GCMs for long-term prediction 3.3
3.3.4 Preparation of GCM-predictor sets
As mentioned earlier, the prediction outputs of the selected GCMs are summarized and combined into the multi-GCM database. Before the multitude of predictor output collected in this way can be used efficiently in the various downscaling techniques, they must be subjected to a complicated screening and selection procedure (Wilby and Wigley 1997). The various steps do this properly are described in this section. Firstly, the GCM-predictor sets are extracted from the
multi-ensembles GCMs and ranked based on their fit to observed data. These GCM-predictor ranks are then written into a daily, monthly and monthly multi-GCM database, which afterwards are transferred to the input dataset of the three major downscaling models used in this study, i.e.
SDSM, LARS-WG and various multi-linear regression (MLR) - models.
Screening of the GCM- predictors 3.3.4.1
As the climate prediction results of the various GCM’s may vary significantly, an initial screening of the available GCMs is needed (Wigley 2008). Gridded outputs from selected GCM datasets from the CMIP3-database (Meehl et al. 2007) are used as a starting set of predictor selection.
The climate variables from the model-grid covering the study region are extracted from selected GCMs, i.e. BCM2, ECHAM5, ECHOG, GISS-ER and PCM1 and subsequently incorporated into the multi-GCM database which will provide the input data for long-term climate predictions. During this process the incomplete and missing predictors from selected GCMs are removed which, finally, results in a total of 1340 available choices of monthly predictors in the multi-GCM database, based on 256 climate predictors from one of the five GCMs mentioned above. For the daily GCM database 58 predictors are available (see also Table 3.3).
In the next step the predictor’s potential for later use in the downscaling of a climate variable in the study region is evaluated. To that avail the cross-correlations between the climate predictors and a local weather/climate variable are evaluated. The cross-correlation coefficient between predictor P and local climate variable C, with mean values ̅ and ̅, is defined as:
where is the predictor time-series of an ensemble in the multi-GCM database and is a local climate time-series, i.e. maximum and minimum temperature, precipitation, wet rate, humidity and solar radiation. The absolute value of is then a measure of the predictor’s potential to predict the local climate.
In this study, monthly time-serial cross-correlations between the GCMs- climate predictors and the meteorological measurements from 24 rainfall-, 4 minimum and maximum temperature-, 4 humidity- and 2 solar radiation- stations within the study area are then computed and ranked.
The results obtained for the average cross-correlation scores of the 58 daily GCM-predictors from the ECHO-G GCM with the observed maximum, minimum temperature and precipitation at all climate stations in the study region are shown in the three barplot-panels of Figure 3.4.
The corresponding average ranks assigned to the various cross-correlation coefficients for each combination of predictor- and observed variable (predictand) are listed in Table 3.8.
The ranks of the top ten predictors from the daily GCM (ECHO-G)- and from the monthly multi-GCM- predictor database for predicting the local climate for all stations in the study region are summarized in Table 3.9.
Based on the predictor-rank results of Table 3.8 and, particularly, of Table 3.9, it can be stated that the air temperature (ta), east wind (ua) and the humidity (hus) are the best predictors for the daily maximum, minimum temperature and precipitation, respectively. The fraction of O3 in the air from the PCM-GCM can best predict the monthly maximum temperature, humidity and the solar radiation. Also the same predictor from the GISS-GCM can best predict the precipitation
∑ ̅ ̅
√∑ ̅ ∑ ̅
(3.2)
and the percentage of wet day. On the other hand, the minimum temperature is best predicted by the outgoing shortwave flux (rsutcs) of the ECHAM5-GCM.
Figure 3.4. Average cross-correlation coefficients between 58 daily ECHO-G GCM predictors and observed daily maximum and minimum temperatures and precipitation for all recording climate stations in the study region for years 1971-2000.
Table 3.8. Average ranks of the cross-correlation coefficients of the daily ECHO-G GCM predictors with the observed local climate variables at all climate stations in the study region for years 1971-2000. See Table 3.7 for meanings of predictor names.
predictor parameter rank Tmax Tmin PCP
hfls hfls 11 13 9
hfss hfss 48 22 2
hus hus.100000 27 10 1
hus.20000 24 20 28
hus.30000 32 16 14
hus.40000 36 19 13
hus.50000 35 15 11
hus.60000 29 12 8
hus.70000 30 11 4
hus.85000 41 18 6
hus.92500 37 17 3
pr pr 33 25 21
pr.1 31 26 26
pr.2 38 24 25
psl psl 14 9 38
rlds rlds 4 7 24
rlus rlus 34 45 17
rlut rlut 42 23 19
rsds rsds 44 41 36
rsus rsus 43 42 37
ta ta.100000 17 50 20
ta.20000 25 31 52
ta.30000 45 28 43
ta.40000 51 30 40
ta.50000 40 36 46
ta.60000 53 54 58
ta.70000 20 34 47
ta.85000 1 8 41
ta.92500 6 32 49
tas tas 19 52 12
tas.1 22 51 22
tas.2 12 56 33
tasmax tasmax 21 47 15
tasmax.1 28 46 23
tasmax.2 18 48 30
tasmin tasmin 8 39 29
tasmin.1 15 40 34
tasmin.2 5 38 45
ua ua.100000 9 1 18
ua.20000 57 27 7
ua.30000 56 29 5
ua.40000 55 37 10
ua.50000 50 53 32
ua.60000 39 33 56
ua.70000 26 21 50
ua.85000 16 6 31
ua.92500 13 5 27
uas uas 10 2 16
va va.100000 2 4 35
va.20000 58 43 42
va.30000 54 44 44
va.40000 49 49 51
Va.50000 46 58 54
Va.60000 47 55 55
Va.70000 52 57 53
Va.85000 23 35 57
Va.92500 7 14 48
vas vas 3 3 39
Table 3.9. Top-ten predictors for daily- (ECHO-G) and monthly- predictor-generating GCMs, ranked based on their average correlation-coefficients with the corresponding climate variable from all stations in the study area. See Table 3.7 for meanings of predictor names.
rank daily GCM monthly GCM
Tmax Tmin PCP Tmax Tmin PCP HMD SLR %Wet
1 ta.
85000 ua.
100000 hus.
100000 PCM.
tro3.92500 ECHAM5.
rsutcs GISS.
tro3.5000 PCM.
tro3.2000 PCM.
tro3.60000 GISS.
tro3.5000 2 va.
100000 Uas hfss PCM.
tro3.70000 PCM.
rsdt GISS.
tro3.3000 GISS.
tro3.2000 PCM.
tro3.50000 GISS.
tro3.3000
3 vas Vas hus.
92500 PCM.
tro3.25000 ECHO-G.
rsdt GISS.
tro3.2000 PCM.
tro3.1000 PCM.
tro3.40000 PCM.
tro3.2000 4 rlds va.
100000 hus.
70000
PCM.
tro3.20000
ECHAM5.
rsdt
GISS.
tro3.7000
GISS.
tro3.5000
PCM.
hur.92500
GISS.
tro3.2000 5 tasmin.2 ua.
92500 ua.
30000 GISS.
tro3.25000 BCCR.
rsdt BCCR.
hus.100000 BCCR.
zg.1000 GISS.
Clt BCCR.
ua.2000 6 ta.
92500 ua.
85000 hus.
85000 GISS.
tro3.30000 GISS.
rsdt BCCR.
rlds GISS.
tro3.10000 PCM.
tro3.30000 BCCR.
ua.5000 7 va.
92500 Rlds ua.
20000 GISS.
rsus ECHO-G.
rsdscs BCCR.
hus.92500 GISS.
tro3.3000 ECHAM5.
Hfss BCCR.
ua.1000 8 tasmin ta.
85000 hus.
60000 GISS.
tro3.20000 ECHAM5.
rsdscs GISS.
huss BCCR.huss ECHAM5.
hur.100000 BCCR.
ua.3000 9 ua.
100000
Psl hfls PCM.
tas
ECHO-G.
rsutcs
BCCR.
zg.2000
GISS.huss GISS.
Mrsos
PCM.
tro3.3000 10 Uas hus.
100000 ua.
40000 PCM.
ta.92500 GISS.
rtmt GISS.
zg.92500 PCM.
tro3.3000 PCM.
Hfss GISS.
huss
Predictor data sets and simulation scenarios 3.3.4.2
The databases and scenarios for the various downscaling simulations are then prepared. There are essentially five predictor data sets, which differ by their time- and grid resolutions and whether they are drawn from a single-GCM- or a multi-GCM database, as shown in Table 3.10.
The single GCM- predictor data set provides both daily- and monthly resolution time-series from ECHO-G. Moreover, the high-resolution GCM (HiRes) with a 0.5°x0.5° grid provides predictors for the MLR- model. The set of multi-GCMs (GCMs) which is a combination of the five GCMs mentioned provides monthly predictor data. In addition to the multi-GCM database, the HiRes GCM is mixed with the multi-GCM database to produce a multi-domain+HiRes GCMs data set (GCMs+Hi-Res) for producing much more complex downscaling input.
As stated in the IPCC-AR4 – report (Solomon et al. 2007), the GCM- experiments were carried out for climate reanalysis in the 20th-century and climate predictions over the 21st-century (Gordon et al. 2000). Therefore, the climate reanalysis GCMs, following 20C3M-scenario predictors for the 1971-1999 time period are used for the downscaling calibration. Beforehand, the daily and monthly available data are separated into a calibration set for 1971-1985, and a verification set for 1986-1999. The available climate projections for years 2000-2096 are subsequently used for downscaled climate projections, following SRES A1B, A2 and B1 scenarios, exclusively, the daily database which is only available for years 2046-2065 and 2081-2100. The calibration, verification and projection periods and the GCM/downscaling combinations used in the later simulations are summarized in Table 3.11. Thus one may note that the conventional downscaling methods SDSM and LARS-WG which require daily predictors are only applied with the daily-GCM database. On the other hand, the new multi-linear regression (MLR) downscaling model can be used with both daily and monthly predictors.
Therefore, the MLR-method is employed to project the future daily climate over years 2046-2065 and 2081-2100 and monthly climate over the whole 21st-century (2000-2096) for all three SRES scenarios.
Table 3.10. GCM- database after selection and classification into five data sets.
no. data set
time-resolution
grid-resolution model source year scenario
1 single GCM (2.5°) daily 2.5°x2.5° ECHO-G 1971-2000 20C3M
(daily ECHO-G) 2046-2065 A1B
2081-2100 A1B
2 single GCM (2.5°) monthly 2.5°x2.5° ECHO-G 1971-2000 20C3M
(monthly ECHO-G) 2000-2096 A1B,A2,B1
3 HiRes GCM (0.5°) monthly 0.5°x0.5° HiRes-CRU 1971-2000 20C3M
(Hi-Res) CGCM2,
HiRes-CSIRO2, HiRes-ECHAM4, HiRes-HadCM3, HiRes-PCM
2001-2096 A1B,A2,B1
4 multi GCMs (2.5°) monthly 2.5°x2.5° ECHO-G, BCCR, ECHAM5 ,
GISS, PCM 1971-1999 20C3M
(GCMs) ECHO-G, BCCR, ECHAM5 ,
GISS, PCM 2000-2096 A1B,A2,B1
5 multi GCMs (2.5°)
+ HiRes GCM (0.5°) monthly 0.5°x0.5° HiRes-CRU 1971-1999 20C3M
(GCMs+Hi-Res) 2.5°x2.5° ECHO-G, BCCR, ECHAM5 ,
GISS, PCM
0.5°x0.5° HiRes-TYN (CGCM2, CSIRO2, ECHAM4, HadCM3, PCM)
2000-2096 A1B,A2,B1
2.5°x2.5° ECHO-G, BCCR, ECHAM5 , GISS, PCM
Table 3.11. Calibration-, verification- and prediction-schemes of the various downscaling models, based on five GCM- predictor data sets.
no method calibration simulation model interval dataset 1 calibration
and 1971-1985 1986-1999 SDSM daily single GCM (2.5°) verification (verification) LARS-WG daily single GCM (2.5°) MLR daily single GCM (2.5°) MLR monthly single GCM (2.5°) MLR monthly HiRes GCM (0.5°) MLR monthly multi GCMs (2.5°)
MLR monthly multi GCMs (2.5°) + HiRes-CRU (0.5°)
2 scenario 1971-1999 2000-2006 MLR monthly single GCM (2.5°) verification (verification) MLR monthly HiRes GCM (0.5°)
MLR monthly multi GCMs (2.5°)
MLR monthly multi GCMs (2.5°) + HiRes-TYN (0.5°)
3 future prediction 3.1 scenario
A1B 1971-1999 2000-2096 MLR monthly single GCM (2.5°) (prediction) MLR monthly HiRes GCM (0.5°)
MLR monthly multi GCMs (2.5°)
MLR monthly multi GCMs (2.5°) + HiRes-TYN (0.5°)
2046-2065 SDSM daily single GCM (2.5°) (prediction) LARS-WG daily single GCM (2.5°) 2081-2100 SDSM daily single GCM (2.5°) (prediction) LARS-WG daily single GCM (2.5°) 3.2 scenario A2 1971-1999 2000-2096 MLR monthly single GCM (2.5°) (prediction) MLR monthly HiRes GCM (0.5°)
MLR monthly multi GCMs (2.5°)
MLR monthly multi GCMs (2.5°) + HiRes-TYN (0.5°)
3.3 scenario B1 1971-1999 2000-2096 MLR monthly single GCM (2.5°) (prediction) MLR monthly HiRes GCM (0.5°)
MLR monthly multi GCMs (2.5°)
MLR monthly multi GCMs (2.5°) + HiRes-TYN (0.5°)
There is a systematic complication with the daily ensembles of the ECHO-G model which provides daily predictors based on a 360-day calendar, while the climate simulations in this study are based on a standard calendar system (Gregorian calendar). Therefore the time-dimension slicing of GCM-predictor is executed in the daily-predictor simulations by using a linear interpolation, where the predictor at time , which is the unknown value between time and that give predictors at upper and lower values and , respectively, is calculated as :
This approach is likewise used in the interpolation of the observation, when their time scale does not fit the predictor time scale.