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-These ensemble experiments underline the importance of both spring sea ice and summer atmospheric forcing to August SIC. In summary, we find that: Spring ice conditions were mostly responsible for the summer SIC anomaly through the end of July, while the atmosphere was mainly responsible for driving SIC to a record low during August. Partitioning the impact of 2020 spring initial sea ice conditions vs. summer atmospheric forcing on the sea ice anomaly at the time of the WS sea ice minimum on August 14 (see “Methods”) attributes ~20% to the initial conditions while ~80% is the due to the atmospheric forcing. Assuming that the increasing presence over the past 40 years of thin ice and open water at the beginning of the melt season (Fig. 5c) is primarily driven by climate change and that the summer atmospheric conditions were due to internal variability, the above 20/80 partitioning provides an approximate measure of the contributions of climate change and internal variability to the 2020 event (see further discussion in “Methods”). Is this 20% climate change signal significant or not? Extreme local events such as storms, heat waves, or floods are almost always dominated by dynamics driven by internal climate variability15. For example, flooding in New York City in response to Superstorm Sandy was on the order of 20% more extreme owing to long-term sea level rise17,18, a signal of the same magnitude we have detected in the present study. Using this example as a scale, we conclude that climate change was indeed a significant contributor to sea ice loss during the summer of 2020 in the WS. Atmospheric variability in context To put 2020 WS sea ice advection into a larger scale context, we consider here the fundamental modes of Arctic atmospheric variability, i.e., the Arctic Oscillation (AO), the Arctic Dipole Mode (ADA), and the Barents Oscillation (BO)19,20. Each of these correspond to the principal components (PCs) of empirical orthogonal functions computed from monthly mean sea level pressure fields north of 30°N (Figs. 7 and S4). During January–March 2020, when sea ice was advected into the Wandel Sea, sea level pressure over the Arctic was low, with a sea level pressure pattern similar to that found in 2017 when the Beaufort Gyre reversed21,22. The resulting onshore ice motion contributed to anomalously thick ice north of Greenland. At this time, the AO and ADA were both very high (the AO was in fact a record), a situation not found in any other year over the 41-year time series. Interestingly, summer 2020 conditions show the opposite, with ice motion westward away from the WS and the AO and ADA near record negative values. It seems clear that the anomalous 2020 WS wind forcing was associated with anomalous large-scale surface wind patterns. Fig. 7: Atmospheric circulation and sea ice motion during 2020. Discussion and conclusions While primarily driven by unusual weather, climate change in the form of thinning sea ice contributed significantly to the record low August 2020 SIC in the WS. Several advection events, some relatively early in the melt season, transported sea ice out of the region and allowed the accumulation of heat from the absorption of solar radiation in the ocean. This heat was mixed upward and contributed to rapid melt during high wind events, notably between August 9 and 16. Ocean-forced melting in this area that is traditionally covered by thick, compact ice is a key finding of this study. However, in some ways this should not be surprising given that this area (like most areas of the Arctic) has experienced a long-term thinning trend. Given the long-term thinning trend and strong interannual variability in atmospheric forcing, it seems reasonable to expect that summer sea ice conditions in the WS will likely become more variable in the future. In fact, mean SIT at the start of summer in 2018 and 2019 was even thinner than in 2020, which implies that with 2020-type atmospheric forcing, we might have seen even lower August SICs in those years, relative to that observed in 2020. In other words, SIC in the WS is now poised to plunge to low summer values, given the right atmospheric forcing. So, is the LIA in trouble? The WS is a key part of the LIA, one that has recently experienced anomalous conditions. We have shown that climate change-associated thinning ice in this region is a prerequisite for the record low ice concentrations seen in August 2020. Further, the unusually high SIT at the start of 2020 suggests that a temporary replenishment of sea ice from other parts of the Arctic may do little to protect this area from eventual sea ice loss. Recent work indicates that while western and eastern sectors of the LIA have distinct physics11, they both are experiencing long-term sea ice thinning and thus are both vulnerable to the processes discussed in this study. Our work suggests a re-examination of climate model simulations in this area, since most do not predict summer 2020-level low SICs until several decades or more into the future. Given that most climate models presently feature a subgrid-scale thickness distribution23 its evolution over time in those models should be a focal point of future investigations (i.e., rather than simply focusing on grid-cell mean thickness). Coupled model simulations where atmosphere and ocean conditions are nudged to 2020 conditions would provide useful insights into the capabilities of the current generation of climate models to replicate our results. In addition, our results should be replicated with ice-ocean models using different resolutions and physics. While the WS is only one part of the LIA, our results should give us pause when making assumptions about the persistence and resilience of summer sea ice in the LIA. Currently, little is known about marine mammal densities and biological productivity in the WS and the broader LIA. Recent studies indicate there may be some transient benefits for polar bears in areas transitioning from thick multi-year ice to thinner first year ice as biological productivity in the system increases. However, this is largely the case in shallow water <300 m in depth and it is unclear if this will occur in multi-year ice regions elsewhere. The assumption that the LIA will be available as a refuge over the next century is inherently linked to projections about species’ population status, because for some species the LIA will be the last remaining summer sea ice habitat e.g. ref. 27. It is critical that future work quantify the resilience of this area for conservation and management of ice-dependent mammals under climate change. Methods Model and model configuration details PIOMAS consists of coupled sea ice and ocean model components. The sea ice model is a multi-category thickness and enthalpy distribution sea ice model which employs a teardrop viscous plastic rheology, a mechanical redistribution function for ice ridging and a LSR (line successive relaxation) dynamics solver. The model features 12 ice thickness categories covering ice up to 28 m thick. Sea ice volume per unit area h provides an “effective sea ice thickness” which includes open water (or leads) and ice of varying thicknesses. Unless otherwise noted, we refer to this quantity as sea ice thickness. The sea ice model is coupled with the Parallel Ocean Program model developed at the Los Alamos National Laboratory. The PIOMAS model domain is based on a curvilinear grid with the north pole of the grid displaced into Greenland. It covers the area north of 49°N and is one-way nested into a similar, but global, ice-ocean model. The average resolution of the model is 30 km but features its highest resolution in the Wandel Sea, with grid cell sizes on the order of 15 × 30 km. Vertical model resolution is 5 m in the upper 30 m, and less than 10 m at depths down to 100 m, a resolution that has been shown sufficient to provide a realistic representation of upper ocean heat fluxes and the NSTM. PIOMAS is capable of assimilating satellite sea ice concentration data using an optimal interpolation approach either over the whole ice-covered area or only near ice edge. In our run HIST, satellite ice concentrations are assimilated only near the ice edge (defined as 0.15 ice concentration). This means that no assimilation is conducted in the areas where both model and satellite ice concentrations are above 0.15. If the observed ice edge exceeds the model ice edge, then sea ice is added to the thinnest sea ice thickness category and sea surface temperature (SST) is set to the freezing point. If the model ice edge exceeds observations, excess ice is removed in all thickness categories proportionally. This ice-edge assimilation approach forces the simulated ice edge close to observations, while preventing satellite-derived ice concentrations (which can be biased low during the summer e.g. ref. ) from inaccurately correcting model ice concentrations in the interior of the ice pack. Ice concentrations used for assimilation are from the Hadley Centre (HadISST v1) for 1979-2006 and from the NSIDC near real time product for 2007 to present. PIOMAS also assimilates SST, using observations provided in the NCAR/NCEP reanalysis (see below for atmospheric forcing) which in turn are derived from NOAA’s OISSTv2.1 data set. SST assimilation is only conducted in the open water areas, not in the ice-covered areas to avoid introducing an additional heat source into the sea ice budget. For this study, we also conducted a number of sensitivity simulations in which no assimilation of ice concentration and SST is performed (see below). Daily mean NCEP/NCAR reanalysis data are used as atmospheric forcing, i.e., 10-m surface winds, 2-m surface air temperature, specific humidity, precipitation, evaporation, downwelling longwave radiation, sea level pressure, and cloud fraction. Cloud fraction is used to calculate downwelling shortwave radiation following Parkinson and Kellogg. Precipitation less evaporation is calculated from precipitation and latent heat fluxes provided by the reanalysis model and specified at monthly time resolution to allow the calculation of snow depth over sea ice and input of fresh water into the ocean. There is no explicit representation of melt-ponds in this version of PIOMAS. River runoff into the model domain is specified from climatology. Because of the uncertainty of net precipitation and river runoff, the surface ocean salinity is restored to a salinity climatology with a 3-year restoring constant. Surface atmospheric momentum and turbulent heat fluxes are calculated using a surface layer model that is part of the PIOMAS framework. Additional model information can be found in in Zhang and Rothrock. PIOMAS has undergone substantial validation and has been shown to simulate sea ice thickness with error statistics similar to the uncertainty of the observations. Validation results for ocean profiles for the WS are shown in S5. Sea ice mass and upper ocean heat budgets Components of the sea ice mass and upper ocean heat budgets are computed directly from model output and residuals. Fprod is calculated as Fprod = Δh/Δt – Fadv and Fbot = Fprod – Fatm-ice. All heat entering the uppermost ocean grid cell is used to melt ice until SIT = 0; however, subsurface shortwave radiation penetration and attenuation are allowed, which can warm the ocean below the uppermost grid cell. Focndyn over the upper 60 m (Eq. (1)) can be partitioned into Focndyn = Focnadv + Fdiff + Fconvect where Focnadv, Fdiff, and Fconvect are heat exchanges between the upper 60 m of the WS and the adjacent ocean via horizontal and vertical advection, horizonal and vertical diffusion, and vertical convection, respectively. Focnadv is calculated directly from model ocean temperatures and velocities, and the sum of Fdiff + Fconvect found as a residual, i.e., Fdiff + Fconvect = ΔH/Δt – Fatm-ocn − Fbot − Focnadv where ΔH/Δt is calculated directly from model temperature profiles. We find that Fdiff + Fconvect is negligible, meaning that horizontal and vertical advection terms (more formally, heat flux convergence) dominate. This is illustrated in Fig. S6, which shows a strong ocean warming within ~100 km of the north Greenland coast owing to lateral heat flux convergence. This is nearly exactly balanced (not shown) by the vertical fluxes, i.e., downwelling, in keeping with previous results. Finally, by comparing the heat budget for summer 2020 simulations with and without data assimilation (i.e., HIST vs. INIT), we find that this numerical effect produces only a negligible heat flux term and so is neglected here (it might be larger in other regions or over a longer time period of simulation). All ice mass and ocean heat budget terms are presented in units of meters of ice melt, assuming an ice density of 917 kg m−3 and latent heat of sea ice fusion of 3.293 × 105 J/kg. HIST, INIT, and ATMOS Runs The single HIST simulation uses data assimilation for the entire simulation period and is the basis for our analysis except for the sensitivity experiments described next. The INIT and ATMOS ensemble runs turn off the assimilation after May 31, 2020. For the JJA period of comparison, differences between the HIST run (which includes assimilation) and the equivalent members from the following ensemble runs (which do not include assimilation) are negligible. Attribution of drivers The INIT and ATMOS ensembles allow a partitioning of the proximate causes of the 2020 sea ice anomaly into those driven by the initial spring conditions (sea ice and ocean) and those related to the evolution of the atmosphere (winds, temperature, radiation, humidity) over the summer. To compute the relative contribution, we calculate spatially averaged SIC and SIT differences between the INIT and ATMOS ensemble medians and the HIST median at the time of the observed and simulated WS SIC sea ice minimum (August 14). The ensemble median here represents normal conditions as the reference to which conditions (sea ice for INIT, atmosphere for ATMOS) being tested are compared. The difference from HIST is considered the contribution of the respective 2020 condition, initial ice thickness for “INIT” and atmosphere for “ATMOS.” This difference in SIC (SIT) is 6.3% (0.35 m) for INIT and 31% (1.5 m) for ATMOS. Adding these differences yields a total SIC response of 37.3%, and with respective fractions for “INIT and ATMOS” yields a 17% (6.3%/37.3%) role of initial conditions and a 83% (31%/37.3%) role for the atmosphere. The impact on SIT is slightly higher with respective contributions of 19% and 81%. This partitioning can be used as a measure of the relative impacts of climate change and internal variability. Loosely following the framework of Trenberth at al. we assume atmospheric variability is governed by internal variability, and initial (i.e., spring) sea ice conditions to be driven by long-term climate change. Therefore the 20%/80% partitioning provides an approximate measure of the contributions of climate change and internal variability on the 2020 event. This separation is not perfect because atmospheric warming appears to be playing a role as evident in the fact that ATMOS ensemble members 2018/2019 both yield ice concentrations well below the 1979–2020 mean/median. The assumption that initial ice conditions are entirely due to climate change is also not entirely correct either, since internal variability also plays a role in sea ice conditions. Nevertheless, our experiments clearly show that the climate signal of thinning sea ice exerts an impact on the magnitude of internally driven extreme events in the WS. Moreover, the fact that dynamic thickening of WS spring sea ice conditions (likely the result of internal variability) did little to improve the resilience of sea ice later in the summer provides an indication that climate change-driven thinning will likely influence future events. Model uncertainties As noted, PIOMAS has undergone substantial validation with respect to sea ice thickness, volume and motion. A measure of the uncertainty of ice-mass budget terms can be obtained from a recent study that compared monthly advection and ice production terms from PIOMAS with another numerical model and estimates derived from satellite observations. Mass budget terms from the three different sources are highly correlated and provide confidence that the relationship of budget terms is correct even if their magnitudes may have error. In addition, our INIT and ATMOS model simulations incur additional uncertainties due to the lack of a direct feedback between the atmosphere and ice-ocean system. However, this problem is less severe in the summer season which is our focus here, because summer thermal contrasts are small between the marine surface and the atmosphere. Future experiments with coupled models that allow for a “replay” of observed variability will be needed to verify this. Sea Ice Outlook: 2023 August Report We thank all the groups and individuals who submitted August Outlooks in this 16th year of the Sea Ice Outlook. We also thank NSF for supporting this year's Outlook with funds from NSF award #1331083. This month we received 29 September pan-Arctic sea-ice extent forecasts. Of these, 10 included regional Alaska sea ice extent forecasts, and 7 included Antarctic sea-ice extent forecasts. The August median forecasted value for pan-Arctic September sea-ice extent is 4.60 million square kilometers with interquartile values of 4.35 and 4.80 million square kilometers, while individual forecasts range from 2.88 to 5.47 million square kilometers. We note that the lowest two forecasts predict a new record September sea-ice extent value (current record is September 2012, with a sea-ice extent of 3.57 million square kilometers), but these forecasts are outliers relative to the other contributions. The median Alaska sea-ice extent forecast is 0.44 million square kilometers and the median Antarctic sea-ice extent forecast is 17.70 million square kilometers, which would be the second lowest Antarctic September sea-ice extent on record. Three of the seven Antarctic forecasts predict a record low sea-ice extent (see below for further details). The August median forecast of 4.60 million square kilometers is slightly lower than the July median (4.66) and slightly higher than the June median (4.54). Interestingly, the interquartile range of August forecasts is slightly higher than the July interquartile range (0.45 compared to 0.36 million square kilometers), illustrating that inter-model uncertainty was not reduced between early July and early August forecasts. The August interquartile range is narrower than the June interquartile range of 0.56 million square kilometers. We also received 13 forecasts of the September Arctic sea-ice extent anomaly. The median anomaly sea-ice extent forecast is +0.17 million square kilometers, suggesting that September 2023 sea-ice extent will be slightly above the expected long term linear trend value. Anomaly forecasts range from -0.31 million square kilometers to +0.68 million square kilometers, and the interquartile range is 0.50 million square kilometers, slightly greater than the interquartile range for absolute September sea-ice extent mentioned above. Nine groups submitted supplemental materials (see: Contributor Full Reports and Supplemental Materials below). The supplemental material contents vary among the contributions, but they may include additional figures and information on methodology including (1) how the forecasts are produced; (2) number of ensemble members used in the forecasts; (3) whether and how bias-corrections are applied; (4) ensemble spread, range of forecasts, uncertainties and other statistics; and (5) whether or not post-processing was performed. This August Outlook Report was developed by lead author Mitch Bushuk, NOAA's Geophysical Fluid Dynamics Laboratory (Executive Summary, Overview of pan-Arctic forecasts), Edward Blanchard-Wrigglesworth, University of Washington (discussion of predictions from spatial fields), Walt Meier, National Snow and Ice Data Center (Discussion of current Arctic conditions); with contributions from Uma Bhatt, University of Alaska Fairbanks (Overview of Alaska regional forecasts, discussion of pan-Arctic anomaly sea-ice forecasts and ice conditions in the Bering and Chukchi seas); François Massonnet, Université catholique de Louvain (Discussion of Antarctic contributions); and with input from Matthew Fisher and the NSIDC Development Team, (statistics and graphs); Betsy Turner-Bogren and Helen Wiggins, ARCUS (report coordination and editing). Note: The Sea Ice Outlook provides an open process for those who are interested in Arctic sea ice to share predictions and ideas; the Outlook is not an operational forecast. 2023 SIO Forecasts (Pan-Attic, Alaska Region, Spatial Forecasts, and Antarctic) The August 2023 Outlook received 29 pan-Arctic contributions (Figure 1). This year's median forecasted value for pan-Arctic September sea-ice extent is 4.60 million square kilometers with interquartile values of 4.35 and 4.80 million square kilometers. This is lower than last year's August median forecast for September, but higher than the three previous years (2019–2021). The lowest sea-ice extent forecast is 2.88 million square kilometers, from UC Louvain, which would be a new record low for the satellite period (1979-present); the highest sea-ice extent forecast is 5.47 million square kilometers, from the NMEFC ArcCFPS Group, which would be the highest September extent since 2006. Two of the outlooks forecast a new record minimum September extent (UC Louvain and the AWI Consortium), with UC Louvain predicting a notable record and AWI Consortium forecasting a value close to the 2012 record low of 3.57 million square kilometers. The observed extent values are from the NSIDC Sea Ice Index (Fetterer et al., 2017), based on the NASA Team algorithm sea ice concentration fields distributed by the NASA Snow and Ice Distributed Active Archive Center (DAAC) at NSIDC (DiGirolamo et al., 2022; Meier et al., 2021). There are 12 dynamical model contributions and 17 contributions from statistical models. The dynamical models have a median forecast of 4.33 million square kilometers with an interquartile range of 4.20 to 4.70 million square kilometers (Figure 2). Compared to the dynamical models, the statistical models generally predict higher values, with a median forecast of 4.64 million square kilometers and an interquartile range of 4.48 to 4.82 million square kilometers. The Outlooks from all methods have medians and interquartile values below last year's observed September extent (4.90), with only a handful of methods yielding an extent higher than last year (Figure 2). Figure 1. Distribution of SIO contributors for August predictions of September 2023 pan-Arctic sea-ice extent. Public/citizen contributions include: Simmons and Sun, Image courtesy of Matthew Fisher, NSIDC. Figure 2. June (left), July (center), and August (right) 2023 pan-Arctic Sea Ice Outlook submissions, sorted by method. The August median of Statistical/ML method (center left in pink) is 4.64 million square kilometers and the median for Dynamic Methods (far right in green) is 4.33 million square kilometers. The flat line represents a single submission that used Mixed/Other Methods in June. There were no submissions using heuristic methods in July or August. Image courtesy of Matthew Fisher, NSIDC. Pan-Arctic Sea-Ice Extent Anomalies This is the third year that the SIO has solicited forecasts of September mean sea-ice extent anomalies. The pan-Arctic anomaly is the departure of the contributors' September extent Outlook relative to their adopted baseline trend (e.g., the trend in historical observations, model hindcasts, etc.). This is motivated by the prospect of reducing SIO extent forecast uncertainty that may originate from models having different trends, mean states, and post-processing methodologies. The 13 anomaly forecasts range from -0.31 to +0.68 million square kilometers, with four at or below and 9 above the contributors' baseline (Figure 3 top). The observed anomalies range from -1.24 (2012) to 0.79 (2006) million square kilometers (Figure 3 bottom). The pan-Arctic 2023 August SIO anomaly forecast has a median of +0.17 million square kilometers and an interquartile range of 0.50 million square kilometers. The uncertainty in the August SIO anomaly forecasts matches that in June, both of which are smaller than the large spread in July. Similar to the pan-Arctic forecasts, statistical methods generally predict higher positive anomalies than dynamical methods. Figure 3. Anomaly pan-Arctic August 2023 forecast ranked by submission (top) and observed anomalies with August forecasts (bottom). The median August 2023 forecast is 0.17 million square kilometers. Alaska Regional Forecasts The multimodel median for the August 2023 SIO forecast for the Alaska seas is 0.44 million square kilometers, and ranges from a minimum of 0.24 to a maximum of 0.81 million square kilometers (Figure 4). The dynamical model forecasts range from a minimum of 0.24 to maximum 0.81 million square kilometers with a median of 0.27 million square kilometers. The statistical model forecasts range from a minimum of 0.33 to a maximum of 0.64 million square kilometers with a median of 0.46 million square kilometers. The statistical forecasts display a smaller spread (interquartile range of 0.05) compared to the dynamical models (interquartile range of 0.48) (Figure 5). To place these in historical perspective, the September median sea-ice extent for the Alaska seas (Bering, Chukchi, and Beaufort) over 2007–2022 is 0.44 million square kilometers, making the forecast for August 2023 SIO forecast match the observed median value (see Figure 3 of 2022 Postseason SIO report). Figure 4. Distribution of SIO contributors for August estimates of September 2023 Alaska Regional sea-ice extent. Figure courtesy of Matthew Fisher, NSIDC. Figure 5. June (left), July (center), and August (right) 2023 Alaska Region Sea Ice Outlook submissions, sorted by method. The observed September 2022 sea-ice extent for the Bering-Chukchi-Beaufort seas was 0.47 million square kilometers. Figure courtesy of Matthew Fisher, NSIDC. Pan-Arctic Forecasts with Spatial Methods We received seven forecasts of September sea-ice probability (SIP), and five of ice-free date (IFD, using both a 15% and an 80% sea-ice concentration threshold). Figure 6. September sea ice probability forecasts from 7 models, the multi-model forecast (bottom middle), and the uncertainty across the forecasts, quantified as the standard deviation (bottom right). The SIP forecasts are in general similar to those in July, with a slight reduction in uncertainty. Interestingly, the forecasts show relatively high SIP values in the Laptev sea (with the exception of the IAP LASG forecast), which in recent years has often undergone significant melt. In contrast, the East Siberian 'sea ice tongue' is forecasted to mostly melt out or show reduced cover. Figure 7. Ice-free date (IFD) forecasts using a 15% SIC threshold (top row) and an 80% SIC threshold (bottom row). The IFD forecasts show that we are near the end of the melt season, with relatively small additional loss of sea ice (shown by the reduced covers of IFD during August or September). These forecasts however help understand the differences across models in their SIP forecasts above. For example, IAP LASG forecasts melt the ice cover during August around the Laptev sea, whereas other models' forecasts maintain the ice cover in this region. Regarding the IFD80 forecasts, there is forecast uncertainty regarding the SIC over the central Arctic region, with some models forecasting significant areas to remain above 80% SIC (GFDL, RASM), whereas others forecast SICs below 80% by the end of summer throughout the central Arctic (e.g., IAP LASG). This month we received two contributions of SIC and sea ice thickness (SIT) initial conditions (Figure 8). Figure 8. SIC and SIT ICs in the RASM and GFDL forecasts. While at large scale there is reasonable agreement in the SIC and SIT ICs, there are also significant differences in particular regions (especially in the Kara and East Siberian seas), which likely help explain some of the SIP and IFD forecast differences between the two models. Antarctic Forecasts Seven outlooks were received for this August call. All groups except one (NCEP-EMC) forecast below-average September mean Antarctic sea-ice extent (Figure 9). We note that the NCEP-EMC forecast is not bias corrected, and we place caution in interpreting it – especially given the increase of the predicted Antarctic sea-ice extent with lead time. On 20 August 2023, the anomaly of daily Antarctic sea-ice extent was 2.2 million square kilometers below the 1981-2020 average, according to the NSIDC sea ice index. This confirms the exceptional behavior of austral sea ice in 2023, that has been following record-low values for eight months. Last month, we stated that it was more likely than not that Antarctic sea-ice would hit a record low in September. Given the continued slow development of sea ice and the consistent sign of the forecasts, we now turn this level of confidence to "very likely". Figure 9. Time-series of observed September Antarctic sea-ice extent and June, July, August individual model forecasts. Also shown are the climatological and anomaly persistence forecasts. Current Conditions Pan-Arctic Conditions During the month of July, sea ice extent decline was near average at 93,300 sq km per day and was fairly steady through the month and near-average decline rates continued through mid-August. At the end of July, extent was 12th lowest in the 45-year satellite record. Thus, conditions were not extreme relative to recent years, but continued a trend of much lower summer extent than before 2007. Figure 10. Daily extent (based on a 5-day running average) through 14 August 2023 and comparisons to the past four years (2019-2022) and the record low minimum year of 2012. The 1981-2010 average is the dark gray line, surrounded by the inter-quartile range (medium gray) and the inter-decile range (light gray). Note: Figure 10 is from NSIDC Charctic, based on NSIDC Sea Ice Index, Fetterer et al., 2017 and the NASA Team sea-ice concentration product at NSIDC (DiGirolamo et al., 2022; Meier et al., 2021). The primary areas of loss during the month were in the Beaufort and Chukchi Seas, where the ice edge retreated far from the coast. The ice also retreated in the eastern East Siberian Sea and the Laptev Sea, though at the end of July a tongue of ice extending to near the coast in the western East Siberian Sea remained. Sea ice also extended to the coast of the Taymyr Peninsula, keeping the Northern Sea Route closed. By mid-August, the tongue of ice in the East Siberian Sea had largely eroded, but ice still remained in the proximity of the Taymyr Peninsula. Ice was beginning to clear out of the channels of the Canadian Archipelago by the end of July and by mid-August the Amundsen (southern) route through the Northwest Passage was open and the northern route was also clearing. Figure 11. Sea ice concentration for 20 August 2023 from the NSIDC Arctic Sea Ice News and Analysis, based on the NSIDC Sea Ice Index (Fetterer et al., 2017) and the NASA team sea ice concentration product at NSIDC (DiGirolamo et al., 2022; Meier et al., 2021). The 1981-2010 median ice edge location is in orange. Note: For current data, see NSIDC Arctic Sea Ice News and Analysis and NSIDC Sea Ice Index. Temperatures during July were moderate over most of the Arctic with the exception of very warm conditions in the eastern Beaufort Sea, where air temperatures at the 925 mb level of the atmosphere were up to 7 degrees C above average. Air temperatures over the Laptev Sea were 1 to 3 degrees C below average. Elsewhere, temperatures were near-average. Figure 12. July 2023 average air temperature anomaly at the 925 mb level. NOAA Physical Sciences Laboratory, Boulder, Colorado (Kalnay et al., 1996). The July sea-level pressure pattern was marked by low pressure over the Laptev Sea and high pressure centered over the Canadian Archipelago. This dipole-anomaly pattern resulted in a fairly strong pressure gradient across the central Arctic, which led to strengthened winds and greater sea ice transport from the Pacific side of the Arctic toward the Atlantic side. Figure 13. Sea level pressure for July 2023. NOAA Physical Sciences Laboratory, Boulder, Colorado (Kalnay et al., 1996). The 500 hPa ('Z500', about 5.5 kilometers up in the atmosphere) geopotential anomalies for 1 June through 16 August 2023 (calculated from the ERA5 reanalysis) show negative anomalies over the Siberian Arctic and positive anomalies over Svalbard and the CAA (Figure 14). The summer pattern of geopotential height anomalies at 500 hPa that covaries with September sea-ice extent can also help account for forecast uncertainty in SIO forecasts, with summers that have low Z500 anomalies tending to have more sea ice (and forecasts that tend to under-predict SIE) and vice versa (Blanchard-Wrigglesworth et al, 2023). In Figure 14 we show the canonical summer pattern, and the so-far (1 June—16 August) observed pattern of Z500 anomalies for summer 2023. Figure 14. (left) The regression of detrended June through September (JJAS) 500 hPa heights on detrended September sea-ice extent over 1979–2022 (in m per million square kilometers) - when central Arctic Z500 heights are low, September SIE tends to be anomalously high, and vice versa -, and (right) anomalous 1 June—16 August 2023 500 hPa heights. As we saw in July, the atmospheric pattern during the current summer is mostly orthogonal to the canonical pattern, and thus, to first order, we do not expect the current summer's circulation to strongly impact September pan-Arctic sea ice extent anomalies. Alaska Regional Conditions The seasonal cycle of daily sea-ice extent in the Alaskan seas in 2023 remained close to climatology during the melt season until mid-July. Since then, the sea-ice extent has fallen steeply, with mid-August values nearly reaching those from 2019 (Figure 15, top). The 2022 sea ice in the Chukchi was lower than mid-August values in 2023, while it was higher in 2022 than 2023 in the Beaufort. The August Alaska sea ice had lower concentrations in 2023 compared to 2022 (Figure 15, bottom). Figure 15. Daily seasonal cycle of Bering-Chukchi-Beaufort Sea Ice Extent from 2008 to present and showing the 1981-2010 median climatology (top). August 20th sea-ice concentration in 2022 (bottom left) and 2023 (bottom right). The average surface air temperature over the Arctic for this past year (October 2021-September 2022) was the 6th warmest since 1900. The last seven years are collectively the warmest seven years on record. Low pressure across the Alaska Arctic and northern Canada sustained warm summer temperatures over the Beaufort Sea and Canadian Archipelago. The Arctic continues to warm more than twice as fast as the rest of the globe, with even greater warming in some locations and times of year. 2022 Arctic sea ice extent was similar to 2021 and well below the long-term average. August 2022 mean sea surface temperatures continued to show warming trends for 1982-2022 in most ice-free regions of the Arctic Ocean. SSTs in the Chukchi Sea were anomalously cool in August 2022. Most regions of the Arctic continued to show increased ocean plankton blooms, or ocean primary productivity, over the 2003-22 period, with the greatest increases in the Eurasian Arctic and Barents Sea. Satellite records from 2009 to 2018 show increasing maritime ship traffic in the Arctic as sea ice declines. The most significant increases in maritime traffic are occurring from the Pacific Ocean through the Bering Strait and Beaufort Sea. NASA’s Oceans Melting Greenland mission used cutting-edge technology to demonstrate that rising ocean temperatures along Greenland’s continental shelf are contributing to ice loss through melting glaciers at the ice sheet’s margins. June 2022 terrestrial snow cover was unusually low over both the North American (2nd lowest in the 56-year record) and Eurasian Arctic (3rd lowest in the record). Winter accumulation was above average, but early snow melt in a warming Arctic contributed to the overall low snow cover. A significant increase in Arctic precipitation since the 1950s is now detectable across all seasons. Wetter-than-normal conditions were observed from October 2021 through September 2022, in what was the 3rd wettest year of the past 72 years. The Greenland Ice Sheet experienced its 25th consecutive year of ice loss. In September 2022, unprecedented late-season warming created surface melt conditions over 36% of the ice sheet, including at the 10,500 ft ice sheet summit. Tundra greening declined from the record high values of the previous two years, with high productivity in most of the North American Arctic, but unusually low productivity in northeastern Siberia. Wildfires, extreme weather events, and other disturbances have become more frequent, influencing the variability of tundra greenness. Striking differences were observed between lake ice durations in Eurasia and North America, with substantially longer than average ice durations in Eurasia and predominantly shorter in North America. Freeze-up of Arctic lakes is occurring later in most of North America, especially in Canada. The distribution, conservation status, and ecology of most Arctic pollinators are poorly known though these insects are critically important to Arctic ecosystems and the food systems of Arctic Indigenous Peoples and Arctic residents. Coordinated long-term monitoring, increased funding, and emerging technologies can improve our understanding of Arctic pollinator habitats and status, and inform effective conservation strategies. In 2022, despite an outbreak of highly pathogenic avian influenza affecting birds throughout North America and variable spring weather conditions, the population sizes of most Arctic geese remained high with increasing or stable trends. Multiple geese species provide food and cultural significance for many peoples. In contrast, communities in the northern Bering and southern Chukchi Sea region reported higher-than-expected seabird die-offs for the sixth consecutive year. Tracking the duration, geographic extent, and magnitude of seabird bird die-offs across Alaska’s expansive and remote coastline is only possible through well-coordinated communication and a dedicated network of Tribal, State, and Federal partners. People experience the consequences of a rapidly changing Arctic as the combined effects of physical conditions, responses of biological resources, impacts on infrastructure, decisions influencing adaptive capacities, and environmental and international influences on economics and well-being. Living and innovating in Arctic environments over millennia, Indigenous Peoples have evolved holistic knowledge providing resilience and sustainability. Indigenous expertise is augmented by scientific abilities to reconstruct past environments and to model and predict future changes. Decision makers (from communities to governments) have the skills necessary to apply this experience and knowledge to help mitigate and adapt to a rapidly changing Arctic. Addressing unprecedented Arctic environmental changes requires listening to one another, aligning values, and collaborating across knowledge systems, disciplines, and sectors of society. What is the longest river in the world? The largest river on the planet, the Amazon, forms from the confluence of the Solimões (the upper Amazon River) and the Negro at the Brazilian city of Manaus in central Amazonas. At the river conjunction, the muddy, tan-colored waters of the Solimões meet the “black” water of the Negro River. The unique mixing zone where the waters meet extends downstream through the rainforest for hundreds of miles, and attracts tourists from all over the world, which has contributed to substantial growth in the city of Manaus. It is the vast quantity of sediment eroded from the Andes Mountains that gives the Solimões its tan color. By comparison, water in the Negro derives from the low jungles where reduced physical erosion of rock precludes mud entering the river. In place of sediment, organic matter from the forest floor stains the river the color of black tea. The Solimões provides nutrient-rich mud to lakes on the floodplain (lower right). The ecology of muddy lakes differs correspondingly from that of nutrient-poor, blackwater rivers and lakes. Solimões water can be seen leaking into the Negro west of the main meeting zone (lower left). The Solimões is much shallower than the Negro because it has filled its valley and bed with great quantities of sediment since the valleys were excavated. Widths of the rivers differ for this reason Global Temperature Key Takeaway: Earth’s global average surface temperature in 2020 statistically tied with 2016 as the hottest year on record, continuing a long-term warming trend due to human activities. This graph (graph_globaltemperature.txt) shows the change in global surface temperature compared to the long-term average from 1951 to 1980. The year 2020 statistically tied with 2016 for the hottest year on record since record keeping began in 1880 (source: graph_globaltemperature.txt). NASA’s analyses generally match independent analyses prepared by National Oceanic and Atmospheric Administration (NOAA) and other institutions. The animation on the right shows the change in global surface temperatures. Dark blue shows areas cooler than average. Dark red shows areas warmer than average. Short-term variations are smoothed out using a 5-year running average to make trends more visible in this map. Methane Key Takeaway: Methane is a powerful heat-trapping gas. The amount of methane in the atmosphere is increasing due to human activities. Methane Basics Methane (CH4) is a powerful greenhouse gas, and is the second-largest contributor to climate warming after carbon dioxide (CO2). A molecule of methane traps more heat than a molecule of CO2, but methane has a relatively short lifespan of 7 to 12 years in the atmosphere, while CO2 can persist for hundreds of years or more. Methane comes from both natural sources and human activities. An estimated 60% of today’s methane emissions are the result of human activities. The largest sources of methane are agriculture, fossil fuels, and decomposition of landfill waste. Natural processes account for 40% of methane emissions, with wetlands being the largest natural source. (Learn more about the Global Methane Budget.) The concentration of methane in the atmosphere has more than doubled over the past 200 years. Scientists estimate that this increase is responsible for 20 to 30% of climate warming since the Industrial Revolution (which began in 1750). Tracking Methane Although it’s relatively simple to measure the amount of methane in the atmosphere, it’s harder to pinpoint where it’s coming from. NASA scientists are using several methods to track methane emissions. One tool that NASA uses is the Airborne Visible InfraRed Imaging Spectrometer - Next Generation, or AVIRIS-NG. This instrument, which gets mounted onto research planes, measures light that is reflected off Earth’s surface. Methane absorbs some of this reflected light. By measuring the exact wavelengths of light that are absorbed, the AVIRIS-NG instrument can determine the amount of greenhouse gases present. NASA added the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to the International Space Station in 2022. Though built principally to study dust storms and sources, researchers found that it could also detect large methane sources, known as “super-emitters.” These aircraft and satellite instruments are finding methane rising from oil and gas production, pipelines, refineries, landfills, and animal agriculture. In some cases, these measurements have led to leaks being fixed, including suburban gas leaks and faulty equipment in oil and gas fields. The Arctic is a source of natural methane from wetlands, lakes, and thawing permafrost. Although a warming climate could change these emissions, scientists do not yet think it will drive a major increase. To this end, NASA’s Arctic Boreal and Vulnerability Experiment, or ABoVE, has been measuring methane coming from natural sources like thawing permafrost in Alaska and Canada. Data Notes and Sources NOAA’s methane data comes from a globally-distributed network of air sampling sites. https://gml.noaa.gov/ccgg/trends_ch4/ Ice core data are from Law Dome (Antarctica) and Summit (Greenland) ice cores, from Etheridge, D.M., L.P. Steele, R.J. Francey, and R.L. Langenfelds, Atmospheric methane between 1000 AD and present: Evidence of anthropogenic emissions and climatic variability. Journal of Geophysical Research, 103, D13, 15,979-15,993, 1998. Data archived at the Carbon Dioxide Information Analysis Center https://cdiac.ess-dive.lbl.gov/trends/atm_meth/lawdome_meth.htm Ocean Warming Ninety percent of global warming is occurring in the ocean, causing the water’s internal heat to increase since modern recordkeeping began in 1955, as shown in the upper chart. (The shaded blue region indicates the 95% margin of uncertainty.) This chart shows annual estimates for the first 2,000 meters of ocean depth. Each data point in the upper chart represents a five-year average. For example, the 2020 value represents the average change in ocean heat content (since 1955) for the years 2018 up to and including 2022. The lower chart tracks monthly changes in ocean heat content for the entire water column (from the top to the bottom of the ocean) from 1992 to 2019, integrating observations from satellites, in-water instruments, and computer models. Both charts are expressed in zettajoules. Heat stored in the ocean causes its water to expand, which is responsible for one-third to one-half of global sea level rise. Most of the added energy is stored at the surface, at a depth of zero to 700 meters. The last 10 years were the ocean’s warmest decade since at least the 1800s. The year 2022 was the ocean’s warmest recorded year and saw the highest global sea level. Ice Sheets Key Takeaway: Antarctica is losing ice mass (melting) at an average rate of about 150 billion tons per year, and Greenland is losing about 270 billion tons per year, adding to sea level rise. Data from NASA's GRACE and GRACE Follow-On satellites show that the land ice sheets in both Antarctica (upper chart) and Greenland (lower chart) have been losing mass since 2002. The GRACE mission ended in June 2017. The GRACE Follow-On mission began collecting data in June 2018 and is continuing to monitor both ice sheets. This record includes new data-processing methods and is continually updated as more numbers come in, with a delay of up to two months. This is important because the ice sheets of Greenland and Antarctica store about two-thirds of all the fresh water on Earth. They are losing ice due to the ongoing warming of Earth’s surface and ocean. Meltwater coming from these ice sheets is responsible for about one-third of the global average rise in sea level since 1993. Sea Level Key Takeaway: Global sea levels are rising as a result of human-caused global warming, with recent rates being unprecedented over the past 2,500-plus years. Sea level rise is caused primarily by two factors related to global warming: the added water from melting ice sheets and glaciers, and the expansion of seawater as it warms. The first graph tracks the change in global sea level since 1993, as observed by satellites. The second graph, which is from coastal tide gauge and satellite data, shows how much sea level changed from about 1900 to 2018. Items with pluses (+) are factors that cause global sea level to increase, while minuses (-) are what cause sea level to decrease. These items are displayed at the time they were affecting sea level. EVIDENCES How Do We Know Climate Change Is Real? There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause. TAKEAWAYS While Earth’s climate has changed throughout its history, the current warming is happening at a rate not seen in the past 10,000 years. According to the Intergovernmental Panel on Climate Change (IPCC), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact."1 Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate. From global temperature rise to melting ice sheets, the evidence of a warming planet abounds. The rate of change since the mid-20th century is unprecedented over millennia. Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives. The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia.1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun’s energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred. Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate. Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century.2 Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response. "Scientific evidence for warming of the climate system is unequivocal." - Intergovernmental Panel on Climate Change Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age. As evidências para mudanças Climáticas rápidas são convincentes: A temperatura global está aumentando A temperatura média da superfície do planeta aumentou cerca de 2 graus Fahrenheit ( 1 graus Celsius ) desde o final do século XIX, uma mudança impulsionada em grande parte pelo aumento das emissões de dióxido de carbono na atmosfera e em outras atividades humanas.4 A maior parte do aquecimento ocorreu nos últimos 40 anos, sendo os sete anos mais recentes os mais quentes. Os anos de 2016 e 2020 estão empatados no ano mais quente já registrado.5 O oceano está ficando mais quente O oceano absorveu grande parte desse aumento de calor, com os 100 metros superiores ( cerca de 328 pés ) do oceano mostrando um aquecimento de 0,67 graus Fahrenheit ( 0,33 graus Celsius ) desde 1969.6 A Terra armazena 90% da energia extra no oceano. As folhas de gelo estão encolhendo As camadas de gelo da Groenlândia e da Antártica diminuíram em massa. Dados da experiência de recuperação de gravidade e clima da NASA mostram que a Groenlândia perdeu uma média de 279 bilhões de toneladas de gelo por ano entre 1993 e 2019, enquanto a Antártica perdeu cerca de 148 bilhões de toneladas de gelo por ano.7 Geleiras estão recuando As geleiras estão recuando em quase todo o mundo —, incluindo nos Alpes, Himalaia, Andes, Montanhas Rochosas, Alasca e África.8 A cobertura de neve está diminuindo Observações por satélite revelam que a quantidade de cobertura de neve na primavera no Hemisfério Norte diminuiu nas últimas cinco décadas e a neve está derretendo mais cedo. Crédito da imagem: NASA / JPL-Caltech9 O nível do mar está aumentando O nível global do mar subiu cerca de 8 polegadas ( 20 centímetros ) no século passado. A taxa nas últimas duas décadas, no entanto, é quase o dobro da do século passado e acelera um pouco a cada ano.10 Gelo do Mar Ártico está diminuindo Tanto a extensão quanto a espessura do gelo do mar do Ártico diminuíram rapidamente nas últimas décadas.11 Eventos extremos estão aumentando em frequência O número de eventos recordes de alta temperatura nos Estados Unidos tem aumentado, enquanto o número de eventos recordes de baixa temperatura vem diminuindo desde 1950. Os EUA também testemunharam um número crescente de intensos eventos de chuvas.12 A acidificação do oceano está aumentando Desde o início da Revolução Industrial, a acidez das águas superficiais do oceano aumentou cerca de 30%.13,14 Esse aumento ocorre devido ao fato de os seres humanos emitirem mais dióxido de carbono na atmosfera e, portanto, serem mais absorvidos pelo oceano. O oceano absorveu entre 20% e 30% do total de emissões antropogênicas de dióxido de carbono nas últimas décadas ( 7,2 a 10,8 bilhões de toneladas métricas por ano ).15,16 Crédito da imagem: NOAA CAUSES The Causes of Climate Change Human activities are driving the global warming trend observed since the mid-20th century. TAKEAWAYS The greenhouse effect is essential to life on Earth, but human-made emissions in the atmosphere are trapping and slowing heat loss to space. Five key greenhouse gases are carbon dioxide, nitrous oxide, methane, chlorofluorocarbons, and water vapor. While the Sun has played a role in past climate changes, the evidence shows the current warming cannot be explained by the Sun. Scientists attribute the global warming trend observed since the mid-20th century to the human expansion of the "greenhouse effect"1 — warming that results when the atmosphere traps heat radiating from Earth toward space. Life on Earth depends on energy coming from the Sun. About half the light energy reaching Earth's atmosphere passes through the air and clouds to the surface, where it is absorbed and radiated in the form of infrared heat. About 90% of this heat is then absorbed by greenhouse gases and re-radiated, slowing heat loss to space. Four Major Gases That Contribute to the Greenhouse Effect: FORCING: Something acting upon Earth's climate that causes a change in how energy flows through it (such as long-lasting, heat-trapping gases - also known as greenhouse gases). These gases slow outgoing heat in the atmosphere and cause the planet to warm. Carbon Dioxide A vital component of the atmosphere, carbon dioxide (CO2) is released through natural processes (like volcanic eruptions) and through human activities, such as burning fossil fuels and deforestation. Methane Like many atmospheric gases, methane comes from both natural and human-caused sources. Methane comes from plant-matter breakdown in wetlands and is also released from landfills and rice farming. Livestock animals emit methane from their digestion and manure. Leaks from fossil fuel production and transportation are another major source of methane, and natural gas is 70% to 90% methane. Nitrous Oxide A potent greenhouse gas produced by farming practices, nitrous oxide is released during commercial and organic fertilizer production and use. Nitrous oxide also comes from burning fossil fuels and burning vegetation and has increased by 18% in the last 100 years. Chlorofluorocarbons (CFCs) These chemical compounds do not exist in nature – they are entirely of industrial origin. They were used as refrigerants, solvents (a substance that dissolves others), and spray can propellants. Another Gas That Contributes to the Greenhouse Effect: FEEDBACKS: A process where something is either amplified or reduced as time goes on, such as water vapor increasing as Earth warms leading to even more warming. Water Vapor Water vapor is the most abundant greenhouse gas, but because the warming ocean increases the amount of it in our atmosphere, it is not a direct cause of climate change. EFFECTS The Effects of Climate Change The effects of human-caused global warming are happening now, are irreversible for people alive today, and will worsen as long as humans add greenhouse gases to the atmosphere. TAKEAWAYS We already see effects scientists predicted, such as the loss of sea ice, melting glaciers and ice sheets, sea level rise, and more intense heat waves. Scientists predict global temperature increases from human-made greenhouse gases will continue. Severe weather damage will also increase and intensify. Earth Will Continue to Warm and the Effects Will Be Profound Global climate change is not a future problem. Changes to Earth’s climate driven by increased human emissions of heat-trapping greenhouse gases are already having widespread effects on the environment: glaciers and ice sheets are shrinking, river and lake ice is breaking up earlier, plant and animal geographic ranges are shifting, and plants and trees are blooming sooner. Effects that scientists had long predicted would result from global climate change are now occurring, such as sea ice loss, accelerated sea level rise, and longer, more intense heat waves. "The magnitude and rate of climate change and associated risks depend strongly on near-term mitigation and adaptation actions, and projected adverse impacts and related losses and damages escalate with every increment of global warming." - Intergovernmental Panel on Climate Change Some changes (such as droughts, wildfires, and extreme rainfall) are happening faster than scientists previously assessed. In fact, according to the Intergovernmental Panel on Climate Change (IPCC) — the United Nations body established to assess the science related to climate change — modern humans have never before seen the observed changes in our global climate, and some of these changes are irreversible over the next hundreds to thousands of years. Scientists have high confidence that global temperatures will continue to rise for many decades, mainly due to greenhouse gases produced by human activities. The IPCC’s Sixth Assessment report, published in 2021, found that human emissions of heat-trapping gases have already warmed the climate by nearly 2 degrees Fahrenheit (1.1 degrees Celsius) since 1850-1900.1 The global average temperature is expected to reach or exceed 1.5 degrees C (about 3 degrees F) within the next few decades. These changes will affect all regions of Earth. The severity of effects caused by climate change will depend on the path of future human activities. More greenhouse gas emissions will lead to more climate extremes and widespread damaging effects across our planet. However, those future effects depend on the total amount of carbon dioxide we emit. So, if we can reduce emissions, we may avoid some of the worst effects. "The scientific evidence is unequivocal: climate change is a threat to human wellbeing and the health of the planet. Any further delay in concerted global action will miss the brief, rapidly closing window to secure a liveable future."2 SOLUTIONS - Intergovernmental Panel on Climate Change Sustainability and Government Resources NASA is an expert in climate and Earth science. While its role is not to set climate policy or prescribe particular responses or solutions to climate change, its job does include providing the scientific data needed to understand climate change. NASA then makes this information available to the global community – the public, policy-, and decision-makers and scientific and planning agencies around the world. (For more information, see NASA's role.) With that said, NASA takes sustainability very seriously. NASA’s sustainability policy is to execute its mission as efficiently as possible. In doing so, we continually improve our space and ground operations. Sustainability involves taking action now to protect the environment for both current and future living conditions. In implementing sustainability practices, NASA supports its missions by reducing risks to the environment and our communities. In executing its mission, NASA's sustainability objectives are to: increase energy efficiency; increase the use of renewable energy; measure, report, and reduce NASA's direct and indirect greenhouse gas emissions; conserve and protect water resources through efficiency, reuse, and stormwater management; eliminate waste, prevent pollution, and increase recycling; leverage agency acquisitions to foster markets for sustainable technologies and environmentally preferable materials, products, and services; design, construct, maintain, and operate high-performance sustainable buildings; utilize power management options and reduce the number of agency data centers; support economic growth and livability of the communities where NASA conducts business; evaluate agency climate change risks and vulnerabilities and develop mitigation and adaptation measures to manage both the short-and long-term effects of climate change on the agency's mission and operations; raise employee awareness and encourage each individual in the NASA community to apply the concepts of sustainability to every aspect of their daily work to achieve these goals; maintain compliance with all applicable federal, state, local or territorial law and regulations related to energy security, a healthy environment, and environmentally-sound operations; and comply with internal NASA requirements and agreements with other entities.  What is the gray circle in the middle of some of the extent maps? Not all satellites pass close enough to the North Pole for their sensors to collect data there. This lack of data is indicated by a gray circle, or “pole hole,” in each image. Created: June 2008 Related question: How will we know if ice at the North Pole melts? Return to top How will we know if ice at the North Pole melts? Historically, lack of satellite data directly over the North Pole has not concerned scientists; they have always assumed that the area underneath is covered with sea ice. However, in recent years, the possibility that there will be no sea ice over the North Pole in summer has become more likely. Fortunately, some satellite sensors are able to obtain data directly over the North Pole; Data from these satellites could be used to fill in data that are missing from other satellite records. For example, the NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) could fill in some missing data because it has a smaller pole hole than other satellites. Or, scientists could use the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, which does collect data over the North Pole and thus has no pole hole. To learn more about how scientists study sea ice, see our Learn about Sea Ice: Science page. Created: June 2008 Related questions: What is the gray circle in the middle of some of the extent maps? Will the ice at the North Pole melt? Return to top What satellite is the sea ice data from? The “Daily image update,” as well as many of the images shown in Arctic Sea Ice News & Analysis, are derived from the Sea Ice Index data product. The Sea Ice Index relies on NASA-developed methods to estimate sea ice conditions using passive-microwave data from the Defense Meteorological Satellite Program (DMSP) the Special Sensor Microwave Imager/Sounder (SSMIS). The basis for the Sea Ice Index is the data set, “Near-Real-Time DMSP SSM/I Daily Polar Gridded Sea Ice Concentrations,” and the NASA-produced “Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data.” For more details, see the Sea Ice Index. Updated: June 2009 Return to top Why is the Sea Ice Index product used to study sea ice? The passive-microwave data used for the Sea Ice Index is especially helpful because the sensor can “see” through clouds and deliver data even during the six months of Arctic darkness and frequently cloudy conditions. Some other satellite sensors cannot penetrate clouds to take data, so the results are sporadic and dependent upon weather conditions. Still other sensors can see through clouds, but they do not cover the entire region of the globe where sea ice exists every day, making near-real-time monitoring difficult. Furthermore, some sensors cannot provide information in winter, when polar darkness prevails. The passive microwave sea ice record dates back to 1979, one of the longest environmental data sets we know of. This provides a long-term product that consistently tracks changes in the ice cover over many years, lending additional confidence to the trends that we observe. So, although NSIDC refers to additional satellite data in developing our analysis, we primarily rely on passive-microwave data for Arctic Sea Ice News & Analysis images and content, and for tracking long-term change. Created: June 2008 Return to top Sometimes readers report that our maps show ice incorrectly, compared to on-the-ground observations or other data sources. Why is this? Quality control for near-real-time-data One reason that ice extent images may have errors is that the satellite derived images in our Daily Image Update are near-real time and have not yet undergone rigorous quality control to correct for conflicting information that is especially likely along coastlines. Areas near land may show some ice coverage where there is not any because the sensor’s resolution is not fine enough to distinguish ice from land when a pixel overlaps the coast. Sometimes, the data we receive have errors in the geolocation data, caused by problems with the instrument, which could affect where ice appears. Near-real-time data may also have areas of missing data, displayed on the daily map as gray wedges, speckles, or spider web patterns. In addition, satellite sensors occasionally have problems and outages, which can affect the near-real-time data. We correct these problems in the final sea ice products, which replace the near-real-time data in about six months to a year. Despite areas of inaccuracy, near-real-time data are still useful for assessing changes in sea ice coverage, particularly when averaged over an entire month. The monthly average image is more accurate than the daily images because weather anomalies and other errors are less likely to affect it. Because of the limitations of near-real-time data, they should be used with caution when seeking to extend a sea ice time series, and should not be used for operational purposes such as navigation. To look at monthly images that have been through quality control, click on “Archived Data and Images” on the Sea Ice Index. Resolution of the data Another reason for apparent errors in ice extent is that the data are averaged over an area of 25 kilometers by 25 kilometers (16 by 16 miles). This means that the ice edge could be off by as much as 25 to 50 kilometers (16 to 31 miles) in passive-microwave data, compared to higher-resolution satellite systems. In addition, we define ice extent as any 25 by 25 kilometer grid cell with with an average of ?at least 15 percent ice. Ice-free areas may nevertheless exist within an area that is defined by our algorithms as ice covered. Passive microwave data characteristics The daily image is derived from remotely sensed passive microwave data, which can be collected even during cloudy or dark conditions. Passive microwave data may show ice where none actually exists due to signal variation between land and water along coastlines, or because of atmospheric interference from rain or high winds over the ice-free ocean. Reasons that passive microwave data may not detect ice include the presence of thin, newly formed ice; the shift in albedo of actively melting ice; and atmospheric interference. Thin, newly formed ice is consistently underestimated by these data. Centers such as the U.S. National Ice Center and the Canadian Ice Service that publish sea ice data for navigation employ higher spatial resolution data that is better able to detect such thin ice. Despite the limitations in passive microwave data, they still yield good large-scale estimates for the overall extent pattern and values of the ice. Plus, the limitations are consistent, affecting the data this year in the same way they have affected it in previous years. So when comparing from year to year, these types of errors do not affect the comparison. While passive microwave data products may not show as much detail or be as accurate “on the ground” as other satellite data, they provide a consistent time series to track sea ice extent going back to 1979. Higher resolution sensors only go back to 2002. This type of long-term, consistent data is important to scientists who study whether or not change is taking place in a system. To learn more about how scientists study sea ice, see our Learn about Sea Ice: Science page. Updated: March 2012 Related question: Do your data undergo quality control? Return to top Do your data undergo quality control? The daily and monthly images that we show in Arctic Sea Ice News & Analysis are near-real-time data. Near-real-time data do not receive the rigorous quality control that final sea ice products enjoy, but it allows us to monitor ice conditions as they develop. Several possible sources of error can affect near-real-time images. Areas near land may show some ice coverage because the sensor has a coarse resolution and though a coastal filter is applied, it is not effective in some situations. Sometimes, the data we receive have geolocation errors, which could affect where ice appears. Near-real-time data may also have areas of missing data, displayed on the daily map as gray wedges, speckles, or spider web patterns. In addition, satellite sensors occasionally have problems and outages, which can affect the near-real-time data. We correct these problems in the final sea ice products, which replace the near-real-time data in about six months to a year. Despite its areas of inaccuracy, near-real-time data are still useful for assessing changes in sea ice coverage, particularly when averaged over an entire month. The monthly average image is more accurate than the daily images because weather anomalies and other errors are less likely to affect it. Because of the limitations of near-real-time data, they should be used with caution when seeking to extend a sea ice time series, and should not be used for operational purposes such as navigation. To look at monthly images that have been through quality control, click on “Archived Data and Images” on the Sea Ice Index. Updated: June 2009 Return to top What is the error range for your images? NSIDC does not have error bars on the time series plot shown in the “Daily Image Update” and the daily time series plot (usually labeled “Figure 2”) because we strive to keep the images concise and easy to read. Plus, the error bars would be quite small compared to the total extent values in the images. We estimate error based on accepted knowledge of the sensor capabilities and analysis of the amount of “noise,” or daily variations not explained by changes in weather variables. For average relative error, or error relative to other years, the error is approximately 20,000 to 30,000 square kilometers (7,700 to 11,600 square miles), a small fraction of the total existing sea ice. For average absolute error, or the amount of ice that the sensor measures compared to actual ice on the ground, the error is approximately 50 thousand to 1 million square kilometers (19,300 to 386,100 square miles), varying over the year. During summer melt and freeze-up in the fall, the extent may be underestimated by 1 million square miles; during mid and late winter before melt starts, the error will be on the low end of the estimates. It is important to note that while the magnitude of the error varies through the year, it is consistent year to year. This gives scientists high confidence in interannual trends at a given time of year. The absolute error values may seem high, but it is important to note that each year has roughly the same absolute error value, so the decline over the long term remains clear. NSIDC has high confidence in sea ice trend statistics and the comparison of sea ice extent between years. Created: June 2008 Related questions: Sometimes readers report that our maps show ice incorrectly. Why? What is standard deviation and how does it relate to sea ice extent? Return to top Why do you use the 1981 to 2010 average for comparisons? NSIDC scientists use the 1981 to 2010 average because it provides a consistent baseline for year-to-year comparisons of sea ice extent. Thirty years is considered a standard baseline period for weather and climate, and the satellite record is now long enough to provide a thirty year baseline period. If we were to recalculate the baseline every year to incorporate the most recent year of data, we couldn’t meaningfully compare between recent years. To borrow a common phrase, we would be comparing apples and oranges. The problem with relying on a sliding average becomes clear over time, when we try to compare new years of data with previous years. For example, if we rely on a standard, unchanging baseline like 1981 to 2010, we can easily and clearly compare September 2007 and September 2008 with each other. However, if we were to use a sliding baseline of 1979 to 2006 for September 2007, and a sliding baseline of 1979 to 2007 for September 2008, we would no longer be comparing “apples to apples” when we compared the two years to the baseline. Arctic Sea Ice News and Analysis and the Sea Ice Index moved to a baseline period of 1981 to 2010 starting July 1, 2013. Previously, NSIDC had used 1979 to 2000 as the comparison period. Updated: July 2013 Related questions: What is the difference between sea ice area and extent? Are you updating the 1981-2010 average to the 1991-2020 average for comparisons? Return to top Are you updating the 1981-2010 average to the 1991-2020 average for comparisons? No. A 30-year climatology is commonly used as a reference period in weather and climate to define “normal” conditions. Thirty years is long enough to average out most natural variations in climate, like El Niño, that can affect the average in the short term. A norm or an average for weather is geared towards operational type of applications. For instance, is the weather warmer than average compared to recent years? If so, should farmers plant crops earlier than last year? Therefore, weather forecast services update their climatology with each new decade. The US National Weather Service, for instance, updated their period from 1981 to 2010 to 1991 to 2020. NSIDC scientists decided against such a shift for analyzing changes to Arctic sea ice. A shifting baseline makes tracking long-term climate change more complicated. As the baseline shifts, anomalies (amount above or below “normal”) and relative (percent per decade) trends will change. For climate, we want to look at long-term changes, so having a consistent baseline makes more sense. That way when new data is collected, there is a consistent baseline for decadal or longer evaluation of change. Ideally, this baseline period would be relatively stable and without much of a trend. This is particularly a problem for Arctic sea ice where the last 10 years have had several extremely low extents. Including these recent years hardly represents “normal” in terms of the long-term climate. If we switch to the 1991-2020 average, then all previous statistics can not be compared with the new baseline. And the new baseline will be more skewed by the downward trend. This would make relative trends, represented as percents per decade, larger in magnitude than they are with 1981-2010 average. For this reason, we plan to maintain the 1981-2010 period as our standard climate record. The period comprises the earliest three full decades in the continuous satellite record. The data for this period have been well validated and consistency has been maintained through careful calibrations between different sensors used in the time series. As a note, ASINA’s sea ice analysis tool allows users to see data relative to a customizable climatology. Updated: October 2021 Return to top The daily image update isn’t current; why? The daily image update is produced from near-real-time operational satellite data, with a data lag of approximately one day. However, visitors may notice that the date on the image is occasionally more than one day behind. Occasional short-term delays and data outages do occur and are usually resolved in a few days. Updated: February 2009 Related question: Do your data undergo quality control? Return to top Are there other sources of sea ice data? How do these sources differ from NSIDC data? Other researchers and organizations monitor sea ice independently, using a variety of sensors and algorithms. While these sources agree broadly with NSIDC data, extent measurements differ because of variation in the formulas (algorithms) used for the calculation, the sensor used, the threshold method to determine whether a region is “ice-covered,” and processing methods. NSIDC’s methods are designed to be as internally consistent as possible to allow for tracking of trends and variability throughout our data record. Links to other sources of sea ice data are listed below: University of Bremen Daily Updated AMSR-E Sea Ice Maps Nansen Environmental & Remote Sensing Center Arctic Regional Ocean Observing System Another source of sea ice data is the operational centers that provide support to ships navigating in the Arctic. There are often discrepancies between information from these centers and our data because they employ additional data sources to capture as much detail on sea ice conditions as possible. However, unlike our data, because the quality and availability of their data sources vary, their products do not provide a long-term, consistent timeseries suitable for tracking climate trends and variability. Several Arctic nations have operational sea ice centers. The two North American centers are: US National Ice Center Canadian Ice Service Updated: February 2020 Related questions: Do your data undergo quality control? What is the difference between sea ice area and extent? Return to top Why do different years appear on the graph? Each year at the beginning of January, the reference year on the daily extent graph changes. The graph of daily sea ice extent for the Northern Hemisphere shows ice extent in the current year, the 1981 to 2010 average, and the year with record low ice extent, (currently 2012). The graph has a five month window. This means that in December, the graph shows the record year of 2012, plus some of 2008 (2007-08) and 2013 (2012-13). When we shift the view in January to show five months beginning in October, the graph shows the end of 2006 and the beginning of 2007 (2006-07) and the end of 2011 and the beginning of 2012 (2011-12). July 2013 Return to top What is the standard deviation range on the daily image? In February 2010, we added the range of standard deviation to our daily extent chart. The gray area around the 1981 to 2010 average line shows the two standard deviation range of the data, which serves as an estimate of the expected range of natural variability. For the past few years, Arctic sea ice extent for most months has been more than two standard deviations below the 1981 to 2010 mean, particularly in summer. Updated: July 2013 Related questions: What is standard deviation and how does it relate to sea ice extent? What is the error range for your images? Return to top What is standard deviation and how does it relate to sea ice extent? Standard deviation is a measure of variation around a mean. One standard deviation is defined as encompassing 68% of the variation, and two standard deviations encompass 95% of the variation. Scientists use standard deviations as a way to estimate the range of variability of data. In the context of climate data like sea ice extent, it provides a sense of the range of expected conditions. Measurements that fall far outside of the two standard deviation range or consistently fall outside that range suggest that something unusual is occurring that can’t be explained by normal processes. For sea ice extent data, the standard deviation is computed for each day of the year from the extent on that day over the 30 years of the baseline period, 1981 to 2010. Doubling the standard deviation to produce a 95% range means that 95% of the daily extents for the years 1981 to 2010 fall within that range. In recent years, ice extent has declined and in the summer especially, it has regularly fallen outside of two standard deviations. This suggests that the recent decline in sea ice extent represents a significant change in conditions from 1981 to 2010 time period. Updated: July 2013 Related question: What is the error range for your images? Return to top Why don’t you publish a global sea ice extent number? The combined number, while easy to derive from our online posted data, is not useful as an analysis tool or indicator of climate trends. Looking at each region’s ice extent trends and its processes separately provides more insight into how and why ice extent is changing. Sea ice in the Arctic is governed by somewhat different processes than the sea ice around Antarctica, and the very different geography of the two poles plays a large role. Sea ice in the Arctic exists in a small ocean surrounded by land masses, with greater input of dust, aerosols, and soot than in the Southern Hemisphere. Sea ice in the Southern Hemisphere fringes an ice-covered continent, Antarctica, surrounded by open oceans. While both regions are affected by air, wind, and ocean, the systems and their patterns are inherently very different. Moreover, at any point in time, the two poles are in opposite seasons, and so a combined number would conflate summer and winter trends, or spring and autumn trends, for the two regions. Why is the daily change in sea ice extent in the northern hemisphere larger at the beginning of each month? If you plot the average daily change in sea ice extent in the northern hemisphere, based on the data from ‘Sea_Ice_Index_Daily_Extent_G02135_v3.0.xlsx,’ you may notice that at the beginning of each month, particularly in the summer, the daily change is larger. This is related to the valid ice masks that are used in the processing of the Sea Ice Index. It is really a land spillover effect: that is, even when there is not ice in a coastal sea, ice can appear to fringe the coast, and fill fjords. This happens because there are mixed land-ocean areas within the sensor’s field of view. That mixture of land and ice looks like sea ice to the algorithms interpreting the sensor data. A correction for land spillover is applied, but it is not perfect. Monthly valid ice masks are also used and these mask out areas where sea ice is not realistic in a given month, including along the coast due to land spillover. When you switch to the next month there is a change in the ice mask. Going from May to June to July, the valid ice mask moves north in the Arctic and crops out more potential ice areas south of the valid ice line. Ice may have receded in a coastal sea by the end of May, for instance, but may still appear to be along the coastline. On the first day of June the new mask removes more of the invalid ice, which is why you see a sudden change in sea ice. Updated: July 2020 Return to top STUDYING SEA ICE What would it mean for Arctic sea ice to recover? Sea ice extent normally varies from year to year, much like the weather changes from day to day. But just as one warm day in October does not negate a cooling trend toward winter, a slight annual gain in sea ice extent over a record low does not negate the long-term decline. Even though the extent of Arctic sea ice has not returned to the record low of 2012, the data show that it is not recovering. To recover would mean returning to within its previous, long-term range. Arctic sea ice extent remains very low. In addition, sea ice remains much thinner than in the past, and so is more vulnerable to further decline. While ice thickness is difficult to measure using satellites, a variety of data sources and estimates indicate that the Arctic ice cover remains thin. For more information on ice thickness, read our Ask a Scientist article, Getting beneath the ice. So what would scientists call a recovery in sea ice? First, a true recovery would continue over a period of multiple years. Second, scientists would expect to see a series of minimum sea ice extents that not only exceed the previous year, but also return to within the range of natural variation. In a recovery, scientists would also expect to see a return to an Arctic sea ice cover dominated by thicker, multiyear ice. Updated: September 2013 Return to top What was sea ice like before the satellite era? The satellite record only dates back to 1979. However, scientists have used historical records of sea ice conditions to estimate sea ice extent before 1979. For more on this topic, read the Ask a Scientist article, How was Arctic sea ice measured before the satellite era? Updated: February 2022 Related questions: Has the Arctic Ocean always had ice in summer? Return to top Has the Arctic Ocean always had ice in summer? We know for sure that at least in the distant past, the Arctic was ice-free. Fossils from the age of the dinosaurs, 65 million years ago, indicate a temperate climate with ferns and other lush vegetation. Based on the paleoclimate record from ice and ocean cores, the last warm period in the Arctic peaked about 8,000 years ago, during the so-called Holocene Thermal Maximum. Some studies suggest that as recent as 5,500 years ago, the Arctic had less summertime sea ice than today. However, it is not clear that the Arctic was completely free of summertime sea ice during this time. The next earliest era when the Arctic was quite possibly free of summertime ice was 125,000 years ago, during the height of the last major interglacial period, known as the Eemian. Temperatures in the Arctic were higher than now and sea level was also 4 to 6 meters (13 to 20 feet) higher than it is today because the Greenland and Antarctic ice sheets had partly melted. Because of the burning of fossil fuels, global averaged temperatures today are getting close to the maximum warmth seen during the Eemian. Carbon dioxide levels now are far above the highest levels during the Eemian, indicating there is still warming to come. According to analyses at NASA and NOAA, the past decade has been the warmest in the observational record dating back to the 19th century and the Arctic has been substantially higher than the global average. Updated: February 2012 Related question: How do we know human activities cause global climate change? Return to top Will the ice at the North Pole melt? Sometimes in everyday use, people associate “the North Pole” with the entire Arctic region. However, when scientists discuss the North Pole, they mean the geographic North Pole, a single point on the globe located at 90 degrees North. The term Arctic generally refers to a much larger region that encompasses the northern latitudes of the globe. The Arctic includes regions of Russia, North America, and Greenland, as well as the Arctic Ocean. The scientific community has a range of predictions concerning when we could see an ice-free Arctic Ocean in summer. Predictions range from sometime between 2030 and 2100. Updated: January 2012 Related questions: What is the gray circle in the middle of some of the extent maps? How will we know if ice at the North Pole melts? Return to top Why don’t I hear much about Antarctic sea ice? NSIDC scientists do monitor sea ice in the Antarctic, and sea ice in the Antarctic is of interest to scientists worldwide. While there are many peer-reviewed journal articles on the topic of Antarctic sea ice and its changes, it has received less attention than the Arctic. Antarctic sea ice has in general changed far less dramatically than Arctic ice. Moreover, changes in Antarctic sea ice are unlikely to have a significant direct impact on the temperate southern latitudes. For more information on Antarctic sea ice, read the Ask a Scientist article, How does Antarctic sea ice differ from Arctic sea ice? Antarctic sea ice data is available on the NSIDC Sea Ice Index. Updated: January 2012 Return to top Is wintertime Antarctic sea ice increasing or decreasing? Wintertime Antarctic sea ice is increasing at a small rate and with substantial year-to-year variation. Monthly sea ice data show trends of increasing sea ice extent that are slightly above the mean year-to-year variability over the satellite record (1979 to present). In more technical terms, the trends are statistically significant at the 95% level, although small (~1% per decade as of 2016). Global climate model projections for sea ice trends around Antarctica are at odds with what is being observed. Nearly all models to date project a slight decline in sea ice extent at present and for the next several decades. The mismatch between model results and observations is a topic of research, and a basis for investigations to find the processes that must be added to the models to align them with what is observed. However, analysis of the variability of Antarctic sea ice in models shows that it is possible that the current trend of increasing sea ice extent is a result of the high variability in the Antarctic sea ice and climate system. The dominant, though subtle, change in the climate pattern of Antarctica has been a gradual increase in the westerly circumpolar winds. Models suggest that both the loss of ozone (the ozone hole that occurs in September/October every year) and increases in greenhouse gases lead to an increase in frequency of this climate pattern. When winds push on sea ice, they tend to move it in the direction they are blowing, but the Coriolis effect adds an apparent push to the left. In the unconfined system of Antarctic sea ice, this pushes the ice northward away from the continent. By spreading sea ice westward and a little northward (and since we measure extent with a 15% cutoff) the gradual trend towards faster mean winds means a gradual trend toward spreading of the ice cover. This general pattern may be part of the explanation for the trend. Recent records of wintertime extents (in 2012, 2013, and 2014) appear to be associated with patterns in air circulation related to the westerly wind regime. The Amundsen Sea Low (ASL), a climate feature of the annual average pressure pattern for Antarctica, varies in both strength and location on a seasonal basis. The ASL tends to be stronger when westerly winds are strong. The ASL, and its effect of sea ice formation and drift, appears to be a major part of the recent string of record winter maximums. More recently (since July 2015) sea ice has returned to near-average conditions, and as of this writing is at a record daily low extent. This highlights the inherent variability in the system. However, one analysis that has attempted to explain both the very large winter extents of 2012, 2013, and 2014, and the subsequent lower and near-average winter maximums in 2015 and 2016 has suggested that the El Niño Southern Oscillation and a Pacific trend called the Pacific Decadal Oscillation (a residual tendency toward El Niño or La Niña in the Pacific that shifts on multi-decadal timescales) may be linked to the change. In other words, the advent of a strong El Niño in late 2015 and early 2016 may have shifted wind and ocean circulation to favor lower extents after a series of La-Niña-prone years (Meehl, 2016). The trend towards stronger circumpolar winds has also caused a sea ice extent decline near the Antarctic Peninsula. In general, the winds tend to dive slightly southward as they approach the Peninsula, an effect of the mountain ridges of the Andes and other circulation features in the Amundsen and Bellingshausen Sea (the ASL mentioned above). A stronger wind from the northwest brings warmer conditions and therefore less ice to the region. Lastly, the El Niño and La Niña cycle also appear to influence sea ice in the Pacific sector. El Niño patterns (a warm eastern tropical Pacific) are associated with warmer winds and less ice; the opposite is true for La Niña. Climate models suggest that the observed increases in Antarctic sea ice are not outside natural variability. However, all models indicate that the ice extent should decrease as greenhouse gases in the atmosphere increase further later in this century. For more information, read the Ask a Scientist article, How does Antarctic sea ice differ from Arctic sea ice? To see data on Antarctic sea ice, see the Sea Ice Index. Updated: December 2016 Related questions: Has the Arctic Ocean always had ice in summer? Return to top CAUSES OF GLOBAL CLIMATE CHANGE AND ICE DECLINE How do we know human activities cause climate change? Fossil fuel burning is responsible for climate change because of the way in which an increased concentration of carbon dioxide in the atmosphere alters the planet’s energy budget and makes the surface warmer. The most fundamental measure of Earth’s climate state is the globally averaged surface air temperature. We define climate change as an extended trend in this temperature. Such a change cannot happen unless something forces the change. Various natural climate forcings exist. For example, periodic changes in the Earth’s orbit about the sun alter the seasonal and latitudinal distribution of solar radiation at the planet’s surface; such variations can be linked to Earth’s ice ages over the past two million years. Changes in solar output influence how much of the sun’s energy the Earth’s surface receives as a whole; more or less solar energy means warmer or cooler global climate. Explosive volcanic eruptions inject sulfur dioxide and dust high into the stratosphere, blocking some of the sun’s energy from reaching the surface and causing it to cool. These are climate forcings because they alter the planet’s radiation or energy budget. An increase in the atmosphere’s concentration of carbon dioxide is also a climate forcing: it leads to a situation in which the planet absorbs more solar radiation than it emits to space as longwave radiation. This means the system gains energy. The globally averaged temperature will increase as a result. This is in accord with a fundamental principle of physics: conservation of energy. As humans burn fossil fuels, adding carbon dioxide to the atmosphere, globally average temperature rises as a result. Arctic Sea Ice 6th Lowest on Record; Antarctic Sees Record Low GrowthIn Brief:The annual Arctic sea ice minimum (lowest) annual extent was the sixth-lowest on record this year, while Antarctic sea ice reached its lowest maximum ever. These both continue a long-term downward trend due to human-caused global warming.Arctic sea ice likely reached its annual minimum extent on Sept. 19, 2023, making it the sixth-lowest year in the satellite record, according to researchers at NASA and the National Snow and Ice Data Center (NSIDC). Meanwhile, Antarctic sea ice reached its lowest maximum extent on record on Sept. 10 at a time when the ice cover should have been growing at a much faster pace during the darkest and coldest months.Scientists track the seasonal and annual fluctuations because sea ice shapes Earth’s polar ecosystems and plays a significant role in global climate. Researchers at NSIDC and NASA use satellites to measure sea ice as it melts and refreezes. They track sea ice extent, which is defined as the total area of the ocean in which the ice cover fraction is at least 15%.Between March and September 2023, the ice cover in the Arctic shrank from a peak area of 5.64 million square miles (14.62 million square kilometers) to 1.63 million square miles (4.23 million square kilometers). That’s roughly 770,000 square miles (1.99 million square kilometers) below the 1981–2010 average minimum of 2.4 million square miles (6.22 million square kilometers). The amount of sea ice lost was enough to cover the entire continental United States.Sea ice around Antarctica reached its lowest winter maximum extent on Sept. 10, 2023, at 6.5 million square miles (16.96 million square kilometers). That’s 398,000 square miles (1.03 million square kilometers) below the previous record-low reached in 1986 – a difference that equates to roughly the size of Texas and California combined. The average maximum extent between 1981 and 2010 was 7.22 million square miles (18.71 million square kilometers).“It’s a record-smashing sea ice low in the Antarctic,” said Walt Meier, a sea ice scientist at NSIDC. “Sea ice growth appears low around nearly the whole continent as opposed to any one region.”This year in the Arctic, scientists saw notably low levels of ice in the Northwest Passage, Meier added. “It is more open there than it used to be. There also seems to be a lot more loose, lower concentration ice – even toward the North Pole – and areas that used to be pretty compact, solid sheets of ice through the summer. That’s been happening more frequently in recent years.”Meier said the changes are a fundamental, decades-long response to warming temperatures. Since the start of the satellite record for ice in 1979, sea ice has not only been declining in the Arctic, but also getting younger. Earlier starts to spring melting and ever-later starts to autumn freeze-up are leading to longer melting seasons. Research has shown that, averaged across the entire Arctic Ocean, freeze-up is happening about a week later per decade, or one month later than in 1979.Nathan Kurtz, lab chief of NASA’s Cryospheric Sciences Laboratory at the agency’s Goddard Space Flight Center in Greenbelt, Maryland, said that as the Arctic warms about four times faster than the rest of the planet, the ice is also growing thinner. “Thickness at the end of the growth season largely determines the survivability of sea ice. New research is using satellites like NASA’s ICESat-2 (Ice, Cloud and land Elevation Satellite-2) to monitor how thick the ice is year-round.”Kurtz said that long-term measurements of sea ice are critical to studying what’s happening in real time at the poles. “At NASA we’re interested in taking cutting-edge measurements, but we’re also trying to connect them to the historical record to better understand what’s driving some of these changes that we’re seeing.”Scientists are working to understand the cause of the meager growth of the Antarctic sea ice, which could include a combination of factors such as El Nino, wind patterns, and warming ocean temperatures. New research has shown that ocean heat is likely playing an important role in slowing cold season ice growth and enhancing warm season melting.This record-low extent so far in 2023 is a continuation of a downward trend in Antarctic sea ice that started after a record high in 2014. Prior to 2014, ice surrounding the continent was increasing slightly by about 1% per decade.Sea ice melting at both poles reinforces warming because of a cycle called “ice-albedo feedback.” While bright sea ice reflects most of the Sun’s energy back to space, open ocean water absorbs 90% of it. With greater areas of the ocean exposed to solar energy, more heat can be absorbed, which warms the ocean waters and further delays sea ice growth.
+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.