Four CO2 concentration inversions and the Global Fire Emissions Database (GFED) versions 2.1 and 3 are used to provide benchmarks for climate‐driven modeling of the global land‐atmosphere CO2 flux ...and the contribution of wildfire to this flux. The Land surface Processes and exchanges (LPX) model is introduced. LPX is based on the Lund‐Potsdam‐Jena Spread and Intensity of FIRE (LPJ‐SPITFIRE) model with amended fire probability calculations. LPX omits human ignition sources yet simulates many aspects of global fire adequately. It captures the major features of observed geographic pattern in burnt area and its seasonal timing and the unimodal relationship of burnt area to precipitation. It simulates features of geographic variation in the sign of the interannual correlations of burnt area with antecedent dryness and precipitation. It simulates well the interannual variability of the global total land‐atmosphere CO2 flux. There are differences among the global burnt area time series from GFED2.1, GFED3 and LPX, but some features are common to all. GFED3 fire CO2 fluxes account for only about 1/3 of the variation in total CO2 flux during 1997–2005. This relationship appears to be dominated by the strong climatic dependence of deforestation fires. The relationship of LPX‐modeled fire CO2 fluxes to total CO2 fluxes is weak. Observed and modeled total CO2 fluxes track the El Niño–Southern Oscillation (ENSO) closely; GFED3 burnt area and global fire CO2 flux track the ENSO much less so. The GFED3 fire CO2 flux‐ENSO connection is most prominent for the El Niño of 1997–1998, which produced exceptional burning conditions in several regions, especially equatorial Asia. The sign of the observed relationship between ENSO and fire varies regionally, and LPX captures the broad features of this variation. These complexities underscore the need for process‐based modeling to assess the consequences of global change for fire and its implications for the carbon cycle.
Key Points
We can model global fire patterns, based mainly on climate
We can model year‐to‐year variations in CO2 uptake by the land
Variation in global total fire depends on climate in complex ways
We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ...ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.
Climate projections show Australia becoming significantly warmer during the 21st century, and precipitation decreasing over much of the continent. Such changes are conventionally considered to ...increase wildfire risk. Nevertheless, we show that burnt area increases in southern Australia, but decreases in northern Australia. Overall the projected increase in fire is small (0.72-1.31% of land area, depending on the climate scenario used), and does not cause a decrease in carbon storage. In fact, carbon storage increases by 3.7-5.6 Pg C (depending on the climate scenario used). Using a process-based model of vegetation dynamics, vegetation-fire interactions and carbon cycling, we show increased fire promotes a shift to more fire-adapted trees in wooded areas and their encroachment into grasslands, with an overall increase in forested area of 3.9-11.9%. Both changes increase carbon uptake and storage. The increase in woody vegetation increases the amount of coarse litter, which decays more slowly than fine litter hence leading to a relative reduction in overall heterotrophic respiration, further reducing carbon losses. Direct CO2 effects increase woody cover, water-use efficiency and productivity, such that carbon storage is increased by 8.5-14.8 Pg C compared to simulations in which CO2 is held constant at modern values. CO2 effects tend to increase burnt area, fire fluxes and therefore carbon losses in arid areas, but increase vegetation density and reduce burnt area in wooded areas.
Future environmental change is expected to modify the global hydrological cycle, with consequences for the regional distribution of freshwater supplies. Regional precipitation projections, however, ...differ largely between models, making future water resource projections highly uncertain. Using two representative concentration pathways and nine climate models, we estimate 21st century water resources across Australia, employing both a process-based dynamic vegetation model and a simple hydrological framework commonly used in water resource studies to separate the effects of climate and vegetation on water resources. We show surprisingly robust, pathway-independent regional patterns of change in water resources despite large uncertainties in precipitation projections. Increasing plant water use efficiency (due to the changing atmospheric CO2) and reduced green vegetation cover (due to the changing climate) relieve pressure on water resources for the highly populated, humid coastal regions of eastern Australia. By contrast, in semi-arid regions across Australia, runoff declines are amplified by CO2-induced greening, which leads to increased vegetation water use. These findings highlight the importance of including vegetation dynamics in future water resource projections.
Spin‐up of UK Earth System Model 1 (UKESM1) for CMIP6 Yool, A.; Palmiéri, J.; Jones, C. G. ...
Journal of advances in modeling earth systems,
August 2020, 2020-08-00, 20200801, 2020-08-01, Letnik:
12, Številka:
8
Journal Article
Recenzirano
Odprti dostop
For simulations intended to study the influence of anthropogenic forcing on climate, temporal stability of the Earth's natural heat, freshwater, and biogeochemical budgets is critical. Achieving such ...coupled model equilibration is scientifically and computationally challenging. We describe the protocol used to spin‐up the UK Earth system model (UKESM1) with respect to preindustrial forcing for use in the sixth Coupled Model Intercomparison Project (CMIP6). Due to the high computational cost of UKESM1's atmospheric model, especially when running with interactive full chemistry and aerosols, spin‐up primarily used parallel configurations using only ocean/land components. For the ocean, the resulting spin‐up permitted the carbon and heat contents of the ocean's full volume to approach equilibrium over 5,000 years. On land, a spin‐up of 1,000 years brought UKESM1's dynamic vegetation and soil carbon reservoirs toward near‐equilibrium. The end‐states of these parallel ocean‐ and land‐only phases then initialized a multicentennial period of spin‐up with the full Earth system model, prior to this simulation continuing as the UKESM1 CMIP6 preindustrial control (piControl). The realism of the fully coupled spin‐up was assessed for a range of ocean and land properties, as was the degree of equilibration for key variables. Lessons drawn include the importance of consistent interface physics across ocean‐ and land‐only models and the coupled (parent) model, the extreme simulation duration required to approach equilibration targets, and the occurrence of significant regional land carbon drifts despite global‐scale equilibration. Overall, the UKESM1 spin‐up underscores the expense involved and argues in favor of future development of more efficient spin‐up techniques.
Plain Language Summary
Earth system models (ESMs) are an important tool for understandingz the Earth and for projecting how climate change may affect natural and human systems. For simulations of ESMs to separate anthropogenic influences on climate from the background state, the stability of the unperturbed system is critical. However, achieving this equilibrium is both scientifically and computationally challenging. Here, we describe how this was achieved for one such model, UKESM1, for the sixth Coupled Model Intercomparison Project (CMIP6). Due to the cost of the full model, especially when running with atmospheric chemistry and aerosols, much of UKESM1's spin‐up to equilibrium made use of ocean‐ and land‐only configurations. Millennial‐scale spin‐up phases of these component‐only models were used to initialize a final centennial‐scale phase of the full model to reach preindustrial equilibrium targets. The stability and realism of UKESM1's spun‐up state was then evaluated across a broad range of properties. A number of lessons were drawn from this spin‐up including the extreme simulation duration required to reach equilibrium. A key conclusion is the importance of developing efficient techniques to spin‐up ESMs.
Key Points
Earth system components and spin‐up protocol of UKESM1 for CMIP6 outlined
Ocean‐only (5,000 years) and land‐only (1,000 years) phases used prior to fully coupled finalizing of spin‐up (500 year)
Evaluation of spin‐up protocol presented, including cross‐component validation of piControl state and drift
The Land surface Processes and eXchanges (LPX) model is a fire-enabled dynamic global vegetation model that performs well globally but has problems representing fire regimes and vegetative mix in ...savannas. Here we focus on improving the fire module. To improve the representation of ignitions, we introduced a reatment of lightning that allows the fraction of ground strikes to vary spatially and seasonally, realistically partitions strike distribution between wet and dry days, and varies the number of dry days with strikes. Fuel availability and moisture content were improved by implementing decomposition rates specific to individual plant functional types and litter classes, and litter drying rates driven by atmospheric water content. To improve water extraction by grasses, we use realistic plant-specific treatments of deep roots. To improve fire responses, we introduced adaptive bark thickness and post-fire resprouting for tropical and temperate broadleaf trees. All improvements are based on extensive analyses of relevant observational data sets. We test model performance for Australia, first evaluating parameterisations separately and then measuring overall behaviour against standard benchmarks. Changes to the lightning parameterisation produce a more realistic simulation of fires in southeastern and central Australia. Implementation of PFT-specific decomposition rates enhances performance in central Australia. Changes in fuel drying improve fire in northern Australia, while changes in rooting depth produce a more realistic simulation of fuel availability and structure in central and northern Australia. The introduction of adaptive bark thickness and resprouting produces more realistic fire regimes in Australian savannas. We also show that the model simulates biomass recovery rates consistent with observations from several different regions of the world characterised by resprouting vegetation. The new model (LPX-Mv1) produces an improved simulation of observed vegetation composition and mean annual burnt area, by 33 and 18% respectively compared to LPX.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation ...models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is ...often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
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.
PDF
