Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models

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Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. / Kjellström, Erik; Boberg, Fredrik; Castro, Manuel; Christensen, Jens Hesselbjerg; Nikulin, Grigory; Sánchez, Enrique.

I: Climate Research, Bind 44, Nr. 2-3, 27.12.2010, s. 135-150.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Kjellström, E, Boberg, F, Castro, M, Christensen, JH, Nikulin, G & Sánchez, E 2010, 'Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models', Climate Research, bind 44, nr. 2-3, s. 135-150. https://doi.org/10.3354/cr00932

APA

Kjellström, E., Boberg, F., Castro, M., Christensen, J. H., Nikulin, G., & Sánchez, E. (2010). Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Climate Research, 44(2-3), 135-150. https://doi.org/10.3354/cr00932

Vancouver

Kjellström E, Boberg F, Castro M, Christensen JH, Nikulin G, Sánchez E. Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Climate Research. 2010 dec 27;44(2-3):135-150. https://doi.org/10.3354/cr00932

Author

Kjellström, Erik ; Boberg, Fredrik ; Castro, Manuel ; Christensen, Jens Hesselbjerg ; Nikulin, Grigory ; Sánchez, Enrique. / Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. I: Climate Research. 2010 ; Bind 44, Nr. 2-3. s. 135-150.

Bibtex

@article{a62870cd9cd142169a154b5b03eb02de,
title = "Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models",
abstract = "We evaluated daily and monthly statistics of maximum and minimum temperatures and precipitation in an ensemble of 16 regional climate models (RCMs) forced by boundary conditions from reanalysis data for 1961-1990. A high-resolution gridded observational data set for land areas in Europe was used. Skill scores were calculated based on the match of simulated and observed empirical probability density functions. The evaluation for different variables, seasons and regions showed that some models were better/worse than others in an overall sense. It also showed that no model that was best/worst in all variables, seasons or regions. Biases in daily precipitation were most pronounced in the wettest part of the probability distribution where the RCMs tended to overestimate precipitation compared to observations. We also applied the skill scores as weights used to calculate weighted ensemble means of the variables. We found that weighted ensemble means were slightly better in comparison to observations than corresponding unweighted ensemble means for most seasons, regions and variables. A number of sensitivity tests showed that the weights were highly sensitive to the choice of skill score metric and data sets involved in the comparison.",
keywords = "Europe, Probability distributions, Regional climate models, Skill score metrics, Weighted ensemble",
author = "Erik Kjellstr{\"o}m and Fredrik Boberg and Manuel Castro and Christensen, {Jens Hesselbjerg} and Grigory Nikulin and Enrique S{\'a}nchez",
year = "2010",
month = dec,
day = "27",
doi = "10.3354/cr00932",
language = "English",
volume = "44",
pages = "135--150",
journal = "Climate Research Online",
issn = "1616-1572",
publisher = "Inter research",
number = "2-3",

}

RIS

TY - JOUR

T1 - Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models

AU - Kjellström, Erik

AU - Boberg, Fredrik

AU - Castro, Manuel

AU - Christensen, Jens Hesselbjerg

AU - Nikulin, Grigory

AU - Sánchez, Enrique

PY - 2010/12/27

Y1 - 2010/12/27

N2 - We evaluated daily and monthly statistics of maximum and minimum temperatures and precipitation in an ensemble of 16 regional climate models (RCMs) forced by boundary conditions from reanalysis data for 1961-1990. A high-resolution gridded observational data set for land areas in Europe was used. Skill scores were calculated based on the match of simulated and observed empirical probability density functions. The evaluation for different variables, seasons and regions showed that some models were better/worse than others in an overall sense. It also showed that no model that was best/worst in all variables, seasons or regions. Biases in daily precipitation were most pronounced in the wettest part of the probability distribution where the RCMs tended to overestimate precipitation compared to observations. We also applied the skill scores as weights used to calculate weighted ensemble means of the variables. We found that weighted ensemble means were slightly better in comparison to observations than corresponding unweighted ensemble means for most seasons, regions and variables. A number of sensitivity tests showed that the weights were highly sensitive to the choice of skill score metric and data sets involved in the comparison.

AB - We evaluated daily and monthly statistics of maximum and minimum temperatures and precipitation in an ensemble of 16 regional climate models (RCMs) forced by boundary conditions from reanalysis data for 1961-1990. A high-resolution gridded observational data set for land areas in Europe was used. Skill scores were calculated based on the match of simulated and observed empirical probability density functions. The evaluation for different variables, seasons and regions showed that some models were better/worse than others in an overall sense. It also showed that no model that was best/worst in all variables, seasons or regions. Biases in daily precipitation were most pronounced in the wettest part of the probability distribution where the RCMs tended to overestimate precipitation compared to observations. We also applied the skill scores as weights used to calculate weighted ensemble means of the variables. We found that weighted ensemble means were slightly better in comparison to observations than corresponding unweighted ensemble means for most seasons, regions and variables. A number of sensitivity tests showed that the weights were highly sensitive to the choice of skill score metric and data sets involved in the comparison.

KW - Europe

KW - Probability distributions

KW - Regional climate models

KW - Skill score metrics

KW - Weighted ensemble

UR - http://www.scopus.com/inward/record.url?scp=78650330433&partnerID=8YFLogxK

U2 - 10.3354/cr00932

DO - 10.3354/cr00932

M3 - Journal article

AN - SCOPUS:78650330433

VL - 44

SP - 135

EP - 150

JO - Climate Research Online

JF - Climate Research Online

SN - 1616-1572

IS - 2-3

ER -

ID: 186941002