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Published ; Journal Article ; This is the final version of the article. Available from Copernicus Publications via the DOI in this record. ; © Author(s) 2017.Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley-Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results. ; This work was primarily funded by Imperial College London as a part of the AXA Chair Programme on Biosphere and Climate Impacts. It is a contribution to the Imperial College initiative on Grand Challenges in Ecosystems and the Environment, and the Ecosystem Modelling And Scaling Infrastructure (eMAST) facility of the Australian Terrestrial Ecosystem Research Network (TERN). TERN is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy (NCRIS). BDS funded by the Swiss National Science Foundation (SNF) and the European Commission's 7th Framework Programme, under grant agreement number 282672, EMBRACE project. WC contributes to the Labex OT-Med (no. ANR-11-LABX-0061) funded by the French government through the A MIDEX project (no. ANR-11-IDEX-0001-02). AGS has been supported by a Natural Environment Research Council grant (NERC grant number NE/I012915/1). VIC simulations utilized the Janus supercomputer, which is supported by the National Science Foundation (award number CNS-0821794) and the University of Colorado Boulder. The Janus supercomputer is a joint effort of the University of Colorado Boulder, the University of Colorado Denver, and the National Center for Atmospheric Research. CERES EBAF data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. CPC soil moisture data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/.

Climate has a huge influence on the way we live. For example, it affects the crops we can grow and the diseases we might encounter in particular locations. It also determines the physical infrastructure we need to build to survive comfortably in the face of extremes of heat, cold, drought and flood. Human emissions of carbon dioxide and other greenhouse gases have changed the composition of the atmosphere over the last two centuries. This is expected to take Earth's climate out of the relatively stable range that has characterised the last few thousand years, during which human society has emerged. Measurements of ice cores and sea-floor sediments show that the current concentration of carbon dioxide, at just over 400 parts per million, has not been experienced for at least three million years. This causes more of the heat from the Sun to be retained on Earth, warming the atmosphere and ocean. The global average of atmospheric temperature has so far risen by about 1˚C compared to the late 19th century, with further increases expected dependent on the trajectory of carbon dioxide emissions in the next few decades. In 2013 and 2014 the Intergovernmental Panel on Climate Change (IPCC) published its fifth assessment report (AR5) assessing the evidence about climate change and its impacts. This assessment considered data from observations and records of the past. It then assessed future changes and impacts based on various scenarios for emissions of greenhouse gases and other anthropogenic factors. In 2015, almost every nation in the world agreed (in the so-called Paris Agreement) to the challenging goal of keeping global average warming to well below 2°C above pre-industrial temperatures while pursuing efforts to limit it to 1.5°C. With the next assessment report (AR6) not due until 2022, it is timely to consider how evidence presented since the publication of AR5 affects the assessments made then. The Earth's climate is a complex system. To understand it, and the impact that climate change will have, requires many different kinds of study. Climate science consists of theory, observation and modelling. Theory begins with well-established scientific principles, seeks to understand processes occurring over a range of spatial and temporal scales and provides the basis for models. Observation includes long time series of careful measurements, recent data from satellites, and studies of past climate using archives such as tree rings, ice cores and marine sediments. It also encompasses laboratory and field experiments designed to test and enhance understanding of processes. Computer models of the Earth climate system use theory, calibrated and validated by the observations, to calculate the result of future changes. There are nevertheless uncertainties in estimating future climate. Firstly the course of climate change is dependent on what socioeconomic, political and energy paths society takes. Secondly there remain inevitable uncertainties induced for example by variability in the interactions between different parts of the Earth system and by processes, such as cloud formation, that occur at too small a scale to incorporate precisely in global models. Assessments such as those of the IPCC describe the state of knowledge at a particular time, and also highlight areas where more research is needed. We are still exploring and improving our understanding of many of the processes within the climate system, but, on the whole, new research confirms the main ideas underpinning climate research, while refining knowledge, so as to reduce the uncertainty in the magnitude and extent of crucial impacts.

This is the final version. Available on open access from the European Geosciences Union via the DOI in this record ; Code and data availability. The benchmarking code is archived at https://zenodo.org/record/3879161#.Xtq-py-z2fU (last access: 5 June 2020) (https://doi.org/10.5281/zenodo.3879161, Kelley, 2020), which also contains the code to produce the figures presented here. The FireMIP model output is archived at https://zenodo.org/record/3555562#.Xell3C2ZOcY (last access: 22 November 2019) (https://doi.org/10.5281/zenodo.3555562, Hantson et al., 2019). Data availability for each reference dataset is provided in Table A1. ; Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. The two older fire models included in the FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less well globally than other models, but it is difficult to distinguish between the remaining ensemble members; some of these models are better at representing certain aspects of the fire regime; none clearly outperforms all other models across the full range of variables assessed. ; European Union FP7 ; German Federal Ministry of Education and Research (BMBF) ; European Research Council (ERC) ; Natural Environment Research Council (NERC) ; Deutsche Forschungsgemeinschaft (DFG) ; National Natural Science Foundation of China ; Strategic Research Area MERGE (ModElling the Regional and Global Earth system) ; Lund University Centre for Studies of Carbon Cycle and Climate Interactions (LUCCI)