ALBERT

Description: 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.

Description: Recent reports by the Global Carbon Project highlight large uncertainties around land surface processes such as land use change, strength of CO2 fertilization, nutrient limitation and supply, and response to variability in climate. Process-based land surface models are well suited to address these complex and emerging global change problems but will require extensive development and evaluation. The coupled Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) framework has been under continuous development by Environment and Climate Change Canada since 1987. As the open-source model of code development has revolutionized the software industry, scientific software is experiencing a similar evolution. Given the scale of the challenge facing land surface modellers, and the benefits of open-source, or community model, development, we have transitioned CLASS-CTEM from an internally developed model to an open-source community model, which we call the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) v.1.0. CLASSIC contains many technical features specifically designed to encourage community use including software containerization for serial and parallel simulations, extensive benchmarking software and data (Automated Model Benchmarking; AMBER), self-documenting code, community standard formats for model inputs and outputs, amongst others. Here, we evaluate and benchmark CLASSIC against 31 FLUXNET sites where the model has been tailored to the site-level conditions and driven with observed meteorology. Future versions of CLASSIC will be developed using AMBER and these initial benchmark results to evaluate model performance over time. CLASSIC remains under active development and the code, site-level benchmarking data, software container, and AMBER are freely available for community use.

Description: Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

Description: Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers of fire, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1900. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trend in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the strongest differences leading to diverging trajectories are related to the way anthropogenic ignitions and suppression, as well as the effects of land-use on vegetation and fire, are incorporated in individual models. This points to a need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire for global change applications. Only two models show a strong response to CO2 and the response to lightning on global scale is low for all models. The sensitivity to climate shows a spatially heterogeneous response and globally only two models show a significant trend. It was not possible to attribute the climate-induced changes in burned area to model assumptions or specific climatic parameters. However, the strong influence of climate on the inter-annual variability in burned area, shown by all the models, shows that we need to pay attention to the simulation of fire weather but also meteorological influences on biomass accumulation and fuel properties in order to better capture extremes in fire behavior.

Description: The El Niño‐-Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) central Pacific (CP) and (ii) eastern Pacific (EP). Both types of El Niño are characterised by above-average sea surface temperature anomalies at the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability following increased tropical near-surface air temperature and decreased precipitation. We employ the dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) within a synthetic experimental framework to examine the sensitivity and potential long-term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the latter half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was small for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.