Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps

Torma, Csaba; Giorgi, Filippo; Coppola, Erika

doi:10.1002/2014jd022781

Cited by 288 publications

(289 citation statements)

References 44 publications

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“…Recently many evaluation studies using RCMs have been conducted through Coordinated Regional Climate Downscaling Experiment (CORDEX) program (Kim et al 2014(Kim et al , 2015Huang et al 2015;Zhou et al 2016;Zou et al 2016;Pattnayak et al 2017). Especially, there have been many interests and efforts regarding added value of RCMs (Di Luca et al 2015;Torma et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

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In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and lowresolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

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Section: Introductionmentioning

confidence: 99%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

et al. 2018

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“…Indeed, convection-permitting simulations (e.g. Schwitalla et al 2008;Chan et al 2013;Torma et al 2015) demonstrate a substantial improvement of heavy and peak precipitation intensities in the summer season. (6) The Bavarian Alps stand out with a systematic dry, and deep river valleys and kettles with a systematic wet bias in both seasons (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Most previous works estimate the relative skill by comparing domain-aggregated scalar evaluation statistics and/or by visually inspecting the fields of the evaluation statistics for the downscaled and the larger-scale driving data (e.g. Duffy et al 2006;Feser 2006;Sotillo et al 2006;Buonomo et al 2007;Sanchez-Gomez et al 2009;Prömmel et al 2010;Di Luca et al 2012;Kendon et al 2012;Cardoso et al 2013;Chan et al 2013;Pearson et al 2015Torma et al 2015, which affects the fidelity of spatial analysis of added value or wholly precludes it. Winterfeldt and Weisse (2009) and Vautard et al (2013) apply few relative metrics only for individual locations, and Winterfeldt et al (2011) and Dosio et al (2015) on a gridpoint basis.…”

Section: Introductionmentioning

confidence: 99%

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

Ivanov

Warrach‐Sagi

Wulfmeyer

2017

Theor Appl Climatol

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A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as 'field' or 'global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 • grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

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“…4) of the S4* and preMet models in space as well as against the subcatchment area, the median of the terrain roughness, the MAE skill score of the subMet with the preMet model as reference, and the MAE skill score of the refRun model with the climatology as reference. The terrain roughness is included since the atmospheric flow in complex terrain is challenging to simulate and atmospheric general circulation models need to filter the topography according to their spatial resolution (Maraun and Widmann, 2015;Torma et al, 2015). The terrain roughness is defined as the difference of the maximum and minimum elevation value within a 3 × 3 pixel window (Wilson et al, 2007).…”

Section: Subcatchmentsmentioning

confidence: 99%

“…transitions between wet and dry or cold and warm seasons) and catchmentspecific hydrological storages (e.g. surface water bodies, soils, aquifers, and snow) and can vary from 0 up to several months (van Dijk et al, 2013;Shukla et al, 2013;Yossef et al, 2013). Indeed, this source of predictability is the rationale behind the application of the ESP approach in operational forecast settings, and it can be further exploited by conditioning on climate precursors (e.g.…”

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confidence: 99%

Monthly streamflow forecasting at varying spatial scales in the Rhine basin

Schick

Rößler

Weingartner

2018

Hydrol. Earth Syst. Sci.

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Abstract. Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981-2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

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Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps

Cited by 288 publications

References 44 publications

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

Monthly streamflow forecasting at varying spatial scales in the Rhine basin

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