Comparing regional precipitation and temperature extremes in climate model and reanalysis products

The aim of this study was to assess soil erosion changes in the mountainous catchment of the Portaikos torrent (Central Greece) under climate change. To this end, precipitation and temperature data were derived from a high-resolution (25 × 25 km) RegCM3 regional climate model for the baseline period 1974–2000 and future period 2074–2100. Additionally, three GIS layers were generated regarding land cover, geology, and slopes in the study area, whereas erosion state was recognized after field observations. Subsequently, the erosion potential model (EPM) was applied to quantify the effects of precipitation and temperature changes on soil erosion. The results showed a decrease (−21.2%) in annual precipitation (mm) and increase (+3.6 °C) in mean annual temperature until the end of the 21st century, and the above changes are likely to lead to a small decrease (−4.9%) in soil erosion potential.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-3413-2007 ◽

2007 ◽

Vol 4 (5) ◽

pp. 3413-3440 ◽

Cited By ~ 18

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.

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Extremal Dependence between Temperature and Ozone over the Continental U.S.

10.5194/acp-2017-1033 ◽

2017 ◽

Author(s):

Pakawat Phalitnonkiat ◽

Wenxiu Sun ◽

Mircea D. Grigoriu ◽

Peter G. M. Hess ◽

Gennady Samorodnitsky ◽

...

Keyword(s):

Climate Model ◽

Heat Waves ◽

Strong Dependence ◽

Measurement Data ◽

The United States ◽

Value Theory ◽

Temperature Extremes ◽

Measured Temperature ◽

Extremal Dependence ◽

Year 2000

Abstract. The co-occurrence of heat waves and pollution events and the resulting high mortality rates emphasizes the importance of the co-occurrence of pollution and temperature extremes. Through the use of extreme value theory and other statistical methods ozone and temperature extremes and their joint occurrence are analyzed over the United States during the summer months (JJA) using Clean Air Status and Trends Network (CASTNET) measurement data and simulations of the present and future climate and chemistry in the Community Earth System Model (CESM1) CAM4-chem. Three simulations using CAM4-chem were analyzed: the Chemistry Climate Model Initiative (CCMI) reference experiment using specified dynamics (REFC1SD) between 1992–2010, a 25-year present-day simulation branched off the CCMI REFC2 simulation in the year 2000 and a 25-year future simulation branched off the CCMI REFC2 simulation in 2100. The latter two simulations differed in their concentration of carbon dioxide (representative of the years 2000 and 2100) but were otherwise identical. A new metric is developed to measure the joint extremal dependence of ozone and temperature by evaluating the spectral dependence of their extremes. Two regions of the U.S. give the strongest measured extreme dependence of ozone and temperature: the northeast and the southeast. The simulations do not capture the relationship between temperature and ozone over the northeast but do simulate a strong dependence of ozone on extreme temperatures over the southeast. In general, the simulations of ozone and temperature do not capture the width of the measured temperature and ozone distributions. While on average the future increase in the 90th percentile temperature and the 90th percentile ozone slightly exceed the mean increase over the continental U.S., in many regions the width of the temperature and ozone distributions decrease. The location of future increases in the tails of the ozone distribution are weakly related to those of temperature with a correlation of 0.3.

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Effect of diversified performance metrics and climate model weighting on global and regional trend patterns of precipitation and temperature

Erdkunde ◽

10.3112/erdkunde.2019.04.04 ◽

2019 ◽

Vol 73 (4) ◽

pp. 303-322

Author(s):

Christoph Ring ◽

Felix Pollinger ◽

Luzia Keupp ◽

Irena Kaspar-Ott ◽

Elke Hertig ◽

...

Keyword(s):

Performance Metrics ◽

Climate Model ◽

Regional Trend ◽

Model Weighting ◽

Precipitation And Temperature

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A statistical framework for evaluating EURO-CORDEX simulations and derived drought characteristics

10.5194/egusphere-egu21-2330 ◽

2021 ◽

Author(s):

David J. Peres ◽

Alfonso Senatore ◽

Paola Nanni ◽

Antonino Cancelliere ◽

Giuseppe Mendicino ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Circulation Model ◽

Regional Climate Models ◽

Monitoring Networks ◽

Global Circulation Model ◽

Drought Characteristics ◽

Precipitation And Temperature

<p>Regional climate models (RCMs) are commonly used for assessing, at proper spatial resolutions, future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic observed climate (temperature and precipitation). We specifically focus on the models&#8217; performance in reproducing drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices. The density of the measurements is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the models based on the combination of the HadGEM2 global circulation model (GCM) with the CLM-Community RCMs are the most skillful in reproducing precipitation and temperature variability as well as drought characteristics. Nevertheless, the ranking of the models may slightly change depending on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, aiding on the selection of the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables.</p>

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Effects of climate model radiation, humidity and wind estimates on hydrological simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-16-305-2012 ◽

2012 ◽

Vol 16 (2) ◽

pp. 305-318 ◽

Cited By ~ 62

Author(s):

I. Haddeland ◽

J. Heinke ◽

F. Voß ◽

S. Eisner ◽

C. Chen ◽

...

Keyword(s):

Bias Correction ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Meteorological Data ◽

Correction Method ◽

Hydrological Models ◽

Temperature And Precipitation ◽

Effects Of Radiation ◽

Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

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