Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid-summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS-Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS-Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ~80 Tg a-1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24-member wetland emission ensemble, only the LPJ-wsl MERRA-2 inventory has these attributes.

East Asia (China, Japan, Koreas and Mongolia) has been the world’s economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid-century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long-lived greenhouse gases (CO2, CH4 and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint bottom-up and top-down approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between 196.9 ± 527.0 Tg CO2eq yr-1 (the top-down approach) and -20.8 ± 205.5 Tg CO2eq yr-1 (the bottom-up approach) during 2000-2019. This net GHG emission includes a large land CO2 sink (-1251.3 ± 456.9 Tg CO2 yr-1 based on the top-down approach and -1356.1 ± 155.6 Tg CO2 yr-1 based on the bottom-up approach), which is being fully offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sector. Emerging data sources and modelling capacities have helped achieve agreement between the top-down and bottom-up approaches to within 20% for all three GHGs, but sizeable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr-1 to a net sink of ~-700 Tg CO2 yr-1.

The long-term net sink of carbon (C), nitrogen (N) and greenhouse gases (GHGs) in the northern permafrost region is projected to weaken or shift under climate change. But large uncertainties remain, even on present-day GHG budgets. We compare bottom-up (data-driven upscaling, process-based models) and top-down budgets (atmospheric inversion models) of the main GHGs (CO2, CH4, and N2O) and lateral fluxes of C and N across the region over 2000-2020. Bottom-up approaches estimate higher land to atmosphere fluxes for all GHGs compared to top-down atmospheric inversions. Both bottom-up and top-down approaches respectively show a net sink of CO2 in natural ecosystems (-31 (-667, 559) and -587 (-862, -312), respectively) but sources of CH4 (38 (23, 53) and 15 (11, 18) Tg CH4-C yr-1) and N2O (0.6 (0.03, 1.2) and 0.09 (-0.19, 0.37) Tg N2O-N yr-1) in natural ecosystems. Assuming equal weight to bottom-up and top-down budgets and including anthropogenic emissions, the combined GHG budget is a source of 147 (-492, 759) Tg CO2-Ceq yr-1 (GWP100). A net CO2 sink in boreal forests and wetlands is offset by CO2 emissions from inland waters and CH4 emissions from wetlands and inland waters, with a smaller additional warming from N2O emissions. Priorities for future research include representation of inland waters in process-based models and compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more in-situ flux observations and improved resolution in upscaling.

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here we construct the first comprehensive bottom-up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in-situ flux measurements and a land cover-based ecosystem flux upscaling approach for the period 2000-2020. Estimates indicate that the permafrost region emitted a mean annual flux of 0.36 (-620, 652) Tg CO2-C y-1, 38 (21, 53) Tg CH4-C y-1, and 0.62 (0.03, 1.2) Tg N2O-N y-1 to the atmosphere throughout the period. While the region was a net source of CH4 and N2O, the CO2 budget was near neutral with large uncertainties. Terrestrial ecosystems remained a CO2 sink, but emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. Including lateral fluxes, the permafrost region was a net source of C and N, releasing 136 (-517, 821) Tg C y-1 and 3.2 (1.9, 4.8) Tg N y-1. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.

Coastal wetlands play a significant role in the storage of ‘blue carbon’, indicating their importance in the carbon biogeochemistry in the coastal zone and in global climate change mitigation strategies. We present airborne eddy-covariance observations of CO2 and CH4 fluxes collected in southern Florida as part of the NASA BlueFlux mission during April 2022, October 2022, February 2023, and April 2023. The flux data generated from this mission consists of over 100 flight hours and more than 6000 km of horizontal distance over coastal saline and freshwater wetlands. We find that the spatial and temporal heterogeneity in CO2 and CH4 exchange is primarily influenced by season, vegetation type, ecosystem productivity, and soil inundation. The largest CO2 uptake fluxes of more than -20 µmol m-2 s-1 were observed over mangroves during all deployments and over swamp forests during flights in April. The greatest CH4 effluxes of more than 250 nmol m-2 s-1 were measured at the end of the wet season in October 2022 over freshwater marshes and swamp shrublands. Although the combined Everglades National Park and Big Cypress National Preserve region was a net sink for carbon, CH4 emissions reduced the ecosystem carbon uptake capacity (net CO2 exchange rates) by 11-91%. Average total net carbon exchange rates during the flight periods were -4 to -0.2 g CO2-eq m-2 d-1. Our results highlight the importance of preserving mangrove forests and point to potential avenues of further research for greenhouse gas mitigation strategies.

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here we construct the first comprehensive bottom-up budgets of CO2, CH4, and N2O across the terrestrial permafrost region using databases of more than 1000 in-situ flux measurements and a land cover-based ecosystem flux upscaling approach for the period 2000-2020. Estimates indicate that the permafrost region emitted a mean annual flux of 0.36 (-620, 652) Tg CO2-C y-1, 38 (21, 53) Tg CH4-C y-1, and 0.62 (0.03, 1.2) Tg N2O-N y-1 to the atmosphere throughout the period. While the region was a net source of CH4 and N2O, the CO2 budget was near neutral with large uncertainties. Terrestrial ecosystems remained a CO2 sink, but emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. Including lateral fluxes, the permafrost region was a net source of C and N, releasing 136 (-517, 821) Tg C y-1 and 3.2 (1.9, 4.8) Tg N y-1. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.

Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach has been developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that 1) significant model-observation disagreements are mainly at short- to intermediate time scales (< 15 days); 2) most of the models can capture the CH4 variability at long time scales (> 32 days) for the boreal and Arctic tundra wetland sites but have limited performance for temperate and tropical/subtropical sites; 3) model error approximates pink noise patterns, indicating that biases at short time scales (< 5 days) could contribute to persistent systematic biases on longer time scales; and 4) differences in error pattern are related to model structure (e.g. proxy of CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability in future wetland CH4 model developments.

Using imaging spectroscopy (hyperspectral imaging), we sought to assess the effects of image pixel resolution, size of mapping windows composed of pixels, and number of spectral species assigned to pixels on the capacity to map plant beta diversity using the biodivMapR algorithm, in support of the planned NASA Surface Biology and Geology (SBG) satellite remote sensing mission. BiodivMapR classifies pixels as spectral species, then calculates beta diversity as dissimilarity of spectral species among mapping windows each composed of multiple pixels. We used NEON airborne 1 m resolution hyperspectral images collected at three sites representing native longleaf pine ecosystems in the southeastern U.S. and aggregated pixels to sizes ranging from 1-90 m for comparative analyses. Plant community composition was groundtruthed. Results show that the capacity to detect plant beta diversity decreases with fewer pixels per mapping window, such that pixel resolution limits the size of mapping windows effective for representing beta diversity. Mapping window size in turn limits the spatial resolution of beta diversity maps composed of mapping windows. Assigning too few pixels per window, as well as assigning too many spectral species per image, results in overestimation of dissimilarity among locations that have plant species in common. This overestimation undermines the capacity to contrast mapping window dissimilarity within versus among community types and reduces the information content of beta diversity maps. These results demonstrate the advantage of maximizing spatial resolution of hyperspectral imaging instruments on the anticipated NASA SBG satellite mission and similar remote sensing projects.

Over the last nearly five decades, optical remote sensing has played a key role in monitoring and quantifying global change, plant diversity, and vegetation functioning across Earth’s terrestrial biomes. As a key tool for researchers, land managers, and policy makers, optical remote sensing facilitates scaling, mapping, and characterizing surface properties over large areas and through time. In addition, steady technological improvements have led to transformational changes in our ability to understand ecosystem state and change, particularly through the expansion of high spectral resolution (i.e. spectroscopic) remote sensing platforms. Point and imaging spectroscopy systems have been used across a range of scales, vegetation types, and biomes to infer plant diversity, leaf traits, and ecosystem functioning. However, despite the acknowledged utility of spectroscopic systems, data availability has been limited to smaller geographic regions given a number of technical challenges, including issues related to data volume and limited spatial coverage by previous Earth Observing (EO) missions (i.e. Hyperion). The NASA Surface Biology and Geology (SBG) mission is designed to fill this gap in ecosystem monitoring. As part of the Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) and Modeling end-to-end traceability (MEET) SBG efforts, we used field, unoccupied aerial system (UAS), and airborne imagery (from NASA’s AVIRIS-NG plafrom) to evaluate the impacts of proposed and theoretical sensor instrument properties on the retrieval of vegetation reflectance across tundra, shrub, and treeline ecosystems in Alaska. Existing observations and open-source tools are used for the simulation of surface reflectance under a range of atmospheric conditions, vegetation types, and different sensor properties. We find that retrieval uncertainty is reduced across all surface types with increasing detector signal-to-noise (SNR) but also key differences across different plant types. Results were also strongly tied to sun-sensor geometry and atmospheric state. Through this exercise we highlight key outcomes to consider for the SBG mission to optimize surface reflectance retrieval in high latitudes that will help to minimize errors in down-stream algorithms, such as functional trait retrievals.

Global environmental science challenges in the limnological research and applications communities can only be advanced when harnessing the collective expertise and capabilities of the satellite remote sensing community and well-established in situ communities such as the Global Lake Ecological Observatory Network (GLEON). At first glance, the groups seem wildly divergent: GLEON is a grass-roots effort which has been active since 2005 and connects researchers and practitioners from around the world to ask and answer questions about lake ecosystems. Earth observing missions can take a decade to plan, build, and launch. NASA and ESA have different missions as space agencies: one primarily focused on exploration and basic research with a year-to-year appropriations cycle, while the other presents a long-term commitment to address societal needs through the Copernicus program Sentinel satellite series. The Surface Biology and Geology (SBG) mission is a future NASA satellite that will launch toward the end of this decade as part of the Earth Systems Observatory. Working together to advance the science of lake ecosystem response to climate change, each group brings different complementary strengths and assets to this societal challenge. Increasing access through open science and cloud computing are creating opportunities for better collaboration. We describe our strategy for international engagement between these groups – cultural and methodological differences aside – to derive new information, learn new insights, and expand the body of knowledge around these unique natural resources.

The Surface Biology and Geology (SBG) mission is one of the core missions of NASA’s Earth System Observatory (ESO). SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~10 TB/day and generate products at ~75 TB/day. As the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing/re- processing, disseminating, and exploiting the SBG data presents new challenges. To address these challenges, we are developing a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight. Our science pipeline is based on the science processing operations technology developed for the Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or an on-site supercomputer. Our effort began by ingesting data and applying workflows from the EO- 1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We have fully implemented the first stage of processing, from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We plan to begin reprocessing the entire 55 TB Hyperion data set by the end of 2021. Work to implement an atmospheric correction module to convert the L1R data to surface reflectance (Level 2) is also underway. Additionally, an effort to develop a hybrid High Performance Computing (HPC)/cloud processing framework has been started to help optimize the cost, processing throughput and overall system resiliency for SBG’s science data system (SDS). Separately, we have developed a method for generating full-volume synthetic data sets for SBG based on MODIS data and have made the first version of this data set available to the community on the data portal of NASA’s Advanced Supercomputing Division at NASA Ames Research Center. The synthetic data will make it possible to test parts of the pipeline infrastructure and other software to be applied for product generation.