Geoscientific Model Development | 2021
ATTRICI v1.1 – counterfactual climate for impact attribution
Abstract
Abstract. Attribution in its general definition aims to quantify drivers of change in\na system. According to IPCC Working Group II (WGII) a change in a natural, human or managed\nsystem is attributed to climate change by quantifying the difference between\nthe observed state of the system and a counterfactual baseline that\ncharacterizes the system s behavior in the absence of climate change, where\n“climate change refers to any long-term trend in climate, irrespective of\nits cause” (IPCC, 2014). Impact attribution following this definition remains a challenge\nbecause the counterfactual baseline, which characterizes the system\nbehavior in the hypothetical absence of climate change, cannot be observed.\nProcess-based and empirical impact models can fill this gap as they allow us to\nsimulate the counterfactual climate impact baseline. In those simulations,\nthe models are forced by observed direct (human) drivers such as land use\nchanges, changes in water or agricultural management but a counterfactual\nclimate without long-term changes. We here present ATTRICI (ATTRIbuting\nClimate Impacts), an approach to construct the required counterfactual\nstationary climate data from observational (factual) climate data. Our\nmethod identifies the long-term shifts in the considered daily climate\nvariables that are correlated to global mean temperature change assuming a\nsmooth annual cycle of the associated scaling coefficients for each day of\nthe year. The produced counterfactual climate datasets are used as forcing\ndata within the impact attribution setup of the Inter-Sectoral Impact Model\nIntercomparison Project (ISIMIP3a). Our method preserves the internal\nvariability of the observed data in the sense that factual and\ncounterfactual data for a given day have the same rank in their respective\nstatistical distributions. The associated impact model simulations allow for\nquantifying the contribution of climate change to observed long-term changes\nin impact indicators and for quantifying the contribution of the observed\ntrend in climate to the magnitude of individual impact events. Attribution\nof climate impacts to anthropogenic forcing would need an additional step\nseparating anthropogenic climate forcing from other sources of climate\ntrends, which is not covered by our method.\n