Scholarly article on topic 'High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2'

High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2 Academic research paper on "Earth and related environmental sciences"

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Academic research paper on topic "High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2"

Earth Syst. Sci. Data Discuss., 6, 779-809, 2013 www.earth-syst-sci-data-discuss.net/6/779/2013/ doi:10.5194/essdd-6-779-2013 © Author(s) 2013. CC Attribution 3.0 License.

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This discussion paper is/has been under review for the journal Earth System Science Data (ESSD). Please refer to the corresponding final paper in ESSD if available.

High resolution atmospheric reconstruction for Europe 1948-2012: coastDat2

B. Geyer

Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany Received: 5 November 2013 - Accepted: 18 November 2013 - Published: 2 December 2013 Correspondence to: B. Geyer (beate.geyer@hzg.de) Published by Copernicus Publications.

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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Abstract 1 1 Instruments

Data Provenance & Structure

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Abstract

The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

1 Motivation

The precursor of coastDat2, coastDat1 (Weisse et al., 2009), was widely used. About 50 % of the coastDat1 users were commercial, while 25 % were academic and another 25% were from authorities. Applications range from assessing long-term variability and change to risk assessment and design, for example of offshore wind farms. As coastDat1 terminated in 2007, and as there were strong requests for an upgrade comprising the most recent years at higher spatial resolution, the coastDat2 effort was implemented. The here described simulation with the community model COSMO-CLM on the current super computer of the German Climate Computing Center (DKRZ) replaces the coastDat1 regional atmospheric simulation done with REM05.0 (Feser et al., 2001; Jacob et al., 2001). For coastal areas the higher resolution is the main advantage. The overall advantage is the availability of the last 5yr.

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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Abstract 1 1 Instruments

Data Provenance & Structure

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2 Model set up

For the reconstruction the COSMO model in CLimate Mode (COSMO-CLM) version 4.8_clm_11 (Rockel et al., 2008; Steppeler et al., 2003) was used. The COSMO model is the non-hydrostatic operational weather prediction model applied and further developed by the national weather services joined in the COnsortium for SMall scale MOdeling (COSMO). The climate mode is applied and developed by the Climate Limited-area Modelling-Community (http://www.clm-community.eu). The use of the model is well supported by the members of the community and documented mainly via the COSMO documentation.

The simulation was done on a regular grid in rotated coordinates with a rotated pole at 170.0° W and 35.0° N with a resolution of 0.22°, a time step of 150 s and hourly output. Figure 1 presents the model domain. 40 vertical levels up to 27.2 km height and 10 soil levels down to 11.5m depth were used. Spectral Nudging after von Storch et al. (2000) was applied for large scale wind speed components in the upper levels (above 850 hPa) to enforce the observed large scale circulation. Every fifth time step in both directions the information of the 5 largest wavelength was nudged with a nudging factor of 0.5. A detailed description on the technique was provided by Müller (2003, p. 50).

Meteorological initial and boundary conditions were taken from NCEP1 reanalysis data (Kalnay et al., 1996; Kistler et al., 2001). The simulation was initialized on first of January 1948 with interpolated fields for the air temperature, zonal and meridional wind component, specific water vapor content, specific cloud water content, surface specific humidity, surface pressure, skin temperature for sea points, thickness of surface snow amount and volume fraction of soil moisture. The interpolation to the coast-Dat2 grid was done by the model chain part int2lm v1.9_clm5 (Schattler, 2011). The soil moisture values of the coarse NCEP-grid require more spin up time than the few days required by atmospheric fields (Denis et al., 2002). Therefore we ran the model for five years, namely 1948 to 1952, and restarted with the gained soil moisture fields.

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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Data Provenance & Structure

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Figure 2 shows the development of the moisture for area means of layers 1 to 8 for the European standard evaluation areas (Rockel and Woth, 2007) adopted by Christensen and Christensen (2007, p. 22). The layers 9 to 10 are hydrologically not active and are undefined, the water draining through layer 8 was added to the runoff. Layer 7 and 8 have the same start value of 0.33 m. After 5 yr the soil moisture reached realistic values for all regions and levels, i.e., that they were not dependent any more on the initial values as they were near to equal for both simulations (5 yr spin up and coastDat2). At the lateral boundaries the relaxation scheme by Davies (1976) was applied. The affected 10 grid boxes, the sponge zone, are cut off for all time series data of the set.

Land surface processes were parameterized with the TERRA-ML scheme (Schrodin and Heise, 2001; Domsetal., 2011). For sea points the NCEP1 skin temperatures were used as lower boundary condition. Cumulus convection was parameterized using the Tiedtke scheme (Tiedtke, 1989). Clouds were determined by the prognostic variables cloud water and cloud ice. We used a 5th order Runge-Kutta time integration scheme.

The hourly output was written in netCDF following the CF-conventions 1.4 (Eaton et al., 2009). In the appendix, Table 3, all output variables are listed.

3 External data

The surface height and orographic roughness length were taken from the gtopo30 data set of the Distributed Active Archive Center (US Geological Survey, 2004), the land-sea fraction, parameters of vegetation, leaf area, root depth and lake fraction from the Global Ecosystems V2.0. The soil type was taken from the Food and Agriculture Organization of the United Nations (FAO). The climatological deep soil temperature was taken from the CRU (Climate research Unit at the University of East Anglia). To generate a file with the merged information on the model grid the so called PrEProcessor (PEP) was used. Detailed information on the data as well as the preprocessor was given by Smiatek et al. (2008).

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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4 Evaluation

The evaluation was done for several parameters. The user demands are manifold, ranging from coastDat-internal forcing for the wave model via air chemistry models to research in the field of wind energy. In this paper we show data set comparisons for the most user-requested quantities: 2 m air temperature, total precipitation, wind speed, cloud cover, and height of boundary layer.

4.1 Reference data sets

The evaluation of the data set for air temperature, precipitation, wind, and cloud cover was done by using common gridded data sets: E-OBS of the ENSEMBLES project version 8.0 (van den Besselaar et al., 2011; Haylock et al., 2008), CRU version ts_3.2 (Jones and Harris, 2011), GPCC (Global Precipitation Climatology Centre) version 6 (Rudolf et al., 2010), and REGNIE from Deutscher Wetterdienst (Dietzer, 2000). For comparison of the height of boundary layer we used station data for Lindenberg, Germany (Beyrich and Leps, 2012).

4.2 Near-surface air temperature

Figure 3 shows the mean differences between the monthly means of air temperature at 2 m height of coastDat2 and e0bs8.0 for 1950-2012. The eObs data were interpolated to the rotated grid of coastDat. From April to September the differences are below ±1 °C for wide areas of mid Europe. High differences with values up to -6 and +6 °C occur over Iceland and North Africa respectively. The precursor data set coastDat1 was used as forcing for biosphere models (e.g. Vetter et al., 2008; Jung et al., 2007), where the diurnal cycle has major importance. Therefore, we determined the differences of the diurnal temperature range. It was calculated as difference between daily maximum 2 m air temperature and daily minimum 2 m air temperature. The means of the monthly

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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Abstract 1 1 Instruments

Data Provenance & Structure

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mean differences between coastDat2 and e0bs8.0 for the period 1950 to 2012 are shown in Fig. 4.

There is a tendency to underestimate the diurnal temperature range for wide areas all over the year, except for the North-Africa part. The differences in the maximum 2 m air temperature are highest for April to August. In the North African part the coastDat2 temperatures are several degrees higher than the values of eObs and in the Northeastern parts of Europe they are several degrees lower than the observations (not shown). The differences between the two data sets in minimum 2 m air temperature are much smaller, with highest deviations occurring for Africa from June to August (not shown). The main source for the differences in the diurnal temperature range is the difference in maximum 2 m air temperature.

4.3 Precipitation

Figure 5 shows the mean differences between the monthly sums of total precipitation of coastDat2 and e0bs8.0. Basis is the period 1950-2012, the eObs data are interpolated to the rotated grid of coastDat. As additional material we added Fig. 10, the mean differences between the monthly sums of total precipitation of coastDat2 and GPCC6, to the appendix. Basis is the period 1950-2010, the GPCC data are interpolated to the rotated grid of coastDat.

As the data sets based on observations (eObs, GPCC and CRU) differ, we calculated the mean minimal differences between CCLM and the three observational data sets and listed them as percentages in Table 1.

To summarize the information we find especially good agreement for December to May for British Islands (A1), Iberian Peninsula (A2), France (A3), the Alps (A6) and Mediterranean (A7): deviations are below 10% of mean observational value. The main systematic negative deviations occur from June to November for Mediterranean (A7) and June to August in East-Europe (A8), while systematic highest positive deviations are found from December to March in Scandinavia (A5). Additionally, we show the monthly mean time series of the 8 regions for CCLM and for the range of all the three

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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observational data sets in the appendix (sorted by season: Fig. 11a to d). As some users of our data set are interested in especially dry or wet seasons, we show absolute values.

The REGNIE data set has a very high spatial resolution of 1 km grid spacing for all over Germany. As the data are daily resolved we have the possibility of statistics on a daily basis. Figure 6 shows a histogram of area mean daily precipitation for Germany. The borders of the classes were chosen following the recommendation of the Global Precipitation Climatology Project. The shape of the REGNIE distribution function is generally well reproduced by CCLM. However, only for the two classes from 1.7 to 3 mmday-1 for all seasons the three data sets show the same frequencies. For all other classes no consistent statement concerning relation between the data sets is possible.

4.4 Wind

The marine near surface wind speeds were analyzed following Winterfeldt et al. (2010). Winterfeldt et al. (2010) found an added value of the regional atmospheric simulation of coastDat1, done with REM05.0, compared to the reanalysis of NCEP with satellite data of quikScat as reference near the coasts. These findings were reproduced for the coastDat2 data set. Important for users of our wind data is the proofed good offshore quality of the surface wind data, although the Brier skill score for wide offshore fields is negative, meaning, that NCEP1 data has a higher agreement with observations than coastDat2. As shown for the precursor data set coastDat1 by Sotillo et al. (2005, Fig. 7), the quality e.g. at platform K1 is very good. Following the idea of Sotillo et al. (2005) our Fig. 7 shows the quantile-quantile plot of observation vs. model data. On the left hand side, results are for Atlantic buoy K1 (at 48.701° N, 12.401° W) and on the right hand side for Aegean Buoy Athos (at 39.97° N, 24.72° E). K1 observations were interpolated from anemometer height of 3 m to 10 m by logarithmic wind profile, where the roughness length depends via Charnock relation on the wind speed. The NCEP1 data were linearly interpolated in time to hourly values. For the Mediterranean buoy Athos

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atmospheric reconstruction: coastDat2

B. Geyer

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Abstract 1 1 Instruments

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NCEP1 data were interpolated to the measurement output interval of 3h. The quality of the wind fields form coastDatl and coastDat2 is comparable with the tendency of better representation of high wind speeds in coastDat2.

4.5 Total cloud cover

5 The comparison of the total cloud cover of coastDat2 and CRU data is shown in Fig. 8. For most of the months and most of the areas the differences are below 10%. Highest differences occur from June to August over North Africa and March to August over Scandinavia. For almost all over the year, the differences for Greenland are high.

4.6 Height of planetary boundary layer

10 Observational data for the height of the boundary layer are hardly available. As this variable is especially important when the coastDat data set is used for air chemistry applications, i.e. to simulate the transport of harmful substances, we show at least a comparison for a short term period (2003-2012) at a single station (Lindenberg, WMO No. 10393, 52.21° N, 14.10° E). As the height of the boundary layer shows a strong diur-15 nal cycle, the data set was divided into four sets depending on the start time of the soundings. The start time of the soundings is 45 to 75 min prior to the reporting time at 00:00, 06:00, 12:00, and 18:00 UTC. Therefore the corresponding values from 23:00, 05:00, 11:00, and 17:00 UTC of the simulated data were selected. The observations are flagged with quality status flags, which were derived by the use of four different 20 methods to calculate the height of the PBL (Beyrich and Leps, 2012). From both data sets the values with observation quality flag "good" were extracted. For comparison both values were related to ground height, because real elevation is 112 m while the model height is 63 m. Figure 9 shows the frequency distribution of boundary layer heights from model and 25 observation by launch time and season. The classes refer to the model levels.

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Both frequency distributions show the shift to higher values during noon for all seasons. In general the accordance of the distributions is highest for the noon soundings. The tendency to wider spread distributions at 18:00 is given for both data sets. Most of the 16 distribution functions of simulated values show a bimodal shape while the observed values functions have clear maxima, for non-noon soundings mostly beneath 213 m. The simulated frequencies in the lowest 5 model levels (in the height of 213 m) are clearly lower than the observations.

In Table 2 the numbers of good-flagged deduced heights of PBL are listed per season and sounding time, and the according medians of these and the simulated heights, and the differences of these values are also shown.

All midnight to noon simulated median values are higher than the observed within a range of 90 to 260 m. Only the autumn 06:00 UTC simulated value is less than the observed one.

5 Conclusions

To our knowledge, the data set described represents the longest regional reconstruction based on global atmospheric reanalyses at such a high spatial and temporal detail. It covers more than 60 yr and shows good agreement with observations, although there are regions where better performance would be desirable. The comparison of the variables near surface air temperature, diurnal temperature range, precipitation, cloud cover, near surface wind speed and height of PBL with observations indicate on exemplary the quality of the data set. The main advantages of the dataset are the huge number of available variables for whole Europe and the inclosure of the recent years.

Acknowledgements. The CCLM is the community model of the German climate research (www.clm-community.eu). The German Climate Computing Center (DKRZ) provided the computer hardware for the Limited Area Modelling simulations in the project "Regional Atmospheric Modelling". The NCEP/NCAR1 reanalysis data was provided by the National Center for Atmospheric Research (NCAR). Thanks to the ENSEMBLES group updating the eObs data set ac-

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High resolution

atmospheric reconstruction: coastDat2

B. Geyer

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cording to newest findings, to the CRU crew providing the CRU time series, GPCC and DWD for allowing us to use their data. We thank the UK Met office for providing the wind measurements at Buoy K1 station number 62029. The Athos buoy data are available via the POSEIDON Operational Oceanography System, Hellenic Centre for Marine Research (www.poseidon.hcmr.gr). We thank Frank Beyrich for providing height of boundary layer data from Deutscher Wetterdienst for Lindenberg. Additionally we want to thank the providers of the external datasets (cited in detail by Smiatek et al., 2008): the FAO for soiltypes data, and USGS for the orography, and Global ecosystem data.

References

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Feser, F., Weisse, R., and von Storch, H.: Multi-decadal atmospheric modeling for Europe yields

multi-purpose data, EOS Trans., 82, 305-310, doi:10.1029/01EO00176, 2001. 780 Geyer, B. and Rockel, B.: coastDat-2 COSMO-CLM, World Data Center for Climate, CERA-DB "coastDat-2_COSMO-CLM", available at: http://cera-www.dkrz.de/WDCC/ui/Compact. jsp?acronym=coastDat-2_COSMO-CLM, 2013. 780, 794 Haylock, M. R., Hofstra, N., Tank, A. M. G. K., Klok, E. J., Jones, P. D., and New, M.: A European daily high-resolution gridded data set of surface temperature and precipitation for 19502006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201, 2008. 783 Jacob, D., Hurk, B. J. J. v. d., Andr®, U., Elgered, G., Fortelius, C., Graham, L. P., Jackson, S. D., Karstens, U., Köpken, C., Lindau, R., Podzun, R., Rockel, B., Rubel, F., Sass, B. H., Smith, R. N. B., and Yang, X.: A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period, Meteorol. Atmos. Phys., 77, 19-44, 2001. 780

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Rockel, B. and Woth, K.: Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations, Climatic Change, 81, 267280,2007. 782, 796

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orol. Z., 17, 347-348, 2008. 781 Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A., and Ziese, M.: GPCC Status Report December 2010 (On the most recent gridded global data set issued in fall 2010 by the Global Precipitation Climatology Centre (GPCC)), GPCC Status Report, available at: http://gpcc. dwd.de, 2010. 783

Scháttler, U.: A Description of the Nonhydrostatic Regional COSMO-Model Part V: Preprocessing: Initial and Boundary Data for the COSMO-Model, Tech. rep., Deutscher Wetterdienst, available at: http://www.cosmo-model.org/content/model/documentation/core/cosmoInt2lm. pdf, 2011. 781

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version of the COSMO model (COSMO-CLM), Meteorol. Z., 17, 395-405, 2008. 782, 788 Sotillo, M. G., Ratsimandresy, A. W., Carretero, J. C., Bentamy, A., Valero, F., and Gonza-ález Rouco, F.: A high-resolution 44-year atmospheric hindcast for the Mediterranean Basin: contribution to the regional improvement of global reanalysis, Clim. Dynam., 25, 219-236, doi:10.1007/s00382-005-0030-7, 2005. 785 Steppeler, J., Doms, G., Scháttler, U., Bitzer, H., Gassmann, A., Damrath, U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75-96, doi:10.1007/s00703-001-0592-9, 2003. 781 Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale

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Center Distributed Active Archive Center (EDC DAAC), 2004. 782 van den Besselaar, E. J. M., Haylock, M. R., van der Schrier, G., and Tank, A. M. G. K.: A European daily high-resolution observational gridded data set of sea level pressure, J. Geophys. Res., 116, D11110, doi:10.1029/2010JD015468, 2011. 783 Vetter, M., Churkina, G., Jung, M., Reichstein, M., Zaehle, S., Bondeau, A., Chen, Y., Ciais, P., Feser, F., Freibauer, A., Geyer, R., Jones, C., Papale, D., Tenhunen, J., Tomelleri, E.,

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Trusilova, K., Viovy, N., and Heimann, M.: Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models, Biogeosciences, 5, 561-583, doi:10.5194/bg-5-561-2008, 2008. 783 von Storch, H., Langenberg, H., and Feser, F.: A spectral nudging technique for dynamical

downscaling purposes, Mon. Weather Rev., 128, 3664-3673, 2000. 781 Weisse, R., von Storch, H., Callies, U., Chrastansky, A., Feser, F., Grabemann, I., Günther, H., Winterfeldt, J., Woth, K., and Pluess, A.: Regional meteorological-marine reanalyses and climate change projections, B. Am. Meteorol. Soc., 90, 849-860, doi:10.1175/2008BAMS2713.1, 2009. 780 Winterfeldt, J., Geyer, B., and Weisse, R.: Using QuikSCAT in the added value assessment of dynamically downscaled wind speed, Int. J. Climatol., 31,1028-1039, doi:10.1002/joc.2105, 2010. 785

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Table 1. Seasonal-mean minimal differences of precipitation [%] over land between CCLM and the ensemble of the three observational data (eObs, CRU, GPCC) for 1950-2010 and the 8 European sub-regions of Fig. 1: British Islands (A1), Iberian Peninsula (A2), France (A3), Mid-Europe (A4), Scandinavia (A5), Alps (A6), Mediterranean (A7), East-Europe (A8).

A1 A2 A3 A4 A5 A6 A7 A8

DJF -1.2 3.9 6.4 15 20 7.8 -0.3 24

MAM 2.1 0.2 1.1 7.7 43 9.5 -1.8 11

JJA -9.6 -34 -29 -23 2.1 -17 -42 -33

SON -9.4 -17 -14 -6.7 7.0 -14 -23 -16

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Table 2. Statistical parameters of comparison between observed and simulated PBL height, split for sounding times in column 3 to 6, and seasons in rows according to the abbreviation in column 2. Listed are the number and the median of used observed values (median O), median of simulated values (median S), and the differences of the medians. Unit of the last three variables is m.

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number DJF MAM JJA SON 490 486 574 424 647 630 700 396 678 665 690 505 598 633 641 509

median O DJF MAM JJA SON 220 202 384 232 136 136 1166 829 98 125 1310 917 127 124 661 109

median S DJF MAM JJA SON 356 356 475 356 258 258 1409 778 258 258 1669 1174 258 258 964 258

difference DJF MAM JJA SON 136 154 91 124 122 122 243 -51 160 133 359 257 131 134 303 149

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Table 3. List of output variables of coastDat2 data set. The time series's published by Geyer and Rockel (2013) are marked in column "ts" with a cross. Time independent variables are labeled with "f" and were merged in the file coastDat2_COSMO-CLM_fx.

variable name unit long name standard name ts/fix

1 AEVAP S kgm-2 surface evaporation water_evaporation_amount x

2 ALB_RAD 1 surface albedo surface_albedo X

3 ALHFL_S Wm-2 av. surface latent heat flux surface_downward_latent_heat_flux X

4 ALWD_S Wm-2 downward longwave radiation at the surface - X

5 ALWU_S Wm-2 upward longwave radiation at the surface X

6 APAB_S Wm-2 av. surface photosynthetic active radiation surface_downwelling_photosynthetic _radiative_flux_in_air X

7 ASHFL_S Wm-2 av. surface sensible heat flux surface_downward_sensible_heat_flux X

8 ASOB_S Wm-2 av. surface net downward shortwave radiation surface_net_downward_shortwave_flux X

9 ASOB_T Wm-2 av. TOA net downward shortwave radiation net_downward_shortwave_flux_in_air X

10 ASOD_T Wm-2 av. solar downward radiation at top - X

11 ASWDIFD_S Wm-2 diffuse downward sw radiation at the surface X

12 ASWDIFU_S Wm-2 diffuse upward sw radiation at the surface X

13 ASWDIR_S Wm-2 direct downward sw radiation at the surface X

14 ATHB_S Wm-2 av. surface net downward longwave radiation surface_net_downward_longwave_flux X

15 ATHB_T Wm-2 av. TOA outgoing longwave radiation net_downward_longwave_flux_in_air X

16 AUMFL S Pa av. eastward stress surface_downward_eastward_stress X

17 AVMFL_S Pa av. northward stress surface_downward_northward_stress X

18 CAPE CON J kg-1 specific convectively avail. potential energy atmosphere_specific_convective_available _potential_energy X

19 CLCH 1 high cloud cover cloud_area_fraction_in_atmosphere_layer

20 CLCL 1 low cloud cover cloud_area_fraction_in_atmosphere_layer

21 CLCM 1 medium cloud cover cloud_area_fraction_in_atmosphere_layer

22 CLCT 1 total cloud cover cloud_area_fraction X

23 DURSUN s duration of sunshine duration_of_sunshine X

24 FC s-1 coriolis parameter coriolis_parameter f

25 FIS m2 s-2 surface geopotential surface_geopotential f

26 FOR_D - ground fraction covered by deciduous forest - f

27 FOR_E - ground fraction covered by evergreen forest f

28 FR LAND 1 land-sea fraction land_area_fraction f

29 H_SNOW m thickness of snow surface_snow_thickness X

30 HBAS CON m height of convective cloud base convective_cloud_base_altitude

31 HHL m height altitude f

32 HMO3 Pa air pressure at ozone maximum air_pressure

33 HPBL m Height of boundary layer - X

34 HSURF m surface height surface_altitude f

35 HTOP_CON m height of convective cloud top convective_cloud_top_altitude

36 HZEROCL m height of freezing level freezing_level_altitude

37 LAI 1 leaf area index leaf_area_index

38 MFLX CON kgm-2s-1 convective mass flux density atmosphere_convective_mass_fluxx

39 P Pa pressure air_pressure

40 PLCOV 1 vegetation area fraction vegetation_area_fraction

41 PMSL Pa mean sea level pressure air_pressure_at_sea_level X

42 PP Pa deviation from reference pressure difference_of_air_pressure_from_model _reference

43 PS Pa surface pressure surface_air_pressure X

44 QC kg kg-1 specific cloud liquid water content mass_fraction_of_cloud_liquid_water_in_air

45 QI kg kg-1 specific cloud ice content mass_fraction_of_cloud_ice_in_air

46 QR kg kg-1 specific rain content mass_fraction_of_rain_in_air

47 QS kg kg-1 specific snow content mass_fraction_of_snow_in_air

48 QV kg kg-1 specific humidity specific_humidity

49 QV_2M kg kg-1 2 m specific humidity specific_humidity X

50 QV_S kg kg-1 surface specific humidity surface_specific_humidity

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Table 3. Continued.

variable name unit long name standard name ts/fix

51 RAIN_CON kgm-2 convective rainfall convective_rainfall_amount X

52 RAIN GSP kgm-2 large scale rainfall large_scale_rainfall_amount X

53 RELHUM 2M % 2m relative humidity relative_humidity X

54 RLAT latitude latitude

55 RLON longitude longitude

56 ROOTDP m root depth root_depth

57 RUNOFF_G kgm-2 subsurface runoff subsurface_runoff_amount X

58 RUNOFF_S kgm-2 surface runoff surface_runoff_amount X

59 SNOW_CON kgm-2 convective snowfall convective_snowfall_amount X

60 SNOW GSP kgm-2 large scale snowfall large_scale_snowfall_amount X

61 SNOWLMT m height of the snow fall limit in m a.s.l. altitude

62 SOBS RAD Wm-2 surface net downward shortwave radiation surface_net_downward_shortwave_flux

63 SOILTYP 1 soil type soil_type f

64 SSO_GAMMA - anisotropy of sub-grid scale orography - f

65 SSO_SIGMA - mean slope of sub-grid scale orography - f

66 SSO_THETA angle between principal axis of orography and east -

67 T K temperature air_temperature

68 T_2M K 2 m temperature air_temperature X

69 T_2M_AV K 2 m temperature air_temperature X

70 T_G K grid mean surface temperature surface_temperature

71 T_S K soil surface temperature - X

72 T SNOW K snow surface temperature surface_temperature_where_snow

73 T_SO K soil temperature soil_temperature

74 TD_2M K 2 m dew point temperature dew_point_temperature X

75 TD_2M_AV K 2 m dew point temperature dew_point_temperature X

76 TDIV_HUM kgm-2 atmosphere water divergence change_over_time_in_atmospheric_water _content_due_to_advection

77 THBS_RAD Wm-2 surface net downward longwave radiation surface_net_downward_longwave_flux

78 TKE CON J kg-1 convective turbulent kinetic energy -

79 TMAX 2M K 2 m maximum temperature air_temperature X

80 TMIN_2M K 2 m minimum temperature air_temperature X

81 TOT_PREC kgm-2 total precipitation amount precipitation_amount X

82 TOT_SNOW kgm-2 total snow amount snow_amount X

83 TQC kgm-2 vertical integrated cloud water atmosphere_cloud_liquid_water_content X

84 TQI kgm-2 vertical integrated cloud ice atmosphere_cloud_ice_content X

85 TQV kgm-2 precipitable water atmosphere_water_vapor_content X

86 TWATER kgm-2 total water content atmosphere_water_content X

87 U m s-1 U-component of wind grid_eastward_wind

88 U_10M m s-1 U-component of 10 m wind grid_eastward_wind X

89 V m s-1 V-component of wind g rid_northward_wind

90 V_10M m s-1 V-component of 10m wind g rid_northward_wind X

91 UVlat_10M m s-1 U and V-component of 10m wind X

92 VGUST_CON m s-1 maximum 10m convective gust wind_speed_of_gust X

93 VGUST_DYN m s-1 maximum 10m dynamical gust wind_speed_of_gust X

94 VIO3 Pa vertical integrated ozone amount equivalent_pressure_of_atmosphere_ozone _content

95 VMAX_10M m s-1 maximum 10m wind speed wind_speed_of_gust X

96 W m s-1 vertical wind velocity upward_air_velocity

97 W_I m canopy water amount canopy_water_amount

98 W_SNOW m surface snow amount lwe_thickness_of_surface_snow_amount

99 W_SO m soil water content lwe_thickness_of_moisture_content_of _soil_layer X

100 W_SO_ICE m soil frozen water content lwe_thickness_of_frozen_water_content_of _soil_layer X

101 WDIRlat_10M X

102 WSS 10M m s-1 wind_speed X

103 Z0 m surface roughness length surface_roughness_length X

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Fig. 1. Orography [m] of model domain of CCLM (colored area). The white frame indicates the 10 pixel wide sponge zone. The red boxes define the borders of the 8 European standard evaluation domains defined by Rockel and Woth (2007).

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Fig. 2. Soil moisture content [m] for the 8 European sub-regions of Fig. 1. crosses: initial value, dashed lines: monthly mean of spin up run, solid: monthly mean of the final (restarted) simulation. The colors belong to the soil levels.

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Fig. 3. Mean differences of monthly mean 2 m air temperatures [K] of 1950-2012: coastDat2-e0bs8.0.

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Fig. 4. Differences of mean diurnal temperature range [K] of 1950-2012: coastDat2-eObs8.0.

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Fig. 5. Differences of mean monthly sums of total precipitation [mm] of 1950-2012: coastDat2-e0bs8.0.

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Fig. 6. Histogram of daily precipitation sums [mm] of 1951-2009: REGNIE (red bars), coast-Dat2 (blue bars), and e0bs8.0 (shaded).The frequency is given in %.

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Fig. 7. Validation of near surface wind speeds: quantile-quantile plot for Atlantic offshore conditions (left: 2007-2012, platform K1 height corrected) and near-shore Mediterranean conditions (right: 2000-2012, buoy Athos).

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Fig. 8. Differences of mean monthly means of total cloud cover [1] of 1950-2010: coastDat2-CRU3.2.

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Fig. 9. Frequency distribution of the planetary boundary layer height [m] of coastDat2 and u observation for 2003-2012.

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Fig. 10. Differences of mean monthly sums of total precipitation [mm] of 1950-2010: coastDat2-GPCC6.

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Fig. 11a. Area mean monthly sums of total precipitation of December, January, and February of 1950-2010: coastDat2 (red), range of GPCC6, e0bs8.0 and CRU3.2 for each season (gray filled).

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Fig. 11b. Area mean monthly sums of total precipitation of March, April, and May of 1950-2010: coastDat2 (red), range of GPCC6, e0bs8.0 and CRU3.2 for each season (gray filled).

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Fig. 11c. Area mean monthly sums of total precipitation of June, July, and August of 19502010: coastDat2 (red), range of GPCC6, e0bs8.0 and CRU3.2 for each season (gray filled).

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Fig. 11d. Area mean monthly sums of total precipitation of September, October, and November of 1950-2010: coastDat2 (red), range of GPCC6, e0bs8.0 and CRU3.2 for each season (gray filled).

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