SOURCE_DOCUMENTS/dataset.txt

The moisture trapped in the soil affects a lot more than the health of
crops and trees. Look at natural--color satellite images and it becomes
clear that most of the water on Earth (about 97 percent) is stored in
the oceans. Next you might notice some on the land: liquid water fills
lakes and rivers, while frozen water blankets the poles and
mountaintops. In the atmosphere, water is visible in the countless tiny
droplets that compose the clouds, though there is plenty of moisture
even in cloud-free skies.

Soil moisture has many expressions and influences in Earth's climate,
from evaporation to freezing and thawing ice to droughts and floods.
(Photos used under a Creative Commons license, courtesy of Guido
Appenzeller (top left), TREEAID (bottom left), and Mike Rosenberg (top
right). NASA Earth Observatory image (bottom right) by Joshua Stevens,
using Landsat data from the U.S. Geological Survey.)

Not immediately visible, however, is the water residing in the soil.
This water does not appear brilliantly blue or white, like the oceans or
ice. In fact, it is hard to spot in natural-color satellite images.

Compared to the amount of water stored elsewhere on the planet, the
amount in the soil is minuscule. But that small volume has great
significance. It can affect when, where, and what a farmer will plant.
It can influence the weather. And at high northern latitudes, soil
moisture has serious implications for global climate.

For all of these reasons, researchers have developed satellite
instruments to measure the water hidden between soil particles. The
instruments are either active or passive.

Active radar sensors transmit microwave radiation toward the ground and
measure the echoes. Depending on the moisture content, the reflected
signal will "look" different---information scientists then use to derive
the amount of soil moisture. (One such sensor flies on the Metop
satellites operated by the European Organization for the Exploitation of
Meteorological Satellites.) The radar approach allows scientists to
measure moisture in very specific areas (high resolution), but it is
less accurate than other approaches.

"Passive radiometers detect microwave wavelengths of light that are
naturally emitted by the soil. Because the signal varies with wetness,
scientists can use the information to estimate the amount of moisture in
the top few inches of soil. These measurements give better estimates of
the amount of water, but over a broader area (coarse spatial resolution)
than active radar. The European Space Agency has flown a passive
radiometer on the Soil Moisture and Ocean Salinity (SMOS) mission since
2009, and NASA put them on the Aqua (2002) and Aquarius (2011)
satellites. NASA's Soil Moisture Active Passive (SMAP) satellite was
launched in January 2015 and carries both a radiometer and radar.
However, the radar stopped transmitting data a few months after launch.

The Soil Moisture Active Passive (SMAP) satellite can observe global
soil moisture daily at a 36-kilometer resolution. (NASA Earth
Observatory map by Joshua Stevens, using data courtesy of JPL and the
SMAP science team.)

All of these platforms, combined with insights from ground-based
sensors, contribute to a growing record of global soil moisture. The
goal is to establish a standardized set of measurements for the entire
planet so that everyone from meteorologists to climate modelers can
track the movement of this small but vital reservoir of water.

The most obvious users of soil moisture data are farmers and ranchers.
There's more to it, however, than the simple fact that plants need water
to grow. Knowing something about the moisture in the soil is important
before, during, and after the growing season. For example, will mud
prevent a tractor from safely driving across the fields? How much water
will fruits, nuts, and vegetables have available at each stage of
growth, from germination through harvest? What is the forecast for crop
yields around the world? How will the amount of moisture and
agricultural output affect trade policy and food aid?

Ground-based sensors can monitor soil moisture over small areas,
typically less than one square meter. To find out what is happening over
larger areas, researchers in several U.S. states have patched together a
network of sensors. In Oklahoma, for example, a network to monitor
weather and climate parameters (including soil moisture) was conceived
after a disastrous flood struck Tulsa in 1984. Interest in this type of
network for agricultural purposes also arose in Stillwater. The result
was an environmental monitoring network called Mesonet, which in 1996
started to include soil moisture sensors.

Today, more than 100 stations across Oklahoma are making measurements at
various soil depths down to 60 centimeters (24 inches). Sensors record
the temperature during and after a imparting a pulse of heat; the amount
of water in the soil can then be inferred from the temperature change.
(Other ground-based methods involve neutron scattering or soil coring.)
Data from these sensors, updated every 30 minutes, can help farmers
quickly figure out where there is inadequate moisture in their fields.

Mesonet is just one of 31 networks and 1,479 stations in North America.
But, in situ networks do not cover all areas of the United States, and
certainly not the planet.

Nearly 1,500 stations track soil moisture in locations across the United
States. As an example, the plot shows data from the Coastal Sage UCI
station (California). As the map shows, the network is relatively sparse
for the size of the nation. (NASA Earth Observatory map and chart by
Joshua Stevens, using data from the TAMU North American Soil Moisture
Database.)

To fill in the gaps, some scientists estimate global soil moisture by
running computer models loaded with precipitation, temperature, and
humidity data. Gathering all of the data to run such models can take as
long as two to three months, which makes real-time applications
impossible.

"What we really want is soil moisture information that can be used to
understand how plants are growing and what's going on in the atmosphere
right now," said Susan Moran, a hydrologist with USDA's Agricultural
Research Service and chair of the SMAP Applications Working Group. "We
have to get soil moisture information to the agriculture community, and
the only way to do that is from satellites."

With the recent satellite missions, Moran and colleagues have been
learning more about how soil moisture affects plant growth and
agricultural productivity, especially during conditions of water
shortage and drought. For instance, she notes there have been drier and
longer droughts than the one currently parching the western U.S., but
none that have been so hot. The combination of heat and the lack of
water is driving soil moisture to unprecedented deficits.

"Data from SMAP will make a giant difference for my work," she said. "We
have already looked at five years of data from SMOS; add SMAP onto that
and we begin to get a good time series of global soil moisture to help
us figure out where vegetation has a high risk of mortality."

Soil moisture has an obvious, visible effect on the landscape. The
high-profile examples are droughts and floods. But the water in the soil
has a more subtle, yet equally important role in day-to-day weather.

Soil moisture forms a vast, thin, and mostly out-of-sight reservoir of
water that accumulates in the root zone of plants. The water is released
to the atmosphere through evaporation and plant transpiration. Averaged
globally, this evapotranspiration contributes to more than 60 percent of
the precipitation that falls over land each year.

Today, satellites can measure soil moisture globally and quickly.
Saturated soils in the map above---measured by SMAP on October 5,
2015---were the result of intense rains that caused flooding in the
southeastern United States. (NASA Earth Observatory map by Joshua
Stevens, using soil moisture data courtesy of JPL and the SMAP science
team.)

"The first time we were struck by the importance of soil moisture for
weather forecasts was in July 1993," said Patricia de Rosnay, a
researcher at the European Centre for Medium-Range Weather Forecasts
(ECMWF). During the first six months of that year, extreme amounts of
rain and snow fell on the central United States. Yet the existing
weather models were not accounting for the storage and evaporation of
all of that water. They could not see how the water on the land was
feeding back into the weather patterns to make the deluge more extreme.
By July 1993, the Upper Mississippi River faced its worst flooding on
record.

July 1993 also happened to be the same month that ECMWF scientists began
testing a new weather forecast model. Their model accounted for soil
moisture in the root zone, allowing researchers to see how the soil
sustained a high level of evaporation and fed the extreme rainfall
event. The new model had produced a closer representation of reality.

The strength of the connection between soil moisture and the weather is
not the same everywhere. According to NASA scientist Randy Koster, there
are hot spots---about 10 percent of Earth's surface where the amount of
soil moisture plays a more critical role in the weather.

Water that evaporates from Earth's surface is linked to the formation of
clouds and rainfall. In dry areas, variations in the amount of
evaporation are too small to have much of an effect on the atmosphere.
In humid regions, particularly the tropics, changes in soil moisture do
not matter much for evaporation because it is limited by the amount of
water that the atmosphere can hold.

Landsat 8 acquired this image N'Djamena, Chad, on October 20, 2015. The
city sits along the Logone River and within the African Sahel. As
pictured here after the rainy season, the river's saturated banks are
surrounded by a dry, sandy landscape. (NASA Earth Observatory image by
Joshua Stevens, using Landsat data from the U.S. Geological Survey.)

The soil moisture hot spots are areas that are neither too dry nor too
wet. They are located in the transition zones between dry and wet
areas---places that have suitably high evaporation that is more
dependent on moisture on the ground than in the atmosphere. The
Midwestern United States is one of those hot spots. So, too, are
northern India and the African Sahel.

Better estimates of soil moisture in weather models will not necessarily
make for perfect long-range forecasts. Randomness in the variables that
cause weather will always hinder the accuracy beyond a few days. But
with better information on the thin reservoir in the soil, forecasters
can tip the scales further in favor of getting weather prediction right.

In the planet's highest northern latitudes, even the water in the soil
is locked away as ice, making it mostly inaccessible to plants. But just
a short distance to the south, in the boreal areas of Alaska, Canada,
Siberia, and Scandinavia, the landscape comes alive each year after the
spring thaw.

The transition is relatively rapid, occurring over just a few weeks, and
coincides with increasing sunlight and spring snowmelt. Rapid warming
releases liquid water. As liquid water becomes more readily available,
plant and animal activity are energized. The land greens up, and animals
return to graze.

"I'm always impressed by how rapidly northern landscapes transition from
frozen and dormant conditions in the winter to a rapid burst of life and
activity in the spring," said John Kimball, a scientist at the
University of Montana.

The transition between frozen and thawed land is something researchers
have observed for more than 30 years with satellites. The Nimbus-7
Pathfinder, the Defense Meteorological Satellite Program (DMSP)
satellites, and Aqua all have carried passive microwave radiometers.
They detect the microwave energy coming from the Earth's surface, which
will have different characteristics depending on whether the soil is
frozen or thawed. When surface water and soil moisture is locked away as
ice, this frozen landscape looks like a desert to a microwave sensor.
Thawed landscapes look comparatively wet, so this large contrast is the
basis for something called a freeze-thaw measurement.

Across a year, the ice and frozen lands advance and retreat in the high
northern and southern latitudes. (NASA Earth Observatory image by Joshua
Stevens, using NASA's Blue Marble data.)

Kimball and colleagues have mined 30 years of freeze-thaw patterns from
the satellite record. In a paper published in 2012, the researchers
showed that soils in the Northern Hemisphere thawed for as many as 7.5
days more in 2008 than they did in 1979. The change was primarily driven
by an earlier start to the spring thaw and coincided with measureable
warming in the region.

"This was a real eye-opener to me," Kimball said. "We found that the
earlier spring-thaw was driving widespread increases in northern growing
seasons." The start and the length of the growing season have
implications for how much carbon is exchanged between the land and
atmosphere.

Each year, half of all global carbon emissions are removed from the
atmosphere by natural processes on the surface. It is sequestered
somewhere on land, and a large amount of that carbon is stored at high
latitudes. According to scientists at the Woods Hole Research Center,
the boreal region covers about 15 percent of the global land surface,
yet holds more than 30 percent of all carbon contained on land.

A longer growing season in the north could make vegetation a more
important "sink," removing carbon dioxide from the atmosphere and
storing it in forest biomass, dead organic matter, and the soil. But
those boreal lands also could become a carbon source though burning,
decay, and thawing soil. Currently, the region is thought by many to be
a net sink, absorbing more carbon than it releases. But how might
thawing soils affect that balance?

The answer is complicated by the fact that the timing of the thaw can
vary dramatically over a small area. Sunlight sweeps over the landscape
at a low angle, so areas with even the slightest rolling topography can
be cast in either shade or sunlight. South-facing slopes thaw first. And
just a few extra weeks of thawing time can have a huge impact on plant
growth.

Another complication in the carbon equation is permafrost. Even where
the top layer of soil has thawed, there is often a long-term frozen
layer below. This frozen layer locks up carbon so that it cannot
decompose. But as seasonal thaws reach greater soil depths, more organic
matter can decompose and get flushed out into the atmosphere by streams
or degassing into the atmosphere. "There is debate as to how stable that
soil will be with continued global warming," Kimball says.

But progress is being made. Freeze-thaw monitoring, according to
Kimball, has made a major advance thanks to the development of
well-calibrated, long-term satellite soil moisture records. As those
observations continue, and as they encompass more of the planet, it
stands to reason that our understanding of the entire water cycle will
improve.

Soil moisture has an obvious, visible effect on the landscape. The
high-profile examples are droughts and floods. But the water in the soil
has a more subtle, yet equally important role in day-to-day weather.

Soil moisture forms a vast, thin, and mostly out-of-sight reservoir of
water that accumulates in the root zone of plants. The water is released
to the atmosphere through evaporation and plant transpiration. Averaged
globally, this evapotranspiration contributes to more than 60 percent of
the precipitation that falls over land each year.

"The first time we were struck by the importance of soil moisture for
weather forecasts was in July 1993," said Patricia de Rosnay, a
researcher at the European Centre for Medium-Range Weather Forecasts
(ECMWF). During the first six months of that year, extreme amounts of
rain and snow fell on the central United States. Yet the existing
weather models were not accounting for the storage and evaporation of
all of that water. They could not see how the water on the land was
feeding back into the weather patterns to make the deluge more extreme.
By July 1993, the Upper Mississippi River faced its worst flooding on
record.

July 1993 also happened to be the same month that ECMWF scientists began
testing a new weather forecast model. Their model accounted for soil
moisture in the root zone, allowing researchers to see how the soil
sustained a high level of evaporation and fed the extreme rainfall
event. The new model had produced a closer representation of reality.

The strength of the connection between soil moisture and the weather is
not the same everywhere. According to NASA scientist Randy Koster, there
are hot spots---about 10 percent of Earth's surface where the amount of
soil moisture plays a more critical role in the weather.

Water that evaporates from Earth's surface is linked to the formation of
clouds and rainfall. In dry areas, variations in the amount of
evaporation are too small to have much of an effect on the atmosphere.
In humid regions, particularly the tropics, changes in soil moisture do
not matter much for evaporation because it is limited by the amount of
water that the atmosphere can hold.

The soil moisture hot spots are areas that are neither too dry nor too
wet. They are located in the transition zones between dry and wet
areas---places that have suitably high evaporation that is more
dependent on moisture on the ground than in the atmosphere. The
Midwestern United States is one of those hot spots. So, too, are
northern India and the African Sahel.

Better estimates of soil moisture in weather models will not necessarily
make for perfect long-range forecasts. Randomness in the variables that
cause weather will always hinder the accuracy beyond a few days. But
with better information on the thin reservoir in the soil, forecasters
can tip the scales further in favor of getting weather prediction right.

Remotely sensed biomass carbon density maps are widely used for myriad
scientific and policy applications, but all remain limited in scope.
They often only represent a single vegetation

type and rarely account for carbon stocks in belowground biomass. To
date, no global product integrates these disparate estimates into an
all-encompassing map at a scale appropriate

for many modelling or decision-making applications. We developed an
approach for harmonizing vegetation-specific maps of both above and
belowground biomass into a single, comprehensive

representation of each. We overlaid input maps and allocated their
estimates in proportion to the relative spatial extent of each
vegetation type using ancillary maps of percent tree

cover and landcover, and a rule-based decision schema. The resulting
maps consistently and seamlessly report biomass carbon density estimates
across a wide range of vegetation types

in 2010 with quantified uncertainty. They do so for the globe at an
unprecedented 300-meter spatial resolution and can be used to more
holistically account for diverse vegetation carbon

stocks in global analyses and greenhouse gas inventories. Background &
Summary Terrestrial ecosystems store vast quantities of carbon (C) in
aboveground and belowground biomass1. At

any point in time, these stocks represent a dynamic balance between the
C gains of growth and C losses from death, decay and combustion. Maps of
biomass are routinely used for benchmarking

biophysical models2,3,4, estimating C cycle effects of disturbance5,6,7,
and assessing biogeographical patterns and ecosystem services8,9,10,11.
They are also critical for assessing

climate change drivers, impacts, and solutions, and factor prominently
in policies like Reducing Emissions from Deforestation and Forest
Degradation (REDD+) and C offset schemes12,13,14.

Numerous methods have been used to map biomass C stocks but their
derivatives often remain limited in either scope or extent12,15. There
thus remains a critical need for a globally

harmonized, integrative map that comprehensively reports biomass C
across a wide range of vegetation types. Most existing maps of
aboveground biomass (AGB) and the carbon it contains

(AGBC) are produced from statistical or data-driven methods relating
field-measured or field-estimated biomass densities and spaceborne
optical and/or radar imagery12,15,16. They largely

focus on the AGB of trees, particularly those in tropical landscapes
where forests store the majority of the region's biotic C in aboveground
plant matter. Land cover maps are often

used to isolate forests from other landcover types where the predictive
model may not be appropriate such that forest AGB maps intentionally
omit AGB stocks in non-forest vegetation

like shrublands, grasslands, and croplands, as well as the AGB of trees
located within the mapped extent of these excluded landcovers17.
Non-forest AGB has also been mapped to some

extent using similar approaches but these maps are also routinely masked
to the geographic extent of their focal landcover18,19,20,21. To date,
there has been no rigorous attempt to

harmonize and integrate these landcover-specific, remotely sensed
products into a single comprehensive and temporally consistent map of C
in all living biomass. Maps of belowground

biomass (BGB) and carbon density (BGBC) are far less common than those
of AGB because BGB cannot be readily observed from space or airborne
sensors. Consequently, BGB is often inferred

from taxa-, region-, and/or climate-specific "root-to-shoot" ratios that
relate the quantity of BGB to that of AGB22,23,24. These ratios can be
used to map BGB by spatially applying

them to AGB estimates using maps of their respective strata5. In recent
years, more sophisticated regression-based methods have been developed
to predict root-to-shoot ratios of some

landcover types based on covariance with other biophysical and/or
ecological factors25,26. When applied spatially, these methods can allow
for more continuous estimates of local BGB5,27.

Like AGBC, though, few attempts have been made to comprehensively map
BGBC for the globe. Despite the myriad of emerging mapping methods and
products, to date, the Intergovernmental

Panel on Climate Change (IPCC) Tier-1 maps by Ruesch and Gibbs28 remains
the primary source of global AGBC and BGBC estimates that transcend
individual landcover types. These maps,

which represents the year 2000, were produced prior to the relatively
recent explosion of satellite-based AGB maps and they therefore rely on
an alternative mapping technique called

"stratify and multiply"15, which assigns landcover-specific biomass
estimates or "defaults" (often derived from field measurements or
literature reviews) to the corresponding classified

grid cells of a chosen landcover map12. While this approach yields a
comprehensive wall-to-wall product, it can fail to capture finer-scale
spatial patterns often evident in the field

and in many satellite-based products12,15. The accuracy of these maps is
also tightly coupled to the quality and availability of field
measurements29 and the thematic accuracy and

discontinuity of the chosen landcover map. Given the wealth of
landcover-specific satellite based AGB maps, a new harmonization method
akin to "stratify and multiply" is needed to

merge the validated spatial detail of landcover-specific remotely sensed
biomass maps into a single, globally harmonized product. We developed
such an approach by which we (i) overlay

distinct satellite-based biomass maps and (ii) proportionately allocate
their estimates to each grid cell ("overlay and allocate").
Specifically, we overlay continental-to-global scale

remotely sensed maps of landcover-specific biomass C density and then
allocate fractional contributions of each to a given grid cell using
additional maps of percent tree cover, thematic

landcover and a rule-based decision tree. We implement the new approach
here using temporally consistent maps of AGBC as well as matching
derived maps of BGBC to generate separate

harmonized maps of AGBC and BGBC densities. In addition, we generate
associated uncertainty layers by propagating the prediction error of
each input dataset. The resulting global maps

consistently represent biomass C and associated uncertainty across a
broad range of vegetation in the year 2010 at an unprecedented 300 meter
(m) spatial resolution. Our harmonization

approach (Fig. 1) relies on independent, landcover-specific biomass maps
and ancillary layers, which we compiled from the published literature
(Table 1). When published maps did not

represent our epoch of interest (i.e. grasslands and croplands) or did
not completely cover the necessary spatial extent (i.e. tundra
vegetation), we used the predictive model reported

with the respective map to generate an updated version that met our
spatial and temporal requirements. We then used landcover specific
root-to-shoot relationships to generate matching

BGBC maps for each of the input AGBC maps before implementing the
harmonization procedure. Below we describe, in detail, the methodologies
used for mapping AGBC and BGBC of each landcover

type and the procedure used to integrate them. Woody tree biomass Since
the first remotely sensed woody AGB maps were published in the early
1990s, the number of available products

has grown at an extraordinary pace16 and it can thus be challenging to
determine which product is best suited for a given application. For our
purposes, we relied on the GlobBiomass

AGB density map30 as our primary source of woody AGB estimates due to
its precision, timestamp, spatial resolution, and error quantification.
It was produced using a combination of

spaceborne optical and synthetic aperture radar (SAR) imagery and
represents the year 2010 at a 100 m spatial resolution -- making it the
most contemporary global woody AGB currently

available and the only such map available for that year. Moreover,
GlobBiomass aims to minimize prediction uncertainty to less than 30% and
a recent study suggests that it has high

fidelity for fine-scale applications31. The GlobBiomass product was
produced by first mapping the growing stock volume (GSV; i.e. stem
volume) of living trees, defined following Food

and Agriculture Organization (FAO) guidelines32 as those having a
diameter at breast height (DBH) greater than 10 centimeters (cm). AGB
density was then determined from GSV by applying

spatialized biomass expansion factors (BEFs) and wood density estimates.
These factors were mapped using machine learning methods trained from a
suite of plant morphological databases

that compile thousands of field measurements from around the globe33.
The resulting AGB estimates represent biomass in the living structures
(stems, branches, bark, twigs) of trees

with a DBH greater than 10 cm. This definition may thereby overlook AGB
of smaller trees and/or shrubs common to many global regions. Unlike
other maps, though, the GlobBiomass product

employs a subpixel masking procedure that retains AGB estimates in 100 m
grid cells in which any amount of tree cover was detected in finer
resolution (30 m) imagery34. This unique

procedure retains AGB estimates in tree-sparse regions like savannahs,
grasslands, croplands, and agroforestry systems where AGB is often
overlooked17, as well as in forest plantations.

The GlobBiomass product is the only global map that also includes a
dedicated uncertainty layer reporting the standard error of prediction.
We used this layer to propagate uncertainty

when converting AGB to AGBC density, modelling BGBC, and integrating
with C density estimates of other vegetation types. Bouvet et al.35 --
some of whom were also participants of the

GlobBiomass project -- independently produced a separate AGB density map
for African savannahs, shrublands and dry woodlands circa 2010 at 25 m
spatial resolution35 (hereafter "Bouvet

map"), which we included in our harmonized product to begin to address
the GlobBiomass map's potential omission of small trees and shrubs that
do not meet the FAO definition of woody

AGB. This continental map of Africa is based on a predictive model that
directly relates spaceborne L-band SAR imagery -- an indirect measure of
vegetation structure that is sensitive

to low biomass densities36 -- with region-specific, field-measured AGB.
Field measurements (n = 144 sites) were compiled from 7 different
sampling campaigns -- each specifically seeking

training data for biomass remote sensing -- that encompassed 8 different
countries35. The resulting map is not constrained by the FAO tree
definition and is masked to exclude grid cells

in which predicted AGB exceeds 85 megagrams dry mater per hectare (Mg
ha−1) -- the threshold at which the SAR-biomass relationship saturates.
To avoid extraneous prediction, it further

excludes areas identified as "broadleaved evergreen closed-to-open
forest", "flooded forests", "urban areas" and "water bodies" by the
European Space Agency's Climate Change Initiative

(CCI) Landcover 2010 map37 and as "bare areas" in the Global Land Cover
      (GLC) 2000 map38. While the Bouvet map is not natively accompanied
      by an uncertainty layer, its authors provided

us with an analytic expression of its uncertainty (SE; standard error of
prediction) as a function of estimated AGB (Eq. 1) which we used to
generate an uncertainty layer for subsequent

error propagation. We combined the GlobBiomass and Bouvet products to
generate a single woody biomass map by first upscaling each map
separately to a matching 300 m spatial resolution

using an area-weighted average to aggregate grid cells, and then
assigning the Bouvet estimate to all overlapping grid cells, except
those identified by the CCI Landcover 2010 map

as closed or flooded forest types (Online-only Table 1) which were not
within the dryland domain of the Bouvet map. While more complex
harmonization procedures based on various averaging

techniques have been used by others39,40, their fidelity remains unclear
since they fail to explicitly identify and reconcile the underlying
source of the inputs' discrepancies41.

We thus opted to use a more transparent ruled-based approach when
combining these two woody biomass maps, which allows users to easily
identify the source of a grid cell's woody biomass

estimate. Given the local specificity of the training data used to
produce the Bouvet map, we chose to prioritize its predictions over
those of the GlobBiomass product when within

its domain. In areas of overlap, the Bouvet map values tend to be lower
in moist regions and higher in dryer regions (Fig. 2), though, where
used, these differences rarely exceed ±25

megagrams C per hectare (MgC ha−1). We then converted all woody AGB
estimates to AGBC by mapping climate and phylogeny-specific biomass C
concentrations from Martin et al.42. Climate

zones were delineated by aggregating classes of the Köppen-Gieger
classification43 (Table 2) to match those of Martin et al.42.
Phylogenetic classes (angiosperm, gymnosperm and mixed/ambiguous)

were subsequently delineated within each of these zones using aggregated
classes of the CCI Landcover 2010 map (Online-only Table 1). Martin et
al.42 only report values for angiosperms

and gymnosperms so grid cells with a mixed or ambiguous phylogeny were
assigned the average of the angiosperm and gymnosperm values and the
standard error of this value was calculated

from their pooled variance. Due to residual classification error in the
aggregated phylogenetic classes, we weighted the phylogeny-specific C
concentration within each climate zone

by the binary probability of correctly mapping that phylogeny where,
within each climate zone, μc is the mean probability-weighted C
concentration of the most probable phylogeny, μm

is the mean C concentration of that phylogeny from Martin et al.42, pm
is the user's accuracy of that phylogeny's classification (Table 3), and
μn and μo are the mean C concentrations

of the remain phylogenetic classes from Martin et al.42. Standard error
estimates for these C concentrations were similarly weighted using
summation in quadrature where is the probability-weighted

standard error of the most probable phylogeny's C concentration and and
are the standard errors of the respective phylogeny-specific C
concentrations from Martin et al.42. Probability-weighted

C concentrations used are reported in Table. Mapped,
probability-weighted C estimates were then arithmetically applied to AGB
estimates. Uncertainty associated with this correction

was propagated using summation in quadrature of the general form (Eq. 4)
is the uncertainty of μf, and , are the respective uncertainty estimates
of the dependent parameters (standard

error unless otherwise noted). Here, μf, is the estimated AGBC of a
given grid cell, and is the product of its woody AGB estimate, and its
corresponding C concentration. Tundra vegetation

biomass The tundra and portions of the boreal biome are characterized by
sparse trees and dwarf woody shrubs as well as herbaceous cover that are
not included in the GlobBiomass definition

of biomass. AGB density of these classes has been collectively mapped by
Berner et al.18,45 for the North Slope of Alaska from annual Landsat
imagery composites of the normalized difference

vegetation index (NDVI) and a non-linear regression-based model trained
from field measurements of peak AGB that were collected from the
published literature (n = 28 sites). Berner

et al.18 note that while these field measurements did not constitute a
random or systematic sample, they did encompass a broad range of tundra
plant communities. In the absence of

a global map and due the sparsity of high quality Landsat imagery at
high latitudes, we extended this model to the pan-Arctic and
circumboreal regions using NDVI composites created

from daily 250 m MODIS Aqua and Terra surface reflectance images46,47
that were cloud masked and numerically calibrated to Landsat ETM
reflectance -- upon which the tundra model is

based -- using globally derived conversion coefficients48. We generated
six separate 80th percentile NDVI composites circa 2010 -- one for each
of the MODIS missions (Aqua and Terra)

in 2009, 2010 and 2011 -- following Berner et al.18. We chose to use
three years of imagery (circa 2010) rather than just one (2010) to
account for the potential influence that cloud

masking may exert upon estimates of the 80th NDVI percentile in a single
year. We then applied the tundra AGB model to each composite, converted
AGB estimates to AGBC by assuming a

biomass C fraction of 49.2% (SE = 0.8%)42 and generated error layers for
each composite from the reported errors of the AGB regression
coefficients and the biomass C conversion factor

using summation in quadrature as generally described above (Eq. 4). A
single composite of tundra AGBC circa 2010 was then created as the
pixelwise mean of all six composites. We also

generated a complementary uncertainty layer representing the cumulative
standard error of prediction, calculated as the pixelwise root mean of
the squared error images in accordance

with summation in quadrature. Both maps were upscaled from their native
250 m spatial resolution to a 300 m spatial resolution using an area
weighted aggregation procedure, whereby

pixels of the 300 m biomass layer was calculated as the area weighted
average of contained 250 m grid cells, and the uncertainty layer was
calculated -- using summation in quadrature

-- as the root area-weighted average of the contained grid cells
squared. Grassland biomass Grassland AGBC density was modelled directly
from maximum annual NDVI composites using a

non-linear regression-based model developed by Xia et al.19 for mapping
at the global scale. This model was trained by relating maximum annual
NDVI as measured by the spaceborne Advanced

Very High-Resolution Radiometer (AVHRR) sensor to globally distributed
field measurements of grassland AGBC that were compiled from the
published literature (81 sites for a total of

158 site-years). Like the tundra biomass training data, these samples
did not constitute a random or systematic sample but do encompass a
comprehensive range of global grassland communities.

Given the inevitable co-occurrence of trees in the AVHRR sensor's 8 km
resolution pixels upon which the model is trained, it's predictions of
grassland AGBC are relatively insensitive

to the effects of co-occurring tree cover. We thereby assume that its
predictions for grid cells containing partial tree cover represent the
expected herbaceous AGBC density in the

absence of those trees. Maximum model predicted AGBC (NDVI = 1) is 2.3
MgC ha−1 which is comparable to the upper quartile of herbaceous AGBC
estimates from global grasslands49 and

suggests that our assumption will not lead to an exaggerated estimation.
For partially wooded grid cells, we used modelled grassland AGBC density
to represent that associated with

the herbaceous fraction of the grid cell in a manner similar to Zomer et
al.17 as described below (See "Harmonizing Biomass Carbon Maps"). We
applied the grassland AGBC model to all

grid cells of maximum annual NDVI composites produced from finer
resolution 16-day (250 m) MODIS NDVI imagery composites circa 201050,51.
Here again, three years of imagery were used

to account for potential idiosyncrasies in a single year's NDVI
composites resulting from annual data availability and quality. As with
AGB of tundra vegetation, annual composites

(2009--2011) were constructed separately from cloud-masked imagery
collected by both MODIS missions (Aqua and Terra; n = 6) and then
numerically calibrated to AVHRR reflectance using

globally derived conversion coefficients specific to areas of herbaceous
cover52. We then applied the AGBC model to each of these composites and
estimated error for each composite

from both the AVHRR calibration (standard deviation approximated from
the 95% confidence interval of the calibration scalar) and the AGBC
model (relative RMSE) using summation in quadrature.

A single map of grassland AGBC circa 2010 was then created as the
pixelwise mean of all six composites and an associated error layer was
created as the pixelwise root mean of the squared

error images. Both maps were aggregated from their original 250 m
resolution to 300 m to facilitate harmonization using the area-weighted
procedure described previously for woody and

tundra vegetation (see section 1.2). Cropland biomass Prior to harvest,
cropland biomass can also represent a sizable terrestrial C stock. In
annually harvested cropping systems, the

maximum standing biomass of these crops can be inferred from annual net
primary productivity (ANPP). While spaceborne ANPP products exist, they
generally perform poorly in croplands53,54.

Instead, cropland ANPP is more commonly derived from crop
yields20,21,53. We used globally gridded, crop-specific yields of 70
annually harvested herbaceous commodity crops circa 2000

by Monfreda et al.20 -- the only year in which these data were
available. These maps were produced by spatially disaggregating
crop-yield statistics for thousands of globally distributed

administrative units throughout the full extent of a satellite-based
cropland map20. These maps were combined with crop-specific parameters
(Online-only Table 2) to globally map AGBC

as aboveground ANPP for each crop following the method of Wolf et al.21.
This method can be simplified as (Eq. 5) where y is the crop's yield (Mg
ha−1), ω is the dry matter fraction

of its harvested biomass, h is its harvest index (fraction of total AGB
collected at harvest) and c is the carbon content fraction of its
harvested dry mass. This simplification assumes,

following Wolf et al.21, that 2.5% of all harvested biomass is lost
between the field and farmgate and that unharvested residue and root
mass is 44% C. Total cropland AGBC density

was then calculated as the harvested-area-weighted average of all
crop-specific AGBC estimates within a given grid cell. Since multiple
harvests in a single year can confound inference

of maximum AGBC from ANPP, we further determined the harvest frequency
(f) of each grid cell by dividing a cell's total harvested area (sum of
the harvested area of each crop reported

within a given grid cell) by its absolute cropland extent as reported in
a complementary map by Ramankutty et al.55. If f was greater than one,
multiple harvests were assumed to have

occurred and AGBC was divided by f to ensure that AGBC estimates did not
exceed the maximum standing biomass density. Since the yields of many
crops and, by association, their biomass

have changed considerably since 200056,57, we calibrated our circa 2000
AGBC estimates to the year 2010 using local rates of annual ANPP change
(MgC ha−1 yr−1) derived as the Theil-Sen

slope estimator -- a non-parametric estimator that is relatively
insensitive to outliers -- of the full MODIS Terra ANPP timeseries
(2000--2015)58. Total ANPP change between 2000 and

2010 for each grid cell was calculated as ten times this annual rate of
change. Since MODIS ANPP represents C gains in both AGB and BGB, we
proportionately allocated aboveground ANPP

to AGBC using the total root-to-shoot ratio derived from the circa 2000
total crop AGBC and BGBC maps (described below). Since error estimates
were not available for the yield maps

or the crop-specific parameters used to generate the circa 2000 AGBC
map, estimated error of the circa 2010 crop AGBC map was exclusively
based on that of the 2000--2010 correction.

The error of this correction was calculated as the pixel-wise standard
deviation of bootstrapped simulations (n = 1000) in which a random
subset of years was omitted from the slope

estimator in each iteration. The 8 km resolution circa 2000 AGBC map and
error layer were resampled to 1 km to match the resolution of MODIS ANPP
using the bilinear method prior to

ANPP correction and then further resampled to 300 m to facilitate
harmonization. Woody crops like fruit, nut, and palm oil plantations
were not captured using the procedure just described

and their biomass was instead assumed to be captured by the previously
described woody biomass products which retained biomass estimates in all
pixels where any amount of tree cover

was detected at the sub-pixel level (see section 1.1). Belowground
biomass carbon maps Matching maps of BGBC and associated uncertainty
were subsequently produced for each of the landcover-specific

AGBC maps using published empirical relationships. With the exception of
savannah and shrubland areas, woody BGBC was modelled from AGBC using a
multiple regression model by Reich

et al.25 that considers the phylogeny, mean annual temperature (MAT),
and regenerative origin of each wooded grid cell and that was applied
spatially using maps of each covariate in

a fashion similar to other studies5,27. Tree phylogeny (angiosperm or
gymnosperm) was determined from aggregated classes of the CCI Landcover
2010 map37 (Online-only Table 1) with

phylogenetically mixed or ambiguous classes assumed to be composed of
50% of each. MAT was taken from version 2 of the WorldClim bioclimatic
variables dataset (1970--2000) at 1 km resolution59

and resampled to 300 m using the bilinear method. Since there is not a
single global data product mapping forest management, we determined tree
origin -- whether naturally propagated

or planted -- by combining multiple data sources. These data included
(i) a global map of "Intact Forest Landscapes" (IFL) in the year 201360
(a conservative proxy of primary, naturally

regenerating forests defined as large contiguous areas with minimal
human impact), (ii) a Spatial Database of Planted Trees (SDPT) with
partial global coverage61, (iii) national statistics

reported by the FAO Global Forest Resources Assessment (FRA) on the
extent of both naturally regenerating and planted forests and woodlands
within each country in the year 201062,

total area of natural and planted trees was equal to the corresponding
FRA estimates. If the FAOSTAT-reported area of tree crops exceeded
FRA-reported planted area, the difference

was added to FRA planted total. All areas mapped as IFL were assumed to
be of natural origin and BGB was modelled as such. Likewise, besides the
exceptions noted below, all tree plantations

mapped by the SDPT were assumed to be of planted origin. In countries
where the extent of the IFL or SDPT maps fell short of the FRA/FAOSTAT
reported areas of natural or planted forests,

respectively, we estimated BGBC in the remaining, unknown-origin forest
grid cells of that country (BGBCu), as the probability-weighted average
of the planted and natural origin estimates

using Eq. 6 and are the respective BGBC estimates for a grid cell
assuming entirely planted and natural origin, respectively, and and are
the respective differences between (i) the

FRA/FAOSTAT and (ii) mapped extent of planted and natural forest within
the given grid cell's country. While the mapped extent of IFL forests
within a given country never exceeded

that country's FRA reported natural forest extent, there were infrequent
cases (n = 22 of 257) in which the mapped extent of tree plantations
exceeded the corresponding FRA/FAOSTAT

estimate of planted forest area. In these cases, we down-weighted the
BGB estimates of SDPT forests in a similar fashion such that the weight
of their planted estimate ( ) was equal

to the quotient of (i) the FRA/FAOSTAT planted area and (ii) the SDPT
extent within the country, and the weight of the natural origin estimate
applied to the SDPT extent ( ) was equal

to A BGBC error layer was then produced using summation in quadrature
from the standard error estimates of the model coefficients, the AGBC
error layer, the relative RMSE of MAT (27%),

and the derived global uncertainty of the phylogeny layer. Phylogeny
error was calculated as the Bernoulli standard deviation (δ) of the
binary probability (p) of correct classification

(i.e. "area weighted user's accuracy"44; Table 3) using Eq. 7. Since
savannahs and shrublands are underrepresented in the regression-based
model25, their BGBC was instead estimated

using static root-to-shoot ratios reported by Mokany et al.22, which are
somewhat conservative in comparison to the IPCC Tier-1 defaults23,24 put
favoured for consistency with methods

used for grasslands (see below). Error was subsequently mapped from that
of the AGBC estimates and the root-to-shoot ratios applied BGBC of
tundra vegetation was mapped from AGBC using

a univariate regression model derived by Wang et al.26 that predicts
root-to-shoot ratio as a function of MAT. We applied the model using the
WorldClim version 2 MAT map59 and propagated

error from the AGBC estimates, the relative RMSE of MAT and the standard
error of regression coefficients. Where tundra AGB exceeded 25 Mg ha−1
-- the maximum field-measured shrub biomass

reported by Berner et al.18 -- vegetation was considered to include
trees and the Reich et al.25 method described earlier for woody
vegetation was used instead. In the absence of a

continuous predictor of grassland root-to-shoot ratios, we applied
climate specific root-to-shoot ratios from Mokany et al.22 to the
corresponding climate regions of the Köppen-Gieger

classification43 (Table 2). Here, again, these ratios vary slightly from
the IPCC Tier-1 defaults23,24 but were chosen for their greater sample
size and specificity. Grassland BGBC

error was mapped from the error of the AGBC estimates and the respective
root-to-shoot ratios. Cropland BGBC was again estimated from
crop-specific yields and morphological parameters

(Online-only Table 2) following Wolf et al.21 and Eq. 8 where y is the
crop's yield (Mg ha−1), r is the root-to-shoot ratio of the crop, and h
is its harvest index. Here again we assume

that 2.5% of all harvested biomass is lost between the field and
farmgate and that root biomass is 44% C, following Wolf et al.21. BGBC
error was mapped from the error of the 2000-to-2010

ANPP correction for BGBC allocation as described above for cropland
AGBC. Harmonizing biomass carbon maps The AGBC and BGBC maps were
harmonized separately following the same general

schema (Fig. 3). Given that our harmonized woody biomass map contains
biomass estimates for grid cells in which any amount of tree cover was
detected at the subpixel level (see section

1.1), we conserved its estimates regardless of the landcover reported by
the 2010 CCI map in order to more fully account for woody biomass in
non-forested areas17. We then used the

MODIS continuous vegetation fields percent tree cover map for 201063 to
allocate additional biomass density associated with the most probable
herbaceous cover (grass or crop) to each

grid cell in quantities complementary to that of the grid cell's
fractional tree cover estimate (Eq. 9) where μT is the total biomass
estimate of a grid cell, μw is the woody biomass

estimate for the grid cell, μh is its herbaceous biomass estimate, and q
is the MODIS fractional tree cover of the grid cell. Since MODIS tree
cover estimates saturate at around 80%64,

we linearly stretched values such that 80% was treated as complete tree
cover (100%). Moreover, we acknowledge that percent cover can
realistically exceed 100% when understory cover

is considered but we were unable to reasonably determine the extent of
underlying cover from satellite imagery. As such, our approach may
underestimate the contribution of herbaceous

C stocks in densely forested grid cells. The most likely herbaceous
cover type was determined from the CCI Landcover 2010 map, which we
aggregated into two "likely herbaceous cover"

classes -- grass or crop -- based on the assumed likelihood of cropland
in each CCI class (Online-only Table 1). However, due to inherent
classification error in the native CCI Landcover

map, when determining the herbaceous biomass contribution we weighted
the relative allocation of crop and grass biomass to a given grid cell
based on the probability of correct classification

by the CCI map (i.e. "user's accuracy", Table 6) of the most probable
herbaceous class ( ) such that μh can be further expressed as (Eq. 10)
where μi is the predicted biomass of the

most probable herbaceous class, and μj is that of the less probable
class. The uncertainty of a grid cell's total AGBC or BGBC estimate ( )
was determined and mapped from that of its

components ( ) by summation in quadrature which can be simplified as
(Eq. 11) is the error of the grid cell's estimated μw, is the error of
its estimated μh, and is the error of its

q.  Here, can be further decomposed and expressed as Eq. 12 to account
    for the accuracy weighted allocation procedure expressed previously
    (Eq. 10) is the error of the estimated biomass

density of the most probable herbaceous class, is the estimated standard
deviation of that class's Bernoulli probability (p; Eq. 7), and is the
error of the estimated biomass density

of the less probable herbaceous subclass. Exceptions to the above schema
were made in the tundra and boreal biomes -- as delineated by the
RESOLVE Ecoregions 2017 biome polygons65 --

where thematic overlap was likely between the woody and tundra plant
biomass maps. A separate set of decision rules (Fig. 3) was used to
determine whether grid cells in these biomes

were to be exclusively allocated the estimate of the tundra plant map or
that of the fractional allocation procedure described above. In general,
any land in these biomes identified

as sparse landcover by the CCI landcover map (Online-only Table 1) was
assigned the tundra vegetation estimate. In addition, lands north of 60°
latitude with less than 10% tree cover

or where the tundra AGBC estimate exceeded that of the woody AGBC
estimate were also exclusively assigned the tundra vegetation estimate.
Lands north of 60° latitude not meeting these

criteria were assigned the woody value with the additional contribution
of grass. Subtle numerical artefacts emerged from the divergent
methodologies employed north and south of 60°N

latitude. These were eliminated by distance weighting grid cells within
1° of 60°N based on their linear proximity to 60°N and then averaging
estimates such that values at or north

of 61°N were exclusively based on the northern methodology, those at
60°N were the arithmetic average of the two methodologies and those at
or south of 59°N were exclusively based

on the southern methodology. This produced a seamless, globally
harmonized product that integrates the best remotely sensed estimates of
landcover-specific C density. Water bodies

identified as class "210" of the CCI 2010 landcover map were then masked
from our final products. Data Records Data layers (n = 4, Table 7) for
the maps of AGBC and BGBC density (Fig.

4)  as well as their associated uncertainty maps which represent the
    combined standard error of prediction (Fig. 5) are available as
    individual 16-bit integer rasters in GeoTiff format.

All layers are natively in a WGS84 Mercator projection with a spatial
resolution of approximately 300 m at the equator and match that of the
ESA CCI Landcover Maps37. Raster values

are in units megagrams C per hectare (MgC ha−1) and have been scaled by
a factor of ten to reduce file size. These data are accessible through
the Oak Ridge National Laboratory (ORNL)

DAAC data repository. In addition, updated and/or derived
vegetation-specific layers that were used to create our harmonized 2010
maps are

available as supplemental data on figshare67. Technical Validation Our
harmonized products rely almost exclusively upon maps and models that
have been rigorously validated by their

original producers and were often accompanied by constrained uncertainty
estimates. Throughout our harmonization procedure, we strived to
conserve the validity of each of these products

by minimizing the introduction of additional error and by tracking any
introductions, as described above, such that the final error layers
represent the cumulative uncertainty of the

inputs used. Ground truth AGB and BGB data are almost always collected
for individual landcover types. Consequently, we are unable to directly
assess the validity of our integrated

estimates beyond their relationships to individual landcover-specific
estimates and the extents to which they were modified from their
original, previously-validated form prior to

and during our harmonization procedure. Modifications to independent
biomass layers Temporal and spatial updates made to existing
landcover-specific maps of non-tree AGB resulted in

relatively small changes to their predictions. For example, we used
numerically calibrated MODIS imagery to extend the Landsat-based tundra
plant AGB model beyond its native extent

(the North Slope of Alaska) to the pan-Arctic region since neither a
comparable model nor a consistent Landsat time series were available for
this extent. We assessed the effects of

these assumptions by comparing our predictions for the North Slope with
those of the original map18 (Fig. 6a). Both positive and negative
discrepancies exist between ours and the original,

though these rarely exceed ±2 MgC ha−1 and no discernibly systematic
bias was evident. Fig. 6 figure 6 Differences between landcover-specific
AGBC estimates from the original published

maps and the modified versions used as inputs to create the 2010
harmonized global maps. Tundra vegetation AGBC (a) is compared to the
Landsat-based map of Berner et al.45 for the

north slope of Alaska after converting it to units MgC ha−1. Here, the
comparison map was subsequently aggregated to a 1 km resolution and
reprojected for visualization. Grassland

AGBC (b) is compared to the AVHRR-based map of Xia et al.19 which
represents the average estimate between 1982--2006. For visualization,
the map was aggregated to a 5 km resolution

and subsequently reprojected after being masked to MODIS IGBP grasslands
in the year 200685 following Xia et al.19. As such, this map does not
necessarily represent the spatial distribution

of grid cells in which grassland estimates were used. Cropland AGBC (c)
is compared to the original circa 2000 estimates to assess the effects
of the 2000-to-2010 correction. The map

is masked to the native extent of the combined yield maps and aggregated
to a 5 km resolution for visualization. For all maps, negative values
indicate that our circa 2010 estimates

are lower than those of the earlier maps while positive values indicate
higher estimates. Full size image Our updated map of grassland biomass
carbon in the year 2010 was similarly

made by applying the original AVHRR-based model to calibrated MODIS
imagery. This too resulted in only subtle changes to the original
biomass map (Fig. 6b) that were rarely in excess

of 0.5 MgC ha−1. In most areas, our estimates were higher than those of
Xia et al.19 who mapped the mean AGBC density between 1986 and 2006.
Most of these elevated estimates corresponded

with areas in which significant NDVI increases ("greening") have been
reported while notably lower estimates in the Argentine Monte and
Patagonian steppe biomes of southern South America,

likewise, correspond with areas of reported "browning"68,69. Both
greening and browning trends are well documented phenomena and have been
linked to climatic changes70. Moreover, we

further compared AGBC estimates from both the original Xia et al.19 map
and our 2010 update to AGBC field measurements coordinated by the
Nutrient Network that were collected from

48 sites around the world between 2007 and 200949. The RMSE (0.68 MgC
ha−1) of our updated map was 10% less that of the Xia et al. map for
sites with less than 40% tree cover. Likewise,

our 2010 estimates were virtually unbiased (bias = −0.01 MgC ha−1) in
comparison to the Xia map (bias = 0.25 MgC ha−1). While still noisy,
these results suggest that our temporal update

improved the overall accuracy of estimated grassland AGBC. Finally,
cropland biomass carbon maps were also updated from their native epoch
(2000) to 2010 using pixel-wise rates of

MODIS ANPP change over a ten-year period. While MODIS ANPP may be a poor
snapshot of crop biomass in a single year, we assumed that its relative
change over time reflects real physiological

shifts affecting the cropland C cycle. This correction also resulted in
only small differences that rarely exceeded ±2 MgC ha−1 and that,
spatially, correspond well with observed declines

in the yields of select crops that have been linked to climate
change71,72 (Fig. 6c). Nonetheless, updated global yield maps comparable
to those available for 2000 would greatly improve

our understanding of the interactions between climate change, crop
yields, and C dynamics. Belowground biomass estimates Belowground
biomass is notoriously difficult to measure, model,

and also to validate. We accounted for the reported uncertainty of
nearly every variable considered when estimating belowground biomass and
pixel-level uncertainty, but we were unable

to perform an independent validation of our harmonized estimates at the
pixel level due to a paucity of globally consistent field data. To
complete such a task, a globally orchestrated

effort to collect more BGB samples data across all vegetation types is
needed. Given this lack of data, we instead compared the estimated
uncertainty of our BGBC maps to that of our

AGBC estimates to infer the sources of any divergence (Fig. 5). As
expected, our cumulative BGBC uncertainty layer generally reveals
greater overall uncertainty than our AGBC estimates,

with BGBC uncertainty roughly twice that of AGBC throughout most of the
globe. The highest absolute uncertainty was found in biomass rich
forests. Arid woodlands, especially those

of the Sahel and eastern Namibia, generally had the greatest relative
BGBC uncertainty, though their absolute uncertainty was quite small
(generally less than 3 MgC ha−1). Here, biomass

estimates of sparse woody vegetation were primarily responsible for
heightened relative uncertainty. High relative and absolute BGBC
uncertainty were also associated with predictions

in select mountainous forests (e.g. east central Chile) as well as
forested areas in and around cities. These patterns were largely driven
by AGB uncertainty in the GlobBiomass product.

Biomass harmonization The GlobBiomass global woody AGB map produced by
Santoro et al.30 comprises the backbone of our integrated products and,
with few exceptions, remains largely

unchanged in our final AGBC map. The native version of the GlobBiomass
map is accompanied by an error layer describing the uncertainty of each
pixel's biomass estimate and this too

forms the core of our integrated uncertainty layers. In areas with tree
cover, the global average error of GlobBiomass estimates is 39 Mg ha−1
or 50% with greater relative uncertainty

in densely forested areas, along the margins of forested expanses like
farm fields and cities, and in similar areas with sparse tree cover.
Adding additional grass or crop biomass

in complementary proportion to a grid cell's tree cover often did not
exceed the estimated error of the original GlobBiomass map (Fig. 7).
Grid cells exceeding GlobBiomass's native

uncertainty comprise less than 40% of its total extent. Exceptions were
primarily found in grassland and cropland dominated regions where tree
cover was generally sparse, and, consequently,

the herbaceous biomass contribution was relatively high. Even so, the
absolute magnitude of these additions remains somewhat small (less than
2.3 MgC ha−1 for grassland and 15 MgC

ha−1 for cropland). Fig. 7 figure 7 Differences between the final
harmonized AGBC map and GlobBiomass AGBC. GlobBiomass AGB was aggregated
to a 300 m spatial resolution and converted

to C density prior to comparison. Negative values indicate areas where
the new map reports lower values than GlobBiomass while positive value
denote higher estimates. Full size image

Larger deviations from GlobBiomass were also present in areas of both
dryland Africa and the Arctic tundra biome, where we used independent
layers to estimate woody biomass. In African

drylands, GlobBiomass likely underestimates woody biomass by adopting
the conservative FAO definition (DBH \> 10 cm), which implicitly omits
the relatively small trees and shrubs that

are common to the region. The Bouvet map of Africa that we used to
supplement these estimates is not bound by this constraint, was
developed from region-specific data, and predicts

substantially higher AGB density throughout much of its extent with
comparatively high accuracy (RMSE = 17.1 Mg ha−1)35. GlobBiomass also
included sporadic biomass estimates throughout

much of the Arctic tundra biome. Trees are generally scarce throughout
this biome, which is instead dominated by dwarf shrubs and herbaceous
forbs and graminoids, so given GlobBiomass's

adherence to FAO guidelines, its predictions here may be spurious. We
thus prioritized the estimates of the independent model developed
specifically to collectively predict biomass

of both woody and herbaceous tundra vegetation. These estimates were
generally higher than GlobBiomass but agreed well with independent
validation data from North America (RMSE = 2.9

Mg ha−1)18. Comparison with the IPCC Tier-1 global biomass carbon map
While far from a perfect comparison, the only other map to
comprehensively report global biomass carbon density

for all landcover types is the IPCC Tier-1 map for the year 2000 by
Ruesch and Gibbs28. As previously described, this map was produced using
an entirely different method ("stratify

and multiply") and distinct data sources23 and represents an earlier
epoch. However, the map is widely used for myriad applications, and it
may thus be informative to assess notable

differences between it and our new products. Ruesch and Gibbs28 report
total living C stocks of 345 petagrams (PgC) in AGBC and 133 PgC in BGBC
for a total of 478 PgC, globally. Our

estimates are lower at 287 PgC and 122 PgC in global AGBC and BGBC,
respectively, for a total of 409 PgC in living global vegetation
biomass. Herbaceous biomass in our maps comprised

9.1 and 28.3 PgC of total AGBC and BGBC, respectively. Half of all
herbaceous AGBC (4.5 PgC) and roughly 6% of all herbaceous BGBC (1.7
PgC) was found in croplands. Moreover, we mapped

22.3 and 6.1 PgC, respectively, in the AGB and BGB of trees located
within the cropland extent. These trees constituted roughly 7% of all
global biomass C and are likely overlooked

by both the Ruesch and Gibbs map28 and by remotely sensed forest C maps
that are masked to forested areas. Zomer et al.17 first highlighted this
potential discrepancy in the Ruesch

and Gibbs map28 when they produced a remarkably similar estimate of 34.2
Pg of overlooked C in cropland trees using Tier-1 defaults. However,
their estimates were assumed to be in

addition to the 474 PgC originally mapped by Ruesch and Gibbs28. Here,
we suggest that the 28.4 PgC we mapped in cropland trees is already
factored into our 409 PgC total. Our AGBC

product predicts substantially less biomass C than Ruesch and Gibbs28
throughout most of the pantropical region and, to a lesser extent,
southern temperate forests (Fig. 8a). This

pattern has been noted by others comparing the Ruesch and Gibbs map28 to
other satellite-based biomass maps73 and may suggest that the IPCC
default values used to create it23 are spatially

biased. In addition, well-defined areas of high disagreement emerge in
Africa that directly correspond with the FAO boundaries of the "tropical
moist deciduous forest" ecofloristic

zone and suggest that this area, in particular, may merit critical
review. Moreover, the opposite pattern is observed in this same
ecofloristic zone throughout South America. Our map

also predicts greater AGBC throughout much of the boreal forest as well
as in African shrublands and the steppes of South America. We observed
similar, though less pronounced discrepancies,

when comparing BGBC maps (Fig. 8b). Notably, our map predicts
substantially more BGBC throughout the tundra biome -- a previously
underappreciated C stock that has recently risen to

prominance74 -- the boreal forest, African shrublands and most of South
America and Australia. However, we predict less BGBC in nearly all
rainforests (Temperate and Tropical). These

differences and their distinct spatial patterns correspond with the
vegetation strata used to make the IPCC Tier-1 map28 and suggest that
the accuracy of the "stratify and multiply"

method depends heavily upon the quality of the referenced and spatial
data considered. Inaccuracies in these data may, in turn, lead to false
geographies. Integrating, continuous spatial

estimates that better capture local and regional variation, as we have
done, may thus greatly improve our understanding of global carbon
geographies and their role in the earth system.

Congruence with IPCC Tier-2 and Tier-3 nationally reported woody carbon
stocks The error and variance between our woody biomass estimates --
when aggregated to the country level -- and

comparable totals reported in the FRA were less for comparisons made
against FRA estimates generated using higher tier IPCC methodologies
than for those based on Tier-1 approaches

(Fig. 9). Across the board for AGBC, BGBC, and total C comparisons, the
relative RMSE (RMSECV) of our estimates, when compared to estimates
generated using high tier methods, was roughly

half of that obtained from comparisons with Tier-1 estimates (Table 8).
Likewise, the coefficient of determination (R2) was greatest for
comparisons with Tier-3 estimates. For each

pool-specific comparison (AGBC, BGBC, and total C), the slopes of the
relationships between Tier-1, 2, and 3 estimates were neither
significantly different from a 1:1 relationship

nor from one another (p \> 0.05; ANCOVA). Combined, these results
suggest that our maps lead to C stock estimates congruent with those
attained from independent, higher-tier reporting

methodologies. Fig. 9 figure 9 Comparison of woody biomass density
estimates to corresponding estimates of the FAO's FRA and the USFS's
FIA. National woody AGBC totals derived from

the woody components of our harmonized maps are compared to national
totals reported in the 2015 FRA62 (a) in relation to the IPCC inventory
methodology used by each country. Likewise,

we derived woody AGBC totals for US states and compared them to the
corresponding totals reported by the 2014 FIA75 (b), a Tier-3 inventory.
We also show the additional effect of considering

non-woody C -- as is reported in our harmonized maps -- in light green.
Similar comparisons were made between our woody BGBC estimates and the
corresponding estimates of both the FRA

(c) and FIA (d). We further summed our woody AGBC and BGBC estimates and
    compared them to the total woody C stocks reported by both the
    FRA (e) and FIA (f). Full size image Table 8

Statistical comparison of woody biomass carbon totals derived from the
2010 harmonized maps and those reported by the FRA in relation to the
IPCC inventory methodology used. Full size

table To explore this association at a finer regional scale, we also
compared our woody C estimates to the United States Forest Service's
Forest Inventory Analysis75 (FIA) and found

similarly strong congruence for AGBC and Total C stocks but subtle
overestimates for BGBC (Fig. 9). The FIA is a Tier-3 inventory of woody
forest biomass C stocks that is based on

extensive and statistically rigorous field sampling and subsequent
upscaling, We used data available at the state level for the year 2014
-- again, the only year in which we could obtain

data partitioned by AGBC and BGBC. Like our FRA comparison, we found a
tight relationship between our woody AGBC totals and those reported by
the FIA (Fig. 9b; RMSECV = 25.7%, R2 =

0.960, slope = 1.10, n = 48). Our woody BGBC estimates, though, were
systematically greater than those reported by the FIA (Fig. 9d; RMSECV =
86.4%, R2 = 0.95, slope = 1.51, n = 48).

This trend has been noted by others27 and suggests that the global model
that we used to estimate woody BGBC may not be appropriate for some
finer scale applications as is foretold

by the elevated uncertainty reported in our corresponding uncertainty
layer (Fig. 5b). Our total woody C (AGBC + BGBC) estimates (Fig. 9f),
however, agreed well with the FIA (RMSECV

= 34.1%, R2 = 0.961, slope = 1.17, n = 48) and thus reflect the outsized
contribution of AGBC to the total woody C stock. When the contribution
of herbaceous C stocks is further added

to these comparisons, our stock estimates intuitively increase in rough
proportion to a state's proportional extent of herbaceous cover. The
effect of this addition is particularly

pronounced for BGBC estimates due to the large root-to-shoot ratios of
grassland vegetation. The relative congruence of our results with
higher-tier stock estimates suggests that our

maps could be used to facilitate broader adoption of higher-tier methods
among countries currently lacking the requisite data and those seeking
to better account for C in non-woody

biomass. This congruence spans a comprehensive range of biophysical
conditions and spatial scales ranging from small states to large
nations. Moreover, a recent study suggests that

the fidelity of the underlying GlobBiomass AGB map may extend to even
finer scales31. While our BGBC estimates may differ from some fine-scale
estimates (Fig. 9d), their tight agreement

with high tier BGBC totals at the national level (Fig. 9c) suggests that
they may still be well suited for many national-scale C inventories --
especially for countries lacking requisite

high tier data. Use of our maps is unlikely to introduce error in excess
of that currently implicit in Tier-1 estimates. Credence, though, should
be given to the associated uncertainty

estimates. To facilitate wider adoption of higher-tier methodologies,
our maps could be used to derive new, region-specific default values for
use in Tier-2 frameworks76 or to either

represent or calibrate 2010 baseline conditions in Tier-3 frameworks. In
so doing, inventories and studies alike could more accurately account
for the nuanced global geographies of

biomass C. Usage Notes These maps are intended for global applications
in which continuous spatial estimates of live AGBC and/or BGBC density
are needed that span a broad range of

vegetation types and/or require estimates circa 2010. They are loosely
based upon and share the spatial resolution of the ESA CCI Landcover
2010 map37, which can be used to extract

landcover specific C totals. However, our products notably do not
account for C stored in non-living C pools like litter or coarse woody
debris, nor soil organic matter, though these

both represent large, additional ecosystem C stocks77,78,79. Our maps
are explicitly intended for global scale applications seeking to
consider C in the collective living biomass of

multiple vegetation types. For global scale applications focused
exclusively on the C stocks of a single vegetation type, we strongly
encourage users to instead use the respective

input map or model referenced in Table 1 to avoid potential errors that
may have been introduced by our harmonization procedure. For AGB
applications over smaller extents, users should

further consider whether locally specific products are available. If
such maps are not available and our maps are considered instead,
credence should be given to their pixel-level

uncertainty estimates. As mentioned above, the biomass of shrublands was
only explicitly accounted for in Africa and the Arctic tundra, since
neither broad-scale maps nor models generalizable

to other areas were available in the existing literature. As such, we
caution against the use of our maps outside of these areas when
shrubland biomass is of particular interest or

importance. Moreover, in contrast to the estimates for all other
vegetation types considered, which we upscaled to a 300 m resolution,
cropland C estimates were largely based on relatively

coarse 8 km resolution data that were downscaled using bilinear
resampling to achieve a 300 m spatial resolution. As such, these
estimates may not adequately capture the underlying

finer-scale spatial variation and should be interpreted with that in
mind. Likewise, we reiterate that some BGBC estimates may differ from
locally derived Tier-3 estimates, and attention

should thus be given to our reported pixel-level uncertainty for all
applications. Finally, our maps should not be used in comparison with
the IPCC Tier-1 map of Ruesch and Gibbs (2008)

to detect biomass change between the two study periods due to
significant methodological differences between these products. An
estimated 720 and 811 million people in the world faced

hunger in 2020, according to the United Nations (UN), and nearly one in
three people in the world (2.37 billion) did not have access to adequate
food in 2020. The vulnerabilities and

inadequacies of global food systems are expected to further intensify
over the coming years. The combination of NASA Earth science data with
socioeconomic data provides key information

for sustainable use of available resources. NASA's Socioeconomic Data
and Applications Center (SEDAC) is the home for NASA socioeconomic data
and is a gateway between the social sciences

and the Earth sciences. SEDAC provides numerous datasets and data
collections that may be useful for studies into agriculture and water
management. SEDAC also provides information

about the connections that support efforts to end hunger, achieve food
security and improved nutrition, and promote sustainable agriculture.
Women in the West African country of Senegal

take a break from crushing millet. The United Nations World Food Program
estimates that 46 percent of households in Senegal lack reliable access
to adequate amounts of food. Credit:

Molly Brown Women in the West African country of Senegal take a break
from crushing millet. The United Nations World Food Program estimates
that 46% of households in Senegal lack reliable

access to adequate amounts of food. Credit: Molly Brown. NASA helps
develop tools to address food security and works with decision-makers
and data users to tailor these tools to specific

locations and user needs. These efforts help address issues like water
management for irrigation, crop-type identification and land use,
coastal and lake water quality monitoring,

drought preparedness, and famine early warnings. Much of this work is
carried out and supported fully or in part by the agency's Applied
Sciences Program, which works with individuals

and institutions worldwide to inform decision-making, enhance quality of
life, and strengthen the economy. The Applied Sciences Program co-leads
the international Earth Observations

for Sustainable Development Goals initiative, which advances global
knowledge about effective ways that Earth observations and geospatial
information can support the SDGs. The NASA

datasets and resources listed below, coupled with other data and
resources in this Data Pathfinder, also help measure progress toward
meeting United Nations' Sustainable Development

Goals (SDGs), particularly SDG 2: Zero Hunger. These data can provide a
better overall view for monitoring the food insecurity of vulnerable
populations, tracking agricultural production

related to incomes of small-scale food producers, and monitoring
environmental impacts to soil, water, fertilizer, pesticide pollution,
and changes in biodiversity. More information

is available in the Connection of Sustainable Development Goals to
Agriculture and Water Management section on the main Pathfinder landing
page. Agriculture and Human Dimensions Agriculture

and Food Security theme landing page Global Agricultural Inputs, v1 The
five datasets in this data collection provide global gridded data and
maps on pesticide application, phosphorus

in manure and chemical fertilizers, and nitrogen in manure and chemical
fertilizers Global Pesticide Grids (PEST-CHEMGRIDS), v1.01 (2015, 2020,
2025) Global coverage; 5 arc-min spatial

resolution; GeoTIFF, netCDF-4 Web Map Service Layers Food Supply Effects
of Climate Change on Global Food Production from SRES Emissions and
Socioeconomic Scenarios, v1 (1970 -- 2080)

Global coverage; national resolution; .xlsx Web Map Service Layers Food
Insecurity Hotspots Data Set, v1 (2009 -- 2019) Global coverage;
national resolution; GeoTIFF, Shapefile Web

Map Service Layers Groundswell Spatial Population and Migration
Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 --
2050) Allows users to understand how slow-onset

climate change impacts on water availability and crop productivity,
coupled with sea-level rise and storm surge, may affect the future
population distribution and climate-related internal

migration in low to middle income countries Crop Production Twentieth
Century Crop Statistics, v1 (1900 -- 2017) Global coverage (selected
countries); national/sub-national resolution;

annual Global Population Projection Grid Data Groundswell Spatial
Population and Migration Projections at One-Eighth Degree According to
SSPs and RCPs, v1 (2010 -- 2050) Climate Change

Impact Effects of Climate Change on Global Food Production from SRES
Emissions and Socioeconomic Scenarios, v1 (1970 -- 2080) Global
coverage; national resolution; .xlsx Web Map Service

Layers Groundswell Spatial Population and Migration Projections at
One-Eighth Degree According to SSPs and RCPs, v1 (2010 -- 2050)
Environmental Performance 2022 Environmental Performance

Index Global coverage; national resolution; .xlsx, csv 15 static maps
Poverty-related Data Humidity is a measure of the amount of water vapor
present in the air. High humidity impairs

heat exchange efficiency by reducing the rate of moisture evaporation
from the skin and other surfaces. This can create challenges for
agricultural workers, as well as the crops they

grow. The Modern-Era Retrospective analysis for Research and
Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due
to the amount of historical data available, MERRA-2

data can be used to look for trends and patterns as well as anomalies.
There are several options available: 1-hourly, 3-hourly, 6-hourly,
daily, and monthly. These options provide

information on precipitation. The NASA Earth Exchange Global Daily
Downscaled Projections (NEX-GDDP) dataset is comprised of
high-resolution, bias-corrected global downscaled climate

projections derived from the General Circulation Model (GCM) runs
conducted under the Coupled Model Intercomparison Project Phase 6
(CMIP6) and across all four "Tier 1" greenhouse

gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs).
This dataset provides a set of global, high resolution, bias-corrected
climate change projections that can be

used to evaluate climate change impacts on processes that are sensitive
to finer-scale climate gradients and the effects of local topography on
climate conditions. Uses include: air

temperature, precipitation volume, humidity, stellar radiation, and
atmospheric wind speed. The atmosphere is a mixture of gases that
surrounds the Earth. It helps make life possible

by providing us with air to breathe, shielding us from harmful
ultraviolet (UV) radiation coming from the Sun, trapping heat to warm
the planet, and preventing extreme temperature

differences between day and night. Without the atmosphere, temperatures
would be well below freezing everywhere on Earth's surface. Instead, the
heat absorbed and trapped by our atmosphere

keeps our planet's average surface temperature at a balmy 15°C (59°F).
Some of the atmosphere's gases, like carbon dioxide, are particularly
good at absorbing and trapping radiation.

Changes in the amounts of these gases directly affect our climate. Gases
in Earth's Atmosphere Each of the planets in our solar system has an
atmosphere, but none of them have the

same ratio of gases or layered structure as Earth's atmosphere. Nitrogen
and oxygen are by far the most common gases in our atmosphere. Dry air
is composed of about 78% nitrogen (N2)

and about 21% oxygen (O2). The remaining less than 1% of the atmosphere
is a mixture of gases, including argon (Ar) and carbon dioxide (CO2).
The atmosphere also contains varying amounts

of water vapor, on average about 1%. There are also many, tiny, solid or
liquid particles, called aerosols, in the atmosphere. Aerosols can be
made of dust, spores and pollen, salt

from sea spray, volcanic ash, smoke, and pollutants introduced through
human activity. Earth's Atmosphere Has Layers The atmosphere becomes
thinner (less dense and lower in air pressure)

the further it extends from the Earth's surface. It gradually gives way
to the vacuum of space. There is no precise top of the atmosphere, but
the area between 100-120 km (62-75 miles)

above the Earth's surface is often considered the boundary between the
atmosphere and space because the air is so thin here. However, there are
measurable traces of atmospheric gases

beyond this boundary, detectable for hundreds of kilometers/miles from
Earth's surface. There are several unique layers in Earth's atmosphere.
Each has characteristic temperatures,

pressures, and phenomena. We live in the troposphere, the layer closest
to Earth's surface, where most clouds are found and almost all weather
occurs. Some jet aircraft fly in the

next layer, the stratosphere, which contains the jet streams and a
region called the ozone layer. The next layer, the mesosphere, is the
coldest because the there are almost no air

molecules there to absorb heat energy. There are so few molecules for
light to refract off of that the sky also changes from blue to black in
this layer. And farthest from the surface

we have the thermosphere, which absorbs much of the harmful radiation
that reaches Earth from the Sun, causing this layer to reach extremely
high temperatures. Beyond the thermosphere

is the exosphere, which represents the transition from Earth's
atmosphere to space. Planetary Atmospheres Earth is not the only world
with an atmosphere. Each of the planets - and

even a few moons - in our solar system have an atmosphere. Some planets
have active atmospheres with clouds, wind, rain and powerful storms.
Scientists use light spectroscopy to observe

the atmospheres of planets and moons in other solar systems . Each of
the planets in our solar system has a uniquely structured atmosphere.
The atmosphere of Mercury is extremely thin

and is not very different from the vacuum of space. The gas giant
planets in our solar system - Jupiter, Saturn, Uranus and Neptune - each
have a thick, deep atmosphere. The smaller,

rocky planets - Earth, Venus and Mars - each have thinner atmospheres,
hovering above their solid surfaces. The moons in our solar system
typically have thin atmospheres, with the

exception of Saturn's moon, Titan. Air pressure at the surface of Titan
is higher than on Earth! Of the five officially recognized dwarf
planets, Pluto has a thin atmosphere that expands

and collapses seasonally, and Ceres has an extremely thin and transient
atmosphere made of water vapor. But only Earth's atmosphere has the
layered structure that traps enough of the

Sun's energy for warmth while also blocking much of the harmful
radiation from the Sun. This important balance is necessary to maintain
life on Earth. Forests are one of the world's

largest banks of carbon-rich biomass. This is why when researchers
mapped biomass in the past, they typically focused on the world's
forests. But this approach leaves out considerable

amounts of biomass in grasslands, shrublands, croplands, and other
biomes. New maps, published at NASA's Oak Ridge National Laboratory
Distributed Active Archive Center (ORNL DAAC)

and described in Nature Scientific Data, combine remotely sensed biomass
data for different land cover types into harmonized global maps of above
and belowground biomass for the year

2010. People often conflate forest biomass with total biomass," said
      Seth Spawn, lead author of the research and doctoral candidate at
      the University of Wisconsin, Madison. "Researchers

have spent a lot of time developing nice remotely sensed maps of
aboveground forest biomass, but they intentionally omit other land
covers and the carbon stored below ground in plant

roots. We haven't had the whole picture." Spawn and his team combined
maps of forest biomass with other land cover specific biomass maps that
use remotely sensed data. They allocated

fractional contributions to a given grid cell using data on land cover,
percent tree cover, and the presence of secondary vegetation. These maps
show a sizable stock of biomass outside

of forested areas, especially in the trees located in savannas and farm
fields. "There's more carbon on croplands than I would have expected,"
said Spawn. Trees on orchards and farms

practicing agroforestry have a carbon stock that is overlooked in
previous biomass maps. Globally, trees on croplands stored about 28
metric gigatons of carbon in 2010, which is 7

percent of the total stock of carbon in plants, according to these new
maps. The team produced maps of uncertainty with the data they used and
published them along with the dataset.

Published in March, the dataset is already being used for a number of
applications. Researchers are using the dataset in integrated assessment
models that incorporate economics and

the Earth system. It is also being used to model carbon emissions from
past and potential land use changes and the carbon impacts of bioenergy
transitions. Water is a key component

of the overall Earth system, cycling through each component, moving
within the atmosphere, the ocean, the cryosphere (including snow cover
and snowpack), surface water of rivers and

lakes, and subsurface water. Water availability is critical for human
consumption, agriculture and food security, industry, and energy
development. Assessing water availability, including

the amount and type of precipitation is critical to monitoring
agricultural practices and water resource availability and for providing
interventions when necessary. According to the

U.N., water use has been growing globally at twice the rate as the
global population is increasing. More and more areas are reaching the
limit at which water services can be sustainably

delivered, especially in arid regions. Groundwater, a major water
resource for maintaining cropland productivity, is declining through the
extensive use of water for agricultural irrigation,

where aquifer recharge cannot keep up with groundwater extraction.
Unfortunately, changes in terrestrial water storage, especially with
regard to groundwater, are poorly known and

sparsely sampled. Complicating matters further, global freshwater is not
only unevenly distributed, but sources of freshwater such as lakes and
rivers often cross geopolitical boundaries.

Integrating satellite data with land-based and other measurements,
geospatial data, and hydrologic models help to better understand
controls on global water resources and how changing

water resources impact social-environmental systems across geopolitical
boundaries. Earth Observation Data by Sensor According to the U.N.,
water use has been growing globally at twice

the rate as the global population is increasing. More and more areas are
reaching the limit at which water services can be sustainably delivered,
especially in arid regions. Groundwater,

a major water resource for maintaining cropland productivity, is
declining through the extensive use of water for agricultural
irrigation, where aquifer recharge cannot keep up with

groundwater extraction. Unfortunately, changes in terrestrial water
storage, especially with regard to groundwater, are poorly known and
sparsely sampled. Complicating matters further,

global freshwater is not only unevenly distributed, but sources of
freshwater such as lakes and rivers often cross geopolitical boundaries.
Integrating satellite data with land-based

and other measurements, geospatial data, and hydrologic models help to
better understand controls on global water resources and how changing
water resources impact social-environmental

systems across geopolitical boundaries. Earth Observation Data by Sensor
GRACE, GRACE-FO Instruments aboard the joint NASA/German Space Agency
Gravity Recovery And Climate Experiment

(GRACE, operational 2002 to 2017) and GRACE Follow-On (GRACE-FO,
launched in 2018) satellites obtain measurements about changes in
Earth's gravity. Since water has mass, changes in

groundwater storage can be detected as changes in gravity. GRACE and
GRACE-FO measurements help assess water storage changes in monthly,
total surface, and groundwater depth. These

data are available from 2002 to present; the data track total water
storage time-variations and anomalies (changes from the time-mean) at a
resolution of approximately 90,000 km2 and

larger. These measurements are unimpeded by clouds and track the entire
land water column from the surface down to deep aquifers. GRACE and
GRACE-FO data are uniquely valuable for

regional studies to determine general trends in land water storage as
well as for assessing basin-scale water budgets (e.g., the balance
between precipitation, evapotranspiration,

and runoff). GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology
Equivalent Water Height dataset provides gridded monthly global water
storage/height anomalies relative to a time-mean.

The data are processed at NASA's Jet Propulsion Laboratory (JPL) using
the mascon approach. Mass Concentration blocks (mascons) are a form of
gravity field basis functions to which

GRACE observations are optimally fit. For more information on this
approach, see the JPL Monthly Mass Grids webpage. Data are represented
as Water Equivalent Thickness (WET), representing

the total terrestrial water storage anomalies from soil moisture, snow,
surface water (including rivers, lakes, and reservoirs), as well as
groundwater and aquifers. Scientists at

NASA's Goddard Space Flight Center use GRACE-FO data to generate weekly
groundwater and soil moisture drought indicators. The drought indicators
describe current wet or dry conditions,

expressed as a percentile showing the probability of occurrence for a
specific location and time of year, with lower values (orange/red)
indicating drier than normal conditions and

higher values (blues) indicating wetter than normal conditions. The
drought model is also used to make forecasts of expected drought
conditions one, two, and three months into the

future. NASA, in collaboration with other agencies, has developed models
of groundwater that incorporate satellite information with ground-based
data (when ground-based data are available).

These models are part of the Land Data Assimilation System (LDAS), which
includes a global collection (GLDAS) and a North American collection
(NLDAS). NASA's Goddard Earth Sciences

Data and Information Services Center (GES DISC) optimally reorganized
some large hydrological datasets as time series (also known as data
rods) for a set of water cycle-related variables

from the NLDAS and GLDAS, the Land Parameter Parameter Model (LPRM),
TRMM, and GRACE data assimilation. These are available at GES DISC
Hydrology Data Rods. The Modern-Era Retrospective

analysis for Research and Applications, Version 2 (MERRA-2) provides
data beginning in 1980. Due to the amount of historical data available,
MERRA-2 data can be used to look for trends

and patterns, as well as anomalies. There are several options available:
hourly and monthly from 1980. Remote sensing is the acquiring of
information from a distance. NASA observes

Earth and other planetary bodies via remote sensors on satellites and
aircraft that detect and record reflected or emitted energy. Remote
sensors, which provide a global perspective

and a wealth of data about Earth systems, enable data-informed decision
making based on the current and future state of our planet. Satellites
can be placed in several types of orbits

around Earth. The three common classes of orbits are low-Earth orbit
(approximately 160 to 2,000 km above Earth), medium-Earth orbit
(approximately 2,000 to 35,500 km above Earth),

and high-Earth orbit (above 35,500 km above Earth). Satellites orbiting
at 35,786 km are at an altitude at which their orbital speed matches the
planet's rotation, and are in what

is called geosynchronous orbit (GSO). In addition, a satellite in GSO
directly over the equator will have a geostationary orbit. A
geostationary orbit enables a satellite to maintain

its position directly over the same place on Earth's surface. Aqua
satellite orbit illustrating polar orbital track. NASA's Aqua satellite
completes one orbit every 99 minutes and

passes within 10 degrees of each pole. This enables the Moderate
Resolution Imaging Spectroradiometer (MODIS) aboard Aqua to acquire full
global imagery every 1-2 days. Credit: NASA

Aqua. Low-Earth orbit is a commonly used orbit since satellites can
follow several orbital tracks around the planet. Polar-orbiting
satellites, for example, are inclined nearly 90

degrees to the equatorial plane and travel from pole to pole as Earth
rotates. This enables sensors aboard the satellite to acquire data for
the entire globe rapidly, including the

polar regions. Many polar-orbiting satellites are considered
Sun-synchronous, meaning that the satellite passes over the same
location at the same solar time each cycle. One example

of a Sun-synchronous, polar-orbiting satellite is NASA's Aqua satellite,
which orbits approximately 705 km above Earth's surface. Non-polar
low-Earth orbit satellites, on the other

hand, do not provide global coverage but instead cover only a partial
range of latitudes. The joint NASA/Japan Aerospace Exploration Agency
Global Precipitation Measurement (GPM) Core

Observatory is an example of a non-Sun-synchronous low-Earth orbit
satellite. Its orbital track acquires data between 65 degrees north and
south latitude from 407 km above the planet.

A medium-Earth orbit satellite takes approximately 12 hours to complete
an orbit. In 24-hours, the satellite crosses over the same two spots on
the equator every day. This orbit is

consistent and highly predictable. As a result, this is an orbit used by
many telecommunications and GPS satellites. One example of a
medium-Earth orbit satellite constellation is

the European Space Agency's Galileo global navigation satellite system
(GNSS), which orbits 23,222 km above Earth. While both geosynchronous
and geostationary satellites orbit at 35,786

km above Earth, geosynchronous satellites have orbits that can be tilted
above or below the equator. Geostationary satellites, on the other hand,
orbit Earth on the same plane as the

equator. These satellites capture identical views of Earth with each
observation and provide almost continuous coverage of one area. The
joint NASA/NOAA Geostationary Operational Environmental

Satellite (GOES) series of weather satellites are in geostationary
orbits above the equator. Observing with the Electromagnetic Spectrum
Electromagnetic energy, produced by the vibration

of charged particles, travels in the form of waves through the
atmosphere and the vacuum of space. These waves have different
wavelengths (the distance from wave crest to wave crest)

and frequencies; a shorter wavelength means a higher frequency. Some,
like radio, microwave, and infrared waves, have a longer wavelength,
while others, such as ultraviolet, x-rays,

and gamma rays, have a much shorter wavelength. Visible light sits in
the middle of that range of long to shortwave radiation. This small
portion of energy is all that the human eye

is able to detect. Instrumentation is needed to detect all other forms
of electromagnetic energy. NASA instrumentation utilizes the full range
of the spectrum to explore and understand

processes occurring here on Earth and on other planetary bodies. Some
waves are absorbed or reflected by atmospheric components, like water
vapor and carbon dioxide, while some wavelengths

allow for unimpeded movement through the atmosphere; visible light has
wavelengths that can be transmitted through the atmosphere. Microwave
energy has wavelengths that can pass through

clouds, an attribute utilized by many weather and communication
satellites. The primary source of the energy observed by satellites, is
the Sun. The amount of the Sun's energy reflected

depends on the roughness of the surface and its albedo, which is how
well a surface reflects light instead of absorbing it. Snow, for
example, has a very high albedo and reflects up

to 90% of incoming solar radiation. The ocean, on the other hand,
reflects only about 6% of incoming solar radiation and absorbs the rest.
Often, when energy is absorbed, it is re-emitted,

usually at longer wavelengths. For example, the energy absorbed by the
ocean gets re-emitted as infrared radiation. All things on Earth
reflect, absorb, or transmit energy, the amount

of which varies by wavelength. Just as your fingerprint is unique to
you, everything on Earth has a unique spectral fingerprint. Researchers
can use this information to identify different

Earth features as well as different rock and mineral types. The number
of spectral bands detected by a given instrument, its spectral
resolution, determines how much differentiation

a researcher can identify between materials. Drought, vegetation health,
and soil moisture all can be tracked remotely. This Data Pathfinder
provides links to NASA Earth observations,

tools, and other resources applicable to agricultural production and
water management. The planet NASA studies the most is Earth. NASA's
end-to-end Earth observations enable agricultural

producers to make informed decisions about global market conditions,
water management, in-season crop conditions, severe weather, and
sustainability. This Data Pathfinder will help

guide you through the process of selecting and using datasets applicable
to agriculture and water management, and provides links to specific data
sources. If you are new to remote

sensing, the What is Remote Sensing? Backgrounder provides a
comprehensive overview. In addition, NASA's Applied Remote Sensing
Training Program (ARSET) provides numerous training

What's big, covered in water, yet 100 times drier than the Sahara
Desert? It's not a riddle, it's the Moon! For centuries, astronomers
debated whether water exists on Earth's closest neighbor. In 2020, data
from NASA's SOFIA mission confirmed water exists in the sunlit area of
the lunar surface as molecules of H2O embedded within, or perhaps
sticking to the surface of, grains of lunar dust. Here is a brief
history of the discoveries leading up to the confirmation of water on
the Moon.

When early astronomers looked up at the Moon, they were struck by the
large, dark spots on its surface. In 1645, Dutch astronomer Michael van
Langren published the first-known map of the Moon referring to the dark
spots as "maria" -- the Latin word for "seas" -- and putting into
writing the widely-held view that the marks were oceans on the lunar
surface. Similar maps from Johannes Hevelius (1647), Giovanni Riccioli
and Francesco Grimaldi (1651) were published over the next few years. We
now know these spots to be plains of basalt created by early volcanic
eruptions, but the nomenclature of 'maria' (plural) or 'mare' (singular)
remains.

American astronomer William Pickering made measurements in the late
1800s that led him to conclude the Moon essentially has no atmosphere.
With no clouds and no atmosphere, scientists generally agreed that any
water on the lunar surface would evaporate immediately. Pickering's
measurements led to a widespread view that the Moon was devoid of water.

As scientists made headway in understanding the behavior of substances
that are prone to vaporize at relatively low temperatures -- called
volatiles -- theoretical physicist Kenneth Watson published a paper in
1961 describing how a substance like water could exist on the Moon.
Watson's paper first popularized the idea that water ice could stick to
the bottom of craters on the Moon that never receive light from the Sun,
while sunlit areas on the Moon would be so hot that water would
evaporate near-instantly. These lightless areas of the Moon are called
"permanently shadowed regions."

The Apollo era brought humans to the lunar surface for the very first
time, giving researchers the opportunity to directly look for signs of
water on the Moon. When tested, soil samples brought back by Apollo
astronauts revealed no sign of water. Scientists concluded that the
lunar surface must be completely dry, and the prospect of water wasn't
seriously considered again for decades.

NASA's Clementine mission launched in 1994 to orbit the Moon for two
months and collect information about its minerals. Clementine data
suggested there was ice in a permanently shadowed region of the Moon.
The Lunar Prospector Mission focused on permanently shadowed craters to
look deeper into the discovery and in 1998 found that the largest
concentrations of hydrogen exist in the areas of the lunar surface that
are never exposed to sunlight. The results indicated water ice at the
lunar poles. However, the images were low resolution so no strong
conclusions could be made.

Capitalizing on major advances in technology since the Apollo era,
researchers from Brown University revisited the Apollo samples. They
found hydrogen inside tiny beads of volcanic glass. Since no volcanoes
are erupting on the Moon today, the discovery presented evidence that
water had existed in the Moon when the volcanoes erupted in the Moon's
ancient past. Additionally, the preserved hydrogen provided clues to the
origins of lunar water: if it emerged from erupting volcanoes, it must
have come from within the Moon. The discovery suggested that water was a
part of the Moon since its early existence -- and perhaps since it first
formed.

A suite of spacecraft enabled exciting discoveries in 2009. None were
designed to look for water on the Moon, yet the Indian Space Research
Organization's Chandrayaan-1 and NASA's Cassini and Deep Impact missions
detected signs of hydrated minerals in the form of oxygen and hydrogen
molecules in sunlit areas of the Moon. Researchers couldn't determine
whether they were seeing hydration by hydroxyl (OH) or water (H2O). They
also debated whether the amount of hydration depended on the time of
day.

The Lunar Crater Observation and Sensing Satellite (LCROSS) spacecraft
and Lunar Reconnaissance Orbiter (LRO) launched together in 2009. Later
that year, LCROSS intentionally discharged a projectile into a crater
believed to contain water ice, and flew through the debris from the
projectile's impact. Four minutes later, LCROSS itself intentionally
impacted the Moon while LRO observed. The combined observations showed
grains of water ice in the ejected material. The LRO and LCROSS findings
added to a growing body of evidence that water exists on the Moon in the
form of ice within permanently shadowed regions. LRO continues to orbit
the Moon and provide data used to characterize and map lunar resources,
including hydrogen.

Data from Moon Mineralogy Mapper (M3), carried by ISRO's Chandrayaan-1,
provided scientists with the first high-resolution map of the minerals
that make up the lunar surface. The NASA instrument flew aboard India's
Chandrayaan-1 mission in 2009. An analysis of the full set of data from
M3, announced in 2018, revealed multiple confirmed locations of water
ice in permanently shadowed regions of the Moon.

In 2020, NASA announced the discovery of water on the sunlit surface of
the Moon. Data from the Strategic Observatory for Infrared Astronomy
(SOFIA), revealed that in Clavius crater, water exists in concentrations
roughly equivalent to a 12-ounce bottle of water within a cubic meter of
soil across the lunar surface. The discovery showed that water could be
distributed across the lunar surface, even on sunlit portions, and not
confined to cold, dark areas.

In 2023, a new map of water distribution on the Moon provided hints
about how water may be moving across the Moon's surface. The map, made
using SOFIA data, extends to the Moon's South Pole -- the intended
region of study for NASA's Artemis missions, including the water-hunting
rover, VIPER.

Researchers have confirmed that water exists both in the sunlit and
shadowed surfaces of the Moon, yet many questions remain. Lunar
scientists continue to investigate the origins of water and its
behavior. There is evidence that the water on the Moon comes from
ancient and current comet impacts, icy micrometeorites colliding on the
lunar surface, and lunar dust interactions with the solar wind. However,
more research is needed to understand the full history, present, and
future of water on the Moon. Writer: Allison Gasparini and Molly
Wasser`\nScience `{=tex}Advisors: Casey Honniball, Tim Livengood, NASA's
Goddard Space Flight Center In 2019, scientists discovered that water is
being released from the Moon during meteor showers. Water on the Moon
could come from a surprising source, our Sun. In 2020, NASA scientists
confirmed the presence of H2O on the Moon.

Viewed from space, one of the most striking features of our home planet
is the water, in both liquid and frozen forms, that covers approximately
75% of the Earth's surface. Geologic evidence suggests that large
amounts of water have likely flowed on Earth for the past 3.8 billion
years---most of its existence. Believed to have initially arrived on the
surface through the emissions of ancient volcanoes, water is a vital
substance that sets the Earth apart from the rest of the planets in our
solar system. In particular, water appears to be a necessary ingredient
for the development and nourishment of life.

Water is practically everywhere on Earth. Moreover, it is the only known
substance that can naturally exist as a gas, a liquid, and solid within
the relatively small range of air temperatures and pressures found at
the Earth's surface. In all, the Earth's water content is about 1.39
billion cubic kilometers (331 million cubic miles), with the bulk of it,
about 96.5%, being in the global oceans. As for the rest, approximately
1.7% is stored in the polar icecaps, glaciers, and permanent snow, and
another 1.7% is stored in groundwater, lakes, rivers, streams, and soil.
Only a thousandth of 1% of the water on Earth exists as water vapor in
the atmosphere.

Despite its small amount, this water vapor has a huge influence on the
planet. Water vapor is a powerful greenhouse gas, and it is a major
driver of the Earth's weather and climate as it travels around the
globe, transporting latent heat with it. Latent heat is heat obtained by
water molecules as they transition from liquid or solid to vapor; the
heat is released when the molecules condense from vapor back to liquid
or solid form, creating cloud droplets and various forms of
precipitation. For human needs, the amount of freshwater on Earth---for
drinking and agriculture---is particularly important. Freshwater exists
in lakes, rivers, groundwater, and frozen as snow and ice. Estimates of
groundwater are particularly difficult to make, and they vary widely.
(The value in the above table is near the high end of the range.)
Groundwater may constitute anywhere from approximately 22 to 30% of
fresh water, with ice (including ice caps, glaciers, permanent snow,
ground ice, and permafrost) accounting for most of the remaining 78 to
70%.

The water, or hydrologic, cycle describes the pilgrimage of water as
water molecules make their way from the Earth's surface to the
atmosphere and back again, in some cases to below the surface. This
gigantic system, powered by energy from the Sun, is a continuous
exchange of moisture between the oceans, the atmosphere, and the land.

Studies have revealed that evaporation---the process by which water
changes from a liquid to a gas---from oceans, seas, and other bodies of
water (lakes, rivers, streams) provides nearly 90% of the moisture in
our atmosphere. Most of the remaining 10% found in the atmosphere is
released by plants through transpiration. Plants take in water through
their roots, then release it through small pores on the underside of
their leaves. In addition, a very small portion of water vapor enters
the atmosphere through sublimation, the process by which water changes
directly from a solid (ice or snow) to a gas. The gradual shrinking of
snow banks in cases when the temperature remains below freezing results
from sublimation.

Together, evaporation, transpiration, and sublimation, plus volcanic
emissions, account for almost all the water vapor in the atmosphere that
isn't inserted through human activities. While evaporation from the
oceans is the primary vehicle for driving the surface-to-atmosphere
portion of the hydrologic cycle, transpiration is also significant. For
example, a cornfield 1 acre in size can transpire as much as 4,000
gallons of water every day.

After the water enters the lower atmosphere, rising air currents carry
it upward, often high into the atmosphere, where the air is cooler. In
the cool air, water vapor is more likely to condense from a gas to a
liquid to form cloud droplets. Cloud droplets can grow and produce
precipitation (including rain, snow, sleet, freezing rain, and hail),
which is the primary mechanism for transporting water from the
atmosphere back to the Earth's surface.

When precipitation falls over the land surface, it follows various
routes in its subsequent paths. Some of it evaporates, returning to the
atmosphere; some seeps into the ground as soil moisture or groundwater;
and some runs off into rivers and streams. Almost all of the water
eventually flows into the oceans or other bodies of water, where the
cycle continues. At different stages of the cycle, some of the water is
intercepted by humans or other life forms for drinking, washing,
irrigating, and a large variety of other uses.

Groundwater is found in two broadly defined layers of the soil, the
"zone of aeration," where gaps in the soil are filled with both air and
water, and, further down, the "zone of saturation," where the gaps are
completely filled with water. The boundary between these two zones is
known as the water table, which rises or falls as the amount of
groundwater changes.

The amount of water in the atmosphere at any moment in time is only
12,900 cubic kilometers, a minute fraction of Earth's total water
supply: if it were to completely rain out, atmospheric moisture would
cover the Earth's surface to a depth of only 2.5 centimeters. However,
far more water---in fact, some 495,000 cubic kilometers of it---are
cycled through the atmosphere every year. It is as if the entire amount
of water in the air were removed and replenished nearly 40 times a year.

Water continually evaporates, condenses, and precipitates, and on a
global basis, evaporation approximately equals precipitation. Because of
this equality, the total amount of water vapor in the atmosphere remains
approximately the same over time. However, over the continents,
precipitation routinely exceeds evaporation, and conversely, over the
oceans, evaporation exceeds precipitation.

In the case of the oceans, the continual excess of evaporation versus
precipitation would eventually leave the oceans empty if they were not
being replenished by additional means. Not only are they being
replenished, largely through runoff from the land areas, but over the
past 100 years, they have been over-replenished: sea level around the
globe has risen approximately 17 centimeters over the course of the
twentieth century.

Sea level has risen both because of warming of the oceans, causing water
to expand and increase in volume, and because more water has been
entering the ocean than the amount leaving it through evaporation or
other means. A primary cause for increased mass of water entering the
ocean is the calving or melting of land ice (ice sheets and glaciers).
Sea ice is already in the ocean, so increases or decreases in the annual
amount of sea ice do not significantly affect sea level.

Throughout the hydrologic cycle, there are many paths that a water
molecule might follow. Water at the bottom of Lake Superior may
eventually rise into the atmosphere and fall as rain in Massachusetts.
Runoff from the Massachusetts rain may drain into the Atlantic Ocean and
circulate northeastward toward Iceland, destined to become part of a
floe of sea ice, or, after evaporation to the atmosphere and
precipitation as snow, part of a glacier.

Water molecules can take an immense variety of routes and branching
trails that lead them again and again through the three phases of ice,
liquid water, and water vapor. For instance, the water molecules that
once fell 100 years ago as rain on your great- grandparents' farmhouse
in Iowa might now be falling as snow on your driveway in California.

Among the most serious Earth science and environmental policy issues
confronting society are the potential changes in the Earth's water cycle
due to climate change. The science community now generally agrees that
the Earth's climate is undergoing changes in response to natural
variability, including solar variability, and increasing concentrations
of greenhouse gases and aerosols. Furthermore, agreement is widespread
that these changes may profoundly affect atmospheric water vapor
concentrations, clouds, precipitation patterns, and runoff and stream
flow patterns. For example, as the lower atmosphere becomes warmer,
evaporation rates will increase, resulting in an increase in the amount
of moisture circulating throughout the troposphere (lower atmosphere).
An observed consequence of higher water vapor concentrations is the
increased frequency of intense precipitation events, mainly over land
areas. Furthermore, because of warmer temperatures, more precipitation
is falling as rain rather than snow.

In parts of the Northern Hemisphere, an earlier arrival of spring-like
conditions is leading to earlier peaks in snowmelt and resulting river
flows. As a consequence, seasons with the highest water demand,
typically summer and fall, are being impacted by a reduced availability
of fresh water.

Warmer temperatures have led to increased drying of the land surface in
some areas, with the effect of an increased incidence and severity of
drought. The Palmer Drought Severity Index, which is a measure of soil
moisture using precipitation measurements and rough estimates of changes
in evaporation, has shown that from 1900 to 2002, the Sahel region of
Africa has been experiencing harsher drought conditions. This same index
also indicates an opposite trend in southern South America and the south
central United States.

While the brief scenarios described above represent a small portion of
the observed changes in the water cycle, it should be noted that many
uncertainties remain in the prediction of future climate. These
uncertainties derive from the sheer complexity of the climate system,
insufficient and incomplete data sets, and inconsistent results given by
current climate models. However, state of the art (but still incomplete
and imperfect) climate models do consistently predict that precipitation
will become more variable, with increased risks of drought and floods at
different times and places.

Orbiting satellites are now collecting data relevant to all aspects of
the hydrologic cycle, including evaporation, transpiration,
condensation, precipitation, and runoff. NASA even has one satellite,
Aqua, named specifically for the information it is collecting about the
many components of the water cycle.

Aqua launched on May 4, 2002, with six Earth-observing instruments: the
Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding
Unit (AMSU), the Humidity Sounder for Brazil (HSB), the Advanced
Microwave Scanning Radiometer for the Earth Observing System (AMSR-E),
the Moderate Resolution Imaging Spectroradiometer (MODIS), and Clouds
and the Earth's Radiant Energy System (CERES).

Since water vapor is the Earth's primary greenhouse gas, and it
contributes significantly to uncertainties in projections of future
global warming, it is critical to understand how it varies in the Earth
system. In the first years of the Aqua mission, AIRS, AMSU, and HSB
provided space-based measurements of atmospheric temperature and water
vapor that were more accurate than any obtained before; the sensors also
made measurements from more altitudes than any previous sensor. The HSB
is no longer operational, but the AIRS/AMSU system continues to provide
high-quality atmospheric temperature and water vapor measurements.

More recent studies using AIRS data have demonstrated that most of the
warming caused by carbon dioxide does not come directly from carbon
dioxide, but rather from increased water vapor and other factors that
amplify the initial warming. Other studies have shown improved
estimation of the landfall of a hurricane in the Bay of Bengal by
incorporating AIRS temperature measurements, and improved understanding
of large-scale atmospheric patterns such as the Madden-Julian
Oscillation.

In addition to their importance to our weather, clouds play a major role
in regulating Earth's climate system. MODIS, CERES, and AIRS all collect
data relevant to the study of clouds. The cloud data include the height
and area of clouds, the liquid water they contain, and the sizes of
cloud droplets and ice particles. The size of cloud particles affects
how they reflect and absorb incoming sunlight, and the reflectivity
(albedo) of clouds plays a major role in Earth's energy balance.

One of the many variables AMSR-E monitors is global precipitation. The
sensor measures microwave energy, some of which passes through clouds,
and so the sensor can detect the rainfall even under the clouds.

Water in the atmosphere is hardly the only focus of the Aqua mission.
Among much else, AMSR-E and MODIS are being used to study sea ice. Sea
ice is important to the Earth system not just as an important element in
the habitat of polar bears, penguins, and some species of seals, but
also because it can insulate the underlying liquid water against heat
loss to the often frigid overlying polar atmosphere and because it
reflects sunlight that would otherwise be available to warm the ocean.

When it comes to sea ice, AMSR-E and MODIS provide complementary
information. AMSR-E doesn't record as much detail about ice features as
MODIS does, but it can distinguish ice versus open water even when it is
cloudy. The AMSR-E measurements continue, with improved resolution and
accuracy, a satellite record of changes in the extent of polar ice that
extends back to the 1970s.

AMSR-E and MODIS also provide monitoring of snow coverage over land,
another key indicator of climate change. As with sea ice, AMSR-E allows
routine monitoring of the snow, irrespective of cloud cover, but with
less spatial detail, while MODIS sees greater spatial detail, but only
under cloud-free conditions.

As for liquid water on land, AMSR-E provides information about soil
moisture, which is crucial for vegetation including agricultural crops.
AMSR-E's monitoring of soil moisture globally permits, for example, the
early identification of signs of drought. Aqua is the most comprehensive
of NASA's water cycle missions, but it isn't alone. In fact, the Terra
satellite also has MODIS and CERES instruments onboard, and several
other spacecraft have made or are making unique water-cycle
measurements.

The Ice, Cloud, and Land Elevation Satellite (ICESat) was launched in
January 2003, and it collected data on the topography of the Earth's ice
sheets, clouds, vegetation, and the thickness of sea ice off and on
until October 2009. A new ICESat mission, ICESat-2, is now under
development and is scheduled to launch in 2015.

The Gravity Recovery and Climate Experiment (GRACE) is a unique mission
that consists of two spacecraft orbiting one behind the other; changes
in the distance between the two provide information about the gravity
field on the Earth below. Because gravity depends on mass, some of the
changes in gravity over time signal a shift in water from one place on
Earth to another. Through measurements of changing gravity fields, GRACE
scientists are able to derive information about changes in the mass of
ice sheets and glaciers and even changes in groundwater around the
world.

CloudSat is advancing scientists' understanding of cloud abundance,
distribution, structure, and radiative properties (how they absorb and
emit energy, including thermal infrared energy escaping from Earth's
surface). Since 2006, CloudSat has flown the first satellite-based,
millimeter-wavelength cloud radar---an instrument that is 1000 times
more sensitive than existing weather radars on the ground. Unlike
ground-based weather radars that use centimeter wavelengths to detect
raindrop-sized particles, CloudSat's radar allows the detection of the
much smaller particles of liquid water and ice in the large cloud masses
that contribute significantly to our weather.

The joint NASA and French Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observations (CALIPSO) is providing new insight into the role
that clouds and atmospheric aerosols (particles like dust and pollution)
play in regulating Earth's weather, climate, and air quality. CALIPSO
combines an active laser instrument with passive infrared and visible
imagers to probe the vertical structure and properties of thin clouds
and aerosols over the globe.

July through October fall within the dry season in the western and
northern Amazon rainforest, but a particularly acute lack of rain during
this period in 2023 has pushed the region into a severe drought. The OLI
(Operational Land Imager) instrument on Landsat 8 captured this image
(right) of the parched Rio Negro in the Brazilian province of Amazonas
near the city of Manaus on October 3, 2023. On that date, the level of
the river, the largest tributary of the Amazon River, had dropped to
15.14 meters (50.52 feet), according to data collected by the Port of
Manaus.

For comparison, the image on the left shows the same area on October 8,
2022, when the water level was 19.59 meters, a more typical level for
October. Rio Negro water levels continued to drop in the days after the
image was collected, reaching a record low of 13.49 meters on October
17, 2023. Some areas in the Amazon River's watershed have received less
rain between July and September than any year since 1980, Reuters
reported. The drought has been particularly severe in the Rio Negro
watershed in northern Amazonas, as well as parts of southern Venezuela
and southern Colombia. "Overall, this is a pretty unusual and extreme
situation," said René Garreaud, an atmospheric scientist at the
University of Chile.

"The primary culprit exacerbating the drought appears to be El Niño."

This cyclical warming of surface waters in the central-eastern Pacific
functions somewhat like a boulder in the middle of a stream, disrupting
atmospheric circulation patterns in ways that lead to wetter conditions
over the equatorial Pacific and drier conditions over the Amazon Basin.
According to news outlets, the low river water levels on the Rio Negro
and other nearby rivers have disrupted drinking water supplies in
hundreds of communities, slowed commercial navigation, and led to fish
and dolphin die-offs.

Manaus, the capital and largest city of the Brazilian state of Amazonas,
is the primary transportation hub for the upper Amazon, serving as an
important transit point for soap, beef, and animal hides. Other
industries with a presence in the city of two million people include
chemical, ship, and electrical equipment manufacturing.

After rapidly growing in volume just a few years earlier, northwest
Iran's Lake Urmia nearly dried out in autumn 2023. The largest lake in
the Middle East and one of the largest hypersaline lakes on Earth at its
greatest extent, Lake Urmia has for the most part transformed into a
vast, dry salt flat.

It stands in contrast to the image from three years earlier (left),
acquired by the OLI on Landsat 8 on September 8, 2020, when water filled
most of the basin and salt deposits were only visible around the
perimeter of the lake.

The replenishment followed a period of above-average precipitation that
sent a surge of freshwater into the basin, expanding its watery
footprint. Drier conditions have since brought levels back down. The
longer-term trend for Urmia has been one toward drying. In 1995, Lake
Urmia reached a high-water mark; then in the ensuing two decades, the
lake level dropped more than 7 meters (23 feet) and lost approximately
90 percent of its area.

Consecutive droughts, agricultural water use, and dam construction on
rivers feeding the lake have contributed to the decline.

A shrinking Lake Urmia has implications for ecological and human health.
The lake, its islands, and surrounding wetlands comprise valuable
habitat and are recognized as a UNESCO Biosphere Reserve, Ramsar site,
and national park.

The area provides breeding grounds for waterbirds such as flamingos,
white pelicans, and white-headed ducks, as well as a stopover for
migratory species. However, with low lake levels, what water remains
becomes more saline and taxes the populations of brine shrimp and other
food sources for larger animals.

A shrinking lake also increases the likelihood of dust from the exposed
lakebed becoming swept up by winds and degrading air quality. Recent
studies have linked the low water levels in Lake Urmia with respiratory
health impacts among the local population. The relative effects of
climate, water usage, and dams on Lake Urmia's water level is a topic of
debate. The lake did see some recovery during a 10-year restoration
program beginning in 2013.

However, the efficacy of that effort has been difficult to parse since
strong rains also fell during that period. Some research has concluded
that climatic factors were primarily responsible for the recovery.

The deep-blue sea is turning a touch greener. While that may not seem as
consequential as, say, record warm sea surface temperatures, the color
of the ocean surface is indicative of the ecosystem that lies beneath.
Communities of phytoplankton, microscopic photosynthesizing organisms,
abound in near-surface waters and are foundational to the aquatic food
web and carbon cycle.

This shift in the water's hue confirms a trend expected under climate
change and signals changes to ecosystems within the global ocean, which
covers 70 percent of Earth's surface. Researchers led by B. B. Cael, a
principal scientist at the U.K.'s National Oceanography Centre, revealed
that 56 percent of the global sea surface has undergone a significant
change in color in the past 20 years.

After analyzing ocean color data from the MODIS (Moderate Resolution
Imaging Spectroradiometer) instrument on NASA's Aqua satellite, they
found that much of the change stems from the ocean turning more green.
The map above highlights the areas where ocean surface color changed
between 2002 and 2022, with darker shades of green representing
more-significant differences (higher signal-to-noise ratio). By
extension, said Cael, "these are places we can detect a change in the
ocean ecosystem in the last 20 years."

The study focused on tropical and subtropical regions, excluding higher
latitudes, which are dark for part of the year, and coastal waters,
where the data are naturally very noisy. The black dots on the map
indicate the area, covering 12 percent of the ocean's surface, where
chlorophyll levels also changed over the study period.

Chlorophyll has been the go-to measurement for remote sensing scientists
to gauge phytoplankton abundance and productivity. However, those
estimates use only a few colors in the visible light spectrum. The
values shown in green are based on the whole gamut of colors and
therefore capture more information about the ecosystem as a whole. A
long time series from a single sensor is relatively rare in the remote
sensing world. As the Aqua satellite was celebrating its 20th year in
orbit in 2022---far exceeding its design life of 6 years---Cael wondered
what long term trends could be discovered in the data. In particular, he
was curious what might have been missed in all the ocean color
information it had collected. "There's more encoded in the data than we
actually make use of," he said.

By going big with the data, the team discerned an ocean color trend that
had been predicted by climate modeling, but one that was expected to
take 30-40 years of data to detect using satellite-based chlorophyll
estimates. That's because the natural variability in chlorophyll is high
relative to the climate change trend. The new method, incorporating all
visible light, was robust enough to confirm the trend in 20 years. At
this stage, it is difficult to say what exact ecological changes are
responsible for the new hues. However, the authors posit, they could
result from different assemblages of plankton, more detrital particles,
or other organisms such as zooplankton.

It is unlikely the color changes come from materials such as plastics or
other pollutants, said Cael, since they are not widespread enough to
register at large scales. "What we do know is that in the last 20 years,
the ocean has become more stratified," he said. Surface waters have
absorbed excess heat from the warming climate, and as a result, they are
less prone to mixing with deeper, more nutrient-rich layers.

This scenario would favor plankton adapted to a nutrient-poor
environment. The areas of ocean color change align well with where the
sea has become more stratified, said Cael, but there is no such overlap
with sea surface temperature changes. More insights into Earth's aquatic
ecosystems may soon be on the way.

NASA's PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) satellite, set
to launch in 2024, will return observations in finer color resolution.
The new data will enable researchers to infer more information about
ocean ecology, such as the diversity of phytoplankton species and the
rates of phytoplankton growth.

On September 10, 2023, a low-pressure storm brought heavy rains to
northeastern Libya, causing deadly flooding and devastation in cities
along the Mediterranean coast. On the coast of Libya's Cyrenaica region,
Al Bayda recorded 414 millimeters (16 inches) of rain in one day.

Nearby, the port city of Derna received more than 100 millimeters (4
inches) over the course of the storm---far exceeding the city's average
monthly rainfall for September of less than 1.5 millimeters (0.1
inches). Derna lies at the end of a long, narrow valley, called a wadi,
which is dry for most of the year.

Floods triggered two dams along Wadi Derna to collapse, sending
floodwater and mud to the city. According to news reports, floodwater
swept away roads and entire neighborhoods. The images above show the
Cyrenaica region before and after the storm. They are false color, which
makes water (blue) stand out from the surroundings. The image on the
right, acquired on September 13, shows water filling low-lying areas and
wadis inland from the coast.

The image on the left shows the same area on September 7. Both images
were acquired with the Moderate Resolution Imaging Spectroradiometer
(MODIS) on NASA's Terra satellite. The flooding and damage in Derna is
difficult to see at this resolution, although sediment flowing into the
Mediterranean is visible in natural color images.

In the days prior to making landfall in Libya, the same low-pressure
storm (named Storm Daniel by the Hellenic National Meteorological
Service) swamped parts of Greece, Turkey, and Bulgaria. As the storm
approached Libya, it developed characteristics of a tropical-like
cyclone, or "medicane," with winds measuring around 70 to 80 kilometers
(43 to 50 miles) per hour.

The natural-color image above, acquired with MODIS on NASA's Terra
satellite, shows the storm on September 10 as it made landfall in
northeastern Libya. Only one or two medicanes typically develop in a
year, according to NOAA.

As of September 13, authorities were still conducting search and rescue
operations in the region. Derna was still largely inaccessible on that
day, making it difficult to assess the full impact of the flood.

Sea surface temperatures have a large influence on climate and weather.
For example, every 3 to 7 years a wide swath of the Pacific Ocean along
the equator warms by 2 to 3 degrees Celsius.

This warming is a hallmark of the climate pattern El Niño, which changes
rainfall patterns around the globe, causing heavy rainfall in the
southern United States and severe drought in Australia, Indonesia, and
southern Asia.

On a smaller scale, ocean temperatures influence the development of
tropical cyclones (hurricanes and typhoons), which draw energy from warm
ocean waters to form and intensify.

These sea surface temperature maps are based on observations by the
Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua
satellite. The satellite measures the temperature of the top millimeter
of the ocean surface. In this map, the coolest waters appear in blue
(approximately -2 degrees Celsius), and the warmest temperatures appear
in pink-yellow (35 degrees Celsius).

Landmasses and the large area of sea ice around Antarctica appear in
shades of gray, indicating no data were collected.

The most obvious pattern shown in the time series is the year-round
difference in sea surface temperatures between equatorial regions and
the poles.

Various warm and cool currents stand out even in monthly averages of sea
surface temperature. A band of warm waters snakes up the East Coast of
the United States and veers across the North Atlanticâ€"the Gulf Stream.

Although short-lived weather events that influence ocean temperature are
often washed out in monthly averages, a few events show up.

For example, in December 2003, strong winds blew southwest from the Gulf
of Mexico over Central America toward the Pacific Ocean, driving surface
waters away from the coast, and allowing cold water from deeper in the
ocean to well up to the surface. These winds are a recurring phenomenon
in the area in the winter; they are known as Tehuano winds.

At the base of the ocean food web are single-celled algae and other
plant-like organisms known as phytoplankton. Like plants on land,
phytoplankton use chlorophyll and other light-harvesting pigments to
carry out photosynthesis, absorbing atmospheric carbon dioxide to
produce sugars for fuel. Chlorophyll in the water changes the way it
reflects and absorbs sunlight, allowing scientists to map the amount and
location of phytoplankton. These measurements give scientists valuable
insights into the health of the ocean environment, and help scientists
study the ocean carbon cycle.

These chlorophyll maps show milligrams of chlorophyll per cubic meter of
seawater each month. Places where chlorophyll amounts were very low,
indicating very low numbers of phytoplankton are blue. Places where
chlorophyll concentrations were high, meaning many phytoplankton were
growing, are dark green. The observations come from the Moderate
Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite.
Land is dark gray, and places where MODIS could not collect data because
of sea ice, polar darkness, or clouds are light gray.

The highest chlorophyll concentrations, where tiny surface-dwelling
ocean plants are thriving, are in cold polar waters or in places where
ocean currents bring cold water to the surface, such as around the
equator and along the shores of continents. It is not the cold water
itself that stimulates the phytoplankton. Instead, the cool temperatures
are often a sign that the water has welled up to the surface from deeper
in the ocean, carrying nutrients that have built up over time. In polar
waters, nutrients accumulate in surface waters during the dark winter
months when plants can't grow. When sunlight returns in the spring and
summer, the plants flourish in high concentrations.

A band of cool, plant-rich waters circles the globe at the Equator, with
the strongest signal in the Atlantic Ocean and the open waters of the
Pacific Ocean. This zone of enhanced phytoplankton growth comes from the
frequent upwelling of cooler, deeper water as a result of the dominant
easterly trade winds blowing across the ocean surface. In many coastal
areas, the rising slope of the sea floor pushes cold water from the
lowest layers of the ocean to the surface. The rising, or upwelling
water carries iron and other nutrients from the ocean floor. Cold
coastal upwelling and subsequent phytoplankton growth are most evident
along the west coasts of North and South America and southern Africa.

In March and April 2023, some earth scientists began to point out that
average sea surface temperatures had surpassed the highest levels seen
in a key data record maintained by NOAA. Months later, they remain at
record levels, with global sea surface temperatures 0.99°C (1.78°F)
above average in July. That was the fourth consecutive month they were
at record levels. Scientists from NASA have taken a closer look at why.
"There are a lot of things that affect the world's sea surface
temperatures, but two main factors have pushed them to record heights,"
said Josh Willis, an oceanographer at NASA's Jet Propulsion Laboratory
(JPL). "We have an El Niño developing in the Pacific, and that's on top
of long-term global warming that has been pushing ocean temperatures
steadily upward almost everywhere for a century."

The map above shows sea surface temperature anomalies on August 21,
2023, when many areas were more than 3°C (5.4°F) warmer than normal. On
that date, much of the central and eastern regions of the equatorial
Pacific were unusually warm, the signature of a developing El Niño. As
has been the case for weeks, large patches of warm water were also
present in the Northwest Pacific near Japan and the Northeast Pacific
near California and Oregon. Portions of the Indian, Southern, and Arctic
Oceans also showed unusual warmth.

The map is based on data from the Multiscale Ultrahigh Resolution Sea
Surface Temperature (MUR SST) project, a JPL effort that blends
measurements of sea surface temperatures from multiple NASA, NOAA, and
international satellites, as well as ship and buoy observations. Rather
than showing absolute temperature, the anomaly reflects the difference
between the sea surface temperature on August 21, 2023, and the
2003-2014 average for that day. The video below, also based on MUR SST
data, shows global sea surface temperatures since April 1, 2023, the
period when they have been at record-breaking levels. The warmest waters
appear dark red.

In March and April 2023, some earth scientists began to point out that
average sea surface temperatures had surpassed the highest levels seen
in a key data record maintained by NOAA. Months later, they remain at
record levels, with global sea surface temperatures 0.99°C (1.78°F)
above average in July.

That was the fourth consecutive month they were at record levels.
Scientists from NASA have taken a closer look at why. "There are a lot
of things that affect the world's sea surface temperatures, but two main
factors have pushed them to record heights," said Josh Willis, an
oceanographer at NASA's Jet Propulsion Laboratory (JPL). "We have an El
Niño developing in the Pacific, and that's on top of long-term global
warming that has been pushing ocean temperatures steadily upward almost
everywhere for a century."

The map above shows sea surface temperature anomalies on August 21,
2023, when many areas were more than 3°C (5.4°F) warmer than normal. On
that date, much of the central and eastern regions of the equatorial
Pacific were unusually warm, the signature of a developing El Niño.

As has been the case for weeks, large patches of warm water were also
present in the Northwest Pacific near Japan and the Northeast Pacific
near California and Oregon. Portions of the Indian, Southern, and Arctic
Oceans also showed unusual warmth. The map is based on data from the
Multiscale Ultrahigh Resolution Sea Surface Temperature (MUR SST)
project, a JPL effort that blends measurements of sea surface
temperatures from multiple NASA, NOAA, and international satellites, as
well as ship and buoy observations. Rather than showing absolute
temperature, the anomaly reflects the difference between the sea surface
temperature on August 21, 2023, and the 2003-2014 average for that day.

The video below, also based on MUR SST data, shows global sea surface
temperatures since April 1, 2023, the period when they have been at
record-breaking levels. The warmest waters appear dark red.

"Over the long term, we're seeing more heat and warmer sea surface
temperatures pretty much everywhere," said Gavin Schmidt, the director
of NASA's Goddard Institute for Space Studies. "That long-term trend is
almost entirely attributable to human forcing---the fact that we've put
such a huge amount of greenhouse gas in the atmosphere since the start
of the industrial era." Schmidt noted that other factors---such as
weather and wind patterns or the distribution of dust and
aerosols---have short-term effects on sea surface temperatures in
certain regions, but they generally have a minor effect on the
longer-term global mean. Previous research shows that as much as 90
percent of the excess heat that has occurred in recent decades due to
increasing greenhouse gas emissions is absorbed by the ocean, with much
of that heat stored near the surface. The most important factor that
helped push sea surface temperatures into record territory in 2023 was
the evolving El Niño in the Pacific, according to Willis. He came to
that conclusion by analyzing the timing and intensity of sea surface
temperature anomalies in several regions and comparing them to the
global trend. "We had a big jump in global surface temperature at the
beginning of April---exactly when the Pacific temperatures jumped up and
also when sea levels in the eastern Pacific started to rise," Willis
said. "The heat waves in the Atlantic are important and will have
serious effects on marine life and weather in Europe in the coming
months. But it's the Pacific that has taken the global mean on a wild
ride this year." What happens in the Pacific tends to have a large
influence on the global sea surface temperatures partly because of its
size.

The Pacific represents about half of the world's ocean area.

Marine heat waves---defined as periods of persistent anomalously warm
ocean temperatures (warmer than 90 percent of the previous observations
for a given time of year)---have occurred recently in several areas.

One NOAA analysis showed that 48 percent of the global oceans were in
the midst of a marine heat wave in August---a larger area than for any
other month since the start of the record in 1991.

Particularly intense events have warmed the North Atlantic and parts of
the Caribbean in recent months.

Willis expects the heat in the equatorial Pacific to have more staying
power than many of the other marine heat waves simmering around the
world. "Many of the marine heat waves we're seeing are ephemeral and
'skin' deep, generally lasting on the order of weeks and driven by
atmospheric forces," explained Willis.

The unusually warm water in the equatorial Pacific associated with the
developing El Niño after three consecutive years of La Niña is expected
to weaken trade winds in ways that reinforce and amplify the warming of
surface waters, fueling the El Niño further.

Forecasters from NOAA say that there is a greater than 95 percent chance
that El Niño conditions will persist throughout the Northern Hemisphere
winter.

"What's happening in the Pacific with El Niño will influence global
weather patterns and sea surface temperatures well into the winter and
possibly even longer," Willis said.

To monitor sea surface temperatures, scientists at NOAA and NASA analyze
observations from sensors and buoys in the oceans, ships, and several
different polar-orbiting and geostationary satellites. Groups of
scientists with NOAA's Physical Sciences Laboratory, NOAA's Coral Reef
Watch, and NASA's Jet Propulsion Laboratory track marine heat waves and
sea surface temperature anomalies closely.

You can use NASA's State of the Ocean Tool on Worldview to monitor daily
sea surface temperature anomalies.

One of the wettest wet seasons in northern Australia transformed large
areas of the country's desert landscape over the course of many months
in 2023. A string of major rainfall events that dropped 690 millimeters
(27 inches) between October 2022 and April 2023 made it the
sixth-wettest season on record since 1900--1901.

This series of false-color images illustrates the rainfall's months-long
effects downstream in the Lake Eyre Basin. Water appears in shades of
blue, vegetation is green, and bare land is brown. The images were
acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on
NASA's Terra satellite between January and July 2023.

In the January 22 image (left), water was coursing through seasonally
dry channels of the Georgina River and Eyre Creek following weeks of
heavy rains in northern Queensland. By April 21 (middle), floodwaters
had reached further downstream after another intense period of
precipitation in March. This scene shows that water had filled in some
of the north-northwest trending ridges that are part of a vast fossil
landscape of wind-formed dunes, while vegetation had emerged in wet soil
upstream. Then by July 26 (right), the riverbed had filled with even
more vegetation.

The Georgina River and Eyre Creek drain approximately 210,000 square
kilometers (81,000 square miles), nearly the area of the United Kingdom.
Visible in the lower part of the images, the lake gets refreshed about
every three years; when it reaches especially high levels, it may take
18 months to 2 years to dry up. Two smaller neighboring lakes flood
seasonally. These three lakes and surrounding floodplains support
hundreds of thousands of waterbirds and are designated as an Important
Bird Area.

Seasonal flooding is a regular occurrence in these desert river systems.
However, the events of the 2022-2023 rainy season stood out in several
ways. They occurred while La Niña conditions were in place over the
tropical Pacific Ocean. (The wettest seasons in northern Australia have
all occurred during La Niña years, according to Australia's Bureau of
Meteorology.) In addition, major rains occurring in succession, as was
the case with the January and March events, have the overall effect of
prolonging floods. That's because vegetation that grows after the first
event slows down the pulse of water that comes through in the next rain
event.

The high water has affected both local communities and ecosystems.
Floods have inundated cattle farms and isolated towns on temporary
islands. At the same time, they are a natural feature of the
"boom-and-bust" ecology of Channel Country, providing habitat and
nutrients that support biodiversity.

After three consecutive years of La Niña, spring 2023 saw the return of
El Niño---a natural climate phenomenon characterized by the presence of
warmer than normal sea surface temperatures (and higher sea levels) in
the central and eastern tropical Pacific Ocean.

El Niño is associated with the weakening of easterly trade winds and the
movement of warm water from the western Pacific toward the western coast
of the Americas. The phenomenon can have widespread effects, often
bringing cooler, wetter conditions to the U.S. Southwest and drought to
countries in the western Pacific, such as Indonesia and Australia.

Satellite- and ocean-based measurements of sea surface temperature are
one way to detect the arrival of El Niño. Its signature also shows up in
satellite measurements of sea surface height, which rises as ocean
temperatures warm up. That's because warmer water expands to fill more
volume, while cooler water contracts.

The map above depicts sea surface height anomalies across the central
and eastern Pacific Ocean as observed from June 1--10, 2023. Shades of
blue indicate sea levels that were lower than average; normal sea level
conditions appear white; and reds indicate areas where the ocean stood
higher than normal.

Data for the map were acquired by the Sentinel-6 Michael Freilich and
Sentinel-3B satellites and processed by scientists at NASA's Jet
Propulsion Laboratory (JPL). Note that signals related to seasonal
cycles and long-term trends have been removed to highlight sea level
anomalies associated with El Niño and other short-term natural
phenomena.

In a report released on June 8, 2023, the NOAA Climate Prediction Center
declared El Niño conditions were present. The report pointed to sea
surface temperatures in the Niño 3.4 region of the tropical Pacific
(from 170° to 120° West longitude) that in May 2023 were 0.8°C (1.4°F)
above the long-term average.

Forecasters expected El Niño conditions to gradually strengthen into the
2023--2024 Northern Hemisphere winter, by which time they called for a
84 percent chance of a moderate strength El Niño developing and a 56
percent chance of a strong El Niño.

As of June 2023, however, El Niño was not as far along as past El Niño
events by the same time of year, according to Josh Willis, an
oceanographer and Sentinel-6 Michael Freilich project scientist at JPL.

"It's still a bit too early to say whether this will be a big one,"
Willis said. "It will probably have some global impacts, but there's
still time for this El Niño to underwhelm."

As spring turned to summer, phytoplankton came to life in the shallow
waters of the North Sea. Sunlight and warm ocean temperatures in June
2023 enabled the microscopic plant-like organisms to rapidly multiply
and form a dazzling turquoise display visible to satellites.

Satellites observed hints of the bloom developing between Scotland and
Norway for about two weeks, but the view from above was mostly hidden by
clouds. Then, mostly clear skies on the afternoon of June 15, 2023,
allowed the Visible Infrared Imaging Radiometer Suite (VIIRS) on the
NOAA-20 satellite to acquire this natural-color image of the abundant
phytoplankton.

Phytoplankton are to the ocean what plants are to land: primary
producers, an essential food source for other life, and the main carbon
recycler for the marine environment. Diatoms, coccolithophores, algae,
and other forms of phytoplankton are floating, plant-like organisms that
soak up sunshine, carbon dioxide, and nutrients to create their own
energy.

This bloom might contain some diatoms---a type of phytoplankton with
silica shells and ample chlorophyll that color the surface waters green.
The color of the water, however, indicates that coccolithophores are
likely abundant. Coccolithophores have calcium carbonate shells that
make the water appear milky blue in satellite imagery, and they
typically peak in abundance at these latitudes around the summer
solstice.

Phytoplankton are typically most abundant in the North Sea in late
spring and early summer when high levels of nutrients are available in
the water. Melting sea ice and increased runoff from European rivers---a
product of melting snow and spring rains---carry a heavy load of
nutrients out to sea. Intense seasonal winds blowing over the relatively
shallow sea also cause a lot of mixing that brings nutrients to the
surface.

Researchers in Norway studied the patterns and timing of phytoplankton
blooms in the North Sea using data from multiple satellite sensors,
including VIIRS and NASA's Moderate Resolution Imaging Spectroradiometer
(MODIS). They found that between 2000 and 2020, blooms in this region of
the North Sea peaked in mid-to-late April. These blooms lasted, on
average, about 46 days. They also found that in the 21-year study
period, phytoplankton blooms in the region were starting later in the
year and lasting slightly longer. The cause of this delay, however, was
not immediately clear.

The composition of phytoplankton blooms near Norway may be changing over
time with warmer sea surface temperatures, the researchers noted, but it
is difficult to tell the species composition of blooms without taking
physical samples. However, a future NASA Plankton, Aerosol, Cloud, ocean
Ecosystem (PACE) satellite mission will enable researchers to infer more
information about ocean ecology, such as the species of phytoplankton
present in blooms and the rates of phytoplankton growth.

Sea ice in the Sea of Okhotsk put on a dazzling display in late May
2023, as the winter's ice pack thinned and broke up. The freely drifting
ice, subject to wind and currents, formed a series of spirals off the
coast of Russia.

The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua
satellite captured this image on May 28, 2023. More-intact ice is
visible on the north side of the P'yagina Peninsula (Poluostrov
P'yagina), at the top of the image, with smaller pieces breaking away
and drifting to the south and west. A group of islands---too small to
see clearly at this scale---off the eastern tip of the land may be
responsible for the small eddies in that area. Spirals such as these can
form downstream of a stationary object that obstructs fluid flow.

The Sea of Okhotsk, which is hemmed in by the Siberian coast and the
Kamchatka Peninsula, is the southernmost sea in the Northern Hemisphere
that freezes seasonally. An influx of frigid Siberian air, in addition
to inflows of freshwater from rivers that lower the salinity and raise
the freezing point of the water, create conducive conditions for ice to
form during the colder months.

During the 2022-2023 winter, the extent of sea ice in the Arctic was
below average. The end-of-winter minimum extent, reached on March 6, was
the sixth lowest in the satellite record, according to data maintained
by the National Snow & Ice Data Center (NSIDC). The NSIDC also noted
that seasonal ice decline picked up in the last several days of May,
when this image was captured.

In a recent study, researchers in Japan found that yearly differences in
ice extent are largely governed by regional cold air masses and
low-pressure systems, along with large-scale patterns associated with
the El Niño/Southern Oscillation (ENSO). Looking at longer term
climate-driven trends, they reported that ice extent in the Sea of
Okhotsk decreased by about 9 percent per decade between 1979 and 2010.

Every fall, millions of people flock to the Shuangtaizi Estuary (also
called the Liao River Estuary) in northeastern China to marvel at its
brilliant red coastal landscapes. They are drawn by the expanses of rare
salt-loving seepweed that thrive in the estuary's alkaline tidal
mudflats.

The small shrubby plants, Suaeda salsa (also called Suaeda heteroptera),
start out greenish-red in the spring but become a bright crimson in fall
as seasonal shifts in rainfall and tides expose seepweed to slightly
saltier, cooler conditions. This leads to the increased production and
accumulation of the red pigment betalain.

However, the estuary has changed significantly in recent decades due to
coastal development, raising questions about the long-term viability of
its colorful seepweed beaches and wetland habitat. The scale of change
is apparent in the pair of Landsat images shown above.

The image on the left, acquired by the Thematic Mapper on Landsat 5,
shows the estuary in 1986; the image on the right, from the Operational
Land Imager-2 (OLI-2) on Landsat 9, shows the same area in 2022. Both
images were acquired in September, around the time when seepweed reaches
its deepest red. The green areas along the river are dominated by
Phragmites australis, a type of reed. The yellow areas are rice fields.
The photo below shows seepweed (red) in the foreground transitioning
into Phragmites australis (green) in the background. Large new
aquaculture facilities---and a new port (lower right of image)---have
replaced tidal flats where seepweed once thrived. The construction of a
dyke and reservoir on the eastern bank of the river has also isolated
part of the estuary from tidal waters, making the area unsuitable for
seepweed. In wider views of the 1986 and 2022 images, notice how
seepweed was further constrained by urbanization and the expansion of
aquaculture to the east of the port, as well as the expansion of gas and
oil drilling on the western bank of the Shuangtaizi River.

Human activities have affected seepweed in other less direct ways in
recent decades. The construction of dams, bridges, and canals caused
spikes in the amount of sediment carried by the river and deposited on
tidal flats downstream. The extra accumulations made it difficult for
new seepweed plants to germinate in some areas. Researchers have also
found evidence indicating that the construction of boardwalks and the
rising number of tourists has harmed seepweed by scaring away waterbirds
that feed on crabs, leading to higher numbers of crabs grazing on
seepweed in certain areas.

Overall, hundreds of square kilometers of wetlands have been lost since
the 1980s, according to one analysis that spans three decades of Landsat
observations. The amount of land with seepweed dropped by roughly 25
percent during that time, though certain parts of the estuary have seen
seepweed areas expand or grow more concentrated.

Seepweed-seeking tourists are not the only group affected by the loss of
tidal flats and wetlands in this area. The estuary provides habitat for
more than 100 water birds, including the critically endangered Siberian
Crane (Leucogeranus leucogeranus), the endangered Oriental stork
(Ciconia boyciana), and the red-crowned Crane (Grus japonensis). The
estuary was named a national nature reserve in 1998 and a Ramsar site in
2005.

For the past few decades, scientists have been observing natural ocean
fertilization events---episodes when plumes of volcanic ash, glacial
flour, wildfire soot, and desert dust blow out onto the sea surface and
spur massive blooms of phytoplankton. But beyond these extreme events,
there is a steady, long-distance rain of dust particles onto the ocean
that promotes phytoplankton growth just about all year and in nearly
every basin.

In a new study published May 5 in the journal Science, a team of
researchers from Oregon State University, the University of Maryland
Baltimore County, and NASA combined satellite observations with an
advanced computer model to home in on how mineral dust from land
fertilizes the growth of phytoplankton in the ocean. Phytoplankton are
microscopic, plant-like organisms that form the center of the marine
food web.

Phytoplankton float near the ocean surface primarily subsisting on
sunlight and mineral nutrients that well up from the depths or float out
to sea in coastal runoff. But mineral-rich desert dust---borne by strong
winds and deposited in the ocean---also plays an important role in the
health and abundance of phytoplankton.

This image, acquired on April 8, 2011, by the Moderate Resolution
Imaging Spectroradiometer (MODIS) on NASA's Terra satellite, shows
Saharan dust over the Bay of Biscay. A phytoplankton bloom in the bay
makes the water appear bright green and blue. Sediment is likely
contributing to some of the color, especially in areas closer to the
shore.

According to the new study, dust deposition onto the ocean supports
about 4.5 percent of yearly global export production---a measure of how
much of the carbon phytoplankton take up during photosynthesis sinks
into the deep ocean. However, this contribution approaches 20 percent to
40 percent in some ocean regions at middle and higher latitudes.

Phytoplankton play a large role in Earth's climate and carbon cycle.
Like land plants, they contain chlorophyll and derive energy from
sunlight through photosynthesis. They produce oxygen and sequester a
tremendous amount of carbon dioxide in the process, potentially on a
scale comparable to rainforests. And they are at the bottom of an
ocean-wide food pecking order that ranges from tiny zooplankton to fish
to whales.

Dust particles can travel thousands of miles before falling into the
ocean, where they nourish phytoplankton long distances from the dust
source, said study coauthor Lorraine Remer, a research professor at the
University of Maryland Baltimore County. "We knew that atmospheric
transport of desert dust is part of what makes the ocean 'click,' but we
didn't know how to find it," she said.

Seasonal allergy sufferers be warned: this story may have you reaching
for the antihistamines. Researchers have determined that "slicks" on the
surface of the Baltic Sea, visible in satellite images, are made up of
pine pollen.

Pollen slicks are visible in these images of the Baltic Sea, acquired on
May 16, 2018, with the MultiSpectral Instrument (MSI) on the European
Space Agency's Sentinel-2A satellite. The images are false-color (bands
8A, 3, and 2) and have been enhanced to increase the visibility of the
pollen. The patterns are caused by wind-driven currents and waves moving
the pollen around on the surface of the water.

The composition of slicks in this region was previously unclear. Other
types of floating material, such as cyanobacteria and marine debris,
have been known to appear in satellite imagery. But by combining
experimental results, ground-based observations, and satellite image
processing, the researchers could confidently attribute the material in
the eddies to pine (Pinus sylvestris) pollen.

The impetus for investigating this phenomenon came from a different
marine event, said Chuanmin Hu, an ocean optics expert at the University
of South Florida who led the research. "This work is inspired by a
recent sea snot event in the Marmara Sea that created a huge problem for
Türkiye and its coastal regions," he said. Sea snot, which is caused by
phytoplankton releasing a gooey substance, coated large swaths of the
sea in May 2021 and caught Hu's attention when it was detected by
satellites.

That led him to wonder if anything comparable was occurring on other
large bodies of water nearby. As it turned out, satellite images of the
Baltic Sea from that time looked similar to the satellite images of sea
snot in the Marmara Sea (to human eyes, at least). But Hu found it
strange that there were no reports of disruptive slime from the large,
heavily trafficked sea.

To identify potential slicks, Hu and colleagues inspected
medium-resolution satellite images from sensors such as the Moderate
Resolution Imaging Spectroradiometer on NASA's Terra and Aqua
satellites. When his team analyzed other satellite data for the spectral
signature of the mystery Baltic Sea substance, they realized it was
distinct from sea snot and other floating matter. The spectral shape had
a characteristically sharp increase between wavelengths of 400 and 500
nanometers.

Given the timing of the slicks and the prevalence of pine trees in the
nine countries surrounding the sea, they suspected pollen as a possible
culprit. Collaborators in Poland had photographs of pollen on the
surface of the water, acquired during fieldwork in May 2013 (below). To
dig deeper, the U.S. and Polish groups conducted laboratory and field
experiments to measure the spectral reflectance of pollen. Indeed, the
results matched what was captured by satellites.

The researchers then looked back at springtime images of the Baltic Sea
from 2000 to 2021 and saw similar slick patterns in 14 of those years.
Notably, the pollen's footprint on the sea in the second half of the
study period was markedly larger than in the first half. In recent
years, slicks often cover some portion of the sea in parts of May and
June.

This observation aligns with trends toward longer pollen seasons and
more pollen production that have been documented in other areas of the
world. For example, one recent study found that pollen season in North
America starts nearly three weeks sooner and lasts about a week longer
than it did in 1990, driven by warming temperatures. In addition, more
carbon dioxide in the atmosphere fueling photosynthesis may increase
plants' potential to produce more pollen.

The profusion of pollen may have larger impacts beyond making people
sneeze. Though not well studied, pollen grains can affect aquatic
ecosystems by supplying carbon to the sea. Much like leaf litter
supports food webs in lakes and streams, pollen grains may be an
important source of nutrients for insect larvae, crustaceans, and other
invertebrates in coastal Baltic Sea waters.

Having cracked the code of distinguishing pollen in satellite imagery,
Hu thinks the imagery may lead to several new insights. "If we can track
pollen aggregation in different places, this may provide useful data for
fisheries studies," he said. Even more, the technique could complement
land-based air quality sensors to monitor allergens---all the more
relevant as human health impacts from allergies intensify.

For several weeks in April 2023, swirls of green and turquoise grew more
vibrant in the waters off the Mid-Atlantic coast of the United States.
Some of the color is due to an abundance of phytoplankton. Though each
of these floating plant-like organisms is microscopic, large groups of
them are visible to satellites.

A phytoplankton bloom was under way on April 20, 2023, when the Moderate
Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite
acquired this image (top). The detailed image below was acquired the
same day with the Operational Land Imager-2 (OLI-2) on Landsat 9.

Phytoplankton are responsible for nearly half of Earth's primary
production. They turn carbon dioxide, sunlight, and nutrients into the
food that feeds almost all other life in the sea, from zooplankton to
finfish to whales.

The type of phytoplankton present in the bloom cannot be definitively
identified based on these natural-color images. But assessments of past
blooms in the area have turned up a mix of diatoms and coccolithophores.
Diatoms, a microscopic form of algae, have silica shells and plenty of
chlorophyll that can make the water appear green. Coccolithophores have
chalky calcium carbonate plates (coccoliths) that reflect light and can
make the water appear bright blue.

Color can also come from other sources, such as sediment or colored
dissolved organic matter (CDOM) that have mixed in the water. Discharge
from the Delaware River delivers sediment and CDOM to the coastal waters
in this region. It can also supply nutrients---contained in the runoff
from farms and urban and suburban areas---that help to fuel large
blooms.

Similar blooms have occurred in recent years, in both 2021 and 2022.
Those blooms, however, developed their most striking colors almost one
month later, around mid-May.

In February 2023, Tropical Cyclone Gabrielle churned south across the
Coral Sea and passed over the Bellona Plateau---a shallow area 600
kilometers (400 miles) west of Grande Terre, the principal island of New
Caledonia. Once a sizable island during the Pleistocene ice ages, the
plateau is now submerged under 25-50 meters of water. It hosts reefs
that teem with corals, coralline algae, mollusks, foraminifera, and many
other types of marine life with calcium carbonate skeletons or shells.

Signs of underwater reefs and carbonate platforms are often subtle in
satellite imagery. But Gabrielle's winds were fierce enough that the
storm left a clear sign of the carbonate ecosystem below the water. The
passing storm stirred up enough carbonate sediment to temporarily
discolor more than 13,000 square kilometers of water, an area about the
size of Puerto Rico. Resuspension events of this size are rare at
Bellona Plateau, with this being only the second time it has happened at
this scale since the launch of the MODIS sensor on the Terra satellite
in 1999.

Gabrielle was passing over the area on February 9, 2023, when the
Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra
satellite acquired the first image above. After the storm clouds
cleared, the satellite observed carbonate sediment that had become
suspended in the water (second image) on February 11, 2023. The sediment
drifted in ocean currents over the span of a week, with water over the
Bellona plateau returning to its normal color by February 20, 2023.

The Operational Land Imager-2 (OLI-2) on Landsat 9 captured the detailed
images (below) showing sediment swirling in eddies around the plateau on
February 12, 2023. The sediment was likely fine-grained carbonate mud
with some larger carbonate sand mixed in. It likely formed due to the
erosion and accumulation of bits of coral skeletons, coralline algae,
and the hard shells of marine organisms that live on the plateau.

"With the right water chemistry and amount of light, plateaus like this
become major calcium carbonate factories," explained James Acker, an
oceanographer with ADNET Systems at the Goddard Earth Sciences Data and
Information Services Center (GES DISC). Previous estimates suggest that
although shallow coastal areas cover just 7 percent of the ocean's area,
they generate about half of the world's marine carbonate sediment.

Acker has been using satellites to observe carbonate resuspension events
since the launch of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS)
in 1997. The goal is to develop better estimates for how much carbonate
sediment from shallow areas ends up getting pushed into deeper waters by
winds, currents, or other processes.

"Deep ocean water dissolves carbonate muds and sands when they sink,"
explained Acker. "That can help counter the ongoing ocean acidification
we're seeing that is caused by the rising levels of carbon dioxide in
the atmosphere." In some cases, depending on the chemistry of the water,
carbonates dissolve at depths as shallow as 500 meters. In others, they
dissolve at depths closer to 4.5 kilometers.

Estimates suggest that oceans absorb about 30 percent of the carbon
dioxide that humans release into the atmosphere. Some of that carbon
gets incorporated into shells and sediment and eventually stored as
calcium carbonate in limestone and other sedimentary rocks, making
carbonate platforms and marine sedimentary rock an important carbon
sink.

However, the estimates for how much carbonate these shallow carbonate
reefs and plateaus produce, export, and store vary significantly. And
there is considerable uncertainty about how the ocean's ability to store
carbon will change as the acidity of the ocean changes. More acidic
ocean waters make it harder for many marine organisms to build calcium
carbonate shells and thrive, so acidification may reduce how much carbon
ends up stored in sedimentary rocks.

The first step to investigating how climate change might be changing the
marine carbon cycle is to simply understand and document how much
carbonate sediment is cycling between shallow and deep water, explained
Acker. That has led Acker and sedimentologist Jude Wilber to examine
decades of satellite data to find out if storm and wind events play an
important role in this cycling from shallow to deep.

At the American Geophysical Union's Oceans meeting in 2022, Acker and
colleagues presented an analysis of a previous resuspension event that
followed Tropical Cyclone Wati hitting the Bellona Plateau in 2006. That
event occurred after the Category 4 storm stalled over the plateau for
two days and battered it with winds that exceeded 209 kilometers (135
miles) per hour.

However, the estimates for how much carbonate these shallow carbonate
reefs and plateaus produce, export, and store vary significantly. And
there is considerable uncertainty about how the ocean's ability to store
carbon will change as the acidity of the ocean changes. More acidic
ocean waters make it harder for many marine organisms to build calcium
carbonate shells and thrive, so acidification may reduce how much carbon
ends up stored in sedimentary rocks.

The first step to investigating how climate change might be changing the
marine carbon cycle is to simply understand and document how much
carbonate sediment is cycling between shallow and deep water, explained
Acker. That has led Acker and sedimentologist Jude Wilber to examine
decades of satellite data to find out if storm and wind events play an
important role in this cycling from shallow to deep.

At the American Geophysical Union's Oceans meeting in 2022, Acker and
colleagues presented an analysis of a previous resuspension event that
followed Tropical Cyclone Wati hitting the Bellona Plateau in 2006. That
event occurred after the Category 4 storm stalled over the plateau for
two days and battered it with winds that exceeded 209 kilometers (135
miles) per hour.

"Due to decades of satellite observations---and dramatic examples like
this---we can say confidently that tropical cyclones play a very
important role," said Acker. "Nothing else exports the volume of
sediment into deeper water that they do. The next step is to demonstrate
that in a more systematic and rigorous way by analyzing the entire
satellite record with machine learning techniques and getting teams out
in the field to better understand the dynamics of transport events."

Water from recent winter storms is needed by farmers, wildlife, and
residents in the region, where precipitation and lake levels in recent
years have been among the lowest since the 1970s. However, scientists
caution that similar large precipitation events in the past have not
been enough to reverse the longer-term depletion of groundwater---a
reserve of water that supplements surface sources used for irrigation
and other purposes.

"The abundant water is expected to recharge the groundwater in the next
few months, as we have seen during similar events in 2011 and 2017,"
said Pang-Wei Liu, a scientist at NASA's Goddard Space Flight Center.
"However, if the climate pattern is the same as before---dry and hot in
summer followed by low precipitation---and the water demands are still
high, then we expect the groundwater drawdown will continue."

The chart above, produced with data provided by Liu, shows a downward
trend in California's terrestrial water storage (dark blue line)
spanning nearly two decades. This includes surface and groundwater, and
water held within the soil and in snow. The rest of the lines show why
this is happening; amid some variability in all types of stored water,
it is groundwater (light blue line) that is sharply decreasing.

Liu and colleagues used data from the Gravity Recovery and Climate
Experiment (GRACE) and GRACE Follow-On satellite missions to show that
the depletion of groundwater in California's Central Valley has been
accelerating since 2003. Their results were published December 2022 in
Nature Communications.

"Even the wettest wet seasons are simply never enough to make up for the
far greater amount of groundwater that California extracts each year,"
said Jay Famiglietti, a global futures professor at Arizona State
University and a co-author of the paper. "Hopefully California's
Sustainable Groundwater Management Act can slow what will otherwise be a
speedy trip to the bottom."

Earth's average surface temperature in 2022 tied with 2015 as the fifth
warmest on record, according to an analysis by NASA. Continuing the
planet's long-term warming trend, global temperatures in 2022 were 0.89
degrees Celsius (1.6 degrees Fahrenheit) above the average for NASA's
baseline period (1951--1980), according to scientists at NASA's Goddard
Institute for Space Studies (GISS).

The past nine years have been the warmest years since modern
recordkeeping began in 1880. This means Earth in 2022 was about 1.11°C
(2°F) warmer than the late 19th century average.

The map above depicts global temperature anomalies in 2022. It does not
show absolute temperatures; instead, it shows how much warmer or cooler
each region of Earth was compared to the average from 1951 to 1980. The
bar chart below shows 2022 in context with temperature anomalies since
1880. The values represent surface temperatures averaged over the entire
globe for the year.

"The reason for the warming trend is that human activities continue to
pump enormous amounts of greenhouse gases into the atmosphere, and the
long-term planetary impacts will also continue," said Gavin Schmidt,
director of GISS, NASA's leading center for climate modeling.

Human-driven greenhouse gas emissions have rebounded following a
short-lived dip in 2020 due to the COVID-19 pandemic. Recently, NASA
scientists, as well as international scientists, determined carbon
dioxide emissions were the highest on record in 2022. NASA also
identified some super-emitters of methane---another powerful greenhouse
gas---using the Earth Surface Mineral Dust Source Investigation (EMIT)
instrument that launched to the International Space Station last year.

The Arctic region continues to experience the strongest warming
trends---close to four times the global average---according to GISS
research presented at the 2022 annual meeting of the American
Geophysical Union, as well as a separate study.

Communities around the world are experiencing impacts scientists see as
connected to the warming atmosphere and ocean. Climate change has
intensified rainfall and tropical storms, deepened the severity of
droughts, and increased the impact of storm surges. Last year brought
torrential monsoon rains that devastated Pakistan and a persistent
megadrought in the U.S. Southwest. In September, Hurricane Ian became
one of the strongest and costliest hurricanes to strike the continental
U.S.

NASA's global temperature analysis is drawn from data collected by
weather stations and Antarctic research stations, as well as instruments
mounted on ships and ocean buoys. NASA scientists analyze these
measurements to account for uncertainties in the data and to maintain
consistent methods for calculating global average surface temperature
differences for every year. These ground-based measurements of surface
temperature are consistent with satellite data collected since 2002 by
the Atmospheric Infrared Sounder on NASA's Aqua satellite and with other
estimates.

NASA uses the period from 1951--1980 as a baseline to understand how
global temperatures change over time. That baseline includes climate
patterns such as La Niña and El Niño, as well as unusually hot or cold
years due to other factors, ensuring it encompasses natural variations
in Earth's temperature.

Many factors can affect the average temperature in any given year. For
example, 2022 was one of the warmest on record despite a third
consecutive year of La Niña conditions in the tropical Pacific Ocean.
NASA scientists estimate that La Niña's cooling influence may have
lowered global temperatures slightly (about 0.06°C or 0.11°F) from what
the average would have been under more typical ocean conditions.

A separate, independent analysis by the National Oceanic and Atmospheric
Administration (NOAA) concluded that the global surface temperature for
2022 was the sixth highest since 1880. NOAA scientists use much of the
same raw temperature data in their analysis and have a different
baseline period (1901--2000) and methodology. Although rankings for
specific years can differ slightly between the records, they are in
broad agreement and both reflect ongoing long-term warming.