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.

While the preindustrial ocean was assumed to be in equilibrium with the atmosphere, the modern ocean is a carbon sink, resulting from natural variability and anthropogenic perturbations, such as fossil fuel emissions and changes in riverine exports over the past two centuries. Here we use a suite of sensitivity experiments based on the ECCO-Darwin global-ocean biogeochemistry model to evaluate the response of air-sea CO2 flux and carbon cycling to present-day lateral fluxes of carbon, nitrogen, and silica. We generate a daily export product by combining point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral fluxes from 5171 watersheds worldwide. From 2000 to 2019, carbon exports increase CO2 outgassing by 0.22 Pg C yr-1 via the solubility pump, while nitrogen exports increase the ocean sink by 0.17 Pg C yr-1 due to phytoplankton fertilization. On regional scales, exports to the Tropical Atlantic and Arctic Ocean are dominated by organic carbon, which originates from terrestrial vegetation and peats and increases CO2 outgassing (+10 and +20%, respectively). In contrast, Southeast Asia is dominated by nitrogen from anthropogenic sources, such as agriculture and pollution, leading to increased CO2 uptake (+7%). Our results demonstrate that the magnitude and composition of riverine exports, which are determined in part from upstream watersheds and anthropogenic perturbations, substantially impact present-day regional-to-global-ocean carbon cycling. Ultimately, this work stresses that lateral fluxes must be included in ocean biogeochemistry and Earth System Models to better constrain the transport of carbon, nutrients, and metals across the land-ocean-aquatic-continuum.

The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single-satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N to 37.4°S). The unique reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS’s indication of water existence. Berkeley-RWAWC provides monthly, near-real-time inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water datasets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal Berkeley-RWAWC’s enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley-RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE), and their relationship with environmental drivers, are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state-of-the-science flux products: NEE constrained by surface and space-based atmospheric CO2 measurements over 2010–2015 and satellite up-scaled GPP from FluxSat over 2001-2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems are dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models.

The rate of increase in atmospheric methane (CH4) has accelerated in recent years, reaching 15 ppb/yr in 2020, with causes that are not well understood. Given methane’s potent global warming potential (85x that of CO2 on a 20-year timescale), this indicates a crucial need to better understand its current budget. Near-global high-precision methane column observations from the TROPOMI satellite sensor offer a major advance for mapping methane fluxes. Here we combine two years of TROPOMI data with the GEOS-Chem adjoint model in a 4D-Var framework to optimize global methane emissions at high spatial resolution. The inversions converge on distinct sets of solutions depending on whether methane loss rates are also simultaneously optimized or not. Findings thus show that even with the dense TROPOMI coverage, methane budget inferences remain sensitive to the prior assumptions for OH. The ensemble of solutions adheres to a close linear relationship between the derived global source and sink terms, with each distinct result successfully improving the simulation of globally-available in-situ data. Solutions with methane loss rates treated as a hard constraint exhibit the best consistency with remote OH and CO measurements and with the background seasonal cycle in methane. We further employ multiple inversion formalisms to test the solution sensitivity to the assumed prior emissions. This presentation will explore the derived emission adjustments in terms of their implications for methane flux drivers and potential missing sources.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity on the global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. Transpiration and gross primary productivity (GPP) that traditional LSMs simulate are not directly measurable from space and they are inferred from spaceborne observations using assumptions that are inconsistent with those of the LSMs, whereas canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we present the land model developed within the Climate Modeling Alliance (CliMA), which simulates global-scale GPP, transpiration, and hyperspectral canopy radiative transfer (RT). Thus, CliMA Land can predict any vegetation index or outgoing radiance, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given measurement geometry. Even without parameter optimization, the modeled spatial patterns of CliMA Land GPP, SIF, NDVI, EVI, and NIRv correlate significantly with existing observational products. CliMA Land is also very useful in its high temporal resolution, e.g., providing insights into when GPP, SIF, and NIRv diverge. Based on comparisons between models and observations, we propose ways to improve future land modeling regarding data processing and model development.

Wetlands are the single largest source of methane to the atmosphere and their emissions are expected to respond to a changing climate. Inaccuracy and uncertainty in inundation extent drives differences in modeled wetland emissions and impacts representation of wetland emissions on inter-annual and seasonal time frames. Existing wetland maps are based on optical or NIR satellite data obscured by clouds and vegetation, often leading to underestimates in wetlands extent, especially in the Tropics. Here, we present new inundation maps based on the CYGNSS satellite constellation, operating in L-band that is not impacted by clouds or vegetation, providing reliable observations through canopy and cloudy periods. We map the temporal and spatial dynamics of the Pantanal and Sudd wetlands, two of the largest wetlands in the world, using CYGNSS data and a computer vision algorithm. We link these inundation maps to methane fluxes via WetCHARTs, a global wetland methane emissions model ensemble. We contrast CYGNSS-modeled methane emissions with WetCHARTs standard runs that use monthly rainfall data from ERA5, as well as the commonly used SWAMPS wetland maps. We find that the CYGNSS-based inundation maps modify the methane emissions in multiple ways. The seasonality of inundation and methane emissions is shifted by two months because of the lag in wetland recharge following peak rainfall. Both inundation and methane emissions also respond non-linearly to wet-season precipitation totals, leading to large interannual variability in emissions. Finally, the annual magnitude of emissions is found to be greater than previously estimated.

Isoprene is a hydrocarbon emitted in large quantities by terrestrial vegetation. It is a precursor to several air quality and climate pollutants including ozone. Emission rates vary with plant species and environmental conditions. This variability can be modelled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). MEGAN parameterizes isoprene emission rates as a vegetation-specific standard rate which is modulated by scaling factors that depend on meteorological and environmental driving variables. Recent experiments have identified large uncertainties in the MEGAN temperature response parameterization, while the emission rates under standard conditions are poorly constrained in some regions due to a lack of representative measurements and uncertainties in landcover. In this study, we use Bayesian model-data fusion to optimize the MEGAN temperature response and standard emission rates using satellite- and ground-based observational constraints. Optimization of the standard emission rate with satellite constraints reduced model biases but was highly sensitive to model input errors and drought stress and was found to be inconsistent with ground-based constraints at an Amazonian field site, reflecting large uncertainties in the satellite-based emissions. Optimization of the temperature response with ground-based constraints increased the temperature sensitivity of the model by a factor of five at an Amazonian field site but had no impact at a UK field site, demonstrating significant ecosystem-dependent variability of the isoprene emission temperature sensitivity. Ground-based measurements of isoprene across a wide range of ecosystems will be key for obtaining an accurate representation of isoprene emission temperature sensitivity in global biogeochemical models.