Probabilistic downscaling of GCM scenarios over southern India

Vigaud, Nicolas; Vrac, Mathieu; Caballero, Yvan

doi:10.1002/joc.3509

Cited by 59 publications

(44 citation statements)

References 22 publications

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“…After this thresholding, only the positive values are corrected by CDF-t with respect to the strictly positive observed values. Other approaches are possible, such as applying a BC model directly on the whole time series including both dry days and rainy days, i.e., without separating the correction methodology into occurrence and intensity (e.g., Vrac et al, 2012;Vigaud et al, 2013;Vrac et al, 2016, among others). The latter approach has also been tested for preliminary tests and the results were not sensibly different from those presented in this article (not shown).…”

Section: The Rank Resampling For Distributions and Dependences (R 2 Dmentioning

confidence: 99%

“…Bias adjustments of the whole distribution through quantilemapping techniques have been quite popular since it allows for adjusting not only the mean and variance but also any quantile of the variable of interest. Hence, many variants have been proposed (e.g., Déqué, 2007;Michelangeli et al, 2009;Kallache et al, 2011;Tramblay et al, 2013;Vrac et al, 2016) and applied in different studies (e.g., Oettli et al, 2011;Colette et al, 2012;Tisseuil et al, 2012;Vigaud et al, 2013). Nevertheless, usually, those approaches only work in a univariate context, which means that they are designed to independently correct one variable at a time, for one location (e.g., grid cell) at a time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction

Vrac¹

2018

Hydrol. Earth Syst. Sci.

101

135

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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 (R 2 D 2 ) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R 2 D 2 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. R 2 D 2 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 R 2 D 2 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-R 2 D 2 is only satisfying in the inter-variable context, 1506d-R 2 D 2 strongly improves inter-site properties and 3012d-R 2 D 2 is able to account for both. Applications of the proposed R 2 D 2 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.

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Section: The Rank Resampling For Distributions and Dependences (R 2 Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction

Vrac¹

2018

Hydrol. Earth Syst. Sci.

101

135

View full text Add to dashboard Cite

show abstract

“…Moreover, the SDM evaluated here is named the "cumulative distribution function transform" (CDF-t) approach. CDF-t has originally been developed for wind downscaling (Michelangeli et al, 2009) but recently applied to temperature and precipitation (Vigaud et al, 2012;Lavaysse et al, 2012). This method aims at modelling local-scale statistical characteristics using a probabilistic downscaling model.…”

Section: Introductionmentioning

confidence: 99%

Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment

Vrac

Drobinski²,

Merlo³

et al. 2012

Nat. Hazards Earth Syst. Sci.

Self Cite

151

156

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Abstract. ERA-40 reanalyses, and simulations from three regional climate models (RCMs) (ALADIN, LMDZ, and WRF) and from one statistical downscaling model (CDFt) are used to evaluate the uncertainty in downscaling of wind, temperature, and rainfall cumulative distribution functions (CDFs) for eight stations in the French Mediterranean basin over 1991-2000. The uncertainty is quantified using the Cramer-von Mises score (CvM) to measure the "distance" between the simulated and observed CDFs. The ability of the three RCMs and CDF-t to simulate the "climate" variability is quantified with the explained variance, variance ratio and extreme occurrence. The study shows that despite their differences, the three RCMs display very similar performance. In terms of global distributions (i.e. CvM), all models perform better than ERA-40 for both seasons and variables. However, looking at variance criteria, RCMs are not always much better than ERA-40 reanalyses, whereas CDF-t produces accurate results when applied to ERA-40. In a second step, a combined statistical/dynamical downscaling approach has been used, consisting in applying CDF-t to the RCM outputs. It shows that CDF-t applied to the RCM outputs does not necessarily produce better results than those from CDF-t directly applied to ERA-40. It also shows that CDF-t applied to RCMs generally improves the downscaled CDFs and that the "additional" added value of CDF-t applied to the RCMs is independent of the performance of the RCMs in terms of CvM, explained variance, variance ratio and extreme occurrence.

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“…The cumulative distribution function-transform (CDFt) method was originally developed by Michelangeli et al (2009) to downscale wind velocity and was later applied to temperature and precipitation in, for example, Vrac et al (2012) and Vigaud et al (2013). The CDFt model is a quantile-mapping-based approach, which consists in relating the local-scale cumulative distribution function (CDF) of the variable of interest to the large-scale CDF (here from NCEP or GCMs) of the same variable.…”

Section: The Cdft Modelmentioning

confidence: 99%

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

Grouillet

Ruelland

Ayar

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydrometeorological situations. The comparison was conducted over a common 20-year period (1986-2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods.

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Probabilistic downscaling of GCM scenarios over southern India

Cited by 59 publications

References 22 publications

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

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Probabilistic downscaling of GCM scenarios over southern India

Cited by 59 publications

References 22 publications

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R&lt;sup&gt;2&lt;/sup&gt;D&lt;sup&gt;2&lt;/sup&gt;) bias correction

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R&lt;sup&gt;2&lt;/sup&gt;D&lt;sup&gt;2&lt;/sup&gt;) bias correction

Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

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Product

Resources

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Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction