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This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai’i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthr

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

As fire seasons in Brazil lengthen and intensify, the need to enhance fire simulations and comprehend fire drivers becomes crucial. Yet determining what drivers burning in different Brazilian biomes is a major challenge, with the highly uncertain relationship between drivers and fire. Finding ways to acknowledge and quantify that uncertainty is critical in ascertaining the causes of Brazil’s changing fire regimes. We propose FLAME (Fire Landscape Analysis using Maximum Entropy), a new fire model that integrates Bayesian inference with the Maximum Entropy (MaxEnt) concept, enabling probabilistic reasoning and uncertainty quantification. FLAME utilizes bioclimatic, land cover and human driving variables to model fires. We apply FLAME to Brazilian biomes, evaluating its performance against observed data for three categories of fires: all fires (ALL), fires reaching natural vegetation (NAT), and fires in non-natural vegetation (NON). We assessed burned area responses to variable groups. The model showed adequate performance for all biomes and fire categories. Maximum temperature and precipitation together are important factors influencing burned area in all biomes. The number of roads and amount of forest boundaries (edge densities), and forest, pasture and soil carbon showed higher uncertainties among the responses. The potential response of these variables displayed similar spatial likelihood of the observations given the model, between the ALL, NAT and NON categories. Overall, the uncertainties were larger for the NON-category, particularly for Pampas and Pantanal. Customizing variable selection and fire categories based on biome characteristics could contribute to a more biome-focused and contextually relevant analysis. Moreover, prioritizing regional-scale analysis is essential for decision-makers and fire management strategies. FLAME is easily adaptable to be used in various locations and periods, serving as a valuable tool for more informed and effective fire pre

The refugia hypothesis, often used to explain Amazonia’s high biodiversity, initially received ample support but has garnered increasing criticism over time. Palynological, phylogenetic, and vegetation model reconstruction studies have been invoked to support the opposing arguments of extensive fragmentation versus a stable Amazonian Forest during Pleistocene glacial maxima. Here, we test the past existence of forest fragments and savanna connectivity by bias-correcting vegetation distributions from a Dynamic Vegetation Model (DVM) driven by paleoclimate simulations for South America during the Last Glacial Maximum (LGM). We find evidence for fragmented forests akin to refugia with extensive tropical humid forests to the west and forest islands in central/southern Amazonia. Drier ecosystems of Northern Llanos, Caatinga and Cerrado may have merged into continuous savanna/grasslands that dominated the continent. However, our reconstructions suggest taller, dense woodland/tropical savanna vegetation and areas of similar bioclimate connected disparate forest fragments across Amazonia. This ecotonal biome may have acted as a corridor for generalist forest and savanna species, creating connectivity that allows for range expansion during glacial periods. Simultaneously, it could have served as a barrier for specialists, inducing diversification through the formation of ‘semi-refugia’.

Estimating fire emissions in the Brazilian Cerrado requires a comprehensive approach, combining data, fire and vegetation modelling techniques, and policy. Although high quantities of global fire emissions come from the Cerrado, research in this area is still overlooked when compared to other savanna countries. This study systematically reviewed 69 papers on fire emissions from the Cerrado. The aim was to provide insights into the placement of the Cerrado in the atmospheric carbon budget and support improved estimation of the Biome’s carbon balance. Our review finds that, in the Cerrado, studies often focus on quantifying fire dynamics parameters and emissions, and that a holistic approach is required to estimate fire emissions, which is hindered due to the difficulty in valuing the qualitative aspects of fire. Evidence suggests a rise in interest in understanding fire emissions in the Cerrado, reflected in the increased number of studies throughout the years. More research is required to understand the aspects of fire dynamics in the Cerrado, how these reflect fire emissions locally and globally and potential mitigation activities. This could be achieved by including fire management representation in land surface models and using observational data to constrain and assess their utility.

Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai’i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic c

Humans can act as fire starters or suppressors, changing fire regimes by increasing the number of ignitions, changing their timing, and altering fuel structure and abundance, which can be considered a human–environmental coupling. Considering the human influences on fire activity, representing socio-economic impacts on fires in global fire models is crucial to underpin the confidence in these modelling frameworks. In this work we implement a socio-economic factor in the fire ignition and suppression parametrisation in INFERNO based on a Human Development Index (HDI). HDI captures human development's income, health, and education dimensions leading to a representation where if there is more effort to improve human development, the population also invests in higher fire suppression. Including this representation of socio-economic factors in INFERNO reduces the annual mean burnt area (between 1997–2016) positive biases found in Temperate North America, Central America, Europe and Southern Hemisphere South America, by more than 100 % without statistically significant impact to other areas. In addition, it improves the representation of the burnt area trends, especially in Africa. Central Asia and Australia where observations show negative trends. Including socio-economic impacts on fire based on HDI in INFERNO provides a simple and linear representation of these effects on fire ignition and suppression, leading to an improvement of the model performance, especially in developed regions, These impacts are especially relevant to understand future climate regimes and inform policymakers on effects of fire policy in a changing climate.

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 Joint UK Land Environment Simulator Earth System configuration (JULES-ES) land surface model with the Inter-Sectoral Impact Model Intercomparison Project second-phase future projections (ISIMIP2b) bias-corrected climate model data from four global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate the performance of JULES-ES against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. 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 set-up, postprocessing and initial evaluation, using the International Land Model Benchmarking (ILAMB) project. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

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 Joint UK Land Environment Simulator Earth System configuration (JULES-ES) land surface model with the Inter-Sectoral Impact Model Intercomparison Project second-phase future projections (ISIMIP2b) bias-corrected climate model data from four global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate the performance of JULES-ES against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. 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 set-up, postprocessing and initial evaluation, using the International Land Model Benchmarking (ILAMB) project. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes. Land surface properties: TreeCover - Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from 1, regridded as per 2. urban cover from the History Database of the Global Environment, Version 3.1 (HYDE) 3,4 crop cover (from HYDE) pas - Pasture Cover (from HYDE)PopDen (population density from HYDE) BurntArea_xxxxx - Burnt area with xxxx denoting different products, provided by fireMIP 5–7: GFED_four: Global Fire Emissions Database, Version 4 (GFED4) 8 GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) 9 MCD_forty_five: MCD45 10 Meris: Fire_CCI4.0 11 MODIS: Fire_CCI5.1 12 Climate: MAP_xxx - Mean annual precipitation where xxx denotes data source: CMORPH 13,14 CRU from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) 15 GPCC: 16 MSWEP: 17 MAT - Mean annual temperature from CRU) MConc_xxx – Mean annual concentration of rainfall as defined by 18, where xxx denotes precip data source (see “MAP_xxx”) MADD_xxx- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”) MDDM_xxx – Mean fractional dry days of the driest month. MADM_xxx – Mean annual precipitation of the driest month. MTWM - Mean Maximum Temperature of the warmest month from CRU MTCM - Mean minimum temperature of the coldest month from CRU SW1 - direct downwards SW simulated using the SLASH model using CRU cloud cover SW2 - diffuse downwards SW simulated using the SLASH model using CRU cloud cover MaxWind (Mean Max Windspeed from CRU-(National Centers for Environmental Prediction 15 ‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains

Unprecedented fire events in recent years are leading to a demand for improved understanding of how climate change is already affecting fires, and how this could change in the future. Increased fire activity in South America is one of the most concerning of all the recent events, given the potential impacts on local ecosystems and the global climate from the loss of large carbon stores under future socio-environmental change. However, due to the complexity of interactions and feedbacks, and lack of complete representation of fire biogeochemistry in many climate models, there is currently low agreement on whether climate change will cause fires to become more or less frequent in the future, and what impact this will have on ecosystems. Here we use the latest climate simulations from the UK Earth System Model UKESM1 to understand feedbacks in fire, dynamic vegetation, and terrestrial carbon stores using the JULES land surface model, taking into account future scenarios of change in emissions and land use. Based on evaluation of model performance for the present day, we address the specific policy-relevant question: how much fire-induced carbon loss will there be over South America at different global warming levels in the future? We find that burned area and fire emissions are projected to increase in the future due to hotter and drier conditions, which leads to large reductions in carbon storage, especially when combined with increasing land-use conversion. The model simulates a 30% loss of carbon at 4°C under the highest emission scenario, which could be reduced to 7% if temperature rise is limited to 1.5°C. Our results provide a critical assessment of ecosystem resilience under future climate change, and could inform the way fire and land-use is managed in the future to reduce the most deleterious impacts of climate change.

Unprecedented fire events in recent years are leading to a demand for improved understanding of how climate change is already affecting fires, and how this could change in the future. Increased fire activity in South America is one of the most concerning of all the recent events, given the potential impacts on local ecosystems and the global climate from the loss of large carbon stores under future socio-environmental change. However, due to the complexity of interactions and feedbacks, and lack of complete representation of fire biogeochemistry in many climate models, there is currently low agreement on whether climate change will cause fires to become more or less frequent in the future, and what impact this will have on ecosystems. Here we use the latest climate simulations from the UK Earth System Model UKESM1 to understand feedbacks in fire, dynamic vegetation, and terrestrial carbon stores using the JULES land surface model, taking into account future scenarios of change in emissions and land use. Based on evaluation of model performance for the present day, we address the specific policy-relevant question: how much fire-induced carbon loss will there be over South America at different global warming levels in the future? We find that burned area and fire emissions are projected to increase in the future due to hotter and drier conditions, which leads to large reductions in carbon storage, especially when combined with increasing land-use conversion. The model simulates a 30% loss of carbon at 4°C under the highest emission scenario, which could be reduced to 7% if temperature rise is limited to 1.5°C. Our results provide a critical assessment of ecosystem resilience under future climate change, and could inform the way fire and land-use is managed in the future to reduce the most deleterious impacts of climate change.

Technical note. The sudden increase in Amazon fires early in the 2019 fire season made global headlines. While it has been heavily speculated that the fires were caused by deliberate human ignitions or human-induced landscape changes, there have also been suggestions that meteorological conditions could have played a role. Here, we ask two questions: were the 2019 fires in the Amazon unprecedented in the historical record, and did the meteorological conditions contribute to the increased burning? To answer this, we take advantage of a recently developed modelling framework which optimises a simple fire model against observations of burnt area and whose outputs are described as probability densities. This allowed us to test the probability of the 2019 fire season occurring due to meteorological conditions alone. The observations show that the burnt area was higher than in previous years in regions where there is already substantial deforestation activity in the Amazon. Overall, 11 % of the area recorded the highest early season (June–August) burnt area since the start of our observational record, with areas in Brazil's central arc of deforestation recording the highest ever monthly burnt area in August. However, areas outside of the regions of widespread deforestation show less burnt area than the historical average, and the optimised model shows that this low burnt area would have extended over much of the eastern Amazon region, including in Brazil's central arc of deforestation with high fire occurrence in 2019. We show that there is a 9 % likelihood of the observed August fires being caused by meteorological conditions alone, decreasing to 6 %–7 % along the agricultural–humid forest interface in Brazil's central states and 8 % in Paraguay and Bolivia dry forests. Our results suggest that changes in land use, cover or management are the likely drivers of the substantial increase in the 2019 early fire season burnt area, especially in Brazil. Burnt area for Septembe

Technical note. The sudden increase in Amazon fires early in the 2019 fire season made global headlines. While it has been heavily speculated that the fires were caused by deliberate human ignitions or human-induced landscape changes, there have also been suggestions that meteorological conditions could have played a role. Here, we ask two questions: were the 2019 fires in the Amazon unprecedented in the historical record, and did the meteorological conditions contribute to the increased burning? To answer this, we take advantage of a recently developed modelling framework which optimises a simple fire model against observations of burnt area and whose outputs are described as probability densities. This allowed us to test the probability of the 2019 fire season occurring due to meteorological conditions alone. The observations show that the burnt area was higher than in previous years in regions where there is already substantial deforestation activity in the Amazon. Overall, 11 % of the area recorded the highest early season (June–August) burnt area since the start of our observational record, with areas in Brazil's central arc of deforestation recording the highest ever monthly burnt area in August. However, areas outside of the regions of widespread deforestation show less burnt area than the historical average, and the optimised model shows that this low burnt area would have extended over much of the eastern Amazon region, including in Brazil's central arc of deforestation with high fire occurrence in 2019. We show that there is a 9 % likelihood of the observed August fires being caused by meteorological conditions alone, decreasing to 6 %–7 % along the agricultural–humid forest interface in Brazil's central states and 8 % in Paraguay and Bolivia dry forests. Our results suggest that changes in land use, cover or management are the likely drivers of the substantial increase in the 2019 early fire season burnt area, especially in Brazil. Burnt area for Septembe

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.

Anthropogenically driven declines in tropical savannah burnt area have recently received attention due to their effect on trends in global burnt area. Large-scale trends in ecosystems where vegetation has adapted to infrequent fire, especially in cooler and wetter forested areas, are less well understood. Here, small changes in fire regimes can have a substantial impact on local biogeochemistry. To investigate trends in fire across a wide range of ecosystems, we used Bayesian inference to quantify four primary controls on burnt area: fuel continuity, fuel moisture, ignitions and anthropogenic suppression. We found that fuel continuity and moisture are the dominant limiting factors of burnt area globally. Suppression is most important in cropland areas, whereas savannahs and boreal forests are most sensitive to ignitions. We quantify fire regime shifts in areas with more than one, and often counteracting, trends in these controls. Forests are of particular concern, where we show average shifts in controls of 2.3–2.6% of their potential maximum per year, mainly driven by trends in fuel continuity and moisture. This study gives added importance to understanding long-term future changes in the controls on fire and the effect of fire trends on ecosystem function.

Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

Anthropogenically driven declines in tropical savannah burnt area have recently received attention due to their effect on trends in global burnt area. Large-scale trends in ecosystems where vegetation has adapted to infrequent fire, especially in cooler and wetter forested areas, are less well understood. Here, small changes in fire regimes can have a substantial impact on local biogeochemistry. To investigate trends in fire across a wide range of ecosystems, we used Bayesian inference to quantify four primary controls on burnt area: fuel continuity, fuel moisture, ignitions and anthropogenic suppression. We found that fuel continuity and moisture are the dominant limiting factors of burnt area globally. Suppression is most important in cropland areas, whereas savannahs and boreal forests are most sensitive to ignitions. We quantify fire regime shifts in areas with more than one, and often counteracting, trends in these controls. Forests are of particular concern, where we show average shifts in controls of 2.3–2.6% of their potential maximum per year, mainly driven by trends in fuel continuity and moisture. This study gives added importance to understanding long-term future changes in the controls on fire and the effect of fire trends on ecosystem function.

Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.