Development of a national strategy for adaptation to climate change adverse impacts in Cyprus CYPADAPT LIFE10 ENV/CY/000723

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1 Development of a national strategy for adaptation to climate change adverse impacts in Cyprus CYPADAPT LIFE10 ENV/CY/ Literature review and evaluation on the state-of-the-science computer-based DELIVERABLE 3.1 Authors: Christos Giannakopoulos, Theodora Kopania, Effie Kostopoulou and Mike Petrakis National Observatory of Athens, Greece

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3 Acknowledgements This report was produced under co-finance of the European financial instrument for the Environment (LIFE+) as the first Deliverable (D3.1) of the third Action (Action 3) of the CYPADAPT Project CYPADAPT (LIFE10ENV/CY/000723) during the implementation of its first Activity (Activity 3.a) on the Literature review and evaluation on the state-of-thescience computer-based. The CYPADAPT team would like to acknowledge the European financial instrument for the Environment (LIFE+) for the financial support. Disclaimer The information included herein is legal and true to the best possible knowledge of the authors, as it is the product of the utilization and synthesis of the referenced sources, for which the authors cannot be held accountable.

4 Contents Executive Summary Review of the state-of-the-science computer based Introduction Data and Climate Simulation Models Observational Data Six Regional Climate Models (RCMs) The PRECIS Regional Climate Model Evaluation of the state-of-the-science computer based climate simulation models Evaluation of the Six Regional Climate Models (RCMs) Modeled and Observed Climatology Modeled and Observed Long-term Trends Modeled and Observed Quantile Distribution Weather Extremes Evaluation of the KNMI-RACMO2 Regional Climate Model Evaluation of the PRECIS Regional Climate Model Modeled and Observed Climatology Modeled and Observed Quantile Distribution Weather Extremes and Trends Conclusions References i P a g e

5 List of Figures Figure 1-1: Map of Cyprus showing the geographical location of the stations of the Cyprus Meteorological Service Figure 2-1: Common RCM domain of the ENSEMBLES simulations (left) and Cyprus map (Marble Desktop Globe, with overlaid the locations studied (right) Figure 2-2: Annual cycle of the climatology for Maximum Temperature, Minimum Temperature and Precipitation Sum for Nicosia, from the RCMs ensemble mean and the observations. The shaded area represents the 1σ range of the 6-member model mean. The insert plots (with the same axes units) are the corresponding biases of the model ensemble mean relative to the observed. The number at the right of the inserts summarizes the annually averaged bias Figure 2-3: As Fig. 2-2, for Limassol Figure 2-4: As Fig. 2-2, for Saittas Figure 2-5: Long-term relative anomalies of the deseasonalized timeseries for Maximum Temperature (Tmax), Minimum Temperature (Tmin) and Total Precipitation (Prcp) for Nicosia, from the six RCMs (CNR, ETH, HAD, KNM, MNO and MPI), their ensemble mean (ENS) and the observations (OBS). The data have been smoothed with a 5-year running mean Figure 2-6: As Fig. 2-5, for Limassol Figure 2-7: As Fig. 2-5, for Saittas Figure 2-8: Quantile quantile plots of Tmax (left, in C), Tmin (middle, in C) and Precipitation (right, in mm/month) for Nicosia (up), Limassol (middle) and Saittas (bottom) from the six RCMs (CNR, ETH, HAD, KNM, MNO and MPI) and the observations (OBS) for Figure 2-9: Curves show the 30-year mean annual cycles of observed (black) and modeled (red) maximum temperature (left), minimum temperature (middle) and precipitation (right) at three representative stations. Temperature is measured in C and precipitation in mm/day Figure 2-10: Map of Cyprus showing the geographical location of the stations of the Cyprus Meteorological Service (x), as well as PRECIS grid points ( ) Figure 2-11: Curves show the 30-year mean annual cycles of observed (ST) and modeled (PRC) maximum temperature (TX), minimum temperature (TN) and mean temperature (TM) at 8 representative stations. Temperature is measured in C. 25 Figure 2-12: Mean annual cycles of the climatology for total precipitation (P) at 8 stations, from the PRECIS model (red) and the observations (blue). Precipitation is measured in mm/day Figure 2-13: Scatter plots of the corresponding pairs of observed and PRECIS mean temperature (TM) for the 9 sites Figure 2-14: Scatter plots of the corresponding pairs of observed and PRECIS maximum temperature (TX) for the 9 sites Figure 2-15: Scatter plots of the corresponding pairs of observed and PRECIS minimum temperature (TN) for the 9 sites ii P a g e

6 Figure 2-16: QQ plots for the annual maximum temperature (TX_ANN), minimum temperature (TN_ANN), precipitation (P_ANN), and 3 indices (TXQ90_JJA, TNQ10_DJF, PQ90_ANN) in two medium elevation continental stations (Nicosia and Lefkara) Figure 2-17: QQ plots for the annual maximum temperature (TX_ANN), minimum temperature (TN_ANN), precipitation (P_ANN), and 3 indices (TXQ90_JJA, TNQ10_DJF, PQ90_ANN) in two mountainous stations (Amiantos and Panagia). 33 Figure 2-18: Annual (ANN), winter (DJF), and summer (JJA) model biases maps for extreme maximum temperature (TX and TXQ90). Units are in degrees Celsius. The circles represent the average model bias in relation to the observational data Figure 2-19: Annual (ANN), winter (DJF), and summer (JJA) model biases maps for extreme minimum temperature (TN and TNQ10). Units are in degrees Celsius. The circles represent the average model bias in relation to the observational data Figure 2-20: Annual (ANN) and winter (DJF) model biases maps for frost nights (TN<0 o C) as well as annual (ANN) and summer (JJA) model biases maps for heatwave days (TX>35 o C). The circles represent the average model bias in relation to the observational data Figure 2-21: Annual (ANN), winter (DJF), and summer (JJA) model biases maps for average precipitation, as well as annual (ANN), winter (DJF), and autumn (SON) model biases maps for PQ90. Units are mm of rain. The circles represent the average model bias in relation to the observational data Figure 2-22: Trends of average maximum temperature (TXave) and average minimum temperature (TNave), for the summer (JJA), derived from PRECIS model (left) and observational data from the stations (right). Filled triangles indicate trends significant at the 0.05 level of significance Figure 2-23: Trends of annual TXQ90 and PQ90 indices, derived from PRECIS model (left) and observational data from the stations (right). Filled triangles indicate trends significant at the 0.05 level of significance iii P a g e

7 List of Tables Table 1-1: Station details in Cyprus (Cyprus Meteorological Service). The colors of the station names correspond to the colors of the crosses in the map of Figure Table 1-2: The six Regional Climate Models used for the purposes of the report Table 2-1: Annual means of absolute biases of climatology of Maximum Temperature (Tmax), Minimum Temperature (Tmin) and Total Precipitation (Prcp) of the 6 RCMs (CNR, ETH, HAD, KNM, MNO, MPI) and their ensemble mean (ENS) and Pearson correlation coefficients from the observed climatology for Nicosia, Limassol and Saittas Table 2-2: Definitions for indices of extremes (from Zhang and Yang 2004) Table 2-3: Indices of extremes from observations (OBS) and the 6-model mean (ENS) for Nicosia Table 2-4: As Table 2-3, for Limassol Table 2-5: As Table 2-3, for Saittas Table 2-6: Indices of extremes calculated from PRECIS model output and from observed data iv P a g e

8 Executive Summary This report presents a literature review and evaluation on the regional climate models used for the assessment of the potential climate changes in Cyprus. In particular, it reviews several high-resolution Regional Climate Models (RCMs) and evaluates their performance using available observational temperature and precipitation data from meteorological stations over Cyprus. The six RCMs data are evaluated by comparing with observations for the climate mean state and the weather extremes during the recent reference period For simulated temperatures, the annual average biases from the model ensemble do not exceed 1.5 C and are within the observed inter-annual variability (1σ). The 6-model ensemble does well in capturing the observed annual cycle in precipitation, although the individual RCMs sometimes fail to reproduce the wet extremes. The model performance is also evaluated based on the quantile quantile relationships in the monthly statistics. Modeled temperatures are generally compact and close to the observed, however simulations perform better for the colder temperatures as well as for meteorological conditions above the night time boundary layer. The larger discrepancies appear from the precipitation plots indicating deteriorating performance toward wetter conditions. The main regional climate model used in this project is PRECIS in which Cyprus lies at the centre of the study domain resulting in more accurate simulations. PRECIS is compared with an integrated set of model simulations, also developed to reproduce climate conditions and future climate changes for Europe. In these simulations, Cyprus is placed in the southeastern part of the domain. The PRECIS dataset satisfactorily reproduces annual cycles, raw data and climate indices for temperature in medium elevation continental sites. In higher altitudes, there are overestimations in temperature, probably due to the lower topography of the model. In coastal areas, the model shows poor skill in reproducing the inter-annual variability of min/max temperature, which might be attributed to the strong sea-influence in the model grids. As far as precipitation is concerned, the model shows an overall underestimation in annual and seasonal rainfall, as well as in extreme precipitation events. In addition, high correlations are also observed between daily model and station data. Regarding QQ plots, the corresponding points are grouped around a line slightly deviated from the 45deg diagonal line, showing that the two distributions are linearly related (less so for Precipitation and PQ90). PRECIS is found to accurately estimate increasing (although not statistically significant) trends in extreme temperature indices. For precipitation, trends are correctly estimated in medium-low altitudes. 1 P a g e

9 1 Review of the state-of-the-science computer based climate simulation models 1.1 Introduction Recent studies on present and future climate have shown that the Eastern Mediterranean and the Middle East (EMME) are among the most vulnerable regions to climate change with pronounced warming and precipitation reductions (IPCC, 2007; Giorgi and Lionello, 2008; Sheffield and Wood, 2008). Climate in the eastern Mediterranean is characterized by mild rainy winters influenced by the westward moving cyclones (Maheras, 2001; Alpert et al., 2004) and long, hot and dry summers brought about by persistent atmospheric subsidence controlled by the Asian monsoon and the Hadley circulation (Ziv et al., 2004). Cyprus lies at the eastern end of the Mediterranean Sea, hence it belongs in the Mediterranean climate zone and therefore, experiences mild winters and hot dry summers. Winters are mild, with some rain and snow on Troodos Mountain. In summer, the extension of the summer Asian Thermal Low is evident throughout the eastern Mediterranean in all seasonal circulation patterns (Kostopoulou and Jones, 2007a, b), associated with high temperatures and abundant sunshine. The average daytime temperature in winter ranges from C. In summer, the average maximum temperature in coastal regions is C. Further inland, the maximum temperature often reaches 40 C. The wet season extends from November to March, with most (approx. 60%) of the rain falling between December and February (Pashiardis, 2002). Precipitation is generally associated with the movement of moist maritime flows to the North, occurring particularly over areas of high elevation (Kostopoulou and Jones, 2007a). Winter precipitation is closely related to cyclogenesis in the region (Pinto et al., 2001). Nevertheless, it is not uncommon for isolated summer thunderstorms to occur, which however contribute to less than 5% to the total annual precipitation amount (Pashiardis, 2002). The characteristic summer aridity of the region has significant implications in several socio-economic sectors. Cyprus is facing its worst ever water shortage in the last few decades. 1.2 Data and Climate Simulation Models Climate models are widely used to project present and future changes of climate variables. Although the ability of models has improved, systematic biases can be found in model simulations. Therefore it is recommended that model simulations of past and present climate are evaluated by comparing the results with observations. This report reviews 7 high-resolution Regional Climate Models (RCMs) over Cyprus and evaluates models accuracy with the use of observational temperature and precipitation data from meteorological stations on the island. 2 P a g e

10 1.2.1 Observational Data The observational data obtained from the Cyprus Meteorological Service archives are daily weather records available from 1976 to present. The stations are located at various geographic locations and altitudes to represent the complex terrain of the island. Table 1-1 provides a list of the latitude/longitude co-ordinates and elevation of each station (Giannakopoulos et al., 2010). It is evident from Table 1-1, that some are coastal stations, while others are continental at low, medium or high altitudes (see also Fig. 1-1). Table 1-1: Station details in Cyprus (Cyprus Meteorological Service). The colors of the station names correspond to the colors of the crosses in the map of Figure 1-1. STATION LATITUDE (N) LONGITUDE (E) ALTITUDE (m) PRODROMOS AMIANTOS PANAGIA STAVROS SAITTAS LEFKARA NICOSIA LIMASSOL LARNACA Figure 1-1: Map of Cyprus showing the geographical location of the stations of the Cyprus Meteorological Service. 3 P a g e

11 1.2.2 Six Regional Climate Models (RCMs) A brief description of the first six models that are reviewed and evaluated in this report is presented below (Kostopoulou et al., 2009). An overview of the models is also available in Table 1-2. Table 1-2: The six Regional Climate Models used for the purposes of the report. INSTITUTE PARENT GCM RCM COUNTRY ABBREVIATION ETHZ HadCM3Q0 CLM Switzerland ETH CNRM ARPEGE ALADIN France CNR KNMI ECHAM5-r3 RACMO2 Netherlands KNM METNO BCM HIRHAM Norway MNO METOHC HadCM3Q0 HadRM3Q0 U.K. HAD MPI ECHAM5-r3 REMO Germany MPI (1) The first model ETHZ-CLM is provided by the Swiss Federal Institute of Technology Zurich/ETHZ (Eidgenössische Technische Hochschule Zürich). This is based on the regional climate model of the international Climate Limited-area Modelling community (CLM Climate Version of Lokal-Modell ) for climate research, driven by the HadCM3Q0 model (Böhm et al., 2006). The model uses 95*85 horizontal grid points at a rotated latitude-longitude projection and 32 vertical levels. (2) The second RCM used CNRM-RM4.5 is the ALADIN-Climate which is developed at Météo-France/CNRM (Centre National de Recherches Météorologiques) and it is described in Déqué and Somot (2007) and Radu et al. (2008). This model uses 31 vertical levels and 128*120 (lat x lon) horizontal grids.resolution (3) The third model KNMI-RACMO2, used in this work, was provided by the Royal Netherlands Meteorological Institute (Koninklijk Nederlands Meteorologisch Instituut, widely known as KNMI). The KNMI regional climate model RACMO2 (Lenderink et al., 2003; van den Hurk et al., 2006) is forced with output from a transient run conducted with the ECHAM5 GCM. This model uses 40 vertical levels in a hybrid sigma-pressure following coordinate system. The horizontal resolution is 25 km 25 km, which produces a European-Mediterranean grid of 85 longitude 95 latitude grid cells in a rotated latitude-longitude projection. (4) The fourth model METNOHIRHAM was developed in the Norwegian Meteorological Institute and it is based on Version 5 of the HIRHAM regional climate model (Christensen et al., 1996; Haugen and Haakenstad, 2006) driven by the Bergen Climate Model (BCM), a fully-coupled, global climate model that provides state-of-the-art computer simulations of the Earth's past, present, and future climate. This model uses 31 vertical levels and horizontal grid of 213*198 (lat x lon) grid points. 4 P a g e

12 (5) The fifth model METO-HC_HadRM3Q0 was produced in the UK Met Office and it is based on the HadCM3Q0 global climate model. The RCM is projected on a rotated pole projection, with regular latitude/longitude. The number of horizontal grid points is 214x220 (lat x lon) and the number vertical levels is 19 (Collins et al, 2006). (6) The sixth model used is the MPI-M-REMO (Jacob, 2001; Jacob et al., 2001) which has been developed in the Max Planck Institute for Meteorology (MPIM), Hamburg in Germany. The parent GCM is the ECHAM5 and the RCM has been projected on a rotated spherical coordinate system. This model covers 109 x 121 grid points horizontally with 27 vertical levels in the atmosphere The PRECIS Regional Climate Model Another model that is used in this report in order to produce daily output data is the PRECIS (Providing Regional Climates for Impact Studies) regional climate model, based on the United Kingdom (UK) Meteorological Office Hadley Centre HadRM3P model (Jones et al., 1995, 2004). PRECIS applies the same formulation of the climate system as its parent Atmosphere-Ocean General Circulation Model (AOGCM), HadCM3 (Collins et al., 2006), which is also used to provide the lateral boundary conditions, and is driven by the IPCC SRES A1B emissions scenario. The model has a horizontal resolution of 0.22 latitude and longitude (about km) and 19 vertical levels (Nakicenovic et al., 2000; Lelieveld et al., ). PRECIS will be the main model used in this project. The model simulations were performed by the Cyprus Institute within the framework of the CIMME project ( which studied Climate Change and Impacts in the Eastern Mediterranean and Middle East. In PRECIS simulations Cyprus lies at the centre of the study domain. PRECIS is compared with an integrated set of model simulations, also developed to reproduce climate conditions and future climate changes for Europe. In these simulations, Cyprus is placed in the south-eastern part of the domain. 5 P a g e

13 2 Evaluation of the state-of-the-science computer based climate simulation models 2.1 Evaluation of the Six Regional Climate Models (RCMs) The evaluation of the six RCMs is based on runs performed in the framework of the EU FP6 ENSEMBLES project ( For brevity, three-letter acronyms from the institution names are used to identify the RCMs throughout the text and in the graphs (Table 1-2 provides more information about the models) (Hadjinicolaou et al., 2011). All models use a common integration area covering Europe, with Cyprus placed in the southeastern part of the domain (Fig. 2-1) and are run in a horizontal resolution of 25 x 25 km. Daily time-series of Tmax, Tmin, Prcp are provided by the Meteorological Service of Cyprus (MSC) for the stations of Nicosia, Limassol and Saittas. These locations are representative of the island s climate and topographical features, ranging from inland (Nicosia), coastal (Limassol) to mountainous (Saittas) (Fig. 2-1). The data were available for the period , which offers a common period of 25 years for comparison with the model data. Figure 2-1: Common RCM domain of the ENSEMBLES simulations (left) and Cyprus map (Marble Desktop Globe, with overlaid the locations studied (right) Modeled and Observed Climatology We first assess (in Figs. 2-2, 2-3, 2-4; Table 2-1) the mean climatological conditions for the three locations in Cyprus (Nicosia, Limassol and Saittas), for the recent past by comparing modeled and observed Tmax, Tmin and Prcp for the period The inserts in the figures show the annual cycle of the biases of the 6-model ensemble from the observations, allowing an evaluation of the RCMs performance for the mentioned period. Nicosia (Fig. 2-2), located in the central plains of the island, experiences the highest temperatures in July and August (with T max 37 C and T min 22 C), while the coldest months 6 P a g e

14 are January and February (with T max 16 C and T min 6 C). The timing of these observed warmest and coldest conditions, as well as the overall shape of the annual cycle, is captured by the six RCM average. There is a small overestimation of T max (1.6 C) and underestimation of T min (0.5 C), comparable to the observed inter-annual variability, as expressed by the annually averaged climatological standard deviations of 1.43 and 1.11 C, respectively (see Table 2-1). Regarding precipitation (monthly totals), the model ensemble reproduces the observed annual cycle, with the wet months from November to March and the very dry summer conditions. The rainfall from the ensemble mean is less than observed for the wet months, resulting in a -17% bias from the climatological observed annual total of 296 mm. The average of biases in the annual cycle of the ensemble is -4 mm/month, or 5.4 mm/month if we consider the absolute biases (Table 2-1), which is a better measure of the model proximity to the observations that overcomes the cancellation effect of averaging over positive and negative biases. These values compare well within the observed standard deviation of 21.2 mm/year, which is much higher than any of the biases of the six RCMs as shown in Table 2-1. In Fig. 2-3, the observations show that the coastal location of Limassol, due to its marine influence, is around 5 C cooler than Nicosia in the summer (with T max 32 C and T min 17 C in July August) and 1 2 C warmer in winter (with T max 20 C and T min 7-8 C in January- February). The mean absolute annual bias of the 6-model average is 1.2 C for T max and 1.2 C for T min, comparable to the observed standard deviation of 1.11 and 1.17 C respectively, as seen in Table 2. There is a seasonality in the ensemble temperature bias (not evident for the other stations) with discrepancies of -2 C of T max in winter and +2 C in summer, indicating a less maritime climate in the models than in reality, possibly related to the difficulty to represent land sea breeze effects by the models. The model ensemble precipitation is less than the observations from December to March, as seen in the monthly biases. The annually averaged model bias is -2 mm/month, while the annual precipitation total is only 6% less than the observed 406 mm. Note the less than 1 mm/month observed climatological values from June to September (in August, for example, no rain was measured by the station during the period studied). In the station of Saittas (Fig. 2-4), located in the lower slopes of the Troodos mountain range, the observed T max and T min in winter do not exceed 13 and 3 C respectively, but in the summer reach 34 and 18 C, while the winter rainfall ranges between 80 and 130 mm/month (Fig. 2-4). The multi-model average captures very well the observed temperatures (the T max and T min biases are -0.9 and 1.0 C, respectively, smaller than the observed inter-annual variability of 2.24 and 1.79 C). The evolution and the amplitude of the observed annual cycle is also captured nicely with an annually averaged positive model bias of 2 mm/month and the model annual precipitation total, which is only 4% less than the observed 662 mm. The agreement for this high elevation station is remarkable but it is partly also fortuitous, since the large positive bias of one model (MNO, see Table 2-1) increases the ensemble mean values. 7 P a g e

15 Figure 2-2: Annual cycle of the climatology for Maximum Temperature, Minimum Temperature and Precipitation Sum for Nicosia, from the RCMs ensemble mean and the observations. The shaded area represents the 1σ range of the 6-member model mean. The insert plots (with the same axes 8 P a g e

16 units) are the corresponding biases of the model ensemble mean relative to the observed. The number at the right of the inserts summarizes the annually averaged bias. Table 2-1: Annual means of absolute biases of climatology of Maximum Temperature (Tmax), Minimum Temperature (Tmin) and Total Precipitation (Prcp) of the 6 RCMs (CNR, ETH, HAD, KNM, MNO, MPI) and their ensemble mean (ENS) and Pearson correlation coefficients from the observed climatology for Nicosia, Limassol and Saittas. The 1σ standard deviations of the observations (OBS) are also shown. 9 P a g e

17 Figure 2-3: As Fig. 2-2, for Limassol. Figure 2-4: As Fig. 2-2, for Saittas Modeled and Observed Long-term Trends We next present the 25-year evolution of T max, T min and Prcp from all RCMs and the observations in order to check the model performance of trends during the recent past. This is an important test of the ability of the A1B emissions to drive the late twentieth century warming and it also reveals some interesting issues regarding the largescale forcing of the modeled regional climate. Figures 2-5, 2-6, and 2-7 show the relative anomalies for the three locations, constructed from the deseasonalized monthly mean time-series of the 6 RCMs, their ensemble mean and the observations, divided by the respective overall average to allow inter-comparison, and smoothed with a 5-year running mean to emphasize the longer-term variations. In Nicosia, the weak cooling observed during the 1980s (Fig. 2-5) is followed by a weak warming in the 1990s. The model ensemble exhibits a very similar evolution with a high 10 P a g e

18 Pearson s correlation coefficient of 0.76, much larger than any individual RCM (see Table 2-1). All six RCMs simulate the positive trends in the 1990s but in certain periods, they show tendencies of opposite sign (for example in ); after averaging, they cancel and result in the very good agreement of the ensemble mean with the observations. The temporal variation of the models is not exactly a spaghetti type, since the pairs of the RCMs, with the same parent GCM vary similarly (e.g. ETH and HAD, KNM and MPI), pointing to the strong influence of the large-scale forcing. The observed minimum temperature, like the T max, is also lower in the first half of the period but shows a very rapid warming in the 1990s that is captured only by the ETH and HAD models after The ensemble mean agrees less well with a correlation coefficient of The observed precipitation exhibits a large variability and an almost linear decrease, resulting in a 20% overall reduction from the late 1970s to the late 1990s. The models appear much more variable (for example HAD and MNO are well outside the envelope) and their ensemble mean, despite the smaller variability (near the observed) and the negative trend in the 1990s, is actually anticorrelated with the observed trend (r =-0.49). For Limassol, the overall observed trend in T max is weak and although the temporal evolution is captured by ETH and HAD (r = 0.71 and 0.67), the ensemble mean does not agree well. The observed T min shows a remarkable warming in the 1980s and 1990s, not captured by the models although their ensemble mean has an r = 0.54 with the observations. The models seem to capture part of the observed precipitation variations with a positive r = 0.45 of the ensemble mean. The observed T max variation in Saittas has larger amplitudes than in the other two stations and a similarly warmer 1990s period than earlier in the record. This is generally captured by most models (apart from ETH and HAD) and results in an ensemble mean positive correlation of In T min, there is a similar observed warming compared to Nicosia and Limassol but much less steep. This suggests that the stronger observed warming over the mainland and coastal cities (that both experienced a rapid population increase after 1974) could possibly be an urbanization effect. Precipitation in this mountainous site is characterized by some large reductions (from 1986 to 1992 and in the end of 1990s) not represented by any model nor the ensemble mean. 11 P a g e

19 Figure 2-5: Long-term relative anomalies of the deseasonalized timeseries for Maximum Temperature (Tmax), Minimum Temperature (Tmin) and Total Precipitation (Prcp) for Nicosia, from the six RCMs (CNR, 12 P a g e

20 ETH, HAD, KNM, MNO and MPI), their ensemble mean (ENS) and the observations (OBS). The data have been smoothed with a 5-year running mean. 13 P a g e

21 Figure 2-6: As Fig. 2-5, for Limassol. 14 P a g e

22 15 P a g e

23 Figure 2-7: As Fig. 2-5, for Saittas. 16 P a g e

24 2.1.3 Modeled and Observed Quantile Distribution The model performance is also evaluated based on the quantile quantile ( qq ) relationships in the monthly statistics. In Figure 2-8, the model values are plotted against the observed ones corresponding to the same quantile. The proximity of the model data (colored lines) to the observed (1:1 straight black line) indicates the degree of agreement between model and observations. For T max at all three locations, most of the models (apart from ETH) slightly underestimate the observations but their distributions are generally compact and close to the observed (they do better for the colder temperatures). For T min, interestingly, the intermodel scatter is least over Saittas and closer to the observations, indicating a better ability of the RCMs to simulate the meteorological conditions above the night time boundary layer, with least influence by local effects. The larger discrepancies appear from the precipitation plots indicating deteriorating performance toward wetter conditions. The best performing models for values between 50 and 100 mm/month are different for each station, and for more than 100 mm/month, KNM follows the observed distribution for Nicosia best and HAD for Limassol and Saittas. 17 P a g e

25 18 P a g e

26 Figure 2-8: Quantile quantile plots of Tmax (left, in C), Tmin (middle, in C) and Precipitation (right, in mm/month) for Nicosia (up), Limassol (middle) and Saittas (bottom) from the six RCMs (CNR, ETH, HAD, KNM, MNO and MPI) and the observations (OBS) for Weather Extremes With the use of the RClimDex software, 14 indices of extremes (temperature and precipitation related) were derived from daily maximum and minimum temperature and daily precipitation for the recent past from the MSC observations and the six RCMs. Table 2-2 shows the index definitions. Tables 2-3, 2-4 and 2-5 list the decadal means of these indices for the observations and the 6-model average, to provide an evaluation of the collective model ability to reproduce the extreme manifestations of the climate in Cyprus. The C.I. range defines the 95% confidence interval (for the models, it coincides with the lower and higher values of the 6-member ensemble, while for the observations, it is based on the 25-year sample). Table 2-2: Definitions for indices of extremes (from Zhang and Yang 2004). The model ensemble performs better for the temperature related indices, though generally underestimating the number of Summer Days and overestimating the Tropical Nights at all locations. Similarly, but only for Nicosia and Limassol, the models slightly underestimate the observed warmest and coldest Tmax (indices TX x and TX n ) while overestimating T min (indices TN x and TN n ). In Saittas, for these indices, the model averages are very close to the observed. For precipitation, the results are more divergent among the stations. For the consecutive dry days CDD, the models perform well, capturing exactly the 4-month dryness of Nicosia, overestimating it by 11 days in Saittas (observed CDD = 80 days) while underestimating it by 40 days in Limassol (where the observed lack of rain persists for 156 days). The other duration index of consecutive wet days is modeled satisfactorily, with the ensemble mean being only 1 2 days longer than the observed 5 7 days. For the threshold (e.g. R10, R17, R20) and intensity (RX1, RX5, SDII) indices, the models underestimate the observations in Nicosia and Saittas but they seem to simulate them more accurately in Limassol. 19 P a g e

27 Table 2-3: Indices of extremes from observations (OBS) and the 6-model mean (ENS) for Nicosia. 20 P a g e

28 Table 2-4: As Table 2-3, for Limassol. 21 P a g e

29 Table 2-5: As Table 2-3, for Saittas. 2.2 Evaluation of the KNMI-RACMO2 Regional Climate Model Further investigation and analysis is performed for one of the six RCMs used in this study, namely KNMI-RACMO2 which was developed at KNMI in the Netherlands (Lenderink et al., 2007). The high spatial resolution of RACMO2 enables a satisfactory representation of the island. Daily output data of the model represent the period (Giannakopoulos et al., 2010). The selection of the model data, used for the evaluation, was based on extracting the four nearest RCM grid point to each observation location. The closest land grid point was found to be the most representative for each site. The mean daily minimum (T min ), maximum (T max ) temperatures and precipitation (RR) for each calendar-day over the evaluation period are calculated to illustrate their seasonal cycles, as represented by both model and observational datasets. Figure 2-9 shows some representative examples of the reproduced annual cycle of maximum temperature (left panels), minimum temperature (middle panels) and precipitation (right panels) for three selected stations. In general, the model captures well the seasonal variability of temperature, in most stations. In particular, the model demonstrates great skill in simulating T max and T min in low altitudinal 22 P a g e

30 regions, whereas T min seems to be better reproduced than T max. As far as maximum temperature is concerned, it is evident that the model tends to overestimate summer maximum temperatures in coastal areas. Regarding T max in high elevated regions, the model presents overestimated values all over the year in the two mountainous stations studied, namely Amiantos and Prodromos (not shown). With respect to precipitation, the model results are generally of the same magnitude as the observational data (apart from few exceptionally high daily amounts) indicating that the fundamental physics is captured by the model. In overall agreement with the results for T max and T min, the model reproduces, to a lesser degree, RR in high elevation areas. According to the station records, the two stations (Amiantos and Prodromos), which are located at considerably higher elevations compared to the rest of the sites, receive the highest precipitation amounts of the region. In these cases the model performs poorly in reproducing precipitation. Figure 2-9: Curves show the 30-year mean annual cycles of observed (black) and modeled (red) maximum temperature (left), minimum temperature (middle) and precipitation (right) at three representative stations. Temperature is measured in C and precipitation in mm/day. The accuracy of the model is further assessed by correlation analyses undertaken between model and station observations. Statistically significant correlation coefficients exceeding 0.8 are defined for all stations and for both T max and T min. Unfortunately, correlations among precipitation datasets do not reveal encouraging results, as in many cases coefficients are below 0.5. An analysis of the daily differences between model and station data results in 23 P a g e

31 findings consistent with those of the seasonal cycle reproductions. In general, the model simulates better (i) T min than T max, (ii) summer than winter temperatures and (iii) low than high altitude locations. Precipitation seems harder to reproduce, however, daily differences reveal an adequate representation by the model. Dry days are better captured by the model, although the model shows a deficiency to capture days with intense precipitation events. Some exceptional differences are detected in high altitude stations, which receive larger rainfall amounts. 2.3 Evaluation of the PRECIS Regional Climate Model Daily output data from the Hadley Centre PRECIS regional climate model, driven by HadCM3P (Collins et al. 2006), are used for the model s evaluation (Lelieveld et al., ). The model simulations are performed at the Cyprus Institute within the framework of the CIMME project ( which studies Climate Change and Impacts in the Eastern Mediterranean and Middle East. Model output is evaluated by comparison with point observations derived from 9 stations located in Cyprus, owned by the Cyprus Meteorological Service, as shown in Figure 2-10 (see also Table 1-2), for the period (except from Nicosia for which the period is ). Figure 2-10: Map of Cyprus showing the geographical location of the stations of the Cyprus Meteorological Service (x), as well as PRECIS grid points ( ). 24 P a g e

32 2.3.1 Modeled and Observed Climatology First, the average patterns of temperature and precipitation for the evaluation period are described and their trends are calculated. The model simulations are evaluated against point observations from 9 stations of Cyprus Meteorological Service (Table 1-2). The average daily maximum (TX), minimum (TN), mean (TM) temperatures and precipitation (P) for each calendar-day over the evaluation period are calculated to illustrate their seasonal cycles, as represented by both model and observational datasets. Figure 2-11: Curves show the 30-year mean annual cycles of observed (ST) and modeled (PRC) maximum temperature (TX), minimum temperature (TN) and mean temperature (TM) at 8 representative stations. Temperature is measured in C. 25 P a g e

33 Figure 2-11 shows some representative examples of the reproduced annual cycle of maximum temperature (TX), minimum temperature (TN), and mean temperature (TM), for eight selected stations. In general, the model captures well the seasonal variability of temperature, in most stations. Regarding high elevated regions, PRECIS presents overestimated values all over the year and especially in the two mountainous stations, namely Amiantos and Prodromos. The overestimation might be attributed to lower model orography. The model presents best skill in medium elevation continental stations (e.g. Lefkara, Nicosia), while sea influence in model grids leads to a weakness of the model to reproduce extreme temperatures in coastal sites (e.g. Limassol, Larnaca). In particular, the model tends to underestimate maximum temperatures and overestimate minimum temperatures in coastal areas. The modeled annual precipitation cycles reproduce the observed wet/dry seasonal precipitation distribution in most cases (Figure 2-12). Nevertheless, for few sites the daily precipitation (P) is found to vary considerably between observations and model, indicating a systematic underestimation of the model simulated precipitation, for example in Prodromos and Amiantos. Although, in general, the model underestimates rainfall in Cypriot stations, in some cases overestimation is noted during the summer. The most prominent case is in Limassol (alt: 31 m), where large discrepancies of summer precipitation are observed. The accuracy of the model is further assessed by correlation analyses undertaken between model and station observations. Scatter plots are produced that show that the correlation coefficients are for TM, for TX and for TN (Figs. 2-13, 2-14, and 2-15). The shape and the direction of the scatter clouds denote positive correlations between the series. In addition, higher correlations are identified in lower altitude stations (e.g. Larnaca, Limassol, Nicosia, Lefkara). 26 P a g e

34 Figure 2-12: Mean annual cycles of the climatology for total precipitation (P) at 8 stations, from the PRECIS model (red) and the observations (blue). Precipitation is measured in mm/day. 27 P a g e

35 Figure 2-13: Scatter plots of the corresponding pairs of observed and PRECIS mean temperature (TM) for the 9 sites. 28 P a g e

36 Figure 2-14: Scatter plots of the corresponding pairs of observed and PRECIS maximum temperature (TX) for the 9 sites. 29 P a g e

37 Figure 2-15: Scatter plots of the corresponding pairs of observed and PRECIS minimum temperature (TN) for the 9 sites Modeled and Observed Quantile Distribution The similarity of PRECIS and station distributions of temperature and precipitation (raw & indices) is also assessed by Quantile-Quantile (QQ) plots. Use of a QQ plot to compare two datasets is a non-parametric and more powerful approach than common techniques of comparing histograms of the two datasets. In a QQ plot shifts in location, shifts in scale, changes in symmetry, and the presence of outliers can be detected. For example, if the two datasets come from populations whose distributions differ only by a shift in location, the points should lie along a straight line that is displaced either up or down from the 45-degree reference line. Figures 2-16 and 2-17 present QQ plots for the annual TX, TN, and precipitation, as well as 3 indices (TXQ90_JJA, TNQ10_DJF, PQ90_ANN) in four representative stations, namely Nicosia, Lefkara, Amiantos, and Panagia. In these Figures, the model values are plotted against the observed ones corresponding to the same quantile. The proximity of the model data to the observed (1:1 straight black line) indicates the degree of agreement between model and observations. 30 P a g e

38 Figure 2-16: QQ plots for the annual maximum temperature (TX_ANN), minimum temperature (TN_ANN), precipitation (P_ANN), and 3 indices (TXQ90_JJA, TNQ10_DJF, PQ90_ANN) in two medium elevation continental stations (Nicosia and Lefkara). 31 P a g e

39 32 P a g e

40 Figure 2-17: QQ plots for the annual maximum temperature (TX_ANN), minimum temperature (TN_ANN), precipitation (P_ANN), and 3 indices (TXQ90_JJA, TNQ10_DJF, PQ90_ANN) in two mountainous stations (Amiantos and Panagia). In general, the produced QQ plots reveal good simulations. However, slight underestimations are detected for TX and precipitation. The model also reveals weakness in reproducing extreme precipitation (PQ90_ANN). At higher elevations (Figure 2-17), overestimation of temperatures and underestimation of precipitation are evident Weather Extremes and Trends The area of interest is particularly vulnerable to extreme climate events such as droughts and heat waves (Kostopoulou et al., ). To assess extreme temperature and precipitation conditions in Cyprus for the evaluation period , climate indices are calculated and expressed as the annual occurrence of a variable exceeding a certain threshold. In particular, the warming conditions are expressed by the number of warm days (days per year with TX>25 C), the number of heatwave days (days per year with TX>35 C) and the number of tropical nights (days per year with TN>20 C). The number of frost nights' are defined by days with TN<0 C. Regarding precipitation, the average number of days (number of wet days) with RR>1.0 mm is used and heavy precipitation is defined by the annual number of days with RR >10 mm (Kostopoulou et al., ). Extreme climate indices (temperature and precipitation related) are calculated for the period Table 2-6 shows the indices of extremes that are derived from PRECIS model and observational data. Table 2-6: Indices of extremes calculated from PRECIS model output and from observed data. TX INDICES TN INDICES PRECIPITATION INDICES Average Annual/seasonal TX No of hot days days (TX>30 C) No of heatwave days (TX>35 C) No of ice days (TX<0 C) Max length of TX >25 C Annual longest spell of TX >25 C Heatwave duration 90th percentile of TX (TXQ90) No of frost nights (TN<0 C) No of very cold nights (TN<-5 C) No of tropical nights (TN>25 C) 10th percentile of TN (TNQ10) No of wet(dry) days (P>1mm,(<1mm)) Max length of wet(dry) spell Annual max total rainfall over 3 days No of days with P >Q95 90th percentile of rain amounts (PQ90) Max no of consecutive dry days 33 P a g e

41 The geographical patterns of the bias (PRECIS output-observations) for selected indices, are presented in Figures 2-18 and The average patterns are investigated in relation to extreme temperature conditions. In particular, spatial patterns are available for average maximum (TX) and minimum (TN) temperatures, as well as extreme indices TXQ90 and TNQ10 in an annual (ANN), winter (DJF) and summer (JJA) basis. Figure 2-18: Annual (ANN), winter (DJF), and summer (JJA) model biases maps for extreme maximum temperature (TX and TXQ90). Units are in degrees Celsius. The circles represent the average model bias in relation to the observational data. Figure 2-19: Annual (ANN), winter (DJF), and summer (JJA) model biases maps for extreme minimum temperature (TN and TNQ10). Units are in degrees Celsius. The circles represent the average model bias in relation to the observational data. The simulated maximum temperature (TX) is overestimated up to 4 C at the western part of the domain while at the eastern part it is underestimated especially during the summer season (Fig. 2-18). 34 P a g e

42 In terms of TN and TNQ10 positive differences dominate large part of the study region with biases reaching +4 C mainly in the central and the western part of the domain during winter (Fig 2-19). Figure 2-20: Annual (ANN) and winter (DJF) model biases maps for frost nights (TN<0 o C) as well as annual (ANN) and summer (JJA) model biases maps for heatwave days (TX>35 o C). The circles represent the average model bias in relation to the observational data. Figure 2-21: Annual (ANN), winter (DJF), and summer (JJA) model biases maps for average precipitation, as well as annual (ANN), winter (DJF), and autumn (SON) model biases maps for PQ90. Units are mm of rain. The circles represent the average model bias in relation to the observational data. 35 P a g e

43 Additional patterns for biases during the evaluation period in frost nights and heatwave days as well as in precipitation indices appear in Figures 2-20 and Frost nights are overestimated for the evaluation period over the larger part of the domain except for a limited area at the northern part of the island. A small overestimation/ underestimation for heatwave days occurs in the western/eastern part of the domain, respectively. The model seems to underestimate precipitation mainly in the western part of the study domain. However, an overestimation of PQ10 is observed during the autumn period. Annual and seasonal temperature and precipitation trends are assessed for the evaluation period. The 30-yr linear trend is determined for each grid point and the Kendall-tau test is employed to estimate the statistical significance of trends. Maps are constructed depicting only grids with significant trends in average TX and TN for summer (Figure 2-22), as well as annual indices TXQ90 and PQ90 (Figure 2-23). Figure 2-22: Trends of average maximum temperature (TXave) and average minimum temperature (TNave), for the summer (JJA), derived from PRECIS model (left) and observational data from the stations (right). Filled triangles indicate trends significant at the 0.05 level of significance. 36 P a g e

44 Figure 2-23: Trends of annual TXQ90 and PQ90 indices, derived from PRECIS model (left) and observational data from the stations (right). Filled triangles indicate trends significant at the 0.05 level of significance. PRECIS is found to accurately estimate increasing, although not statistically significant, trends in extreme temperature indices (Fig 2-22, 2-23). As far as precipitation is concerned, the model seems to fail in reproducing PQ90 trends except for sites at medium-low altitudes (Fig 2-23 bottom). 37 P a g e

45 3 Conclusions The six RCMs data are evaluated by comparing with observations for the climate mean state and the weather extremes during the recent reference period For simulated temperatures, the annual average biases from the model ensemble do not exceed 1.5 C and are within the observed inter-annual variability (1σ). The multi-model mean does well in capturing the observed annual cycle in precipitation, although the individual RCMs sometimes fail to reproduce the wet extremes. The evaluation of the KNMI-RACMO2 model against the observational data shows that simulated fields such as maximum and minimum temperature closely resemble the observed records. The simulations are poorer in regions situated at higher altitudes. As far as the estimated precipitation is concerned, the model performs generally well, despite the fact that it tends to underestimate high precipitation amounts compared to the measured data. PRECIS is the main model used in this project. In PRECIS simulations Cyprus lies at the centre of the study domain. PRECIS is compared with an integrated set of model simulations, also developed to reproduce climate conditions and future climate changes for Europe. In these simulations, Cyprus is placed in the south-eastern part of the domain. The PRECIS dataset satisfactorily reproduces annual cycles, raw data and climate indices for temperature in medium elevation continental sites. In higher altitudes, there are overestimations in temperature, probably due to the lower topography of the model. In coastal areas, the model shows poor skill in reproducing the inter-annual variability of min/max temperature, which might be attributed to the strong sea-influence in the model grids. Regarding precipitation, the model shows an overall underestimation in annual and seasonal rainfall, as well as in extreme precipitation events. High correlations are also observed among daily model and station data. Regarding QQ plots, the corresponding points are grouped around a line slightly deviated from the 45deg diagonal line, showing that the two distributions are linearly related (less so for Precipitation and PQ90). PRECIS is found to accurately estimate increasing (although not statistically significant) trends in extreme temperature indices. For precipitation, trends are correctly estimated in medium-low altitudes. Overall, the evaluation results are encouraging for the use of such RCM data to estimate potential effects of climate change in Cyprus. 38 P a g e