Long-term climate prediction results
For the assessment of the effects of the three SRES-scenarios on the prediction of the local climate in the study area, the 2000-2006 climate predictions with the multi-domain MLR-model employing GCMs+HiRes/HadCM3 sub-model is examined for each of these SRES. The prediction accuracies, as measured by the Nash–Sutcliffe (NS) model efficiency coefficient, obtained for the three SRES scenarios are listed in Table 3.29. The results demonstrate that for all climate variables SRES A2 is the most accurate. Therefore, SRES A2 is considered as the most appropriate to describe the recent (2000-2006) climate in the study area.
Table 3.29. Performance, as measured by the Nash–Sutcliffe efficiency coefficient, of the best GCM (HadCM3) (see Table 3.28) /multi-domain MLR- downscaling model, calibrated for years 1971-1999, for three SRES, to predict the 2000-2006 monthly climate in the study region..
predictor set predictand
average Nash–Sutcliffe model efficiency coefficient 1971-1999
20c3m
2000-2006 SRES scenario
A1B A2 B1
HiRes
Tmax 0.71 -0.24 -0.23 -0.26
Tmin 0.93 0.53 0.54 0.51
HMD 0.75 0.47 0.46 0.47
SLR 0.71 0.31 0.32 0.31
PCP 0.65 0.01 -0.01 0.01
%Wet 0.71 0.52 0.51 0.52
GCMs
Tmax 0.52 -0.22 -0.22 -132.67
Tmin 0.84 0.66 0.73 -0.74
HMD 0.59 0.36 0.37 -213.05
SLR 0.74 0.31 0.22 0.10
PCP 0.61 -16.89 0.10 -140.24
%Wet 0.68 0.09 0.39 -129.08
GCMs +HiRes
Tmax 0.73 -0.34 -0.32 -41.84
Tmin 0.94 -1.72 0.61 -2.97
HMD 0.76 -25.68 0.44 -110.74
SLR 0.78 0.28 0.30 0.15
PCP 0.69 -12.28 0.11 -58.20
%Wet 0.74 -16.73 0.46 -28.67
Note: The optimal scenario in each group is highlighted in bold italics
3.7.2 Long-term prediction of the future climate
The prediction of the future climate in the study region, using the multi-domain MLR- downscaling model with predictors from multi-domain GCMs and HiRes GCM (HadCM3) is emphasized in this section. Since it has been advocated in climate downscaling studies to employ at least 20-30 years of climate time-series for the definition of the baseline climate (Semenov and Barrow 2002), the observed climate variables minimum and maximum temperatures and precipitation in years 1971-1999 are used to calibrate the downscaling models. In this case, the 20c3m-simulation (years 1971-1999) is the baseline simulation to represent the climate in the 20th-century. At the end of this baseline simulation, the future downscaled GCM- predictions obtained with the three SRES A1B, A2 and B1 are projected from the final state of the 20c3m-model and run through the whole 21st-century, i.e. from year 2000 to 2096 (Covey et al. 2009).
Monthly HadCM3-GCM- predicted climate time-series, available over this whole timespan, are downscaled by the multi-domain MLR-model to investigate the potential changes of the future climate. For comparison, the two conventional statistical downscaling models, i.e. SDSM (Wetterhall et al. 2006) and LARS-WG (Khan et al. 2006) which used daily predictors are also
applied. Because of the different availability of the GCM-climate predictors, the future (daily) projections of these two downscaling techniques could be made only for years 2046-2065.
Figure 3.26 to Figure 3.28 illustrate the projected variations and trends of the average downscaled climate time-series of the monthly minimum and maximum temperatures from 4 climate stations, monthly precipitation, probability of wet day, humidity and solar radiation from 24 meteorological stations for the full simulated (predicted) time period 1971-2096. For the 1971-1999 timespan (20c3m period) and the subsequent verification period 2000-2006 (see above) the observed monthly climate time-series are also plotted. One may notice from these figures a rather good agreement, at least in terms of the linear- and of the 12-month moving trend, between the predicted and observed climate series for these two past periods. However, some extreme variations in the maximum temperature and the rainfall amounts cannot be described properly by the corresponding downscaled predictors, for example, the uncommonly increasing maximum temperatures in years 1995-2003, extreme rainfall over 10 mm/day, and bigger fluctuation of the solar radiation. However, such large differences between downscaled and observed data are often found in in climate downscaling (Cubasch et al. 1996, Wilby et al.
1998), and they are usually attributed to local climate effects (Mearns et al. 1999). In fact, a direct look at the parent GCM-predictors indicates that the predicted maximum temperature cannot identify the unusual large observed changes of this variable during 1995-2003, and this negative situation cannot be remedied anymore by the subsequent downscaling.
This means then that trends of climate change obtained directly with a regional climate model and with a downscaling technique should be comparatively assessed (Wilby et al. 1998, Mearns et al. 1999). Consequently, the trend changes of the surface air temperature and of the precipitation obtained directly from the Hi-Res GCM model (grid model) are examined further in Table 3.30.
One may notice from the table that the change rates of the three climate variables obtained with the HiRes- downscaling models are similar to those obtained using the HiRes GCM-grid predictors directly, i.e. an increasing trend for the temperatures and no noteworthy trend for the precipitation. On the other hand, when using MLR-downscaling with the combination-set of GCMs+HiRes, the rainfall trends are slightly positive, however it is still averagely constant.
The climate predictions for the 21st-century obtained with the multi-domain MLR- downscaling model applied to the optimal predictor-set derived from the best combination of multi-domain GCMs and Hi-Res GCM indicate higher temperatures over the 21st–century, whereby SRES A2 scenario results in the strongest increase of the temperature and also of the rainfall. For the former, depending on the SRES scenario, the rate of change ranges between +0.1 and +0.3°C/decade, wherefore the maximum temperature is increasing faster than the minimum one. The rate changes for the precipitation are rather small and lie between +0 and +0.2 mm/day/decade (6 mm/month per decade).
While here only the most prevalent trends of the future climate in the study region are given, more details with regard to its spatial and seasonal variations will be provided in Chapter 7.
Figure 3.26. Monthly averages of projected 4-site minimum and maximum temperatures (top) and 24-site precipitation (bottom) over years 1971-1999 (20c3m) and future years 2000-2096 under the SRES-scenarios A1B, A2 and B1, using the multi-domain MLR-downscaling method.
Tmax
Tmin
Figure 3.27. Similar to Figure 3.26, but for the 4-site averaged monthly wet-day probability (top) and the 4-site averaged monthly humidity (bottom).
Figure 3.28. Similar to Figure 3.26, but for the two-site monthly average of the solar radiation Table 3.30. Linear trends, as measured by the change per decade, of the future downscaled predictions for the monthly maximum and minimum temperatures and the precipitation in the study region for years 2000-2096, under the three SRES- scenarios A1B, A2 and B1, for the different combinations of GCMs /downscaling methods.
method models Tmax (°C/decade) Tmin (°C/decade) PCP (mm/day/decade)
A1B A2 B1 A1B A2 B1 A1B A2 B1
atmospheric
predictor monthly HiRes-GCM
predictors +0.6* +0.5* +0.2* - 0.1 - 0.1 - 0.0 SDSM
LARS-WG
daily ECHO-G** +0.1 +0.2 +0.1 +0.1 +0.1 +0.1 +0.9 +0.9 +0.8 daily ECHO-G** +0.3 +0.4 +0.2 +0.3 +0.4 +0.2 +0.3 +0.3 +0.3
MLR
monthly best-domain +0.3 +0.3 +0.1 +0.2 +0.2 +0.1 +0.1 +0.2 +0.0 monthly HiRes +0.4 +0.3 +0.1 +0.5 +0.4 +0.2 - 0.1 - 0.1 - 0.0 monthly GCMs - 0.0 - 0.0 - 0.0 - 0.1 - 0.1 - 0.1 +0.1 +0.1 +0.1 monthly GCMs+HiRes +0.3 +0.4 +0.3 +0.3 +0.2 +0.1 +0.1 +0.2 +0.0
*HiRes-GCM grid provides only mean temperature
**daily climate ECHO-G- predictors for downscaling are available only for years 2046-2065