Abstract
Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell × number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.
Highlights
Climate change impact studies aim to investigate and understand the consequences of the potential evolutions of the climate system
The common point of those impact studies is that they use global (GCM) or regional climate model (RCM) simulations of different variables over future time periods according to some scenarios as inputs into impact models to project consequences of climate change
A new multivariate bias correction approach was proposed, allowing to correct the marginal distributions of the climate variables of interest and the statistical dependences between the variables, as well as the dependences between the different locations over a given geographical domain. This approach relies on the previously developed “Empirical Copula – Bias Correction” (EC-BC, Vrac and Friederichs, 2015) method, whose all dependence structures – inter-variable, inter-site and overall temporal were taken from reference data and exactly reproduced by the EC-BC correction
Summary
Climate change impact studies aim to investigate and understand the consequences of the potential evolutions of the climate system. The common point of those impact studies is that they use global (GCM) or regional climate model (RCM) simulations of different variables over future time periods according to some scenarios as inputs into impact models to project (e.g., hydrological, ecological) consequences of climate change. Vrac: Multivariate bias correction: the R2D2 method suffer from statistical biases with respect to observations – or more generally reference data This means that some of their statistical properties, such as mean, variance, distribution or even temporal, spatial or inter-variable dependence structures may not be representative of what is observed in the reference dataset. Before employing climate simulations to feed an impact model, it is often mandatory to “bias correct” (or to “adjust”) them in order to correct some of their statistical properties (e.g., Christensen et al, 2008; Muerth et al, 2013)
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