Role of OH variability in the stalling of the global atmospheric CH4 growth rate from 1999 to 2006

The growth in atmospheric methane (CH4) concentrations over the past two decades has shown large variability on a timescale of several years. Prior to 1999 the globally averaged CH4 concentration was increasing at a rate of 6.0 ppb/yr, but during a stagnation period from 1999 to 2006 this growth rate slowed to 0.6 ppb/yr. From 2007 to 2009 the growth rate again increased to 4.9 ppb/yr. These changes in growth rate are usually ascribed to variations in CH4 emissions. We have used a 3-D global chemical transport model, driven by meteorological reanalyses and variations in global mean hydroxyl (OH) concentrations derived from CH3CCl3 observations from two independent networks, to investigate these CH4 growth variations. The model shows that between 1999 and 2006, changes in the CH4 atmospheric loss contributed significantly to the suppression in global CH4 concentrations relative to the pre-1999 trend. The largest factor in this is relatively small variations in global mean OH on a timescale of a few years, with minor contributions of atmospheric transport of CH4 to its sink region and of atmospheric temperature. Although changes in emissions may be important during the stagnation period, these results imply a smaller variation is required to explain the observed CH4 trends. The contribution of OH variations to the renewed CH4 growth after 2007 cannot be determined with data currently available

Evaluating year-to-year anomalies in tropical wetland methane emissions using satellite CH₄ observations

Natural wetlands are the largest source of methane emissions, contributing 20–40% of global emissions and dominating the inter-annual variability. Large uncertainties remain on their variability and response to climate change. This study uses atmospheric methane observations from the GOSAT satellite to evaluate methane wetland emission estimates. We assess how well simulations reproduce the observed methane inter-annual variability by evaluating the detrended seasonal cycle. The latitudinal means agree well but maximum differences in the tropics of 28.1–34.8 ppb suggest that all simulations fail to capture the extent of the tropical wetland seasonal cycle. We focus further analysis on the major natural wetlands in South America: the seasonally flooded savannah of the Pantanal (Brazil) and Llanos de Moxos (Bolivia) regions; and the riverine wetlands formed by the Paraná River (Argentina). We see large discrepancies between simulation and observation over the Pantanal and Llanos de Moxos region in 2010, 2011 and 2014 and over the Paraná River region in 2010 and 2014. We find highly consistent behaviour between the time and location of these methane anomalies and the change in wetland extent, driven by precipitation related to El Niño Southern Oscillation activity. We conclude that the inability of land surface models to increase wetland extent through overbank inundation is the primary cause of these observed discrepancies and can lead to under-estimation of methane fluxes by as much as 50% (5.3–11.8 Tg yr −1 ) of the observed emissions for the combined Pantanal and Paraná regions. As the hydrology of these regions is heavily linked to ENSO variability, being able to reproduce changes in wetland behaviour is important for successfully predicting their methane emissions

Role of regional wetland emissions in atmospheric methane variability

Atmospheric methane (CH4) accounts for ~20% of the total direct anthropogenic radiative forcing by long-lived greenhouse gases. Surface observations show a pause (1999-2006) followed by a resumption in CH4 growth, which remain largely unexplained. Using a land surface model, we estimate wetland CH4 emissions from 1993 to 2014 and study the regional contributions to changes in atmospheric CH4. Atmospheric model simulations using these emissions, together with other sources, compare well with surface and satellite CH4 data. Modelled global wetland emissions vary by ±3%/yr (σ=4.8 Tg), mainly due to precipitation-induced changes in wetland area, but the integrated effect makes only a small contribution to the pause in CH4 growth from 1999 to 2006. Increasing temperature, which increases wetland area, drives a long-term trend in wetland CH4 emissions of +0.2%/yr (1999 to 2014). The increased growth post-2006 was partly caused by increased wetland emissions (+3%), mainly from Tropical Asia, Sourthern Africa and Australia

Analysis of recent atmospheric methane trends using models and observations

Over the past two decades the growth rate of methane has shown large variability on multi-year timescales, the reasons for which are not well understood. The JULES land surface model, TOMCAT 3-D chemical transport model and observations have been used to investigate causes for these variations, with a specific focus on wetland emissions and atmospheric loss. The role of atmospheric variability in the recent methane trends was investigated using TOMCAT, driven by variations in global mean hydroxyl concentrations derived from methyl chloroform observations. Results show that between 1999 and 2006, a stall in the atmospheric methane growth rate was, in part, caused by changes in the atmospheric loss. This was due largely to relatively small changes in global mean hydroxyl concentrations over time, with minor contributions from variations in atmospheric transport and temperature. Methane emissions from various wetland inventories were evaluated using TOMCAT and observations, and recent trends in emissions were investigated. Emissions calculated by JULES were spatially and temporally similar to a top-down emission inventory and produced good agreement with satellite observations when used in TOMCAT (R = 0.84). Emissions derived for the period 1993 – 2012 show a statistically significant (95%-level) positive trend of 0.43 Tg/yr. This suggests a long-term positive trend in wetland emissions that may continue. During the stall in methane growth (1999-2006) modelled wetland emissions were 0.4 Tg/yr lower than average. This suggests that a decrease in wetland emissions contributed to the observed stall in methane growth. The wetland methane processes within JULES were developed to include transport, oxidation, sulphate suppression, unsaturated production and methane storage pools. The parameters required for the additional processes were derived using a perturbed parameter ensemble to optimise the fit with observed fluxes. This slightly increased model performance at flux sites from R = 0.32 in the standard model to R = 0.34 in the updated model. The new version of JULES was tested using TOMCAT and satellite observations, and model agreement improved from R = 0.84 to R = 0.87, additionally the root-mean-squared-error reduced from 17.17 ppb to 15.09 ppb. This suggests the optimised additional model processes slightly improved model performance

A global fuel characteristic model and dataset for wildfire prediction

Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurrence and propagation. Fuel load and fuel moisture content are essential variables for forecasting fire occurrence, and whilst existing operational systems incorporate dead fuel moisture content, both live fuel moisture content and fuel load are either approximated or neglected. We propose a mid-complexity model combining data driven and analytical methods to predict fuel characteristics. The model can be integrated into earth system models to provide real-time forecasts and climate records taking advantage of meteorological variables, land surface modelling, and satellite observations. Fuel load and moisture is partitioned into live and dead fuels, including both wood and foliage components. As an example, we have generated a 10-year dataset which is well correlated with independent data and largely explains observed fire activity globally. While dead fuel moisture correlates highest with fire activity, live fuel moisture and load are shown to potentially enhance prediction skill. The use of observation data to inform a dynamical model is a crucial first step toward disentangling the contributing factors of fuel and weather to understand fire evolution globally. This dataset, with high spatiotemporal resolution (∼9 km, daily), is the first of its kind and will be regularly updated.</p

Carbon budget for 1.5 and 2oC targets lowered by natural wetland and permafrost feedbacks

Methane emissions from natural wetlands and carbon release from permafrost thaw have a positive feedback on climate, yet are not represented in most state-of-the-art climate models. Furthermore, a fraction of the thawed permafrost carbon is released as methane, enhancing the combined feedback strength. We present simulations with an intermediate complexity climate model which follow prescribed global warming pathways to stabilisation at 1.5°C or 2.0°C above pre-industrial levels by the year 2100, and that incorporates a state-of-the-art global land surface model with updated descriptions of wetland and permafrost carbon release. We demonstrate that the climate feedbacks from those two processes are substantial. Specifically, permissible anthropogenic fossil fuel CO2 emission budgets are reduced by 17-23% (47-56 GtC) for stabilisation at 1.5°C, and 9-13% (52-57 GtC) for 2.0°C stabilisation. In our simulations these feedback processes respond faster at temperatures below 1.5°C, and the differences between the 1.5°C and 2°C targets are disproportionately small. This key finding is due to our interest in transient emission pathways to the year 2100 and does not consider the longer term implications of these feedback processes. We conclude that natural feedback processes from wetlands and permafrost must be considered in assessments of transient emission pathways to limit global warming

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement

The CO2 Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO2, CH4 and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO2 Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements.The Copernicus Atmosphere Monitoring Service is operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission as part of the Copernicus Programme (http://copernicus.eu). The CHE and CoCO2 projects have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776186 and No 958927. We also thank the FLUXNET and TCCON PIs for providing the data used for the validation of the nature run dataset.Peer Reviewed"Article signat per 27 autors/es: Anna Agustí-Panareda, Joe McNorton, Gianpaolo Balsamo, Bianca C. Baier, Nicolas Bousserez, Souhail Boussetta, Dominik Brunner, Frédéric Chevallier, Margarita Choulga, Michail Diamantakis, Richard Engelen, Johannes Flemming, Claire Granier, Marc Guevara, Hugo Denier van der Gon, Nellie Elguindi, Jean-Matthieu Haussaire, Martin Jung, Greet Janssens-Maenhout, Rigel Kivi, Sébastien Massart, Dario Papale, Mark Parrington, Miha Razinger, Colm Sweeney, Alex Vermeulen & Sophia Walther "Postprint (published version

Attribution of recent increases in atmospheric methane through 3-D inverse modelling

The atmospheric methane (CH4) growth rate has varied considerably in recent decades. Unexplained renewed growth after 2006 followed 7 years of stagnation and coincided with an isotopic trend toward CH4 more depleted in 13C, suggesting changes in sources and/or sinks. Using surface observations of both CH4 and the relative change of isotopologue ratio (δ13CH4) to constrain a global 3-D chemical transport model (CTM), we have performed a synthesis inversion for source and sink attribution. Our method extends on previous studies by providing monthly and regional attribution of emissions from six different sectors and changes in atmospheric sinks for the extended 2003–2015 period. Regional evaluation of the model CH4 tracer with independent column observations from the Greenhouse Gases Observing Satellite (GOSAT) shows improved performance when using posterior fluxes (R=0.94–0.96, RMSE =8.3–16.5 ppb), relative to prior fluxes (R=0.60–0.92, RMSE =48.6–64.6 ppb). Further independent validation with data from the Total Carbon Column Observing Network (TCCON) shows a similar improvement in the posterior fluxes (R=0.87, RMSE =18.8 ppb) compared to the prior fluxes (R=0.69, RMSE =55.9 ppb). Based on these improved posterior fluxes, the inversion results suggest the most likely cause of the renewed methane growth is a post-2007 1.8±0.4 % decrease in mean OH, a 12.9±2.7 % increase in energy sector emissions, mainly from Africa–Middle East and southern Asia–Oceania, and a 2.6±1.8 % increase in wetland emissions, mainly from northern Eurasia. The posterior wetland flux increases are in general agreement with bottom-up estimates, but the energy sector growth is greater than estimated by bottom-up methods. The model results are consistent across a range of sensitivity analyses. When forced to assume a constant (annually repeating) OH distribution, the inversion requires a greater increase in energy sector (13.6±2.7 %) and wetland (3.6±1.8 %) emissions and an 11.5±3.8 % decrease in biomass burning emissions. Assuming no prior trend in sources and sinks slightly reduces the posterior growth rate in energy sector and wetland emissions and further increases the magnitude of the negative OH trend. We find that possible tropospheric Cl variations do not influence δ13CH4 and CH4 trends, although we suggest further work on Cl variability is required to fully diagnose this contribution. While the study provides quantitative insight into possible emissions variations which may explain the observed trends, uncertainty in prior source and sink estimates and a paucity of δ13CH4 observations limit the robustness of the posterior estimates

Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.</p