A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period

Jacob, Daniela; Hurk, Bart van den; Andræ, Ulf; Elgered, Gunnar; Fortelius, Carl; Graham, L. Phil; Jackson, Stephen D.; Karstens, Ute; Köpken, Chr.; Lindau, Ralf; Podzun, R.; Rockel, Burkhardt; Rubel, Franz; Sass, Bent Hansen; Smith, Robin; Yang, Xiaohua

doi:10.1007/s007030170015

Cited by 256 publications

(135 citation statements)

References 37 publications

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“…The hydrostatic REMO model (Jacob et al 2001) is based on the ECHAM4 physical package using radiation parameterisation of Morcrette et al (1986), the Sundquist (1978) large-scale cloud parameterisation and the Tiedtke (1989) and Nordeng (1994) convective parameterisations. For the simulations for Germany, Jacob et al (2007b) used a double nesting approach with a coarse nest of about 50 km and a fine nest of about 10 km, both with 27 vertical levels to dynamically downscale realisation one and two of the ECHAM5 GCM simulations.…”

Section: Rcmsmentioning

confidence: 99%

High resolution regional climate model simulations for Germany: Part II—projected climate changes

Wagner

Berg²,

Schädler³

et al. 2012

Clim Dyn

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The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of -2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.

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Section: Rcmsmentioning

confidence: 99%

High resolution regional climate model simulations for Germany: Part II—projected climate changes

Wagner

Berg²,

Schädler³

et al. 2012

Clim Dyn

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“…The physical parameterisations were taken form the GCM ECHAM4 (Roeckner et al, 1996). An extended description is given in Jacob (2001) and Jacob et al (2001). REMO solves the prognostic equations for temperature, u and v component of the wind, surface pressure, mixing ratio of water vapour and of cloud water.…”

Section: Remomentioning

confidence: 99%

A statistical model for the urban heat island and its application to a climate change scenario

Hoffmann

Krueger

Schlünzen

2011

Intl Journal of Climatology

106

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A linear statistical model relating the nocturnal urban heat island (UHI) intensity of Hamburg with meteorological conditions is constructed from observations taken by the German Meteorological Service (DWD). To find the appropriate predictors the relationship between different meteorological variables and the UHI of Hamburg is analyzed. Results and physical plausibility suggest that cloud cover, wind speed and relative humidity are the relevant variables and can be used to construct a statistical model. The parameters for the statistical model are determined with the generalized least square method. With the help of the statistical model up to 42% of the UHI variance can be explained. The statistical model is then used to statistically downscale results from climate runs of the regional climate models (RCM) REMO and CLM. Both RCMs were driven with the A1B SRES emission scenario runs of the global climate model ECHAM5/MPI-OM. The resulting values for the future UHI are analyzed with respect to monthly averages and the frequency distribution. Results show that changes in the UHI are different for the different months. Significant change (decrease of UHI) in the results of both RCMs and for both realizations of the A1B scenario can be found for April in at the middle and the end of the century and in December at the end of the century. For the summer months which are most relevant to the development of adaption strategies the results differ between the RCMs. REMO results show no significant changes for the summer, while analyses of CLM suggest significant increase in July and August. The frequency distribution of the summer UHI shows no significant changes for REMO and only in one realization of CLM a significant increase in moderate and strong UHI days can be found for the end of the century.

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“…The regional climate model REMO (Jacob 2001;Jacob et al 2001) is a three-dimensional hydrostatic atmospheric model. It has been developed at the Max-PlanckInstitute for Meteorology in the context of the Baltic Sea Experiment (BALTEX).…”

Section: Modelmentioning

confidence: 99%

A high-resolution 43-year atmospheric hindcast for South America generated with the MPI regional model

Silvestri

Vera

Jacob³

et al. 2008

Clim Dyn

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An evaluation of the present-day climate in South America simulated by the MPI atmospheric limited area model, REMO, is made. The model dataset was generated by dynamical downscaling from the ECMWF-ERA40 reanalysis and compared to in-situ observations. The model is able to reproduce the low-level summer monsoon circulation but it has some deficiencies in representing the South American Low-Level Jet structure. At upper levels, summer circulation features like the Bolivian High and the associated subtropical jet are well simulated by the model. Sea-level pressure fields are in general well represented by REMO. The model exhibits reasonable skill in representing the general features of the mean seasonal cycle of precipitation. Nevertheless, there is a systematic overestimation of precipitation in both tropical and subtropical regions. Differences between observed and modeled temperature are smaller than 1.5°C over most of the continent, excepting during spring when those differences are quite large. Results also show that the dynamical downscaling performed using REMO introduces some enhancement of the global reanalysis especially in temperature at the tropical regions during the warm season and in precipitation in both the subtropics and extratropics. It is then concluded that REMO can be a useful tool for regional downscaling of global simulations of present and future climates.

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A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period

Cited by 256 publications

References 37 publications

High resolution regional climate model simulations for Germany: Part II—projected climate changes

High resolution regional climate model simulations for Germany: Part II—projected climate changes

A statistical model for the urban heat island and its application to a climate change scenario

A high-resolution 43-year atmospheric hindcast for South America generated with the MPI regional model

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