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Abstract 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.

Abstract The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near‐term climate change. Emission inventories generated through surface activity measurements are reported annually or bi‐annually, and evaluated periodically through a “Global Stocktake.” Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (also called smoothing error), as‐well‐as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state‐of‐the‐art inventory (EDGAR v6.0) using satellite‐based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite‐based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, −11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25–1.3 Tg CH4/yr/yr), primarily in the Indo‐Pakistan region, sub‐tropical Africa, and the Southern Brazilian; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

The evolution of the Amazon forest is tightly coupled to its terrestrial water balance. Here, the authors show that forest biomass changes in the Amazon are a driver of the spatiotemporal variation of evapotranspiration, and such changes could have a larger impact on water availability in the dry regions (southern, eastern) of the Amazon.

Sector-based methane emissions can be backed out from observed methane fluxes, using a Bayesian optimal estimation method. This could help with monitoring gas leaks from industry.

Abstract Wetland methane (CH4) emissions comprise about one‐third of the global CH4 source. The latitudinal distribution and climate sensitivity of wetland CH4 fluxes are the key determinants of the global CH4‐climate feedback. However, large differences exist between bottom‐up estimates, informed by ground‐based flux measurements, and top‐down estimates derived from spaceborne total column CH4. Despite the extensive coverage of satellite CH4 concentration observations, challenges remain with using top‐down estimates to test bottom‐up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite‐based top‐down CH4 flux estimates (2010–2012) to test and refine 42 bottom‐up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom‐up models and satellite‐based fluxes innovatively accounts for cross‐correlations and spatial uncertainties typically found in top‐down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite‐constrained wetland CH4 ensemble product derived from assembling the highest‐performance bottom‐up models, which estimates global wetland CH4 emissions of 148 (117–189, 5th–95th percentile) Tg CH4 yr−1. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower‐than‐expected temperature sensitivity agrees better with atmospheric CH4 measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom‐up wetland CH4 emission estimates, their latitudinal distributions, and their sensitivity to climate.

Abstract While the long‐term drought effect on tropical forests has been observed in ground‐based and remote sensing measurements, the feedback of reduced forest biomass on subsequent rainfall is not well understood. We evaluate the impact of slow forest recovery after the 2005 Amazonian drought on local evapotranspiration (ET) and wet season onset (WSO) using remotely sensed precipitation, deuterium retrievals, reanalysis data, and a new ET product. A comparison to the 2009 rainy season, which exhibits similar large‐scale moisture flux convergence, shows that 2006 experienced a 25% ET reduction and 20 days of postponed WSO in the dry‐to‐wet transition. Our results imply that ET reduction due to drought‐driven legacy effect on the Amazon rainforest could be a crucial factor triggering WSO delay in the transitional season following drought events.

The drivers of the increase in atmospheric methane since 2006 remain unclear. Here, the authors use satellite and in situ measurements of CO and CH4 to show that fossil fuels and biogenic sources contribute 12–19 Tg CH4per year and 12–16 Tg CH4per year respectively to the recent atmospheric methane increase.

The net climate impact of gas and coal life-cycle emissions are highly dependent on methane leakage. Every molecule of methane leaked alters the climate advantage because methane warms the planet significantly more than CO _2 over its decade-long lifetime. We find that global gas systems that leak over 4.7% of their methane (when considering a 20-year timeframe) or 7.6% (when considering a 100 year timeframe) are on par with life-cycle coal emissions from methane leaking coal mines. The net climate impact from coal is also influenced by SO _2 emissions, which react to form sulfate aerosols that mask warming. We run scenarios that combine varying methane leakage rates from coal and gas with low to high SO _2 emissions based on coal sulfur content, flue gas scrubber efficiency, and sulfate aerosol global warming potentials. The methane and SO _2 co-emitted with CO _2 alter the emissions parity between gas and coal. We estimate that a gas system leakage rate as low as 0.2% is on par with coal, assuming 1.5% sulfur coal that is scrubbed at a 90% efficiency with no coal mine methane when considering climate effects over a 20 year timeframe. Recent aerial measurement surveys of US oil and gas production basins find wide-ranging natural gas leak rates 0.65% to 66.2%, with similar leakage rates detected worldwide. These numerous super-emitting gas systems being detected globally underscore the need to accelerate methane emissions detection, accounting, and management practices to certify that gas assets are less emissions intensive than coal.

Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). While ESMs currently project large warming in the high northern latitudes, the magnitude and sign of the future carbon balance of Arctic-Boreal ecosystems are highly uncertain. The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is intended to improve future climate projections. Here, we benchmark the Coupled Model Intercomparison Project (CMIP) 5 and 6 (8 CMIP5 members and 12 CMIP6 members) with the International Land Model Benchmarking (ILAMB) tool over the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America. We show that the projected average net biome production (NBP) in 2100 from CMIP6 is higher than that from CMIP5 in the ABoVE domain, despite the model spread being slightly narrower. Overall, CMIP6 shows better agreement with contemporary observed carbon cycle variables (photosynthesis, respiration, biomass) than CMIP5, except for soil carbon and turnover time. Although both CMIP ensemble members project the ABoVE domain will remain a carbon sink by the end of the 21st century, the sink strength in CMIP6 increases with CO _2 emissions. CMIP5 and CMIP6 ensembles indicate a tipping point defined here as a negative inflection point in the NBP curve by 2050–2080 independently of the shared socioeconomic pathway (SSP) for CMIP6 or representative concentration pathway (RCP) for CMIP5. The model ensembles therefore suggest that, if the carbon sink strength keeps declining throughout the 21st century, the Arctic-Boreal ecosystems in North America may become a carbon source over the next century.