Recent intensification of wetland methane feedback

The positive response of wetland methane (CH4) emissions to climate change is an important yet uncertain Earth-system feedback that amplifies atmospheric CH4 concentrations. Here, using a wetland model, we report intensified wetland CH4 emissions during 2000–2021, corresponding with 2020 and 2021 being exceptional years of growth. Our results highlight the need for sustained monitoring and observations of global wetland CH4 fluxes to document emerging trends, variability and underlying drivers. Atmospheric methane concentrations are increasing and a process-based model now estimates greater methane emissions from wetlands since 2007 than previous studies. Substantial increases in 2020 and 2021 contributed to record-high growth rates in the atmospheric methane burden.

The positive response of wetland methane (CH 4 ) emissions to climate change is an important yet uncertain Earth-system feedback that amplifies atmospheric CH 4 concentrations. Here, using a wetland model, we report intensified wetland CH 4 emissions during 2000-2021, corresponding with 2020 and 2021 being exceptional years of growth. Our results highlight the need for sustained monitoring and observations of global wetland CH 4 fluxes to document emerging trends, variability and underlying drivers.
Methane (CH 4 ) is a potent greenhouse gas with a warming potential that is 84 times stronger than CO 2 on a 20-year horizon 1 . Because of its relatively short lifetime (~10 years) in the atmosphere, anthropogenic methane emission reductions are an important mitigation option for limiting near-term warming well below a 1.5 °C or 2 °C increase in global temperature 2,3 . The rapidly rising atmospheric methane concentrations in recent decades in addition to the record-high growth rates in 2020 and 2021 (hereafter, 2020/2021) raise concerns, however, that climate change is amplifying natural CH 4 emissions. The reason for the recent rise of methane is still unclear because of the limited understanding of the interplay between methane sources and sinks. However, the continuously depleting trend of atmospheric 13 C-CH 4 suggest probably strong contributions from increasing biogenic sources [4][5][6][7][8] , pointing to either constant increases from agricultural and waste sectors or wetland CH 4 feedback, or both. Here we report synergies between climate change and the rapid increase in global wetland CH 4 emissions in recent years.
Paleoclimate records suggest that positive warming-wetland CH 4 feedbacks can increase atmospheric methane during rapid, decadal, time scales 9,10 . The wetland CH 4 feedback to global warming is mainly hypothesized to be a result of (1) the effect of rising temperatures on microbial activities (for example, methanogenesis) and on thawing permafrost and (2) the expansion of wetlands with increased total precipitation, to the first order due to the physical relationship (that is, the Clausius-Clapeyron relationship) between rising temperatures and atmospheric water content 11 . Future climate projections 12,13 suggest that wetland emissions will increase by 30-50 TgCH 4 yr −1 globally by 2050 with respect to the 2010 level in the Representative Concentration Pathway (RCP8.5) scenario, in which warming follows emissions trajectories that exceed 3-5 °C. Assuming that anthropogenic methane emissions must decline by 30-60% 14 (~122 TgCH 4 yr −1 ) for warming to stay below 1.5 °C, this projected increase of wetland emissions could offset 25-40% of the reduction. Recent observational studies suggest that the tropical hydrological cycle has already intensified due to strengthened Walker circulation 15 . In higher latitudes, long-term warming 16 appears to be driving increases in growing-season emissions of wetland methane 17 . Satellite-based observations suggest increases in tropical biogenic CH 4 emissions may already be driving the atmospheric CH 4 upward 18-20 due to intensified rainfall and rising temperature.
Despite the potential for positive methane-climate feedbacks from global wetlands, most Earth System Models (ESMs) and Integrated Assessment Models (IAMs) that informed the last Assessment Report of the IPCC do not directly incorporate this process. These models focus on long-term atmospheric methane concentration increases coming from human activities such as industry and agriculture. From a modelling perspective, wetland emissions are considered to impact mainly the interannual variability (IAV) in the atmospheric CH 4 growth rate (except for years with severe fire events when biomass burning emissions play a larger role), which is regulated by climate phenomena such as the El Niño-Southern Oscillation 21 .
Here we apply a wetland methane model developed to represent tropical and permafrost wetlands with two different climate datasets, one based on ground meteorological stations and one from reanalysis, to evaluate climate-change-driven wetland CH 4 emissions from Brief Communication https://doi.org/10.1038/s41558-023-01629-0 2000 to 2021. The CH 4 emission parameterizations are calibrated against a benchmark dataset of wetland fluxes from an independent atmospheric inversion 22 and thus differ with the two climate datasets (Methods). This treatment is for consistency with the setup of a previous future projection study 12 , which used the same land surface model driven by a full ensemble of bias-corrected Coupled Model Intercomparison Project Phase 5 (CMIP5) climate datasets.
We estimate that the wetland CH 4 emissions from simulations based on ground-based and reanalysis-based climate forcing significantly increased (P < 0.01; linear regression) at a rate of 1.3-1.4 TgCH 4 yr −1 from 2000 to 2021 (Fig. 1). The estimated increase is higher than the ensemble average under the high warming climate scenario RCP8.5 (at 0.9 TgCH 4 yr −1 ) 12 . The larger wetland CH 4 increases from the observational-based simulations are probably due to the differences in temperature and precipitation between the CMIP5 models and observation-based climate datasets for the wetland regions. This indicates that global wetlands in high-latitude and tropical regions are experiencing stronger impacts of climate change than predicted in the most intensive climate warming in the CMIP5 models. The simulated wetland dynamics based on a prognostic hydrologic approach shows a good agreement with the land-water mass anomalies from the Gravity Recovery and Climate Experiment satellite ( Supplementary Fig. 1). Global

Brief Communication
https://doi.org/10.1038/s41558-023-01629-0 are 14-26 TgCH 4 yr −1 in 2020 and 13-23 TgCH 4 yr −1 in 2021, each of which has a 5% probability of occurring in the 20-year horizon when excluding the positive trend. The uncertainties of annual estimates reflect different IAV across regions, with reanalysis-based estimates generally giving a higher variability than observational-based estimates. Both simulations suggest that tropical wetlands dominate the increase, although with diverse regional differences (Extended Data Fig. 1), mainly due to uncertainty in spatiotemporal patterns of precipitation between the two climate forcing datasets (Extended Data Fig. 2). The simulations driven by the ground-based climate forcing data indicate trends over South America as the single largest contributor, while the reanalysis-based simulations suggest that trends over Africa, South Asia and Southeast Asia are also responsible for high emissions. Despite the difference in estimated emissions magnitudes and regional changes, both simulations agree that tropical wetlands are emerging hotspots, with 2020 and 2021 being highly anomalous years. This finding is in line with recent atmospheric inversions 18,20,23 , which suggest that tropical methane emissions contributed to a large portion of the atmospheric methane growth rate in 2020 and 2021. The detrended and deseasonalized time series (Fig. 2b) shows that the global annual anomalies in 2020 and 2021 are larger than 1 for the period of 2000-2021 in the reanalysis-based simulation. Even though no strong peak anomalies are detected by the ground-based run, the positive trends are higher than those of the reanalysis-based run (Extended Data Fig. 3). This is mainly due to different parameterization schemes against the different climatic forcings, which results in higher temperature sensitivity and lower wetland extent in the ground-based run compared to the reanalysis-based run (Supplementary Table 3). The reanalysis-based simulation suggests that Africa has a 3 anomalous peak CH 4 emission in 2019 due to extremely large rainfall events, which coincides with strong XCH 4 (that is, column-averaged CH 4 concentration) enhancement during 2019 over East Africa recorded by two satellite datasets 24 . This pattern is not captured by the ground-based run, highlighting the influence of precipitation inputs on CH 4 estimation for regions with sparse measurements (see Methods for the descriptions about climatic inputs).
Our results suggest the probable emergence of a strong positive wetland CH 4 feedback under current climate-change-driven warming and changes in precipitation. With the uncertainty in climate datasets, it is unclear whether rising temperature or strengthened precipitation plays a more prominent role in the rise of wetland CH 4 . Further evidence of intensified wetland CH 4 emissions from top-down inversions would help constrain the representation of processes and parameter uncertainties in the land surface models. Sustained and enhanced multiscale monitoring and observations will also help track sources and changes of methane emissions, particularly in remote areas 25,26 that have sparse measurement coverage and strong potential for climate feedbacks, like many tropical wetlands. While the high-latitude wetlands appear to have only moderate CH 4 increases, the climate-permafrost thaw feedback on future CH 4 emissions remains a concern 12 . The emergence of a wetland-climate feedback emphasizes that coordination between the scientific community on integrating rapidly changing biospheric processes within remaining carbon budgets is a priority for staying below 1.5 °C and 2.0 °C.

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Brief Communication
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LPJ-wsl model
The Lund-Potsdam-Jena-Wald, Schnee and Landschaft (LPJ-wsl) model applied in this study is a process-based dynamic global vegetation model developed for carbon cycle applications. This study extends the Zhang et al. 27 analysis, which ended in 2016, through to 2021. The LPJ-wsl model accounts for the major global processes controlling wetland CH 4 emissions such as soil permafrost, the rate of microbial decomposition and wetland extent dynamics. The version of the LPJ-wsl model applied in this study includes a hydrology model, TOPMODEL, to determine the wetland area and its inter-and intra-annual dynamics 28 , a permafrost and dynamic snow model 29 , and a wetland CH 4 emission model 30 , each of which is incorporated into the LPJ-wsl framework with explicit representation of the effects of snow and freeze-thaw cycles on soil temperature and moisture, and thus CH 4 emissions. The permafrost module simulates the freeze-thaw cycle for eight discrete layers of soil thickness, where the soil heat capacity and its thermal conductivity are affected by the volumetric fractions of the soil physical components, such as the water-ice fraction, mineral soil or peat.

Wetland CH 4 module
Generally, wetland CH 4 emissions are modelled as a function of the CH 4 emitting factor, soil temperature at the upper soil depth (0-50 cm), ecosystem heterotrophic respiration and wetland extent. The CH 4 emitting factor (F) for grid cell X is calculated as a combination of latitudinal scaling factors and surface temperature using the equation: where σ(X) is exp(T(X) - T max ), T(X) is the mean soil temperature between 1960 and 1990 at 0-50 cm depth, and maximum temperature T max = 303.35 K. F T and F B are two latitudinal factors representing typical tropical and boreal wetlands, respectively. F T and F B were fit to match a benchmark of wetland CH 4 fluxes from an independent atmospheric inversion study 22 for consistency with the magnitude of the future projection study 12 where R 1 and R 2 are the CH 4 flux at temperatures T 1 and T 2 , respectively. The results suggest that the LPJ-modelled temperature dependence for each season (Supplementary Fig. 3) is generally comparable with the observational estimates and is slightly lower than the measurements during spring (March, April, May), summer ( June, July, August) and autumn (September, October, November). The LPJ-wsl model is shown to have relatively low weighted root mean square error compared to satellite-constrained top-down wetland CH 4 estimates among process-based wetland CH 4 models when compared with atmospheric measurements 34 . Notably, recent studies show that process-based wetland models underestimate the magnitude of annual total emissions for some of the underrepresented tropical wetland hotspots, such as African wetlands 25 and South American wetlands 26 .

Model simulations
Two gridded meteorological datasets were used for the simulations. The ground-based input dataset was a monthly climatic observation (denoted as CRU) based on meteorological stations that was developed by the CRU at the University of East Anglia. The reanalysis-based climate dataset was a daily climatic dataset from one-hourly reanalysis MERRA2 from the National Aeronautics and Space Administration Global Modeling and Assimilation Office. The CRU dataset is formed by interpolation of site-level measurements to cover land worldwide, while the MERRA2 reanalysis relies on atmospheric models that assimilate multiple sources from satellite observations and ground measurements. The difference in methodology between these two types of climatic inputs results in different spatiotemporal variability in climate variables, especially for precipitation which determines the spatiotemporal patterns of wetland inundation dynamics. Two sets of factorial runs for the period of 2007-2021 were conducted with the climatology data of CRU and MERRA2 to disentangle the drivers of post-2007 wetland methane emissions on CH 4 emissions (Supplementary Table 2). Each set of factorial runs included four runs forced with climatological values (average monthly and/or daily fields for 2000-2006) for air temperature, precipitation and CO 2 concentration. The difference between the baseline simulation and factorial runs provides the individual contributions of climate variables to the anomaly of CH 4 emissions in the post-2007 period.

Decomposition of the time series
The changes in wetland emissions were due to a combination of long-term trends, seasonal cycles and anomalies (IAV, peak emission of a year is especially notable). The components were calculated using equation (3): The function first determines the trend component T[t] for time t using a 12-month moving average with equal weights. The seasonal component S[t] is then centred. The residual r[t] is the anomaly that is determined by removing trends and seasonal cycles from the original time series.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
All the wetland CH 4 emission data 35 and regional masks used in this study are available in the publicly accessible Zenodo repository (https://doi.org/10.5281/zenodo.7595223).