A climate scenario for the Alpine region reclip:more ...wegc fileand diagnostic post-processing...

Wegener Center for Climate and Global Change University of Graz Leechgasse 25, A-8010 Graz

Regional and Local Climate Modelling and Analysis Research Group

A climate scenario for the Alpine region reclip:more project year 3 – WegCenter progress report Andreas Gobiet, Heimo Truhetz, Andreas Riegler

Wegener Center for Climate and Global Change, University of Graz, Austria Email: [email protected] 28 November 2006

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Table of Contents 1 Introduction 1 2 Model setup for 10-year simulations (WegCenter v40) 1 3 Evaluation of the Hindcast Simulation 2

3.1 Correlative Datasets and Evaluation Methodology 3

3.2 Temperature 5

3.3 Precipitation 5 4 Climate Change Signals 9

4.1 Temperature Change 9

4.2 Precipitation Change 10 5 Wind Downscaling 12 6 Publications 20

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1 Introduction The 3-year project “Research for Climate Protection: Model Run Evaluation” (reclip:more) is a coop-eration of five academic institutions in Austria (Austrian Research Centers Systems Research (ARC-sys), Department of Meteorology and Geophysics, Univ. of Vienna (IMG), Institute for Meteorology, Univ. of Natural Resources and Applied Life Sciences Vienna (BOKU-Met), Central Institute for Me-teorology and Geodynamics (ZAMG), and Wegener Center for Climate and Global Change, Univ. of Graz (WegCenter)) and is led by ARC-sys. The major aim of the project is to evaluate the capability of dynamical and statistical downscaling methods in the Alpine region to create climate scenarios at mesoscale (10 km grid spacing) and microscale (≤ 1 km grid spacing) resolutions. Two regional cli-mate models (RCMs), ALADIN (http://www.cnrm.meteo.fr/aladin) and the PSU/NCAR mesoscale model MM5 [Dudhia et al., 2005], are applied for dynamical downscaling of parts of the ERA-40 re-analysis (period 1981-1990) [Uppala et al., 2004] and global climate scenarios of the German ECHAM5 global circulation model (GCM) [Roeckner et al., 2003] (periods 1981-1990 and 2041-2050) in order to provide a climate scenarios on regional-scale resolutions (10 km). Further down-scaling to 1 km and finer grids is accomplished by statistical and diagnostic methods.

In project year 3 (PJ3, Nov. 2005 to Oct. 2006), the reclip:more-team focused on conducting the long-term climate simulations, evaluated the model results and finished the development of statistical and diagnostic post-processing tools. WegCenter contributed with the final definition of the MM5 setup for the long-term simulations with MM5 (Sect. 2), their conduction, evaluation, and preliminary climate change diagnosis (Sects. 3 and 4), and the setup and evaluation of a very-high resolution diagnostic wind downscaling technique for the Vienna Basin region (Sect. 5).

2 Model setup for 10-year simulations (WegCenter v40) Based on the experiences from project years 1 and 2 [Gobiet et al., 2004; Truhetz et al., 2005] and on the decisions made at the reclip:more meeting on 14.10.2005 in Vienna, a detailed set up for MM5 to be used for the reclip:more 10-years simulations has been elaborated (“WegCenter v40”).

The spatial setup consists of two nested domains (30 km and 10 km horizontal grid point distance) and builds a compromise between a favoured larger domain (“BOKU 4”, cf. Truhetz et al., 2005) and the available computational resources. The number of grid points has been reduced from 105 x 135 to 100 x 124 in domain 1 and from 79 x 115 to 79 x 109 grid points in domain 2 (see Figure 2.1).

Figure 2.1: WegCenter v40 MM5 setup. Right panel: Model domain 1 (30 km x 30 km grid spacing). Left panel: Model domain 2 (10 km x 10 km grid spacing). The altitude above sea level is shown in colors. The transition zone where the model state is nudged to lateral boundary conditions (LBCs) (seven grid cells) is marked.

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In vertical, 29 model levels are used with a top layer at 100 hPa. Most model parameterizations re-mained the same as in previous settings (for details see Table 2.1 and Truhetz et al. [2005]), except the MRF boundary layer scheme which was replaced by the ETA scheme since tests showed that both are comparable in terms of computational demands and the ETA scheme includes a prognostic formulation of the turbulent kinetic energy, which is advantageous for further downscaling of the wind field. The originally favoured two-way nesting strategy has been abandoned in order to avoid discon-tinuities in the meteorological fields of domain 1 which occur between the area covered by domain 2 and the area outside domain 2. These discontinuities could adversely affect the analysis of domain 1-results. Table 2.1 summarizes the physical parameterizations and the spatial and temporal set-tings.

Parameterization Setting v40 Domain-settings Domain 1 Domain 2

MM5 model version MM5 v3.7.3 Num. grid points Y 100 79

Cumulus scheme Kain-Fritsch 2 - with Num. grid points X 124 109shallow convection Grid distance [km] 30 10

Boundary layer scheme ETA Num. layers 29 29Explicit moisture scheme Mixed phase (Reisner 1) Top layer [hPa] 100 100Radiation scheme RRTM longwave Time step [s] 90 30Surface scheme NOAH-LSM Nest type one-way one-way

Table 2.1: Physical parameterizations and spatial and temporal settings of MM5 WegCenter v40.

In order to minimize possible interpolation errors, the surface boundary conditions were set up based on highly resolved input-data. MM5 standard datasets from USGS at 2 min resolution (domain 1) and 30 sec resolution (domain 2) were used (ftp://ftp.ucar.edu/mesouser/MM5V3/TERRAIN_DATA), ex-cept for the digital elevation model of domain 2: it was changed from the original USGS GTOPO30 to NASA’s new SRTM30 dataset (ftp://e0mss21u.ecs.nasa.gov/srtm).

Three 11-years MM5 simulations have been performed. The first year of each period serves as spin-up and enables the model to reach an equilibrium state in the slower changing model fields (soil) and is not regarded in the analysis of the results. Henceforth, only the ten-year “analysis period” will be described.

1) Hindcast simulation: LBCs from the ERA-40 reanalysis [Uppala et al., 2004] of the European Cen-tre for Medium Range Weather Forecasts (ECMWF) for the decade 1981 – 1990.

2) Control simulation: LBCs from the ECHAM5 general circulation model (GCM) in T106 resolution (~120 km grid spacing) forced by obsreved greenhouse gas concentrations for the decade 1981 – 1990. The GCM LBC data have been provided by M. Wild and P. Tschuck of ETH Zurich (see Beck and Ahrens [2005] for details).

3) Scenario simulation: LBCs from ECHAM5 forced by greenhouse gas concentrations based on the IS92a emission scenario [Leggett et al., 1992] for the decade 2041 – 2050. The GCM LBC data have been provided by M. Wild and P. Tschuck of ETH Zurich (see Beck and Ahrens [2005] for de-tails).

3 Evaluation of the Hindcast Simulation Though a more detailed evaluation of the reclip:more simulations is conducted by other project part-ners (BOKU-met, IMG-VERA), a first look at the results was necessary in order to check the overall model performance. We concentrate on temperatures at 2 m above ground (T2m) and precipitation (P) of the hindcast simulation which is driven by „near perfect“ LBCs from ERA-40. This allows to judge the performance of MM5 separately from eventual biases in the GCM LBCs used in the control and scenario simulations.

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3.1 Correlative Datasets and Evaluation Methodology

Model output has been compared to several observation-based datasets. T2m was compared to the widely used global dataset of the climate research unit (CRU) [Mitchell and Jones, 2005]), P to CRU, the Global Precipitation Climatology Project (GPCP) [Adler et al., 2003] and two new high-resolution precipitation analyses for the Alpine region, the monthly HISTALP dataset [Auer et al., 2005; Efthy-miadis et al., 2006], and a daily precipitation analysis of ETH and MeteoSwiss (FREI, [Frei et al., 2006]). GPCP is given on a 2.5° x 2.5° grid, CRU on a 0.5° x 0.5° grid, HISTALP and FREI on 20 x 20 km grids.

All comparisons are based on simple differences or relative differences [%] between the MM5 results and the correlative datasets, i.e. positive values correspond to positive model biases compared to correlative data. MM5 results have been resampled on the coarser grids of the correlative datasets and are analyzed in several sub-regions around the Alpine ridge. Figure 3.1 shows the two sub-regional classifications we used. The first is an objective classification by Auer et al. [2005] based on the HISTALP dataset, the second is a subjective classification of IMG-VERA. While the VERA classi-fication distinguishes more regions around the Alps which is advantageous for the analysis of smaller-scale regional climate features and MM5’s ability to reproduce them, the HISTALP classifica-tion draws a sharp north-south dividing line along the main Alpine ridge which is probably more suit-able to display the climatic north-south contrast in the Alpine region. For evaluation purposes, only the VERA classification is shown. The HISTALP subregions will be additionally used for the analysis of the climate change signal in Sect. 4.

Figure 3.1: Climatologic sub-regions. Left panel: HISTALP classifications; right panel: VERA classification. In order to demonstrate the importance of small-scale regional analysis of the climate in the greater Alpine region, the annual precipitation cycles based on the HISTALP dataset in the VERA subre-gions are displayed in Figure 3.2. Major differences exist between the north-western regions with high interannual variability and a secondary precipitation maximum in winter reflecting maritime influ-ences and the eastern areas featuring low variability and a single summer maximum representing continental characteristics. Several regional modifications of these basic characteristics, particularly with respect to autumn precipitation can be found. One example is region 4 (roughly Slovenia) featur-ing a secondary maximum in autumn which can be attributed to cyclones over the northern Mediter-ranean Sea, but no wintertime maximum like the regions north and north-west of the Alps.

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Figure 3.2: Annual precipitation cycle in WegCenter v40 model domain 2 (REGION TOTAL) and the VERA subregions (REGION 1 to REGION 7). Data source: HISTALP.

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3.2 Temperature

Figure 3.3 (left panel) shows the 10-year mean annual cycle of the MM5 (v40, domain 2) T2m bias compared to CRU. Though the CRU dataset is known to be inaccurate in mountainous regions and contains, compared to MM5, inconsistencies in the surface elevation, MM5 is clearly cold biased in spring, summer, and autumn. During winter (December, January, February) the bias is small (-0.5 K in the entire model domain 2), during summer (June, July, August) it its most pronounced (2 K in the entire model domain 2). Figure 3.3 (right panel) demonstrates that the bias is smaller in VERA subregion 7, the mountainous area (-0.2 K in winter and -1.2 K in summer).

Sensitivity studies and published literature [e.g., McCaa and Bretherton, 2004] show that this bias is related to moist convection parameterisation schemes, particularly to the Kain-Fritsch parameterisa-tion (e.g., Tadross et al., [2006]) and is a known feature of MM5. It could probably be reduced by using the Betts Miller cumulus scheme [Betts and Miller, 1993]. However, since the Kain-Fritsch scheme performs excellent with regard to the much more complex parameter precipitation (see Sect. 3.3), since temperature biases can be reasonably accounted for by suitable post-processing algo-rithms (which is much more difficult for precipitation), and since biases in the range of 0.2 to 2 K rep-resent the “state of the art” in regional climate modelling compared to many other RCM experiments, we accepted this bias in favour of a better representation of precipitation.

Figure 3.3: Annual cycle of MM5 (v40, domain 2) T2m bias compared to CRU (1981 – 1990) in the enire model domain without nudging zone (left panel) and in the VERA sub-region 7 (Alps) (right panel). Red bars: 10-year monthly mean bias; green bars: mean bias ± standard deviation; blue bars: maximum and minimum bias.

3.3 Precipitation

Monthly mean precipitation (P) has been compared to various precipitation analyses (Sect. 3.1) in order to capture model biases on one hand, and uncertainties in the correlative datasets on the other hand. While the comparison with HISTALP, FREI and CRU has been performed for the VERA subregions separately, the coarse GPCP data set has only been used to evaluate the domain-average precipitation in the entire model domain 2 (without nudging zone, see Figure 2.1).

Figure 3.4 shows a map of the annual mean MM5 (v40, domain 2) P bias compared to HISTALP. Regarding the area mean, MM5 is virtually unbiased (0.1 mm/day) but two prominent regional details stand out: A wet bias over the Alpine tops and a dry bias over Slovenia. Both features will be dis-cussed on the basis of Figure 3.5 showing mean (1981 – 1990) annual P cycles as simulated in MM5

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(black line) compared to correlative datasets (colored lines) in the VERA subregions (see Sect. 3.1) and in the entire model domain without nudging zones.

MM5 captures various climatic features of the small subregions around the Alps very well. In the north-west, the slightly maritime conditions with a secondary winter precipitation maximum are well reproduced (subregions 1 and 2) and in the easter part the more continental conditions expressed by a convective summer precipitation maximum are captured as well (e.g., sub-region 3). Additionally, smaller scale features like a small autumn maximum in the Po valley (sub-region 6) are very well captured which demonstrates the value of the high model resolution.

On the other hand, MM5 shows a clear deficiency in properly capturing autumn precipitation in some sub-regions, particularly in sub-region 4 (Slovenia). Though this effect has not been analysed in de-tail yet, there is strong indication that it is caused by an under-representation of northern Mediterra-nean cyclones in MM5 which are the primary precipitation source in Slovenia in autumn.

Further remarkable differences are the moist MM5 wintertime conditions in the mountainous region (sub-region 7) compared to the correlative datasets. We expect that major parts of these differences are cause by the well-known (wind-related) negative bias in precipitation measurements mountain-ous regions in winter. Still, the possibility of a model bias contributing to this difference can not be excluded.

Figure 3.4: Annual mean (1981 – 1990) MM5 precipitation bias [mm/day] relative to HISTALP in model domain 2 (nudging zone removed).

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Figure 3.5: Annual precipitation cycle (1981 – 1990 mean) in MM5 (black) (v40, domain 2) and in the observational datasets HISTALP (red), FREI (green), CRU (blue), and GPCP (yellow) in the entire model

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domain (nudging zone removed) (TOTAL) and in VERA subregions (REGION 1 to REGION 7). In order to demonstrate the model’s ability to capture not only monthly mean precipitation Figure 3.6 (created by BOKU-Met) depicts the probability distribution of percentage of model area featuring pre-cipitation over a certain threshold (1, 5, and 10 mm/day, respectively). Since these results are pre-sented in more detail in the reclip:more project year 3 progress report of BOKU-Met, only the overall conclusion is drawn here: MM5 only slightly overestimates the rainy area compared to observations and shows a good performance in this respect.

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Figure 3.6: Probability distribution of fraction of the evaluation area featuring precipitation over 1 mm/day (top line), 5 mm/day (middle line), and 10 mm/day (bottom line). Left column: FREI; right column: MM5 hindcast. Source: P. Haas, BOKU-Met.

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4 Climate Change Signals This Section presents an overview of the T2m and P climate change signals derived form the com-parison of the MM5 scenario (2041 – 2050) and control simulation (1981 – 1990) both driven by LBCs from ECHAM5 (sse Sect. 2). The values presented correspond to the expected climate changes during a 60-year period under the IS92a emission scenario. The results focus on the change in mean climate (seasonal means) and only very preliminary results of variability changes are presented.

4.1 Temperature Change

Figure 4.1 displays the seasonal T2m change derived from the MM5 (v40, domain 2) simulations (scenario – control). The annual mean change over the entire region (not shown) amounts +2.2 K. Seasonally, the T2m signal increases from winter (December, January, February, DJF) (1.8 K) over spring (March, April, May, MAM) (2.2 K), and summer (June, July, August, JJA) (2.3 K), to reach its maximum in autumn (September, October, November, SON) (2.7 K). The area mean seasonal signal can be expected to be dominated by large scale processes, i.e., by the lateral boundary conditions from ECHAM5. However, the high resolution of the RCM adds several regional-seasonal features like stronger warming at high altitudes or the weaker signal in summer over the northern Adriatic Sea.

Figure 4.1: Seasonal mean T2m differences [K] between the MM5 (v40, domain 2) scenario (2041 to

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2050) and control (1981 to 1990) simulation. Top left: winter (DJF); top right: spring (MAM); bottom left: summer (JJA); bottom right: autumn (SON).

4.2 Precipitation Change

Figure 4.2 displays the seasonal precipitation change (in percent of the control value) derived from the MM5 (v40, domain 2) simulations (scenario – control). The annual mean change over the entire region (not shown) is small (+4 %) but seasonally and sub-regionally large changes are projected. Over the entire area, the changes amount +7.9 % (DJF), +1.2 % (MAM), -12.3 % (JJA), and -13.9 % (SON). In Figure 4.3, the changes are displayed separated into the HISTALP (left) and VERA (right) sub-regions. Values range from -30 % to +20 % depending on season and sub-region.

In summer and autumn, the Alpine ridge acts as a sharp barrier separating drier conditions in the south from moister conditions in the north. The Austrian territory is strongest affected in DJF (in-creasing P) and SON (decreasing P) in the eastern part of the country.

Figure 4.2: Seasonal mean precipitation relative differences [%] between the MM5 (v40, domain 2) scenario (2041 – 2050) and control (1981 – 1990) simulation. Top left: winter (DJF); top right: spring (MAM); bottom left: summer (JJA); bottom right: autumn (SON).

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Figure 4.3: Seasonal mean precipitation relative differences [%] between the MM5 (v40, domain 2) scenario (2041 – 2050) and control (1981 – 1990) simulation separated into the HISTALP (left panel) and VERA (right panel) sub-regions. In Figures 4.4 and 4.5, the climate change signal for precipitation intensity and frequency is dis-played. Intensity is increasing in winter, even in regions where frequency is decreasing. This can be regarded as indication for more extreme conditions in future in winter (stronger but less frequent pre-cipitation). Further intensity signals are either very weak or very local and their significance has to be analysed in detail before interpretation. The frequency signals (Figure 4.5) are strong and spatially much more homogeneous. Less rain days can be expected in summer and autumn over the entire Alpine region and in winter in the north-western part.

Figure 4.4: Seasonal mean rain day intensity difference [mm/day] between the MM5 scenario (2041 –

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2050) and control simulation (1981 – 1990). Top left: winter (DJF); top right: spring (MAM); bottom left: summer (JJA); bottom right: autumn (SON).

Figure 4.5: Seasonal mean rain day frequency difference [days/month] (day with P > 1 mm/day are counted as rain days) between the MM5 (v40, domain 2) scenario (2041 – 2050) and control (1981 – 1990) simulation. Top left: winter (DJF); top right: spring (MAM); bottom left: summer (JJA); bottom right: autumn (SON). Generally, it has to be emphasized that the climate change signals for precipitation carry much larger uncertainties than the temperature signals. The precipitation signals found here have to be take seri-ous since they represent a possible and probable evolution of future climate, but they cannot be re-garded as strongly significant regarding all uncertainties involved.

5 Wind Downscaling The wind downscaling method described and evaluated for the Hohe Tauern region in Truhetz et al. [2005; 2007] has been adapted to a new study area, an extended region around Vienna (120 km x 90 km) encompassing the Südliche Wiener Becken, Neusiedl am See, Dunkelsteiner Wald, and the Weinviertel (referred to as Vienna Basin) (Figure 5.1). The method combines dynamical downscaling (conducted in reclip:more to create the main climate scenarios, see Sect. 2) with a diagnostic wind field model CALMET [Scire et al., 1999]. The preparation of initial wind fields and surface boundary conditions and the CALMET setup remained similar to the setup in the Hohe Tauern region, using the Lambert conformal conic projection, horizontal grid spacing of 200 m, 20 levels in the vertical,

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and placing the model top level at 1250 m above ground level (a.g.l.). However, the CORINE land cover dataset has been updated from CLC90 to CLC2000, version 8/2005 [EEA, 2006].

It is planned to apply the wind downscaling procedure to all three MM5 long-term climate simulations (MM5 fields of domain 2 from WegCenter v40 with 10 km horizontal grid spacing, Sect. 2) for the Vienna Basin (see Fig. 5.1). Since the importance of higher resolved initial wind fields in for moun-tainous regions has been demonstrated [Truhetz et al., 2007], the error characteristics for method in flat regions (Vienna Basin) based on 10 km initial fields and eventual performance gains due to higher resolved initial fields have to be evaluated.

Figure 5.1: Study region (120 km x 90 km) (Vienna Basin, blue rectangle) and the observations sta-tions for evaluation; the CORINE land use classes are shown in the background.

In order to investigate the effect the horizontal resolution of initial fields for the diagnostic down-scaling step a new domain setting for the dynamic downscaling step (WegCenter v34) was intro-duced. WegCenter v34 combines the geometrics and the spatial-temporal discretisation of v33 [Truhetz et al., 2005] (3 nested domains with 45 km, 15 km, and 5 km grid spacing, but with domain 3 located over the Vienna Basin) and the physical parameterisations of WegCenter v40 (i.e. the parameterisations used for the long-term climate scenarios, see Sect. 2). Both dynamic downscaling settings (v34 and v40) were conducted for the whole year 1999 and are driven by LBCs derived from the ERA-40 reanalysis [Uppala et al., 2004]. The diagnostic wind downscaling step was conducted for the Mesoscale Alpine Programme (MAP) Special Observing Period (SOP) ranging from Sep. 7 to Nov. 15 1999 based on both the WegCenter v34 (case A, 5 km grid spacing) and WegCenter v40 (case B, 10 km grid spacing) input data to generate highly resolved wind fields (200 m grid spacing, hourly time slices).

The modelled wind fields were directly compared to observation data of 13 surface observation sites (Figure 5.1 and Tab 5.1) retrieved from the MAP database [Bougeault et al., 2001]. Based on the differences between modelled and observed wind fields (wind speeds < 0.5 m/s and their corre-sponding direction were neglected) several error statistics (biases, standard deviations of speed and direction, root mean square errors for vectors, and Pearson’s linear correlation coefficient) were cal-culated to evaluate the method’s performances.

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name of station ϕ [°] λ [°] alt. [m] h [m] N WMO

Grossenzersdorf 48.19944 16.56194 155.0 10.0 1344 11037Gumpoldskirchen 48.04083 16.28278 218.0 10.0 1084 11082

Krems_Ffw_Zentrale 48.41833 15.62083 203.0 10.0 844 11070Langenlois 48.47222 15.69778 204.0 10.0 1172 11075

Leiser_Berge 48.56028 16.37194 457.0 25.0 1019 11083Lilienfeld_Tars 48.01667 15.58333 681.0 10.0 1247 11078

Neusiedl_am_See 47.94250 16.85778 135.0 18.0 1289 11194Poysdorf_Ost 48.67000 16.63833 202.0 10.0 1161 11032

St_Poelten 48.18028 15.61111 285.0 10.0 1001 11028Wien_Hohewarte 48.24861 16.35639 203.0 35.0 1310 11035Wien_Mariabrunn 48.20806 16.23056 227.0 9.5 1097 11080

Wien_Unterlaa 48.12750 16.42278 200.0 15.0 1407 11040Zwerndorf 48.33778 16.83222 146.0 10.0 1244 11085

Table 5.1: General information on observation stations: latitude (ϕ), longitude (λ), altitude (alt.), anemometer height a.g.l. (h), number of valid data values (N), and World Meteorological Organisa-tion (WMO) identification number according to Figure 5.1.

An overview of the error statistics for the Vienna Basin region of case A, case B and the driving ERA-40 data is given in Table 5.2 and Figure 5.2 from which two distinctive conclusions can be drawn. On one hand, the general success of the method is demonstrated by the improvement of any error measure in both cases, A (Table 5.2b) and B (Table 5.2d), when compared to the driving data (ERA-40, Table 5.2e), On the other hand, improvements due to CALMET can be demonstrated by error-distributions with respect to the observed wind speeds, shown in Figure 5.2. In case A and B under-estimation of velocity at higher wind speeds (> 5 m/s) is cured, or at least strongly reduced by CAL-MET compared to the corresponding dynamically downscaled wind fields.

The the increased resolution of the dynamic downscaling step from 10 km (case B) to 5 km (case A) grid spacing does not seem to improve the results. In contrary, case A features an additionally weak directional bias of about 10°. Comparing Figure 5.2b and Figure 5.2d shows that the wind fields of case A cannot be regarded to be of higher quality than wind fields of case B in the flat Vienna Basin area. In general, low wind speeds are slightly overestimated in both cases.

In contrast, the error statistics for case A and case B in the Hohe Tauern region (Table 5.3 and Fig-ure 5.3, [Truhetz et al., 2007]) demonstrate that the increased resolution of the dynamic downscaling step (case A) clearly improves CALMET’s wind fields over complex orography, especially at medium (5-15 m/s) and high (> 15 m/s) wind speeds.

The strongly divergent performance of the wind downscaling method in the Hohe Tauern region and the Vienna Basin reflects the different types of orography. In the Hohe Tauern region near surface flows are mostly influenced by terrain-induced kinematic effects (i.e. deflections and flow perturba-tions due to steep slopes, in particular at stable conditions and higher wind speeds). These effects are better captured by dynamic modelling approaches (especially at higher resolutions) leading to significantly improved initialisation fields for CALMET. The terrain in the Vienna Basin is much smoother and kinematic effects have less impact. Therefore, only marginal benefits of increased resolution of dynamic downscaling can be expected.

These results justify the direct application of the diagnostic downscaling model to the basic reclip:more climate simulations (with 10 km grid spacing) in the Vienna Basin to generate 10-year climatologies of near surface wind with 200 m grid spacing.

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a) MM5 5 km (case A) V MEAN [m/s] V RMS [m/s] V BIAS [m/s] V STDEV [m/s] DIR BIAS [°] DIR STDEV [°] r V [1]Grossenzersdorf 3.0 4.0 2.6 1.9 8.2 59.5 0.62

Gumpoldskirchen 2.8 4.1 3.0 1.9 -0.6 53.1 0.57Krems_Ffw_Zentrale 1.8 4.6 3.1 2.1 29.0 63.0 0.45

Langenlois 1.9 4.6 3.2 2.2 22.6 60.6 0.46Leiser_Berge 7.4 4.6 0.1 2.7 2.0 43.9 0.59

Lilienfeld_Tars 3.0 4.9 3.3 2.6 3.1 49.7 0.53Neusiedl_am_See 3.3 4.8 3.1 2.2 13.0 54.2 0.49

Poysdorf_Ost 2.5 5.5 3.7 2.2 38.5 46.2 0.44St_Poelten 2.9 4.2 2.9 2.4 8.4 54.8 0.56

Wien_Hohewarte 3.5 4.2 2.4 2.1 15.8 51.7 0.53Wien_Mariabrunn 2.7 4.3 3.0 2.0 13.4 58.1 0.53

Wien_Unterlaa 3.8 4.3 1.9 2.3 15.9 57.9 0.54Zwerndorf 3.9 3.9 1.4 2.3 15.5 60.5 0.51

13 station average 3.3 4.4 2.6 2.2 14.1 54.8 0.53b) CALMET 200 m (case A) V MEAN [m/s] V RMS [m/s] V BIAS [m/s] V STDEV [m/s] DIR BIAS [°] DIR STDEV [°] r V [1]

Grossenzersdorf 3.0 3.8 2.5 1.9 5.7 59.2 0.62Gumpoldskirchen 2.8 4.7 3.6 2.1 3.4 53.0 0.57

Krems_Ffw_Zentrale 1.8 4.8 3.3 2.2 36.4 58.9 0.45Langenlois 1.9 4.4 3.1 2.3 20.9 60.0 0.46

Leiser_Berge 7.4 4.8 0.9 2.8 -0.9 44.0 0.59Lilienfeld_Tars 3.0 5.1 3.0 2.3 26.8 58.0 0.53

Neusiedl_am_See 3.3 4.7 3.1 2.2 11.3 53.8 0.49Poysdorf_Ost 2.5 5.2 3.6 2.3 35.4 46.6 0.44

St_Poelten 2.9 4.2 2.9 2.4 8.0 55.9 0.56Wien_Hohewarte 3.5 4.4 2.7 2.3 13.5 53.3 0.53Wien_Mariabrunn 2.7 4.0 2.7 1.9 11.1 58.1 0.53


13 station average 3.3 4.5 2.6 2.2 15.0 55.4 0.53c) MM5 10 km (case B) V MEAN [m/s] V RMS [m/s] V BIAS [m/s] V STDEV [m/s] DIR BIAS [°] DIR STDEV [°] r V [1]

Grossenzersdorf 3.0 3.1 1.5 1.5 -4.9 57.8 0.57Gumpoldskirchen 2.8 3.5 1.8 1.8 -9.6 56.3 0.38


Leiser_Berge 7.4 4.7 -1.7 -1.7 -13.8 46.7 0.58Lilienfeld_Tars 3.0 3.0 0.7 0.7 -6.1 53.8 0.44


St_Poelten 2.9 3.3 1.6 1.6 -0.1 61.5 0.61Wien_Hohewarte 3.5 4.0 2.4 2.4 0.7 53.2 0.56Wien_Mariabrunn 2.7 3.0 1.5 1.5 4.1 57.4 0.49

Wien_Unterlaa 3.8 3.3 0.7 0.7 -1.6 59.6 0.54Zwerndorf 3.9 3.6 0.8 0.8 -4.1 60.1 0.59

13 station average 3.3 3.5 1.3 1.3 1.2 56.5 0.51d) CALMET 200 m (case B) V MEAN [m/s] V RMS [m/s] V BIAS [m/s] V STDEV [m/s] DIR BIAS [°] DIR STDEV [°] r V [1]

Grossenzersdorf 3.0 4.1 2.5 2.2 -8.0 57.0 0.56Gumpoldskirchen 2.8 4.4 2.7 2.6 -7.3 55.7 0.41


Leiser_Berge 7.4 5.0 0.2 2.9 -13.8 46.1 0.59Lilienfeld_Tars 3.0 4.2 1.7 1.8 26.7 62.5 0.50



Wien_Unterlaa 3.8 3.3 0.7 2.3 -0.7 59.6 0.52Zwerndorf 3.9 3.6 0.9 2.1 -6.1 60.4 0.61

13 station average 3.3 3.7 1.6 2.1 4.1 56.1 0.51e) ERA-40 T106 V MEAN [m/s] V RMS [m/s] V BIAS [m/s] V STDEV [m/s] DIR BIAS [°] DIR STDEV [°] r V [1]

Grossenzersdorf 3.0 8.0 3.8 4.1 24.6 87.8 0.15Gumpoldskirchen 2.8 6.3 2.7 3.8 0.1 74.6 0.02


Leiser_Berge 7.5 15.9 5.4 6.0 24.4 90.3 0.01Lilienfeld_Tars 3.0 3.5 0.3 2.6 19.5 65.9 0.05




13 station average 3.3 8.5 3.8 4.3 21.0 84.2 0.15 Table 5.2: Vienna Basin: statistics of the differences between modeled and observed wind fields at observation stations of the dynamic and the diagnostic downscaling step of case A (a, b), case B (c, d), and for the driving data ERA-40 (e). Observed mean wind speed (VMEAN), root mean square error for vectors (VRMS), biases and standard deviations for speed (VBIAS, VSTDEV) and direction (DIRBIAS, DIRSTDEV) and linear correlation coefficient of wind speed (rV) over the period from 7 Sep. to 15 Nov. 1999 are given.

16 Berichtstitel

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1.7 m/s -0.4 m/s

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0 5 10 15 20 25observed wind speed [m/s]

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median mean quantiles (25 %, 75 %)

0.000 0.005 0.010 0.015 0.020 0.025 0.030rel. frequency of events [1]

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17.2 deg 14.5 deg

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17.9 deg 11.8 deg

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Figure 5.2: Vienna Basin: differences between modeled and observed wind speeds (left panels) and directions (right panels). Case A: MM5 data with 5 km grid spacing (a) is downscaled to 200 m (b). Case B: MM5 data with 10 km grid spacing (c) is downscaled to 200 m (d). Panel (e): Error statistics of the common driving data (ERA-40).

17

a) MM5 5 km (case A)V MEAN

[m/s]V RMS

[m/s]V BIAS

[m/s]V STDEV

[m/s]DIR BIAS

[°]DIR STDEV

[°]r V

[1]

Innsbruck-Flugplatz (IF) 2.3 4.8 1.0 2.0 -1.0 115.2 0.37Patscherkofel (PK) 8.8 9.5 -5.8 6.4 -22.8 85.3 0.59Rudolfshuette (RH) 7.0 7.3 -1.4 6.1 -4.8 67.9 0.29

Sonnblick (SB) 11.6 9.3 -5.6 5.7 -4.9 55.1 0.66Schmittenhoehe (SH) 4.6 5.2 -2.6 2.8 -0.7 96.9 0.19

Ellboegen (EB) 5.0 4.1 -0.5 2.8 13.6 67.8 0.736 station average 5.8 6.3 -2.1 4.1 -2.9 84.3 0.44

b) CALMET 200 m (case A)V MEAN

[m/s]V RMS

[m/s]V BIAS

[m/s]V STDEV

[m/s]DIR BIAS

[°]DIR STDEV

[°]r V

[1]

Innsbruck-Flugplatz (IF) 2.3 3.7 -0.4 1.8 -38.0 101.4 0.40Patscherkofel (PK) 8.8 6.8 -1.1 5.9 -15.4 63.6 0.63Rudolfshuette (RH) 7.0 7.0 -0.9 5.5 -8.6 69.3 0.50

Sonnblick (SB) 11.6 8.5 -0.3 5.5 -0.7 53.6 0.70Schmittenhoehe (SH) 4.6 5.2 -1.6 3.1 -0.5 85.4 0.16

Ellboegen (EB) 5.0 4.3 0.6 3.1 11.3 62.7 0.696 station average 5.8 5.6 -0.7 4.0 -8.5 75.0 0.48

c) MM5 10 km (case B)V MEAN

[m/s]V RMS

[m/s]V BIAS

[m/s]V STDEV

[m/s]DIR BIAS

[°]DIR STDEV

[°]r V

[1]

Innsbruck-Flugplatz (IF) 2.3 5.0 1.7 2.4 -30.1 84.9 0.46Patscherkofel (PK) 8.8 8.3 -5.0 5.7 15.4 68.8 0.78Rudolfshuette (RH) 7.0 6.5 -2.2 5.1 -5.4 64.0 0.56

Sonnblick (SB) 11.6 10.6 -5.7 5.6 -13.7 58.4 0.68Schmittenhoehe (SH) 4.6 4.8 -1.5 2.9 27.0 68.4 0.21

Ellboegen (EB) 5.0 6.0 -0.9 3.0 34.3 64.6 0.706 station average 5.8 6.3 -1.8 3.9 8.8 69.0 0.54

d) CALMET 200 m (case B)V MEAN

[m/s]V RMS

[m/s]V BIAS

[m/s]V STDEV

[m/s]DIR BIAS

[°]DIR STDEV

[°]r V

[1]

Innsbruck-Flugplatz (IF) 2.3 3.0 -0.5 2.0 -1.4 80.3 0.20Patscherkofel (PK) 8.8 7.8 -2.3 5.3 7.0 68.3 0.70Rudolfshuette (RH) 7.0 7.8 -0.9 5.9 -8.9 77.6 0.33

Sonnblick (SB) 11.6 10.9 -3.2 6.4 -9.9 59.4 0.51Schmittenhoehe (SH) 4.6 6.5 1.6 3.7 24.8 67.2 0.17

Ellboegen (EB) 5.0 4.9 -0.7 3.0 17.4 65.4 0.696 station average 5.8 6.3 -0.6 4.2 7.6 70.9 0.42

e) ERA-40 T106V MEAN

[m/s]V RMS

[m/s]V BIAS

[m/s]V STDEV

[m/s]DIR BIAS

[°]DIR STDEV

[°]r V

[1]

Innsbruck-Flugplatz (IF) 2.3 5.6 2.4 3.5 27.7 87.7 0.11Patscherkofel (PK) 8.8 12.2 -5.0 7.8 -21.9 101.0 -0.04Rudolfshuette (RH) 6.9 10.4 -4.8 6.0 -57.2 100.5 0.15

Sonnblick (SB) 11.7 14.1 -11.1 7.4 -0.9 135.4 -0.04Schmittenhoehe (SH) 4.6 5.9 -3.5 2.8 -3.6 122.8 0.10

Ellboegen (EB) 5.1 6.4 -3.6 4.1 1.5 91.0 0.066 station average 6.4 8.8 -4.1 5.1 -10.8 106.0 0.06

Table 5.3: Hohe Tauern region: statistics of the differences between modeled and observed wind fields at observation stations of the dynamic and the diagnostic downscaling step of case A (a, b), case B (c, d), and for the driving data ERA-40 (e). Observed mean wind speed (VMEAN), root mean square error for vectors (VRMS), biases and standard deviations for speed (VBIAS, VSTDEV) and direc-tion (DIRBIAS, DIRSTDEV) and linear correlation coefficient of wind speed (rV) over the period from 7 Sep. to 15 Nov. 1999 are given.

18 Berichtstitel

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6.0 deg 3.0 deg -1.3 deg

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10.9 deg 21.4 deg 5.8 deg

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Figure 5.3: Hohe Tauern region: differences between modeled and observed wind speeds (left panels) and directions (right panels). Case A: MM5 data with 5 km grid spacing (a) is downscaled to 200 m (b). Case B: MM5 data with 10 km grid spacing (c) is downscaled to 200 m (d). Panel (e): Error statistics of the common driving data (ERA-40).

19

6 Publications During project year 3 the WegCenter reclip:more team published following reclip:more related arti-cles and presentatios as leading or co-author:

Beck, A., M. Dorninger, A. Gobiet, H. Formayer, H. Truhetz (2006) Assessing the performance of high resolution climate hindcasts over the Alpine Region (oral), 6th annual meeting of EMS and 6th ECAC, Ljubljana, Slovenia, EMS Annual Meeting Abstracts, 3, EMS2006-A-00232. Dalla-Via, A., A. Gobiet, R. Kurzmann, I. Oberauner, F. Prettenthaler, M. Steiner, H. Truhetz, N. Vetters, G. Zakarias (2006) Adaptation in the Water Supply Sector of Eastern Styria (Austria) (oral), Joint OECD and Wengen-2006 International and Interdisciplinary WS Adaptation to the Impacts of Climatic Change in the European Alps, Wengen, Switzerland. Formayer, H., J. Zueger, M. Dorninger, A. Gobiet, P. Haas, T. Gorgas, H. Truhetz, W. Loibl (2006) Influence of the land-surface scheme on the modelled precipitation in the European Alps (poster), EGU 2006 General Assembly, Vienna, Austria, Geophysical Research Abstracts, 8, 09186, SRef-ID: 1607-7962/gra/EGU06-A-09186. Gobiet, A., H. Truhetz, H. Formayer, M. Themeßl, A. Riegler, and G. Kirchengast (2006) High Resolution Climate Scenarios for Austria, Proc. 9. Österr. Klimatag, March 2006, Vienna, Austria, V14. Gobiet, A., A. Beck, H. Truhetz, M. Dorninger, H. Formayer, A. Riegler, W. Loibl (2006) High resolution climate hindcasts and scenarios for the Alpine Region (oral), EGU 2006 General Assembly, Vienna, Austria, Geophysical Research Abstracts, 8, 06788, SRef-ID: 1607-7962/gra/EGU06-A-06788. Gobiet, A., A. Beck, H. Truhetz (2006) Future scenarios of climate mean and variability over the Alpine Region at high resolution (oral), 6th annual meeting of EMS and 6th ECAC, Ljubljana, Slovenia, EMS Annual Meeting Abstracts, 3, EMS2006-A-00222. Loibl, W., and the reclip:more team (2006) Project reclip:more - overview: Objectives, tasks and results 2004-2005 (oral), 9th Austrian Day of Climate (9. sterreichischer Klimatag), Vienna, Austria. Themel, M., A. Gobiet, H. Truhetz, and G. Kirchengast (2006) Regionalisierung von Temperatur und Niederschlag in der Alpinen Region HoheTauern (Downscaling of Temperature and Precipitation in the Alpine Region HoheTauern), Proc. 9. sterr. Klimatag, March 2006, Vienna, Austria, P24. Truhetz, H., A. Gobiet, and G. Kirchengast (2005) reclip:more-WegCenter Report #2 project year 2 (Nov 2004 - Oct 2005), Tech. Report for ARC No. 1/2005, Wegener Center, Univ. of Graz, Austria. Truhetz, H., A. Gobiet, W. Loibl, G. Kirchengast (2005) Downscaling of near surface wind in the Alpine region, in Use of CORINE Land Cover and IMAGE 2000 – CLC 2000 Applications CD, European Environment Agency (EEA) and Joint Research Centre (JRC), Institute for Environment and Sustainability (IES). Truhetz, H., A. Gobiet, and G. Kirchengast (2007) Evaluation of a dynamic-diagnostic modeling approach to generate highly resolved wind fields in the Alpine region, Meteorol. Z., under revision. Truhetz, H., A. Gobiet, G. Kirchengast (2006) Generation of highly resolved wind climatologies in the Alpine region at the 100 m scale (poster), EGU 2006 General Assembly, Vienna, Austria, Geophysical Research Abstracts, 8, 06484, SRef-ID: 1607-7962/gra/EGU06-A-06484. Truhetz, H., A. Gobiet, and G. Kirchengast (2006) Generation of highly resolved wind climatologies in Austria, Proc. 9. Österr. Klimatag, March 2006, Vienna, Austria, V08.

20

References

Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P.P. Xie, J. Janowiak, B. Rudolf, U. Schneider et al. (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present), J. Hydrometeorol. 4 (6), 1147-1167. Auer, I., R. Böhm, A. Jurkovic, A. Orlik, R. Potzmann, W. Schöner, M. Ungersbock et al. (2005) A new instrumental precipitation dataset for the greater alpine region for the period 1800-2002, Int. J. Climatol. 25 (2), 139-166, DOI:10.1002/joc.1135. Beck, A. and B. Ahrens (2005) Dynamical downscaling of reanalysis data using ALADIN, IMG Report for ARC (reclip:more, project year 2), Dept. of Meteorology and Geophysics, University of Vienna, Austria. Betts, A.K. and M.J. Miller (1993) The Betts-Miller scheme. The representation of cumulus convection in numerical models, K. A. Emanuel and D. J. Raymond (Eds.), Amer. Meteor. Soc., 246 pp. Bougeault, P., P. Binder, A. Buzzi, R. Dirks, R. Houze, J. Kuettner, R.B. Smith, R. Steinacker, and H. Volkert (2001) The MAP Special Observing Period, Bull. Amer. Meteor. Soc., 83, 3, 433-462. Dudhia, J. (1993) A nonhydrostatic version of the Penn State – NCAR mesoscale model: Validation and simulation of an atlantic cyclone and cold front, Mon. Wea. Rev. 121, 1493-1513. Efthymiadis, D., P.D. Jones, K.R. Briffa, I. Auer, R. Böhm, W. Schöner, C. Frei, J. Schmidli (2006) Construction of a 10-min-gridded precipitation data set for the Greater Alpine Region for 1800-2003, J. Geophys. Res. 111 (D1), D01105, DOI:10.1029/2005JD006120. European Environment Agency (EEA) (1995) CORINE Land Cover, European Environment Agency Report, Commission of the European Communities OPOCE (Office for official publications of the European Communities), Luxembourg. Gobiet, A., H. Truhetz, and G. Kirchengast (2004) reclip:more - IGAM Report #1, Project Year 1 (Nov 2003 -Jun 2004), Tech. Report for ARC No. 1/2004, Inst. for Geophys., Astrophys., and Meteorol., Univ. of Graz, Austria. Leggett, J., W.J. Pepper, and R.J. Swart (1992) Emissions scenarios for the IPCC: an update in Climate change 1992: the supplementary report to the IPCC scientific assessment edited by J.T. Houghton, B.A. Callander, and S.K. Varney, pp.75-95, Cambridge Univ. Press, Cambridge. McCaa, J.R., and C.S. Bretherton (2004) A New Parameterization for Shallow Cumulus Convection and Its Application to Marine Subtropical Cloud-Topped Boundary Layers. Part II: Regional Simulations of Marine Boundary Layer Clouds, Mon. Wea. Rev., 132, 833-896. Mitchell, T.D., and P.D. Jones (2005) An improved method of constructing a database of monthly climate observa-tions and associated high-resolution grids. Int. J. Climatol., 25, 693-712. Roeckner, E., G. Baeuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, A. Tompkins (2003) The Atmospheric General Circulation Model ECHAM5. Part 1: Model Description, Report 349, Max Planck Institute for Meteorology (MPI), Hamburg. Scire, J.S., F.R. Robe, M.E. Fernau, R.J. Yamartino (2000) A User’s Guide for the CALMET Meteorological Model (Version 5), software manual, Earth Tech Inc., Concorde. Tadross, M.A., W.J. Gutowski Jr., B.C. Hewitson, C. Jack, and N. New (2006) MM5 simulations ot the interannual change and the diurnal cycle of southern African regional climate, Theor. Apll. Climatol., 86, 63-80.

21

Truhetz, H., A. Gobiet, and G. Kirchengast (2005) reclip:more-WegCenter Report #2 project year 2 (Nov 2004 \u2013 Oct 2005), Tech. Report for ARC No. 1/2005, Wegener Center, Univ. of Graz, Austria. Truhetz, H., A. Gobiet, and G. Kirchengast (2006) Evaluation of a dynamic-diagnostic modeling approach to generate highly resolved wind climatologies in the Alpine region, Meteorol. Z., submitted. Uppala, S., P. Kållesberg, A. Hernandez, S. Saarinen, M. Fiorino, X. Li, K. Onogi, U. Andrea, and V. da Costa Bech-told (2004) ERA-40: ECMWF 45-years reanalysis of the global atmosphere and surface conditions 1957–2002, ECMWF Newsletter, 101, 2-21.

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