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Over the last few years, multivariate bias correction methods have been developed to adjust spatial and/or inter-variable dependence properties of climate simulations. Most of them do not correct – and sometimes even degrade – the associated temporal features. Here, we propose a multivariate method to adjust the spatial and/or inter-variable properties while also accounting for the temporal dependence, such as autocorrelations. Our method consists of an extension of a previously developed approach that relies on an analogue-based method applied to the ranks of the time series to be corrected rather than to their "raw" values. Several configurations are tested and compared on daily temperature and precipitation simulations over Europe from one Earth system model. Those differ by the conditioning information used to compute the analogues and can include multiple variables at each given time, a univariate variable lagged over several time steps or both – multiple variables lagged over time steps. Compared to the initial approach, results of the multivariate corrections show that, while the spatial and inter-variable correlations are still satisfactorily corrected even when increasing the dimension of the conditioning, the temporal autocorrelations are improved with some of the tested configurations of this extension. A major result is also that the choice of the information to condition the analogues is key since it partially drives the capability of the proposed method to reconstruct proper multivariate dependences. [ABSTRACT FROM AUTHOR]

Over the last few years, multivariate bias correction methods have been developed to adjust spatial and/or inter-variable dependence properties of climate simulations. Most of them do not correct - and sometimes even degrade - the associated temporal features. Here, we propose a multivariate method to adjust the spatial and/or inter-variable properties while also accounting for the temporal dependence, such as autocorrelations. Our method consists in an extension of a previously developed approach that relies on an analogue-based method applied to the ranks of the time series to be corrected, rather than applied to their "raw" values. Several configurations are tested and compared on daily temperature and precipitation simulations over Europe from one Earth System Model. Those differ by the conditioning information used to compute the analogues, and can include multiple variables at each given time, a univariate variable lagged over several time steps, or both - multiple variables lagged over time steps. Compared to the initial approach, results of the multivariate corrections show that, while the spatial and inter-variable correlations are still satisfactorily corrected even when increasing the dimension of the conditioning, the temporal autocorrelations are improved with some of the tested configurations of this extension. A major result is also that the choice of the information to condition the analogues is key since it partially drives the capability of the proposed method to reconstruct proper multivariate dependencies. [ABSTRACT FROM AUTHOR]

Rohmer, J.; Louisor, J.; Le Cozannet, G.; Naveau, P.; Thao, S., and Bertin, X., 2020. Attribution of extreme wave height records along the North Atlantic coasts using hindcast data: Feasibility and limitations. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1268-1272. Coconut Creek (Florida), ISSN 0749-0208. Extreme Event Attribution (EEA) aims at answering questions about how much climate change influenced the probability or intensity of a specific type of extreme meteo-oceanic event. Here, we focus on wave record breaking i.e. the occurrence of an extreme significant wave height (hs) value that exceeds all past observations. The objective is to evaluate the probability changes related to climate change by estimating the fraction of attributable risk far=1–p0/p1, where p0 and p1 are probabilities of the wave record event in two different worlds: a counterfactual world without anthropogenic forcings i.e. a "world that might have been", and the factual world, i.e. "world that is." To define these different worlds, we rely on a wave hindcast database, which provides very long time series (1900-2008) of hs over the whole North Atlantic Ocean Basin (NAOB). We assume that the counterfactual world corresponds to the series of annual hs maxima over the period 1900-1930, and that the factual world corresponds to the annual hs maxima over the period 1978-2008. The extreme event attribution approach dedicated to record breakings was applied over NAOB, and we show large far values (>0.5) along the northern Canadian, Scottish and southwestern Norwegian coasts (over the latitudes 50°-65°N). These results are, however, carefully discussed with respect to different uncertainty sources, namely the validity of the assumptions underlying the EEA, the statistical uncertainties, the use of hindcast data instead of global climate model's results, and the limitations in the hindcast database setup. [ABSTRACT FROM AUTHOR]