Search

Despite methane’s importance as a greenhouse gas, the Earth System Models that contributed to Phase 6 of the Coupled Model Intercomparison Project (CMIP6) typically prescribe surface methane concentrations - following either historical observations or specified future shared socioeconomic pathways. Here, we make use of novel methane emissions-driven capability in the UK’s Earth System Model to explore the role of an interactive methane cycle, including wetland emissions, on the model’s equilibrium climate sensitivity and its transient climate response to changes in carbon dioxide concentration.The climate response to external forcings is strongly influenced by climate feedbacks and with the inclusion of interactive methane in Earth System Models, it becomes important to understand the effects of changing carbon dioxide and meteorology on wetland emissions. This work re-evaluates the CMIP6 assessment of the methane wetland emissions feedback in UKESM1 by taking account of wetland emissions’ sensitivity to both meteorology and carbon dioxide. This presentation demonstrates the need for including interactive methane in Earth System Models. By allowing changes in natural methane emissions to influence methane concentrations and climate, this novel capability enables scientists to determine the consequences of methane emission reduction policies or climate feedbacks on natural methane sources towards meeting global climate as well as global air quality targets.

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.

Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth System models is often affected by the atmospheric models’ climate biases, leading to errors in land surface projections. Here we run the JULES-ES land surface model with ISIMIP2b bias-corrected climate model data from 4 global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation setup, postprocessing and initial evaluation using ILAMB. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

Methane (CH4) is a potent greenhouse gas with a warming potential 84 times that of carbon dioxide (CO2) over a 20-year period. Atmospheric CH4 concentrations have been rising since the nineteenth century but the cause of large increases post-2007 is disputed. Tropical wetlands are thought to account for ∼20% of global CH4 emissions, but African tropical wetlands are understudied and their contribution is uncertain. In this work, we use the first airborne measurements of CH4 sampled over three wetland areas in Zambia to derive emission fluxes. Three independent approaches to flux quantification from airborne measurements were used: Airborne mass balance, airborne eddy-covariance, and an atmospheric inversion. Measured emissions (ranging from 5 to 28 mg m−2 hr−1) were found to be an order of magnitude greater than those simulated by land surface models (ranging from 0.6 to 3.9 mg m−2 hr−1), suggesting much greater emissions from tropical wetlands than currently accounted for. The prevalence of such underestimated CH4 sources may necessitate additional reductions in anthropogenic greenhouse gas emissions to keep global warming below a threshold of 2°C above preindustrial levels.

In the version of this Article originally published, a parallelization coding problem, which meant that a subset of model grid cells were subjected to erroneous updating of atmospheric gas concentrations, resulted in incorrect calculation of atmospheric CO2 for these grid cells, and therefore underestimation of the carbon uptake by land through vegetation growth and eventual increases to soil carbon stocks. Having re-run the simulations using the corrected code, the authors found that the original estimates of the impact of the natural wetland methane feedback were overestimated. The permafrost and natural wetland methane feedback requires lower permissible emissions of 9–15% to achieve climate stabilization at 1.5 °C, compared with the original published estimate of 17–23%. The Article text, Table 1 and Fig. 3 have been updated online to reflect the revised numerical estimates. The Supplementary Information file has also been amended, with Supplementary Figs 6, 7, 8 and 9 replaced with revised versions produced using the corrected model output. As the strength of feedbacks remain significant, still require inclusion in climate policy and are nonlinear with global warming, the overall conclusions of the Article remain unchanged.

Brazil's CH4 emissions over the period 2010–2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010–2018 are 33.6±3.6Tgyr-1, which are comprised of 19.0±2.6Tgyr-1 from anthropogenic (primarily related to agriculture and waste), 13.0±1.9Tgyr-1 from wetlands and 1.7±0.3Tgyr-1 from biomass burning sources. In addition, between the 2011–2013 and 2014–2018 periods, Brazil's mean emissions rose by 6.9±5.3Tgyr-1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015–2016 El Niño by 3.7±2.7Tgyr-1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.

Methane (CH4) is the second most important anthropogenic greenhouse gas. Its Global Warming Potential over 100 years (GWP100) exceeds 28 times that of CO2. Methane surface concentrations have steadily increased since the pre-industrial due to industrialisation combined with fossil fuel use. In 1850, around the onset of heavy industrialisation in Europe and North America, the CH4 mole fraction was approximately 700 ppbv, and since then it has increased 2.5-fold to slightly more than 1830 ppbv in 2015. Fossil fuel use, raising of livestock, and cultivation of rice are the dominant contributions to the atmospheric methane burden at present with significant emissions from natural wetlands also playing a central role.Here we present first results from the UK Earth System Model (UKESM1.0). The default release version of UKESM1.0 has been extended to represent the methane cycle fully interactively, including dynamic wetlands with global CH4, full stratospheric-tropospheric CH4 chemistry, and CH4 surface deposition. The extended configuration is capable of simulating the climate feedbacks on methane wetland emissions which are typically neglected in current Earth system models. Our simulation is driven with anthropogenic CH4 emissions from CMIP6. We conducted fully-coupled transient simulations of the atmospheric CH4 burden from 1850 to 2100 based on the historic and two future scenarios (SSP3-7.0 and SSP1-2.6) scenarios.We compare the time series of global CH4 surface concentrations between the default CH4 concentration-driven configuration of UKESM1.0 with the fully-interactive emissions-driven configuration. Surface concentrations for the emissions-driven simulation show reasonable agreement with the concentration-driven simulation, but a low bias in the fully interactive simulation gradually emerges after about 1920 which reaches approximately -250 ppbv in the 2000s. We then present a full-cycle CH4 budget analysis based on decadal means for every 50 years between 1850 and 2100. We demonstrate that methane burden and surface mole fractions are expected to return to their 1930s values under SSP1-2.6, albeit with the natural methane sources still heavily disturbed from their original state. We also produce a detailed analysis of the contribution of wetland CH4 emissions for the 250 years of simulation.

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this addition is sufficient to reproduce the observed seasonal dynamics of methane emissions in fully saturated wetland sites, at the same time as reproducing the annual mean emissions. We find that a more complex scheme used in recent Earth system models does not add predictive power. The sites used span a range of climatic conditions, with the majority in high latitudes. The difference in apparent temperature sensitivity seasonally versus spatially cannot be recreated by the non‐microbial schemes tested. We therefore conclude that microbial dynamics are a strong candidate to be driving the seasonal cycle of wetland methane emissions. We quantify longer‐term temperature sensitivity using this scheme and show that it gives approximately a 12% increase in emissions per degree of warming globally. This is in addition to any hydrological changes, which could also impact future methane emissions. publishedVersion

We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC.

We present the first spatially‐resolved wetland δ13C(CH4) source signature map based on data characterizing wetland ecosystems and demonstrate good agreement with wetland signatures derived from atmospheric observations. The source signature map resolves a latitudinal difference of ~10‰ between northern high‐latitude (mean ‐67.8‰) and tropical (mean ‐56.7‰) wetlands, and shows significant regional variations on top of the latitudinal gradient. We assess the errors in inverse modeling studies aiming to separate CH4 sources and sinks by comparing atmospheric δ13C(CH4) derived using our spatially‐resolved map against the common assumption of globally uniform wetland δ13C(CH4) signature. We find a larger interhemispheric gradient, a larger high‐latitude seasonal cycle and smaller trend over the period 2000‐2012. The implication is that erroneous CH4 fluxes would be derived to compensate for the biases imposed by not utilizing spatially‐resolved signatures for the largest source of CH4 emissions. These biases are significant when compared to the size of observed signals.Plain Language SummaryConcentrations of methane are increasing in the atmosphere. In order to understand the reasons behind such variations, carbon isotopes are used to help identify changes in emission sources and sinks. We present a new global map of the carbon isotope signature associated with wetland methane emissions, the largest global source of methane to the atmosphere. We show how this newly synthesized information can lead to more accurate understanding of the causes of variations in the amount and rate of increase of methane in the atmosphere.

We present the latest global land configuration of the Joint UK Land Environment Simulator (JULES) model as used in the latest international coupled model intercomparison project (CMIP6). The configuration is defined by the combination of switches, parameter values and ancillary data, which we provide alongside a set of historical forcing data that defines the experimental setup. In addition, we provide a standardised modelling system that runs on the NERC JASMIN cluster accessible to all with links to JULES. This is provided so that users can test and evaluate their own science against the standard configuration to promote community engagement in the development of land surface modelling capability through JULES. It is intended that JULES configurations should be independent of the underlying code base and thus they will be available at the latest release of the JULES code. This means that different code releases will produce scientifically comparable results for a given configuration version. Versioning is therefore determined by the configuration as opposed to the underlying code base.

We present a computationally efficient modelling system, IMOGEN, designed to undertake global and regional assessment of climate change impacts on the physical and biogeochemical behaviour of the land surface. A pattern-scaling approach to climate change drives a gridded land surface and vegetation model MOSES/TRIFFID. The structure allows extrapolation of General Circulation Model (GCM) simulations to different future pathways of greenhouse gases, including rapid first-order assessments of how the land surface and associated biogeochemical cycles might change. Evaluation of how new terrestrial process understanding influences such predictions can also be made with relative ease.

Global 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 inverted intermediate complexity climate model, which follows prescribed global warming pathways to stabilization at 1.5 or 2.0 °C above pre-industrial levels by the year 2100, and which 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 stabilization at 1.5 °C, and 9–13% (52–57 GtC) for 2.0 °C stabilization. In our simulations these feedback processes respond more quickly at temperatures below 1.5 °C, and the differences between the 1.5 and 2 °C targets are disproportionately small. This key finding holds for transient emission pathways to 2100 and does not account for 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.

We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0): the latest science configurations of the Met Office Unified Model and JULES land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that between them address four critical errors identified in previous configurations: excessive precipitation biases over India, warm/moist biases in the tropical tropopause layer, a source of energy non-conservation in the advection scheme and excessive surface radiation biases over the Southern Ocean. They also include two new parametrisations, namely the UKCA Glomap-mode aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for the coupled climate models and Earth system models that will use these configurations in submissions to CMIP6. In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing in GA7.0, whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3-GC3.1 and UKESM1 climate models.

Modelling land surface water flow is of critical importance for simulating land surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL (TOPography-based hydrological MODEL), and the most important parameter of this model is the well-known topographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically conditioned HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) data using the GA2 algorithm (GRIDATB 2). At 15 arcsec resolution, these layers are 4 times finer than the resolution of the previously best-available topographic index layers, the compound topographic index of HYDRO1k (CTI). For the largest river catchments occurring on each continent we found that, in comparison with CTI our revised values were up to 20% lower in, e.g. the Amazon. We found the highest catchment means were for the Murray–Darling and Nelson–Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. For the majority of large catchments, however, the spread of our new GA2 index values is very similar to those of CTI, yet with more spatial variability apparent at fine scale. We believe these new index layers represent greatly improved global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

Wetlands are a major emission source of methane (CH4) globally. In this study, we evaluate wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including, for the first time, total methane columns derived from the SCIAMACHY instrument on board the ENVISAT satellite. Two JULES wetland emission estimates are investigated: (a) from an offline run driven with Climatic Research Unit–National Centers for Environmental Prediction (CRU-NCEP) meteorological data and (b) from the same offline run in which the modelled wetland fractions are replaced with those derived from the Global Inundation Extent from Multi-Satellites (GIEMS) remote sensing product. The mean annual emission assumed for each inventory (181 Tg CH4 per annum over the period 1999–2007) is in line with other recently published estimates. There are regional differences as the unconstrained JULES inventory gives significantly higher emissions in the Amazon (by ~36 Tg CH4 yr−1) and lower emissions in other regions (by up to 10 Tg CH4 yr−1) compared to the JULES estimates constrained with the GIEMS product. Using the UK Hadley Centre's Earth System model with atmospheric chemistry (HadGEM2), we evaluate these JULES wetland emissions against atmospheric observations of methane. We obtain improved agreement with the surface concentration measurements, especially at high northern latitudes, compared to previous HadGEM2 runs using the wetland emission data set of Fung et al. (1991). Although the modelled monthly atmospheric methane columns reproduce the large-scale patterns in the SCIAMACHY observations, they are biased low by 50 part per billion by volume (ppb). Replacing the HadGEM2 modelled concentrations above 300 hPa with HALOE–ACE assimilated TOMCAT output results in a significantly better agreement with the SCIAMACHY observations. The use of the GIEMS product to constrain the JULES-derived wetland fraction improves the representation of the wetland emissions in JULES and gives a good description of the seasonality observed at surface sites influenced by wetlands, especially at high latitudes. We find that the annual cycles observed in the SCIAMACHY measurements and at many of the surface sites influenced by non-wetland sources cannot be reproduced in these HadGEM2 runs. This suggests that the emissions over certain regions (e.g. India and China) are possibly too high and/or the monthly emission patterns for specific sectors are incorrect. The comparisons presented in this paper show that the performance of the JULES wetland scheme is comparable to that of other process-based land surface models. We identify areas for improvement in this and the atmospheric chemistry components of the HadGEM Earth System model. The Earth Observation data sets used here will be of continued value in future evaluations of JULES and the HadGEM family of models.

International audience; Anthropogenic aerosols in the atmosphere have the potential to affect regional-scale land hydrology through solar dimming. Increased aerosol loading may have reduced historical surface evaporation over some locations, but the magnitude and extent of this effect is uncertain. Any reduction in evaporation due to historical solar dimming may have resulted in an increase in river flow. Here we formally detect and quantify the historical effect of changing aerosol concentrations, via solar radiation, on observed river flow over the heavily industrialized, northern extra-tropics. We use a state-of-the-art estimate of twentieth century surface meteorology as input data for a detailed land surface model, and show that the simulations capture the observed strong inter-annual variability in runoff in response to climatic fluctuations. Using statistical techniques, we identify a detectable aerosol signal in the observed river flow both over the combined region, and over individual river basins in Europe and North America. We estimate that solar dimming due to rising aerosol concentrations in the atmosphere around 1980 led to an increase in river runoff by up to 25% in the most heavily polluted regions in Europe. We propose that, conversely, these regions may experience reduced freshwater availability in the future, as air quality improvements are set to lower aerosol loading and solar dimming.

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (similar to biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH(4) yr(-1), range 540-568. About 60% of global emissions are anthropogenic (range 50-65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH(4) yr(-1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (similar to 64% of the global budget, amp;lt;30 degrees N) as compared to mid (similar to 32 %, 30-60 degrees N) and high northern latitudes (similar to 4 %, 60-90 degrees N). Top-down inversions consistently infer lower emissions in China (similar to 58 TgCH(4) yr(-1), range 51-72, -14 %) and higher emissions in Africa (86 TgCH(4) yr(-1), range 73-108, + 19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project. Funding Agencies|Swiss National Science Foundation; NASA [NNX14AF93G, NNX14AO73G]; National Environmental Science Program - Earth Systems and Climate Change Hub; European Commission [283576, 633080]; ESA Climate Change Initiative Greenhouse Gases Phase 2 project; US Department of Energy, BER [DE-AC02-05CH11231]; FAO member countries; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [2-1502]; ERC [322998]; NERC [NE/J00748X/1]; Swedish Research Council VR; Research Council of Norway [244074]; NSF [1243232, 1243220]; National Science and Engineering Research Council of Canada (NSERC); Chinas QianRen Program; CSIRO Australia; Australian Bureau of Meteorology; Australian Institute of Marine Science; Australian Antarctic Division; NOAA USA; Meteorological Service of Canada; National Aeronautic and Space Administration (NASA) [NAG5-12669, NNX07AE89G, NNX11AF17G, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G]; Department of Energy and Climate Change (DECC, UK) [GA01081]; Commonwealth Scientific and Industrial Research Organization (CSIRO Australia); Bureau of Meteorology (Australia); Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]

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. Modeled 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, Southern Africa, and Australia.

Emissions from wetlands are the single largest source of the atmospheric greenhouse gas (GHG) methane (CH4). This may increase in a warming climate, leading to a positive feedback on climate change. For the first time, we extend interactive wetland CH4 emissions schemes to include the recently quantified, significant process of CH4 transfer through tropical trees. We constrain the parameterisations using a multi-site flux study, and biogeochemical and inversion models. This provides an estimate and uncertainty range in contemporary, large-scale wetland emissions and their response to temperature. To assess the potential for future wetland CH4 emissions to feedback on climate, the schemes are forced with simulated climate change using a ‘pattern-scaling’ system, which links altered atmospheric radiative forcing to meteorology changes. We perform multiple simulations emulating 34 Earth System Models over different anthropogenic GHG emissions scenarios (RCPs). We provide a detailed assessment of the causes of uncertainty in predicting wetland CH4–climate feedback. Despite the constraints applied, uncertainty from wetland CH4 emission modelling is greater that from projected climate spread (under a given RCP). Limited knowledge of contemporary global wetland emissions restricts model calibration, producing the largest individual cause of wetland parameterisation uncertainty. Wetland feedback causes an additional temperature increase between 0.6% and 5.5% over the 21st century, with a feedback on climate ranging from 0.01 to 0.11 Wm−2 K−1. Wetland CH4 emissions amplify atmospheric CH4 increases by up to a further possible 25.4% in one simulation, and reduce remaining allowed anthropogenic emissions to maintain the RCP2.6 temperature threshold by 8.0% on average.

Future changes in atmospheric greenhouse gas concentrations and associated influences on climate could affect the future sustainability of tropical forests. The authors report on tropical forest projections from the new Hadley Centre Global Environmental Model version 2 Earth System configuration (HadGEM2-ES) and compare them to results from the previous generation model [third climate configuration of the Met Office Unified Model in lower resolution with carbon cycle (HadCM3LC)], which had projected near-complete dieback of the Amazon rain forest for a business as usual scenario. In contrast, HadGEM2-ES projects minimal change in Amazon forest extent. The main aim of this study is a preliminary investigation of this difference between the two models. It is found that around 40% of the difference in forest dieback projections is associated with differences in the projected change in dry-season length. Differences in control climatologies of temperature and dry-season length, projected regional warming, and the forest response to climate and CO2 also all contribute to the increased survival of forest in HadGEM2-ES. However, HadGEM2-ES does not invalidate HadCM3LC: Amazon dieback remains a possible scenario of dangerous change that requires further understanding. The authors discuss the relevance to assessments of dieback risk and future work toward narrowing uncertainty about the fate of the Amazon forest.

River basin managers concerned with maintaining water supplies and mitigating flood risk in the face of climate change are taking outputs from climate models and using them in hydrological models for assessment purposes. While precipitation is the main output used, evaporation is attracting increasing attention because of its significance to the water balance of river basins. Climate models provide estimates of actual evaporation that are consistent with their simplified land surface schemes but do not naturally provide the estimates of potential evaporation (PE) commonly required as input to hydrological models. There are clear advantages in using PE estimates controlled by atmospheric forcings when using stand-alone hydrological models with integral soil-moisture accounting schemes. The atmosphere–land decoupling approximation that PE provides can prove to be of further benefit if it is possible to account for the effect of different, or changing, land cover on PE outside of the climate model. The methods explored here estimate Penman–Monteith PE from vegetated surfaces using outputs from climate models that have an embedded land surface scheme. The land surface scheme enables an examination of the dependence of canopy stomatal resistance on atmospheric composition, and the sensitivity of PE estimates to the choice of canopy resistance values under current and changing climates is demonstrated. The conclusions have practical value for climate change impact studies relating to flood, drought, and water management applications.

This article reviews some of the major lines of recent scientific progress relevant to the choice of global climate policy targets, focusing on changes in understanding since publication of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Developments are highlighted in the following major climate system components: ice sheets; sea ice; the Atlantic Meridional Overturning Circulation; tropical forests; and accelerated carbon release from permafrost and ocean hydrates. The most significant developments in each component are identified by synthesizing input from multiple experts from each field. Overall, while large uncertainties remain in all fields, some substantial progress in understanding is revealed.

This study tests the ability of five Dynamic Global Vegetation Models (DGVMs), forced with observed climatology and atmospheric CO2, to model the contemporary global carbon cycle. The DGVMs are also coupled to a fast ‘climate analogue model’, based on the Hadley Centre General Circulation Model (GCM), and run into the future for four Special Report Emission Scenarios (SRES): A1FI, A2, B1, B2. Results show that all DGVMs are consistent with the contemporary global land carbon budget. Under the more extreme projections of future environmental change, the responses of the DGVMs diverge markedly. In particular, large uncertainties are associated with the response of tropical vegetation to drought and boreal ecosystems to elevated temperatures and changing soil moisture status. The DGVMs show more divergence in their response to regional changes in climate than to increases in atmospheric CO2 content. All models simulate a release of land carbon in response to climate, when physiological effects of elevated atmospheric CO2 on plant production are not considered, implying a positive terrestrial climate-carbon cycle feedback. All DGVMs simulate a reduction in global net primary production (NPP) and a decrease in soil residence time in the tropics and extra-tropics in response to future climate. When both counteracting effects of climate and atmospheric CO2 on ecosystem function are considered, all the DGVMs simulate cumulative net land carbon uptake over the 21st century for the four SRES emission scenarios. However, for the most extreme A1FI emissions scenario, three out of five DGVMs simulate an annual net source of CO2 from the land to the atmosphere in the final decades of the 21st century. For this scenario, cumulative land uptake differs by 494 Pg C among DGVMs over the 21st century. This uncertainty is equivalent to over 50 years of anthropogenic emissions at current levels.

An assessment of the contribution of plant physiological effects to future changes in continental water runoff suggests that flooding risk under future global warming scenarios may be greater than was assumed. The stomatal pores that allow CO2 to enter plants and water to escape open less widely when CO2 concentrations are high, reducing water loss from the plant and thus leaving more water at the land surface. This effect may have contributed to the increase in continental runoff observed during the twentieth century, but most predictions of future changes in runoff don't account for it. The concept of 'CO2 equivalent', widely used to compare the effects of greenhouse gases on climate, does not account for this effect, so it may need to be revisited in light of these findings. An ensemble of experiments is used with a global climate model to assess the contribution of plant 'physiological forcing' to future changes in continental runoff. It is found that the effect increases simulated global mean runoff by 6 per cent when the concentration of carbon dioxide is doubled relative to pre-industrial levels; an increase that is comparable to that simulated in response to climate change caused by radiative forcing. This finding suggests that the risk of flooding may be greater than previously assumed under future global warming scenarios. In addition to influencing climatic conditions directly through radiative forcing, increasing carbon dioxide concentration influences the climate system through its effects on plant physiology1. Plant stomata generally open less widely under increased carbon dioxide concentration2, which reduces transpiration1,3,4,5,6 and thus leaves more water at the land surface7. This driver of change in the climate system, which we term ‘physiological forcing’, has been detected in observational records of increasing average continental runoff over the twentieth century8. Here we use an ensemble of experiments with a global climate model that includes a vegetation component to assess the contribution of physiological forcing to future changes in continental runoff, in the context of uncertainties in future precipitation. We find that the physiological effect of doubled carbon dioxide concentrations on plant transpiration increases simulated global mean runoff by 6 per cent relative to pre-industrial levels; an increase that is comparable to that simulated in response to radiatively forced climate change (11 ± 6 per cent). Assessments of the effect of increasing carbon dioxide concentrations on the hydrological cycle that only consider radiative forcing9,10,11 will therefore tend to underestimate future increases in runoff and overestimate decreases. This suggests that freshwater resources may be less limited than previously assumed under scenarios of future global warming, although there is still an increased risk of drought. Moreover, our results highlight that the practice of assessing the climate-forcing potential of all greenhouse gases in terms of their radiative forcing potential relative to carbon dioxide does not accurately reflect the relative effects of different greenhouse gases on freshwater resources.

The potential impacts of climate change on human health are significant, ranging from direct effects such as heat stress and flooding, to indirect influences including changes in disease transmission and malnutrition in response to increased competition for crop and water resources. Development agencies and policy makers tasked with implementing adaptive strategies recognize the need to plan for these impacts. However at present there is little guidance on how to prioritize their funding to best improve the resilience of vulnerable communities. Here we address this issue by arguing that closer collaboration between the climate modelling and health communities is required to provide the focused information necessary to best inform policy makers. The immediate requirement is to create multidisciplinary research teams bringing together skills in both climate and health modelling. This will enable considerable information exchange, and closer collaboration will highlight current uncertainties and hopefully routes to their reduction. We recognize that climate is only one aspect influencing the highly complex behaviour of health and disease issues. However we are optimistic that climate-health model simulations, including uncertainty bounds, will provide much needed estimates of the likely impacts of climate change on human health.

Hydrological impacts of climate change are frequently assessed by off-line forcing of a hydrological model with climatic scenarios from either Global Circulation Models (GCMs) or simpler analogue models. Most hydrological models require a daily time step or smaller while observed climatology and GCM and analogue model output is generally available on a monthly time step. This study investigates and improves a rainfall disaggregation model currently used to convert monthly rainfall totals down to the daily time step. The performance of the model is evaluated using daily data from a network of raingauges covering the Nile basin and contrasted with data from a relatively dense raingauge network from the Blackwater Catchment, in the Southeast of the UK. Whilst the model preserves the mean properties of rainfall occurrence and depth, there is significant overestimation of rainfall variability. Regional calibration and better formulation of the generator improve simulation of variability as well as other aspects of rainfall properties. Hence the parameters required by the weather generator model cannot be regarded as universal. Proportional correction of daily amounts is applied to insure that monthly totals are preserved, allowing retention of interannual variability, and this was shown to have little effect on the distribution of wet day amounts. The calibration of parameter estimation equations has investigated spatial dependence of climate variables and parameters and found that (as expected) rainfall properties exhibit scale-dependence, which may be utilized to transfer data from one spatial scale to another. In order to complete the framework, a model is developed to estimate the wet fraction from monthly total when the former is not available.

Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

Concern about tropical deforestation has often focused on local climate or ecological change. Less research has been carried out on its possible effects on the global climate, although possible mechanisms for such an occurrence have been proposed. Here we use a general circulation model (GCM) to show that complete Amazonian deforestation can result in changes in the climate far afield from the region of deforestation. In particular, the model predicts statistically significant changes to winter rainfall over the NE Atlantic, extending towards western Europe. These are associated with large-scale circulation changes in middle and high latitudes. Simulation of these circulation changes with a simple model confirms the physical mechanism responsible (planetary wave propagation) implying that the result is robust and relatively independent of the GCM used.

The impact of land surface representation on GCM simulations of climate change is analyzed using eight climate change experiments, carried out with four GCMs each utilizing two different land surface schemes (LSSs). In the regions studied (Amazonia, the Sahel, and southern Europe) the simulations differ markedly in terms of their predicted changes in evapotranspiration and soil moisture. These differences are only partly as a result of differences in the predicted changes in precipitation and available energy. A simple “bucket model” characterization of each LSS demonstrates that the different hydrological sensitivities are also strongly dependent on properties of the LSS, most notably the runoff, which occurs when evaporation is marginally soil moisture limited. This parameter, “Yc,” varies significantly among the LSSs, and influences both the soil moisture in the 1 × CO2 control climate, and the sensitivity of both evaporation and soil moisture to climate change. It is concluded that uncertaint...

The possible impact of anthropogenic climate change on the Asian summer monsoon is investigated in several time-slice experiments using prescribed sea-surface temperature (SST) and sea-ice anomalies. The study is carried out with four different atmospheric general circulation models (GCMs), each being involved in two pairs of experiments differing only by the treatment of the land surface hydrology. The objective is to assess the robustness of the simulated climate change, and its possible sensitivity to the land surface scheme. Despite the use of identical SST anomalies, the four GCMs do not predict similar monsoon responses on the regional scale. All models produce a stronger warming over the Asian continent than over the Indian Ocean, but this warming is not a good predictor of the monsoon response to increased CO 2 level. There is a significant spread in the summer precipitation anomalies despite a general weakening of the monsoon circulation, showing that the response of the monsoon rainfall is not solely related to the changes in the large-scale dynamics. In a warmer climate, the monsoon precipitation can increase despite a weakening of the monsoon flow, due to an increase in the atmospheric water content. For decades to come, the increase in the atmospheric water content could be more important than the increase in the land-sea thermal gradient for understanding the evolution of the monsoon precipitation. Though it does not represent a major source of uncertainty, the treatment of the surface hydrology is liable to affect significantly the regional response of the monsoon to CO 2 doubling. A slight change in evapotranspiration is enough to induce a significant change in precipitation. A simple analysis of the regional water budget indicates that this sensitivity is not only related to changes in the horizontal transport of water vapor, but also to changes in the precipitation efficiency, which depends on the treatement of the land surface hydrology.

Using atmospheric forcing data generated from a general circulation climate model, sixteen land surface schemes participating in the Project for the Intercomparison of Land-surface Parametrization Schemes (PILPS) were run off-line to equilibrium using forcing data from a GCM representative of a tropical forest and a mid-latitude grassland grid point. The values for each land surface parameter (roughness length, minimum stomatal resistance, soil depth etc.) were provided. Results were quality controlled and analyzed, focusing on the scatter simulated amongst the models. There were large differences in how the models' partitioned available energy between sensible and latent heat. Annually averaged, simulations for the tropical forest ranged by 79 W m-2 for the sensible heat flux and 80 W m-2 for the latent heat flux. For the grassland, simulations ranged by 34 W m-2 for the sensible heat flux and 27 W m-2 for the latent heat flux. Similarly large differences were found for simulated runoff and soil moisture and at the monthly time scale. The models' simulation of annually averaged effective radiative temperature varied with a range, between all the models, of 1.4 K for tropical forest and 2.2 K for the grassland. The simulation of latent and sensible heat fluxes by a standard 'bucket' models was anomalous although this could be corrected by an additional resistance term. These results imply that the current land surface models do not agree on the land surface climate when the atmospheric forcing and surface parameters are prescribed. The nature of the experimental design, it being offline and with artificial forcing, generally precludes judgements concerning the relative quality of any specific model. Although these results were produced de-coupled from a host model, they do cast doubt on the reliability of land surface schemes. It is therefore a priority to resolve the disparity in the simulations, understand the reasons behind the scatter and to determine whether this lack of agreement in decoupled tests is reproduced in coupled experiments.

We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.

Many atmospheric constituents besides carbon dioxide (CO 2 ) contribute to global warming, and it is common to compare their influence on climate in terms of radiative forcing, which measures their impact on the planetary energy budget. A number of recent studies have shown that many radiatively active constituents also have important impacts on the physiological functioning of ecosystems, and thus the ‘ecosystem services’ that humankind relies upon. CO 2 increases have most probably increased river runoff and had generally positive impacts on plant growth where nutrients are non-limiting, whereas increases in near-surface ozone (O 3 ) are very detrimental to plant productivity. Atmospheric aerosols increase the fraction of surface diffuse light, which is beneficial for plant growth. To illustrate these differences, we present the impact on net primary productivity and runoff of higher CO 2 , higher near-surface O 3 , and lower sulphate aerosols, and for equivalent changes in radiative forcing. We compare this with the impact of climate change alone, arising, for example, from a physiologically inactive gas such as methane (CH 4 ). For equivalent levels of change in radiative forcing, we show that the combined climate and physiological impacts of these individual agents vary markedly and in some cases actually differ in sign. This study highlights the need to develop more informative metrics of the impact of changing atmospheric constituents that go beyond simple radiative forcing.

This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both world-leading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very high-profile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.

The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Many studies have demonstrated the important role of the land surface in the functioning of the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of climate change, increasing atmospheric carbon dioxide concentrations, changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. This paper describes the consolidation of these advances in the modelling of carbon fluxes and stores, in both the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.

We have reviewed the available scientific literature on how natural sources and the atmospheric fate of methane may be affected by future climate change. We discuss how processes governing methane wetland emissions, permafrost thawing, and destabilization of marine hydrates may affect the climate system. It is likely that methane wetland emissions will increase over the next century. Uncertainties arise from the temperature dependence of emissions and changes in the geographical distribution of wetland areas. Another major concern is the possible degradation or thaw of terrestrial permafrost due to climate change. The amount of carbon stored in permafrost, the rate at which it will thaw, and the ratio of methane to carbon dioxide emissions upon decomposition form the main uncertainties. Large amounts of methane are also stored in marine hydrates, and they could be responsible for large emissions in the future. The time scales for destabilization of marine hydrates are not well understood and are likely to be very long for hydrates found in deep sediments but much shorter for hydrates below shallow waters, such as in the Arctic Ocean. Uncertainties are dominated by the sizes and locations of the methane hydrate inventories, the time scales associated with heat penetration in the ocean and sediments, and the fate of methane released in the seawater. Overall, uncertainties are large, and it is difficult to be conclusive about the time scales and magnitudes of methane feedbacks, but significant increases in methane emissions are likely, and catastrophic emissions cannot be ruled out. We also identify gaps in our scientific knowledge and make recommendations for future research and development in the context of Earth system modeling. © 2010 by the American Geophysical Union.

International audience; Exchange of non-CO 2 trace gases between the land surface and the atmosphere plays an important role in atmospheric chemistry and climate. Recent studies have highlighted its importance for interpretation of glacial-interglacial ice-core records, the simulation of the pre-industrial and present atmosphere, and the potential for large climate-chemistry and climate-aerosol feedbacks in the coming century. However, spatial and temporal variations in trace gas emissions and the magnitude of future feedbacks are a major source of uncertainty in atmospheric chemistry, air quality and climate science. To reduce such uncertainties Dynamic Global Vegetation Models (DGVMs) are currently being expanded to mechanistically represent processes relevant to non-CO 2 trace gas exchange between land biota and the atmosphere. In this paper we present a review of im-Correspondence to: A. Arneth (almut.arneth@nateko.lu.se) portant non-CO 2 trace gas emissions, the state-of-the-art in DGVM modelling of processes regulating these emissions, identify key uncertainties for global scale model applications , and discuss a methodology for model integration and evaluation.

A critical function of a land surface scheme, used in climate and weather prediction models, is to partition the energy from insolation into sensible and latent heat fluxes. Many use a soil moisture function to control the surface moisture fluxes through the transpiration. The validity and global distribution of the parameters used to calculate this soil moisture stress function are difficult to assess. This work presents a method to map soil moisture stress globally from an earth observation vegetation index and precipitation data, and it compares the resulting distributions with output from the Joint U.K. Land Environment Simulator (JULES) land surface scheme. A number of model runs with different soil and vegetation parameters are compared. These examine the sensitivity of the seasonality of soil moisture stress, within the model, to the parameterization of soil hydraulic properties and the seasonality of leaf area index in the vegetation. It is found that the seasonality of soil moisture within the model is more sensitive to the soil hydraulic properties than the leaf area index. The partitioning of throughfall into evaporation and runoff, in the model, is the dominant factor in determining the timing of soil moisture stress.

[1] Different representations of runoff generation processes were implemented in the MOSES land surface model which is used with mesoscale and global atmospheric models. The standard model was compared with versions in which runoff generation was described by parameterizations based on either the Probability Distributed Model (PDM) or modified forms of TOPMODEL, all of which used probability functions to describe the subgrid distribution of soil moisture. The model results were compared with observed streamflow in three catchments in southern France. After calibration, the PDM- and TOPMODEL-based parameterizations performed substantially better than the standard model. The TOPMODEL approach gave the best results through allowing a more responsive subsurface flow that contributed to peak flows and also better captured the slower changes during recessions. This approach was sensitive to uncertainty in the value of the topographic index. The PDM-based model only changed the calculation of surface runoff and retained the standard description of subsurface runoff, and this limited the possible improvement in performance. In principle the values of the new parameters can be determined from observations, but in practice calibration is likely to be required. However, for each new parameterization, a single set of parameter values was found that performed well in all catchments. The simulated soil moisture and surface heat fluxes during summer dry periods were affected by the choice of runoff parameterization.