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Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1° × 1° resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000–2020 emissions through 2025. In the test data (∼ 30 % of the total dataset), the emulator achieved a global R2 of 0.65 ± 0.003 (mean ± 95 % CI, hereafter) and an RMSE of 5.49±0.12×10-3 Tg CH4 yr−1. The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021–2025 (157.8 ± 2.4 Tg CH4 yr−1) are not significantly higher (∼ 0.05 Tg CH4 yr−1) than the 2000–2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 and 0.35 ± 0.03 Tg CH4 yr−1, respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (−0.95 ± 0.19 and -0.11±0.02 Tg CH4 yr−1, respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026). 

Abstract Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet. 

Abstract. Understanding and quantifying the global methane (CH4) budgetis important for assessing realistic pathways to mitigate climate change.Atmospheric emissions and concentrations of CH4 continue to increase,making CH4 the second most important human-influenced greenhouse gas interms of climate forcing, after carbon dioxide (CO2). The relativeimportance of CH4 compared to CO2 depends on its shorteratmospheric lifetime, stronger warming potential, and variations inatmospheric growth rate over the past decade, the causes of which are stilldebated. Two major challenges in reducing uncertainties in the atmosphericgrowth rate arise from the variety of geographically overlapping CH4sources and from the destruction of CH4 by short-lived hydroxylradicals (OH). To address these challenges, we have established aconsortium of multidisciplinary scientists under the umbrella of the GlobalCarbon Project to synthesize and stimulate new research aimed at improvingand regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paperdedicated to the decadal methane budget, integrating results of top-downstudies (atmospheric observations within an atmospheric inverse-modellingframework) and bottom-up estimates (including process-based models forestimating land surface emissions and atmospheric chemistry, inventories ofanthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated byatmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximumestimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or∼ 60 % is attributed to anthropogenic sources, that isemissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009),and 24 Tg CH4 yr−1 larger than the one reported in the previousbudget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4emissions have been tracking the warmest scenarios assessed by theIntergovernmental Panel on Climate Change. Bottom-up methods suggest almost30 % larger global emissions (737 Tg CH4 yr−1, range 594–881)than top-down inversion methods. Indeed, bottom-up estimates for naturalsources such as natural wetlands, other inland water systems, and geologicalsources are higher than top-down estimates. The atmospheric constraints onthe top-down budget suggest that at least some of these bottom-up emissionsare overestimated. The latitudinal distribution of atmosphericobservation-based emissions indicates a predominance of tropical emissions(∼ 65 % of the global budget, < 30∘ N)compared to mid-latitudes (∼ 30 %, 30–60∘ N)and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methanebudget is attributable to natural emissions, especially those from wetlandsand other inland waters. Some of our global source estimates are smaller than those in previouslypublished budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due toimproved partition wetlands and other inland waters. Emissions fromgeological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overalldiscrepancy between bottom-up and top-down estimates has been reduced byonly 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methanebudget include (i) a global, high-resolution map of water-saturated soilsand inundated areas emitting methane based on a robust classification ofdifferent types of emitting habitats; (ii) further development ofprocess-based models for inland-water emissions; (iii) intensification ofmethane observations at local scales (e.g., FLUXNET-CH4 measurements)and urban-scale monitoring to constrain bottom-up land surface models, andat regional scales (surface networks and satellites) to constrainatmospheric inversions; (iv) improvements of transport models and therepresentation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/orco-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded fromhttps://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from theGlobal Carbon Project.