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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Global benefits of non-continuous flooding to reduce greenhouse gases and irrigation water use without rice yield penalty

Non-continuous flooding is an effective practice for reducing greenhouse gas emissions (GHGs) and irrigation water use (IRR) in rice fields. However, advancing global implementation is hampered by the lack of comprehensive understanding of GHGs and IRR reduction benefits without compromising rice yield. Here, we present the largest observational data set for such effects as of yet. By using Random Forest regression models based on 636 field trials at 105 globally georeferenced sites, we identified the key drivers of effects of non-continuous flooding practices and mapped maximum GHGs or IRR reduction benefits under optimal non-continuous flooding strategies. The results show that variation in effects of non-continuous flooding practices are primarily explained by the UnFlooded days Ratio (UFR, that is the ratio of the number of days without standing water in the field to total days of the growing period). Non-continuous flooding practices could be feasible to be adopted in 76% of global rice harvested areas. This would reduce the global warming potential (GWP) of CH4 and N2O combined from rice production by 47% or the total GWP by 7% and alleviate irrigation water use by 25%, while maintaining yield levels. The identified UFR targets far exceed currently observed levels particularly in South and Southeast Asia, suggesting large opportunities for climate mitigation and water use conservation, associated with the rigorous implementation of non-continuous flooding practices in global rice cultivation.

climate change mitigation↗
NASA NTRS2022

Imaging the End-to-End Dynamics of the Global Solar Wind-Magnetosphere Interaction

Much of what we know about the solar wind’s interaction with the Earth’s magnetosphere has been gained from isolated in situ measurements by single or multiple spacecraft. Based on their observations, we know that reconnection, whether on the dayside magnetopause or deep within the Earth’s magnetotail, controls the flow of solar wind energy into and through the global system. We know that nightside activity provides the energized particles that power geomagnetic storms. But by their very nature these isolated in situ measurements cannot provide an instantaneous global view of the entire system or its cross-scale dynamics. As a result, we don’t know which mode of reconnection prevails on the dayside magnetopause or within the magnetotail as a function of solar wind and geomagnetic conditions. We don’t know which mode or modes of nightside activity supply the most energized particles to the ring current. Nor do we know the dominant loss mode for ring current decay: precipitation, magnetopause outflow, or charge exchange with neutrals. Nor do we know how processes deep within the magnetosphere provide feedback to those happening in the outer magnetosphere. The answers to these questions could have an impact far beyond magnetospheric physics, since magnetic reconnection, particle acceleration, and charge-exchange are fundamental plasma processes that operate at other planets and throughout the universe. Comprehensive end-to-end global imaging of the key micro, meso-, and macro-scale plasma structures that comprise the magnetosphere will provide the answers to these questions via observations with a spatial resolution that exceeds anything possible with in situ measurements. Each proposed interaction mechanism generates a diagnostic plasma structure or boundary signature. Global, end-to-end, imaging provides the pathway to understanding the system as a whole, its constituent parts, and its cross-scale processes on a continuous basis, as needed to quantify the flow of solar wind energy through the global magnetospheric system. The significance of each mechanism is the product of its amplitude and occurrence rate. This white paper describes how a comprehensively-instrumented single spacecraft in a high-latitude circular polar orbit can provide the essential observations needed to track and quantify the flow of solar wind energy through the magnetosphere, including the solar wind plasma and magnetic field input, the magnetopause location in soft X-rays, the auroral oval in far ultraviolet, the ring current in energetic neutrals, the plasmasphere in extreme ultraviolet, the exosphere in Lyman-, the microstructure of the nightside auroral oval in ground-based all sky cameras, and the magnetic perturbations of ionospheric current patterns seen by ground-based magnetometers.

D G Sibeck↗
NASA NTRS2023

Version 2 of the global catalogue of large anthropogenic and volcanic SO 2 sources and emissions derived from satellite measurements

Sulfur dioxide (SO 2 ) measurements from the Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite (OMPS), and TROPOspheric Monitoring Instrument (TROPOMI) satellite spectrometers were used to update and extend the previously developed global catalogue of large SO 2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources releasing from about 10 kt yr −1 to more than 4000 kt yr −1 of SO 2 , that have been identified and grouped by country and primary source origin: volcanoes (106 sources); power plants (477); smelters (74); and sources related to the oil and gas industry (102). There are several major improvements compared to the original catalogue: it combines emissions estimates from three satellite instruments instead of just OMI, uses a new version 2 of the OMI and OMPS SO 2 dataset, and updated consistent site-specific air mass factors (AMFs) are used to calculate SO 2 vertical column densities (VCDs). The newest TROPOMI SO 2 data processed with the Covariance-Based Retrieval Algorithm (COBRA), used in the catalogue, can detect sources with emissions as low as 8 kt yr −1 (in 2018–2021) compared to the 30 kt yr −1 limit for OMI. In general, there is an overall agreement within ±12 % in total emissions estimated from the three satellite instruments for large regions. For individual emission sources, the spread is larger: the annual emissions estimated from OMI and TROPOMI agree within ±13 % in 50 % of cases and within ±28 % in 90 % of cases. The version 2 catalogue emissions were calculated as a weighted average of emission estimates from the three satellite instruments using an inverse-variance weighting method. OMI, OMPS, and TROPOMI data contribute 7 %, 5 %, and 88 % to the average, respectively, for small (<30 kt yr −1 ) sources and 33 %, 20 %, and 47 %, respectively, for large (>300 kt yr −1 ) sources. The catalogue data show an approximate 50 % decline in global SO 2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years. The version 2 of the global catalogue has been posted at the NASA global SO 2 monitoring website (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022).

volcanic↗
NASA NTRS2023

AgMIP-Wheat Multi-Model Simulations on Climate Change Impact and Adaptation for Global Wheat

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS2023

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy Data Fusion to Map Global Surface Ozone Concentration and Associated Uncertainty

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R 2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m −3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Ozone↗
NASA NTRSDate not supplied

Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as those left wild. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have established a comprehensive global consumption database directly linked to distinct fuels within various fire danger categories. This database, featuring a spatial resolution of approximately 300 meters, builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized Fuel Characteristic Classification System (FCCS) biomass parameters. Consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. We define ‘residual burned area’ as area burning in a region that has burned on a previous day for the same fire season, and ‘new burned area’ as area burning in a region that has not recently burned. This product serves as a valuable tool when used in conjunction with burned area data to rapidly estimate the carbon consumed and released into the atmosphere. Previously, we developed a similar emissions method utilizing satellite information, in conjunction with the FCCS 30-meter United States fuelbed dataset. We implemented this approach on fires, documented during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets.

Emily Gargulinski↗
NASA NTRS2024

Where Dust Comes from: Global Assessment of Dust Source Attributions with AeroCom Models

The source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long-range transport. Recently, a multi-model experiment, namely the AeroCom-III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from seven participating global models. The BASE run and a series of runs with nine tagged regions were made to estimate the contribution of dust emitted in East- and West-Africa, Middle East, Central- and East-Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bodélé and Taklimakan Deserts. The models generally agree in large scale mean dust distributions, however models show large diversity in dust source attribution. The inter-model differences are significant with the global model dust diversity in 30%–50%, but the differences in regional and seasonal scales are even larger. The multi-model analysis estimates that North Africa contributes 60% of global atmospheric dust loading, followed by Middle East and Central Asia sources (24%). Southern hemispheric sources account for 10% of global dust loading, however it contributes more than 70% of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the seven models.

Dust source attribution, Aerosol, Model↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
DOE OSTI2025

Tradeoffs between uniform land protection and biodiversity-specific land protection with <2 °C global warming

Nearly 200 countries have pledged to conserve 30% of terrestrial ecosystems to stop the global biodiversity crisis. However, biodiversity is not uniformly distributed across countries. Adequately addressing this crisis requires a scientific basis for selecting protected land that considers both ecological benefits and impacts to humans. We use the global change analysis model to evaluate land use tradeoffs of four land protection cases under two climate cases. We find that biodiversity-specific land protection up to 39% globally can reduce land use constraints and food prices compared to protecting 30% of land uniformly in each country (’30 × 30’ initiative). Valuing terrestrial carbon for climate change mitigation reduces land conversion pressure and can complement protection strategies. Global impacts to agriculture of additional land protection are small, but regional impacts vary and may be considerable. Overall, biodiversity-specific land protection has greater potential than a uniform target to meet both ecological and human needs.

biodiversity↗
NASA NTRS2022

Atmospheric Energy Spectra in Global Kilometre-Scale Models

Eleven 40-day long integrations of five different global models with horizontal resolutions of less than 9 km are compared in terms of their global energy spectra. The method of normal-mode function decomposition is used to distinguish between balanced (Rossby wave; RW) and unbalanced (inertia-gravity wave; IGW) circulation. The simulations produce the expected canonical shape of the spectra, but their spectral slopes at mesoscales, and the zonal scale at which RW and IGW spectra intersect differ significantly. The partitioning of total wave energies into RWs an IGWs is most sensitive to the turbulence closure scheme and this partitioning is what determines the spectral crossing scale in the simulations, which differs by a factor of up to two. It implies that care must be taken when using simple spatial filtering to compare gravity wave phenomena in storm-resolving simulations, even when the model horizontal resolutions are similar. In contrast to the energy partitioning between the RWs and IGWs, changes in turbulence closure schemes do not seem to strongly affect spectral slopes, which only exhibit major differences at mesoscales. Despite their minor contribution to the global (horizontal kinetic plus potential available) energy, small scales are important for driving the global mean circulation. Our results support the conclusions of previous studies that the strength of convection is a relevant factor for explaining discrepancies in the energies at small scales. The models studied here produce the major large-scale features of tropical precipitation patterns. However, particularly at large horizontal wavenumbers, the spectra of upper tropospheric vertical velocity, which is a good indicator for the strength of deep convection, differ by factors of three or more in energy. High vertical kinetic energies at small scales are mostly found in those models that do not use any convective parameterisation.

DYAMOND↗
DOE OSTI2024

Anthromes and forest carbon responses to global change

Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900-present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO 2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO 2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO 2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land-use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dissolved Organic Carbon in Coastal Waters: Global Patterns, Stocks and Environmental Physical Controls

Abstract Dissolved organic carbon (DOC) in coastal waters is integral to biogeochemical cycling, but global and regional drivers of DOC are still uncertain. In this study we explored spatial and temporal differences in DOC concentrations and stocks across the global coastal ocean, and how these relate to temperature and salinity. We estimated a global median coastal DOC stock of 3.15 Pg C (interquartile range (IQR) = 0.85 Pg C), with median DOC concentrations being 2.2 times higher than in open ocean surface waters. Globally and seasonally, salinity was the main driver of DOC with concentrations correlated negatively with salinity, without a clear relationship to temperature. DOC concentrations and stocks varied with region and season and this pattern is likely driven by riverine inputs of DOC and nutrients that stimulate coastal phytoplankton production. Temporally, high DOC concentrations occurred mainly in months with high freshwater input, with some exceptions such as in Eastern Boundary Current margins where peaks are related to primary production stimulated by nutrients upwelled from the adjacent ocean. No spatial trend between DOC and temperature was apparent, but many regions (19 out of 25) had aligned peaks of seasonal temperature and DOC, related to increased phytoplankton production and vertical stratification at high temperatures. Links of coastal DOC with salinity and temperature highlight the potential for anthropogenic impacts to alter coastal DOC concentration and composition, and thereby ecosystem status.

Lønborg, Christian [Section for Marine Diversity a↗
DOE OSTI2025

Harnessing Satellite Data Alone for Mapping Global Thermal Anisotropy

Mapping thermal anisotropy across global lands is critical for advancing a wide range of Earth science studies. However, a comprehensive understanding of global thermal anisotropy intensity (TAI) and its governing factors remains missing. We introduce a novel data-driven methodology to quantify global TAI exclusively using multi-angle MODIS land surface temperature time series observations. Our analysis reveals distinct seasonal and diurnal TAI patterns, with global mean summertime TAI exceeding 2.9°C. Furthermore, we identify strong associations between TAI and key surface and atmospheric parameters, such as leaf area index and downward shortwave radiation. Our findings advocate for a paradigm shift from model-based to data-driven approaches in correcting thermal anisotropy, thereby addressing a critical bottleneck in Earth observation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Emergent constraints on future methane emissions from global wetlands

Future methane (CH 4 ) emissions from natural wetlands are predicted to increase due to global warming, leading to positive feedback on climate change. However, the magnitude of this increase remains highly uncertain. Here we present novel ensemble simulations of seven state-of-the-art terrestrial biosphere models to estimate wetland CH 4 emissions (eCH 4 ) during the twenty-first century. Our estimates suggest that for every 1 °C increase in global land surface temperature, there is a 24 ± 10 Tg CH 4 yr −1 increase in eCH 4 . We also identify an emergent relationship between contemporary temperature dependence and projected eCH 4 . When constrained by 163 site-year eddy-covariance measurements of eCH 4 , we show that wetland emissions can increase by 50–60% by the 2090s relative to the 2010s under a high-warming scenario. The projected decadal increase in eCH 4 from the 2010–2019 baseline to the 2030s would very likely (90% probability) offset an amount equivalent in scale to 8–10% of anthropogenic eCH 4 at the 2020 level, comparable to the reductions committed under the Global Methane Pledge. However, the constraint is dominated by mid- and high-latitude observations, with limited tropical coverage, and uncertainties in projected wetland inundation contribute substantially to uncertainty in eCH 4 . Our findings reduce the uncertainty in projected wetland methane–climate feedback and highlight its potential impacts on methane mitigation efforts to slow global warming.

Zhang, Zhen [Chinese Academy of Sciences (CAS), Be↗
DOE OSTI2026

Ozone pollution reduction partially offsets the negative impact of climate change mitigation efforts on global hunger

Studies warning of the potential negative effects of climate mitigation on food security through the competing use of land for bioenergy and afforestation have overlooked the impact of reduced ozone and its potential enhancement of crop yields. Here we use six global agro-economic models to compare the impacts of climate change with climate mitigation policy and ozone reduction on agriculture. We find that ozone reduction could reduce the negative impact of a 1.5 °C-consistent climate change mitigation policy on global hunger by 15% in 2050. Sub-Saharan Africa and India, where hunger is most severe, account for 56% of this global reduction. Our findings indicate that the negative effects of climate mitigation on global hunger could be partially offset by the ozone reduction impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Seeing the forest for the trees: implementing dynamic representation of forest management and forest carbon in a long-term global multisector model

Abstract Studies have found that understanding forest management is critical in understanding the interaction between the carbon cycle and the integrated human-Earth system. This makes effectively representing forest management decisions such as planting and harvesting important. Here, we implement a novel dynamic forest harvest model in a global state of the art multi-sector dynamics model, namely the Global Change Analysis Model (GCAM). We implement an approach that explicitly tracks forest age and generates rotation ages for forest harvest that are responsive to changes in wood prices, changes in forest age and regional preferences for forest rotation. Furthermore, the forest sector in GCAM competes for investment with other land use types in the future years based on expected profit. Our baseline scenario results indicate that with the new forest harvest model, the current global wood product demand in GCAM can be met with minimal loss of old growth forest through the age-based harvest decisions. We find that economic pressure for deforestation and consequent loss of forest carbon is a bigger driver of global forest change than wood harvests, especially in developing regions. Under alternative scenarios where an economic value is placed on carbon across the terrestrial and energy systems, while there is an increase in forest plantations, there can be corresponding decreases in forest cover in some regions as forest land competes with land for bio-energy crops. When the carbon in forests is assigned a price, we find that the average rotation age for wood harvests can be reduced across regions to harvest forests in a more carbon efficient manner.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

Wetlands are the single largest natural source of atmospheric methane (CH 4 ), contributing approximately 30% of total surface CH 4 emissions, and they have been identified as the largest source of uncertainty in the global CH 4 budget based on the most recent Global Carbon Project CH 4 report. High uncertainties in the bottom–up estimates of wetland CH 4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH 4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH 4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH 4 emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH 4 yr -1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH 4 yr -1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH 4 yr -1 ). Bottom–up estimates are subject to significant uncertainties (~80 Tg CH 4 yr -1 ), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH 4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH 4 emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top–down inversions to improve bottom–up estimates. Recent advances in surface measurements of CH 4 fluxes (e.g. FLUXNET-CH 4 ) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom–up estimates of wetland CH 4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH 4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH 4 observations directed at wetlands.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Web Map Services (WMS) Global Mosaic

The WMS Global Mosaic provides access to imagery of the global landmass using an open standard for web mapping. The seamless image is a mosaic of Landsat 7 scenes; geographically-accurate with 30 and 15 meter resolutions. By using the OpenGIS Web Map Service (WMS) interface, any organization can use the global mosaic as a layer in their geospatial applications. Based on a trade study, an implementation approach was chosen that extends a previously developed WMS hosting a Landsat 5 CONUS mosaic developed by JPL. The WMS Global Mosaic supports the NASA Geospatial Interoperability Office goal of providing an integrated digital representation of the Earth, widely accessible for humanity's critical decisions.

web map services↗