Comparative validation of statistical and dynamical downscaling models on a dense grid in central Europe: temperature

Huth, Radan; Mikšovský, Jiří; Štěpánek, Petr; Belda, Michal; Farda, Aleš; Chládová, Zuzana; Pišoft, Petr

doi:10.1007/s00704-014-1190-3

Cited by 32 publications

(43 citation statements)

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“…(), who compared extreme spells in several PP and MOS methods; and the recent study by Huth et al . (), which investigated temporal aspects in both statistical and dynamical downscaling methods. But all these studies still include only a rather limited range of methods.…”

Section: Introductionmentioning

confidence: 99%

The VALUE perfect predictor experiment: Evaluation of temporal variability

Maraun

Huth

Gutiérrez

et al. 2017

Intl Journal of Climatology

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Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short‐term variations, spells and variability from interannual to long‐term trends. The EU‐COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long‐term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.

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Section: Introductionmentioning

confidence: 99%

The VALUE perfect predictor experiment: Evaluation of temporal variability

Maraun

Huth

Gutiérrez

et al. 2017

Intl Journal of Climatology

Self Cite

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show abstract

“…The rich variety of methods and applications demonstrates the flexibility of ESD but has also hindered the development of coordinated multi-method programs. Some studies have intercompared ESD and RCM performances (e.g., [69][70][71][72]), and the results have generally suggested both advantages and limitations of the two approaches. Concerning VARGCM and HIRGCM downscaling, several systems are today available [4], but only limited coordinated intercomparison efforts for present day climate conditions have been conducted to date [73,74].…”

Section: A Brief Review Of Coordinated Regional Projection Projects Pmentioning

confidence: 99%

Coordinated Experiments for Projections of Regional Climate Change

Giorgi

Gutowski

2016

Curr Clim Change Rep

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We review coordinated efforts for producing regional climate projections through dynamical and statistical downscaling tools driven by global climate model output. Such projections are affected by multiple sources of uncertainty both at the global model and at the downscaling levels. The characterization of these uncertainties and the production of robust regional to local projections for use in impact studies require the completion of properly designed large ensembles of experiments. Toward this purpose, several regional coordinated efforts have been conducted in the past, particularly involving regional climate models, but because of the lack of a common experiment protocol, the transfer of know-how across them has been difficult. This problem is being addressed in the Coordinated Regional Downscaling Experiment (CORDEX), a framework designed to produce the next generation of worldwide high-resolution regional climate projections through a fully coordinated experiment protocol.

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“…Both approaches were compared, e.g. by HUTH et al (2015). There is also a combined approach, named statistical-dynamical downscaling, which has also gained importance in climate research in recent years.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Dynamical Downscaling of ERA-Interim Using the WRF Regional Climate Model for the Area of Poland. Part 1: Model Configuration and Statistical Evaluation for the 1981–2010 Period

Kryza¹,

Wałaszek²,

Ojrzyńska³

et al. 2017

Geoinformatics and Atmospheric Science

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Abstract-In this work, we present the results of high-resolution dynamical downscaling of air temperature, relative humidity, wind speed and direction, for the area of Poland, with the Weather Research and Forecasting (WRF) model. The model is configured using three nested domains, with spatial resolution of 45 km 9 45 km, 15 km 9 15 km and 5 km 9 5 km. The ERA-Interim database is used for boundary conditions. The results are evaluated by comparison with station measurements for the period 1981-2010. The model is capable of reproducing the main climatological features of the study area. The results are in very close agreement with the measurements, especially for the air temperature. For all four meteorological variables, the model performance captures seasonal and daily cycles. For the air temperature and winter season, the model underestimates the measurements. For summer, the model shows higher values, compared with the measurements. The opposite is the case for relative humidity. There is a strong diurnal pattern in mean error, which changes seasonally. The agreement with the measurements is worse for the seashore and mountain areas, which suggests that the 5 km 9 5 km grid might still have an insufficient spatial resolution. There is no statistically significant temporal trend in the model performance. The larger year-to-year changes in the model performance, e.g. for the years 1982 and 2010 for the air temperature should therefore be linked with the natural variability of meteorological conditions.

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Comparative validation of statistical and dynamical downscaling models on a dense grid in central Europe: temperature

Cited by 32 publications

References 50 publications

The VALUE perfect predictor experiment: Evaluation of temporal variability

The VALUE perfect predictor experiment: Evaluation of temporal variability

Coordinated Experiments for Projections of Regional Climate Change

High-Resolution Dynamical Downscaling of ERA-Interim Using the WRF Regional Climate Model for the Area of Poland. Part 1: Model Configuration and Statistical Evaluation for the 1981–2010 Period

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