Abstract
Abstract. 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.
Highlights
Solid EarthDynamic global vegetation models (DGVMs) are widely used in the assessment of climate change impacts on ecosystems, and feedbacks through ecosystem processes (Cramer et al, 1999; Scholze et al, 2006; Sitch et al, 2008; Scheiter amnoddHeligpgroinjesc, t2io0n0s9)o.fTHthoheweveevgCeer,tratythioeornesrpaershepoleanrrsgeeetodisfcfeerneanrcioess in of future changes in atmospheric CO2 concentration and climate (Friedlingstein et al, 2006; Denman et al, 2007; Sitch et al, 2008)
LPJ scores 1.07 and Land Processes and eXchanges (LPX) scores 1.14 using normalised mean error (NME) for seasonal concentration, compared to 1.00 for the mean and 1.41 ± 0.006 for random resampling. This means that the seasonal concentration of fapar in the dynamic global vegetation models (DGVMs) is, respectively, 7 % and 14 % worse than the mean of the data compared to observations
As found by Heimann et al (1998), the Simple Diagnostic Biosphere Model (SDBM) produces a good simulation of the seasonal cycle of atmospheric CO2 concentration
Summary
Dynamic global vegetation models (DGVMs) are widely used in the assessment of climate change impacts on ecosystems, and feedbacks through ecosystem processes (Cramer et al, 1999; Scholze et al, 2006; Sitch et al, 2008; Scheiter amnoddHeligpgroinjesc, t2io0n0s9)o.fTHthoheweveevgCeer,tratythioeornesrpaershepoleanrrsgeeetodisfcfeerneanrcioess in of future changes in atmospheric CO2 concentration and climate (Friedlingstein et al, 2006; Denman et al, 2007; Sitch et al, 2008). Assessing the uncertainty around vegetationmodel simulations would provide an indicator of confidence in model predictions under different climates. Such a system would serve several functions, including the following: comparing the performance of different models; identifying. Kelley et al.: Benchmarking system for evaluating global vegetation models processes in a particular model that need improvement; and checking that improvements in one part of a model do not compromise performance in another
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