Abstract
Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes.
Highlights
Weather and climate extreme events such as heat waves, droughts or storms cause major impacts upon human societies and ecosystems (IPCC, 2012)
This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe
A clear distinction between bias correction and statistical downscaling is crucial (Maraun, 2013): while the resampling bias correction is designed to account for the former, no attempt at statistical downscaling or bridging any scale mismatches is made. Notwithstanding these limitations we show the functionality of the novel bias correction scheme that might be a useful and physically consistent alternative to conventional statistical bias correction as long as global and regional dynamic climate models suffer from pertinent biases
Summary
Weather and climate extreme events such as heat waves, droughts or storms cause major impacts upon human societies and ecosystems (IPCC, 2012). In recent years, these climatic events have changed in intensity and frequency in many parts of the world (Barriopedro et al, 2011; Donat et al, 2013; Seneviratne et al, 2014) and changes are likely to continue throughout the 21st century (Sillmann et al., 2013). Heat and drought events trigger ecological responses (Reyer et al, 2013; Frank et al, 2015), which in turn induce changes to the cycling of water and carbon through such systems with potential feedback to the atmosphere and cli-. It has been demonstrated that a single large event such as the European heat and drought summer of 2003 alone might undo several years of ecosystem carbon sequestration (Ciais et al, 2005), potentially jeopardizing the terrestrial carbon sink potential (Lewis et al, 2011)
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