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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. [ABSTRACT FROM AUTHOR]

Fluxes of organic and inorganic carbon within the Amazon basin are considerably controlled by annual flooding, which triggers the export of terrigenous organic material to the river and ultimately to the Atlantic Ocean. The amount of carbon imported to the river and the further conversion, transport and export of it, depend on terrestrial productivity and discharge, as well as temperature and atmospheric CO2. Both terrestrial productivity and discharge are influenced by climate and land use change. To assess the impact of these changes on the riverine carbon dynamics, the coupled model system of LPJmL and RivCM (Langerwisch et al., 2015) has been used. Vegetation dynamics (in LPJmL) as well as export and conversion of terrigenous carbon to and within the river (RivCM) are included. The model system has been applied for the years 1901 to 2099 under two deforestation scenarios and with climate forcing of three SRES emission scenarios, each for five climate models. The results suggest that, following deforestation, riverine particulate and dissolved organic carbon will strongly decrease by up to 90 % until the end of the current century. In parallel, discharge increases, leading to roughly unchanged net carbon transport during the first decades of the century, as long as a sufficient area is still forested. During the following decades the amount of transported carbon will decrease drastically. In contrast to the riverine organic carbon, the amount of riverine inorganic carbon is only determined by climate change forcing, namely increased temperature and atmospheric CO2 concentration. Mainly due to the higher atmospheric CO2 it leads to an increase in riverine inorganic carbon by up to 20 % (SRES A2). The changes in riverine carbon fluxes have direct effects on the export of carbon, either to the atmosphere via outgassing, or to the Atlantic Ocean via discharge. Basin-wide the outgassed carbon will increase slightly, but can be regionally reduced by up to 60 % due to deforestation. The discharge of organic carbon to the ocean will be reduced by about 40 % under the most severe deforestation and climate change scenario. The changes would have local and regional consequences on the carbon balance and habitat characteristics in the Amazon basin itself but also in the adjacent Atlantic Ocean. [ABSTRACT FROM AUTHOR]

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 hinders 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 non-preservation 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 to carefully consider 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 past, current and future extremes. [ABSTRACT FROM AUTHOR]

Carbon fluxes in the Amazon Basin are considerably influenced by annual flooding during which terrigenous organic material is imported to the river. This regular interaction affects carbon pools within the riverine system, terrestrial carbon, and carbon exported to the ocean and released to the atmosphere. The processes of generation, conversion, and transport of organic carbon in this coupled terrigenous–riverine system strongly interact and are climate-sensitive, yet their response to climate change is still largely unknown. To quantify climate change effects on carbon pools and on carbon fluxes within the river and to the ocean and the atmosphere, we developed the riverine carbon model RivCM, which is directly coupled to the well-established dynamic vegetation and hydrology model LPJmL. We show here that RivCM successfully reproduces observed values in exported carbon and riverine carbon concentration. We evaluate future changes in riverine carbon by applying RivCM for climate forcing from five climate models and three CO2 emission scenarios (SRES). We find that climate change causes a doubling of riverine organic carbon in the Southern and Western basin while reducing it by 20% in the eastern and northern parts. In contrast, the amount of riverine inorganic carbon shows a 2- to 3-fold increase in the entire basin, independent of the SRES scenario. The export of carbon to the atmosphere increases as well with an average of about 30 %. In contrast, changes in future export of organic carbon to the Atlantic Ocean depend on the SRES scenario and are projected to either decrease by about 8.9% (SRES A1B) or increase by about 9.1% (SRES A2). Such changes in the terrigenous–riverine system could have local and regional impacts on the carbon budget of the whole Amazon Basin and parts of the Atlantic Ocean. Changes in the riverine carbon could lead to a shift in the riverine nutrient supply and pH, while changes in the exported carbon to the ocean leads to changes in the supply of organic material that acts as food source in the Atlantic. On the larger scale the increased outgassing of CO2 could turn the Amazon Basin from a sink of carbon to a considerable source. Therefore we propose that the coupling of terrestrial and riverine carbon budget should be included in subsequent analysis of the future regional carbon budget. [ABSTRACT FROM AUTHOR]