scholarly journals Supplementary material to "Is time a variable like the others in multivariate statistical downscaling and bias correction?"

2021 ◽  
Author(s):  
Yoann Robin ◽  
Mathieu Vrac
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Multivariate Statistical ◽  
Supplementary Material

scholarly journals Is time a variable like the others in multivariate statistical downscaling and bias correction?

2021 ◽  
Vol 12 (4) ◽  
pp. 1253-1273
Author(s):  
Yoann Robin ◽  
Mathieu Vrac
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Synthetic Data ◽  
The Other ◽  
Strong Increase ◽  
Temporal Properties

Abstract. Bias correction and statistical downscaling are now regularly applied to climate simulations to make then more usable for impact models and studies. Over the last few years, various methods were developed to account for multivariate – inter-site or inter-variable – properties in addition to more usual univariate ones. Among such methods, temporal properties are either neglected or specifically accounted for, i.e. differently from the other properties. In this study, we propose a new multivariate approach called “time-shifted multivariate bias correction” (TSMBC), which aims to correct the temporal dependency in addition to the other marginal and multivariate aspects. TSMBC relies on considering the initial variables at various times (i.e. lags) as additional variables to be corrected. Hence, temporal dependencies (e.g. auto-correlations) to be corrected are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a vector auto-regressive (VAR) process. In a second evaluation, we work in a “perfect model” context where a regional climate model (RCM) plays the role of the (pseudo-)observations, and where its forcing global climate model (GCM) is the model to be downscaled or bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing the number of lags too much does not necessarily improve the temporal properties, and an overly strong increase in the number of dimensions of the dataset to be corrected can even imply some potential instability in the adjusted and/or downscaled results, calling for a reasoned use of this approach for large datasets.

Statistical Downscaling for Climate Science

2019 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Climatic Changes ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections

Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.

Supplementary material to "A method to preserve trends in quantile mapping bias correction of climate modeled temperature"

2017 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Ioannis N. Daliakopoulos ◽  
Ioannis K. Tsanis
Keyword(s):  
Bias Correction ◽  
Quantile Mapping ◽  
Mapping Bias ◽  
Supplementary Material

scholarly journals Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

2021 ◽  
Vol 25 (6) ◽  
pp. 3493-3517
Author(s):  
Hossein Tabari ◽  
Santiago Mendoza Paz ◽  
Daan Buekenhout ◽  
Patrick Willems
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Climate Change Impact ◽  
General Circulation ◽  
Frequency Change ◽  
Total Precipitation ◽  
Dry Spells ◽  
Dry Spell ◽  
Change Impact

Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

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