Following the definition of climate given by the World Meteorological Organization (WMO), the climate datasets are divided in 5 time-slices, each 30 years long, for the period 1951-2100. A comparison is made for the daily precipitation (P) and daily mean temperature (T), between model data and the CRU E-OBS (CRU from now on) data for the 2 periods in which CRU reference data are available (1951-1980 and 1981-2010). For each grid point and for these variables (P and T), they are evaluated monthly averages of the variable for each year and each 30-years period, and then compared three statistical indicators of CRU dataset, namely the absolute error (AEA) of monthly averages, the absolute error of the monthly fluctuations (AEF) and the absolute error of the extremes (AEE). Since both precipitation and temperature play a major role in determining a basin's hydrologic response, it is necessary to identify for the basin a unique group of models to use for future analysis, satisfying a criteria of minimal error; this is obtained by introducing two a-dimensional skill score indicators for mean values and fluctuations and for extremes. An effective way to represent AEA and AEF skill indicators, and to evaluate model performance for the study area, is a Cartesian plot, in which AEA is x coordinate and AEF is y coordinate; as the errors (both of monthly averages and of
76 fluctuations) within the first climatic period are well correlated with the corresponding during the second period, the skill evaluation can be definitely set for the entire period 1951 to 2010. In order to get a final analysis, results of the two variables are combined, making it possible to get the AEA-AEF adimensional plots shown in Figure 5.3.
Figure 5.3 – Gaza: AEA-AEF skill scores plot for each of the 14 RCMs for the climatic period 1951- 2010, obtained combining precipitation and 2-meters temperature errors.
In this case, the best four models are: ECH_REM, ECH_RMO, BCM_HIR and HCS_HIR. Unfortunately, data for BCM_HIR and HCS_HIR are available only until year 2050, so that these two RCMs are not considered for further analysis in this study. Therefore, in order to gain at least 4 models to be analyzed, in this part of the study are used other 2 RCMs (ECH_RCA, HCH_RCA) having them got quite good skill ratings. Hence, within this study (and within CLIMB project too, from which this consideration are coming) they are used modeled results coming from ECH_REM, ECH_RMO, HCH_RCA and ECH_RCA, being them two different RCMs initialized with the same GCM initial condition and boundary conditions and two runs of the same RCM fed with two different GCMs.
Yet, as the best ‘performance’ is detected to be for the ECH_RMO climate model, results coming from these model are furthermore analysed in the following, focusing most attention to the only gridded point inside the Gaza Strip (p2362).
After the auditing procedure above illustrated, both modeled temperatures and precipitation variables have been bias corrected with reference to CRU dataset for all the
77 4 chosen RCM models, as CMs are supposed to simulate the climate, reproducing as well the seasonally cycle of that region, within monthly and seasonally biases.
A variety of methods can be used to account for the systematic mismatch between observed and simulated climate variables over a considered control period (Anandhi et al., 2011; Stoll et al., 2011). In order to reduce the climate scenario errors, the QQplot approach, known in literature as ‘daily translation method’ (Mpelasoka and Chiew, 2009) is applied with modeled and observed (CRU) daily precipitation and temperatures data in the Gaza Strip. The ‘daily translation method’ has been shown to perform as well as more sophisticated statistical downscaling methods (Themeßl et al., 2011) and to be skilful in other hydrologic impact studies (Wood et al., 2004; Maurer and Hidalgo, 2008). The basilar assumption of this methodology is that CMs are supposed to simulate the climate, which is essentially the statistics of precipitation, temperature, wind and other meteorological parameter in a given region over long periods (30 years for WMO), reproducing as well the seasonally cycle of that region. CMs are, at least, seasonally biased, and they can be bias corrected by correcting their statistics; a possible solution is to correct directly seasonal probability distribution function.
Establishing a correspondence between modeled and measured values linked by the same value of the cumulative distribution function (CDF), it is possible to eliminate the CDF variable and obtain directly a calibration plot; so that, a quantile scaling technique (Sulis et al., 2012) is used to establish a relationship for the control period (1981-2010) between CRU and RCM-simulated daily values at the different ranks/percentiles defined by interpolating directly from the empirical CDF for each time series. Assuming that the biases are stationary in time, this relationship is separately applied for each of the 12 months to translate the future climate model data.
The results of the bias correction for the 4 chosen models are not further analysed here, excepted for ECH_RMO model. In Figure 5.4 they are graphically compared precipitation values, in terms of mean monthly values, for the period 1981-2010, relative to ECH_RMO outputs (p2362) with and without CRU bias correction for p2362, and measured values in the Rainfall Station (KY) nearest to p2362. It is evident that modeled climate precipitations are underestimated in comparison with real values for all the year, with exception for the months from June to September (months from 6 to 9, dry season).
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Figure 5.4 – Comparison of monthly mean values of precipitation for the period 1981-2010: ECH_RMO outputs (p2362) with and without CRU bias correction, and measured values in the
nearest to p2362 Rainfall Station (KY).
The same bias correction is applied to modeled future precipitation values, supposing that the applied correction can be reasonably the same applied on the modeled past values. In Figure 5.5 they are graphically compared precipitation values, in terms of monthly mean values, for the period 2011-2040 (left part) and 2041-2070 (right part), relative to ECH_RMO outputs for p2362, with and without CRU bias correction.
Figure 5.5 - Comparison of monthly mean values of precipitation for the period 2011-2040 (left) and 2041-2070 (right): ECH_RMO outputs (p2362) with and without CRU bias correction
In Figure 5.6 they are graphically compared temperatures values, in terms of mean monthly averaged (tavg), minimum (tmin) and maximum (tmax) values, for the period 1981-
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Figure 5.6 - Comparison of monthly mean values of temperatures for the period 1981-2010: ECH_RMO outputs (p2362) with and without CRU bias correction.
In Figure 5.7 they are graphically compared temperatures values, in terms of monthly mean values, for the period 2011-2040 and 2041-2070 relative to ECH_RMO outputs for p2362 with CRU bias correction.
Figure 5.7 – Comparison of monthly mean values of temperatures for the periods 1981-2010, 2011- 2040 and 2041-2070: ECH_RMO outputs (p2362) with CRU bias correction