scholarly journals Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

2016 ◽  
Vol 52 (1) ◽  
pp. 471-494 ◽  
Author(s):  
Andreas Langousis ◽  
Antonios Mamalakis ◽  
Roberto Deidda ◽  
Marino Marrocu
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Relative Effectiveness ◽  
Distribution Mapping ◽  
Rainfall Statistics

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

scholarly journals Uncovering the shortcomings of a weather typing based statistical downscaling method

2019 ◽  
Author(s):  
Els Van Uytven ◽  
Jan De Niel ◽  
Patrick Willems
Keyword(s):  
Statistical Downscaling ◽  
Impact Analysis ◽  
Climate Model ◽  
Weather Type ◽  
User Needs ◽  
End User ◽  
Statistical Downscaling Method ◽  
Downscaling Method ◽  
Weather Typing ◽  
General Statistical

Abstract. In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology. The skill is evaluated by the verification of the general statistical downscaling assumptions, and by the perfect predictor experiment that includes hydrological impact analysis. The approach has been tested for an advanced weather typing based SDM and for impact analysis on river peak flows in a Belgian river catchment. Significant shortcomings of the selected SDM were uncovered such as biases in the frequency of weather types and non-stationarities in the extreme precipitation distribution per weather type. Such evaluation of SDMs becomes of use for future tailoring of SDM ensembles to end user needs.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

scholarly journals Uncovering the shortcomings of a weather typing method

2020 ◽  
Vol 24 (5) ◽  
pp. 2671-2686 ◽  
Author(s):  
Els Van Uytven ◽  
Jan De Niel ◽  
Patrick Willems
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Precipitation Amount ◽  
Winter Season ◽  
Added Value ◽  
Typing Method ◽  
Weather Typing ◽  
Precipitation Changes

Abstract. In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.

scholarly journals Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers

2019 ◽  
Vol 23 (3) ◽  
pp. 1741-1749
Author(s):  
Jan Hnilica ◽  
Martin Hanel ◽  
Vladimír Puš
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Dependence Structure ◽  
Climate Projections ◽  
Model Simulations ◽  
Correlation Structures ◽  
Climate Model Simulations ◽  
Dependence Structures

Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures.

scholarly journals Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

2017 ◽  
Vol 21 (6) ◽  
pp. 2649-2666 ◽  
Author(s):  
Matthew B. Switanek ◽  
Peter A. Troch ◽  
Christopher L. Castro ◽  
Armin Leuprecht ◽  
Hsin-I Chang ◽  
...  
Keyword(s):  
Error Correction ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Climate Change Signal ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
Distribution Mapping ◽  
Projected Changes ◽  
Time Invariant

Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

scholarly journals Correção de tendência das projeções climáticas futuras simuladas pelo modelo regional Eta-Hadgem2-Es para a Bacia Hidrográfica do Rio Mundaú, Nordeste do Brasil

2020 ◽  
Vol 5 (3) ◽  
pp. 288-301
Author(s):  
Carolyne Wanessa Lins de Andrade Farias ◽  
Suzana Maria Gico Lima Montenegro ◽  
Frederico Abraão Costa Lins ◽  
Abelardo Antônio de Assunção Montenegro
Keyword(s):  
Hydrologic Modeling ◽  
Climate Model ◽  
Linear Scaling ◽  
Distribution Mapping

Os efeitos das mudanças climáticas futuras decorrentes de ações antrópicas sobre os recursos hídricos têm sido uma das principais questões estudadas atualmente. A compreensão dessas mudanças é de extrema importância para que ações de prevenção e mitigação sejam tomadas. Uma das formas de avaliar tais efeitos é por meio da inclusão das previsões climáticas em modelos hidrológicos. Entretanto, os dados oriundos de modelos climáticos globais/regionais geralmente possuem erros sistemáticos (vieses), e não podem ser utilizados diretamente na modelagem hidrológica. Neste contexto, objetivou-se realizar a correção de tendência de dados de precipitação e temperatura simulados pelo modelo regional Eta-HadGEM2-ES para a Bacia Hidrográfica do Rio Mundaú, testando dois métodos para a correção de tendência, utilizando o software Climate Model for Hydrologic Modeling (CMHyd). Os dados de precipitação e temperatura simulados pelo modelo regional Eta-HadGEM2-ES foram disponibilizados pelo Instituto Nacional de Pesquisas Espaciais (INPE). A correção de tendência foi realizada testando-se dois métodos para a correção: Linear Scaling (LS) e Distribution Mapping (DM). Ambos os métodos foram capazes de corrigir a tendência dos dados de precipitação e temperatura da projeção climática do modelo regional Eta-HadGEM2-ES, mas o método DM mostrou-se mais eficaz na correção quando comparado ao LS.

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