scholarly journals Nonparametric Statistical Downscaling of Precipitation from Global Climate Models

10.5772/32910 ◽  
2012 ◽  
Author(s):  
Paul Nyeko-Ogiramoi ◽  
Patrick Willems ◽  
Gaddi Ngirane-Katashaya ◽  
Victor Ntegek
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Nonparametric Statistical

scholarly journals Comparing dynamical, stochastic and combined downscaling approaches – lessons from a case study in the Mediterranean region

2012 ◽  
Vol 9 (8) ◽  
pp. 9847-9884
Author(s):  
N. Guyennon ◽  
E. Romano ◽  
I. Portoghese ◽  
F. Salerno ◽  
S. Calmanti ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Max Planck ◽  
Basin Scale ◽  
Quantile Transform

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the stochastic statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to examine the relative benefits of each downscaling approach and their combination in making the GCM scenarios suitable for basin scale hydrological applications. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterized by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile transform. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modeled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the trend spatial heterogeneity and time evolution predicted by the GCM, although the comparison with observations resulted still underperforming. The best results were obtained through the combination of both DD and SD approaches.

scholarly journals CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles

2020 ◽  
Author(s):  
Chiranjib Chaudhuri ◽  
Colin Robertson
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Similarity Index ◽  
Wasserstein Distance ◽  
Regional Level ◽  
Global Climate Models ◽  
Annual Maximum ◽  
Maximum Precipitation ◽  
Structural Loss

Despite numerous studies in statistical downscaling methodology, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

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.

Projection of future daily precipitation series and extreme events by using a multi-site statistical downscaling model over the great Montréal area, Québec, Canada

2012 ◽  
Vol 44 (1) ◽  
pp. 147-168 ◽  
Author(s):  
D. I. Jeong ◽  
A. St-Hilaire ◽  
T. B. M. J. Ouarda ◽  
P. Gachon
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Historical Period ◽  
Precipitation Series ◽  
Statistical Downscaling Model ◽  
The Future ◽  
Consistent Increase ◽  
Ipcc Sres

This study suggested strategies to project future precipitation series based on a multi-site hybrid SDM (statistical downscaling model), which can downscale precipitation series at multiple observation sites simultaneously by combining the multivariate multiple linear regression (MMLR) model and the stochastic randomization procedure. The hybrid SDM and future projection methodologies applied to 10 observation sites located in the great area of Montréal, Québec, Canada. Six future independent precipitation series were projected from six sets of future atmospheric predictors using three AOGCMs (Atmosphere-Ocean Global Climate Models, i.e. CGCM2, CGCM3, HadCM3) and three IPCC SRES emission scenarios (B2, A1B and A2). Downscaled climate change signals on wet/dry sequences and extreme indices of precipitation time series were evaluated over the future period from 2060 to 2099 with respect to the historical period from 1961 to 2000. The future scenarios of all three AOGCMs showed a consistent increase of 7.9–44.6% in winter while only those of HadCM3 and CGCM3 showed a decrease of 2.3–23.0% in summer compared to their historical values. Precipitation series of CGCM2 A2 and CGCM3 A2 scenarios yielded the largest increase in winter, while those of HadCM3 B2 and A2 scenarios yielded the largest decrease in summer for all statistics indices.

Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling

2018 ◽  
Author(s):  
Aristita Busuioc ◽  
Alexandru Dumitrescu
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Local Climate

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

scholarly journals Benefits from using combined dynamical-statistical downscaling approaches – lessons from a case study in the Mediterranean region

2013 ◽  
Vol 17 (2) ◽  
pp. 705-720 ◽  
Author(s):  
N. Guyennon ◽  
E. Romano ◽  
I. Portoghese ◽  
F. Salerno ◽  
S. Calmanti ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Max Planck ◽  
Fifth Generation ◽  
Basin Scale

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD permits to obtain more suitable climate scenarios for basin scale hydrological applications starting from GCM simulations. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterised by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modelled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the spatial heterogeneity of trends and the long-term time evolution predicted by the GCM. The best results were obtained through the combination of both DD and SD approaches.

A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain

2012 ◽  
Vol 25 (1) ◽  
pp. 262-281 ◽  
Author(s):  
Ethan D. Gutmann ◽  
Roy M. Rasmussen ◽  
Changhai Liu ◽  
Kyoko Ikeda ◽  
David J. Gochis ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Meteorological Variables ◽  
Small Scale ◽  
Spatial Distributions ◽  
Significant Difference

Abstract Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

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