Validation of the single-domain downscaling tools

To consider the comparative performance of downscaling model using a single-GCM in climate prediction, the SDSM and LARS-WG, and single-domain MLR models, which employ ECHO-G daily ECHO-GCM data in downscaling climate in study area, are validated here according to the calibration and verification schemes. The comparative downscaling results and predicting performance from these three models are demonstrated and evaluated here.

Validation of SDSM- and LARS-WG- models 3.6.1.1

In the validation of SDSM, the calibrated model is employed to downscale daily climate variables of maximum and minimum temperature and rainfall in the Eastern seaboard of Thailand year 1971-1999. As mentioned in the previous chapter, data from four temperature- and 24 precipitation sites (see Figure 2.2) are employed for model calibration and verification.

Originally, the SDSM model employed gridded data from the National Centre for Environmental Prediction (NCEP), i.e. the NCEP-reanalysis data set (Kalnay et al. 1996) to execute the model calibration. However, SDSM is here adapted to be compatible with the ECHO-G GCM, which is the common parent GCM used in most downscaling experiments of this study.

Using 30 individual different random seeds, SDSM simulation generates 30 realizations of daily climate time-series for the 1986-2000 –verification period, as shown in Figure 3.14. Although the average values of these 30 climate realizations are close to the observed series, the extreme values, i.e. the high and low peaks of the downscaled climate realizations often extend beyond those of the observed series. Notice that the mean of the downscaled maximum temperatures in the 1995-2000 time period is does not exhibit the increasing trend in the observed time series which is, most likely, due to biases in the ECHO-G predictions for the study region.

Figure 3.14. 1971-2000 observed and downscaled predictions of Tmax and Tmin temperatures at station 48478 and precipitation at station 48092, using the SDSM-model with parent ECHO-G ECHO-GCM-predictors, and calibrated in the 1971-1985 time interval.

Daily maximum temperature

Daily minimum temperature

Daily precipitation

Figure 3.15. Similar to Figure 3.14, but using the LARS-WG downscaling model with ECHO-G ECHO-GCM- predictor input.

Daily maximum temperature

Daily minimum temperature

Daily precipitation

LARS-WG is validated in a similar manner, wherefore site-base and scenario files containing the climate characters and change factors of future climate are used to synthesize 30 realizations of daily precipitation and temperature for the 1986-2000 period, based on 1971-1985 site-based data. The results are shown in Figure 3.15, which indicates that, similar to SDSM, the realizations of the LARS-WG predictions are within the range of the observed climate and cannot mimic well the increasing observed maximum temperatures in the 1995- 2000 period.

Comparing Figure 3.14 and Figure 3.15 one may notice that the peaks of the observed temperatures are fit somewhat better with LARS-WG than with SDSM. The opposite is true for the peaks of the observed rainfall, which are better simulated by SDSM than by LARS-WG.

The visual differences between the observed and predicted climate series exhibited in Figure 3.14 and Figure 3.15 are further quantified by the various measures of the residual errors between observed and predicted data series, as mentioned earlier, which are ME, RMSE and the NS-coefficient. Table 3.18 and Table 3.19 summarize the values of these error measures for the SDSM- and the LARS-WG downscaling predictions, respectively, wherefore the estimations have been done separately for the 1971-1985- calibration- and the 1986-1999- verification periods. In addition, the error measures are not only calculated for the original daily time climate data, but for monthly aggregated (daily) climate series as well.

As shown in Table 3.18, the prediction performance of SDSM in both daily and monthly series is rather low and mostly unacceptable (NS<0). The results of LARS-WG in Table 3.19 demonstrate that the predictions of daily and monthly minimum temperatures and of the monthly precipitation are acceptable (NS>0), while the predictions of the minimum temperature are good result (NS>0.5) at the aggregated monthly time scale.

Table 3.18. Performances of the SDSM downscaling model, as measured by the ME, RMSE and NS-coefficient, in generating daily and monthly aggregated temperatures and precipitation for the 1971-1985- calibration- and the 1986-1999- verification periods.

type predictand unit no of stations calibration performance verification performance n avg. RMSE NS n avg. RMSE NS daily

Tmax C 4 5479 -0.1 1.8 -0.20 5479 -0.9 2.0 -0.36 Tmin C 4 5479 -0.1 2.5 -0.03 5479 -0.6 2.8 -0.33 PCP mm/day 24 5479 2.6 10.6 -0.30 5479 2.3 11.8 -0.29 monthly

Tmax C 4 180 -0.1 1.1 -0.17 180 -0.9 1.5 -0.69

Tmin C 4 180 -0.1 2.0 0.04 180 -0.6 2.2 -0.40

PCP mm/day 24 180 2.6 5.4 -2.27 180 2.3 5.7 -1.92 Table 3.19. Similar to Table 3.18, but for the LARS-WG downscaling model.

type predictand unit no of stations calibration performance verification performance n avg. RMSE NS n avg. RMSE NS daily

Tmax C 4 5479 0.0 1.5 0.07 5479 -0.9 2.0 -0.20

Tmin C 4 5479 0.1 1.8 0.53 5479 -0.4 2.0 0.40

PCP mm/day 24 5479 0.0 9.9 -0.12 5479 -0.4 11.0 -0.09 monthly

Tmax C 4 180 0.0 1.5 0.07 180 -0.9 1.4 -0.27

Tmin C 4 180 0.1 1.8 0.53 180 -0.4 1.0 0.71

PCP mm/day 24 180 0.0 9.9 -0.12 180 -0.4 2.9 0.41

This comparative assessment of the two downscaling methods with regard to their abilities to generate daily and monthly time-series will be extended further in Chapter 5 by including results of the simulations of the new daily weather generator developed there.

Validation of the single-domain MLR-model 3.6.1.2

In this section the accuracy and performance of multi-linear regression (MLR) model in downscaling climate by using a single-domain GCM, i.e. ECHO-G, is examined here and compared with the results of SDSM and LARS-WG that also employ ECHO-G GCM data. As ECHO-G provides both daily and monthly predictors (see Table 3.1), both variants of the single-domain MLR model (MLR-daily and MLR-monthly) are employed in this validation. The single-domain MLR is different from multi-domain MLR in that the latter mainly focuses on monthly data by using multi-domain and high-resolution GCMs in downscaling as will be examined further in the next section.

The single-domain MLR- model is validated by generating daily climate time-series for years 1971-2000. This time period is then divided into a 1971-1985 calibration- and a 1986-2000 verification period. The time-series generated in this are shown in Figure 3.16, from which one can notice that although the downscaled synthetic minimum temperatures are close to the observed ones, for the time series of the maximum temperatures and the rainfall in the study area, the discrepancies between observed and simulated values are much larger. This indicates that the MLR-model cannot describe the full range of fluctuation, i.e. peaks or extreme values for these two climate variables, but only their average trends, similarly to what has been found for SDSM and LARS-WG in the previous section.

The prediction skills of the single-domain MLR-downscaling model in generating climate on the daily and monthly scale, as measured by the named statistical performance evaluators ME, RMSE and NS-coefficients are listed in Table 3.20. The MLR-daily provides daily output (MLR-daily), while the MLR-monthly is driven by monthly GCM-predictors for the monthly output (MLR-monthly). In addition, the MLR-daily-daily output can be – likewise to what has been done previously for SDSM and LARS-WG - summed up to provide an aggregated monthly series (MLR-daily-monthly). The results of Table 3.20 indicate that the predicting performance of the two monthly prediction variants MLR-daily-monthly and MLR-monthly are consistently, Table 3.20. Model performance, as measured by the ME, RMSE and NS-coefficient, in calibration- and verification years 1971-1985 and 1986-1999, respectively, of the single-domain MLR-downscaling model in combination with the parent GCM ECHO-G for (a) generating daily climate from daily predictors daily), (b) monthly climate from daily predictors (MLR-daily-monthly) and (c) generating monthly climate from monthly predictors (MLR-monthly).

model predictand unit n station calibration performance verification performance

N avg. RMSE NS n avg. RMSE NS

MLR-daily

Tmax ◦C 4 5479 0.0 1.5 0.11 5479 -0.7 1.8 -0.09 Tmin ◦C 4 5479 0.0 1.9 0.43 5479 -0.4 2.0 0.30 PCP mm/day 24 5479 0.0 9.2 0.04 5479 -0.2 10.4 0.03

MLR- daily-monthly

Tmax ◦C 4 180 0.0 0.8 0.31 180 -0.7 1.2 -0.15

Tmin ◦C 4 180 0.0 1.0 0.74 180 -0.4 1.2 0.59

PCP mm/day 24 180 0.0 2.6 0.33 180 -0.2 3.1 0.30

MLR-monthly

Tmax ◦C 4 180 0.0 0.8 0.35 168 -0.6 1.1 0.00

Tmin ◦C 4 180 0.0 0.9 0.80 168 -0.4 0.9 0.73

PCP mm/day 24 180 0.0 2.5 0.40 168 0.5 3.0 0.37

Note: NS>0.5, i.e. which are at the satisfactory level, are highlighted in bold italics

Figure 3.16. 1971-2000 observed and downscaled predictions of Tmax and Tmin temperatures at station 48478 (upper chart) and precipitation at station 48092 (lower chart), using the single-domain MLR-downscaling model with parent ECHO-G GCM-predictors, and calibrated in the 1971-1985 time interval. The corresponding linear regression equations are listed above the charts.

Tmax (ECHO-G)

-15.25 +0.136*tasmin.2 +0.015*rlds -0.224*tas.2 +0.046*tasmin.1 +0.150*tasmax.2 +0.028*ta.70000 +0.003*hfls Tmin (ECHO-G)

51.92 +0.478*ua.100000 +0.340*vas +0.147*va.100000 +0.026*rlds 0.195*ua.85000 +0.001*psl -119.891*hus.100000 +258.157*hus.70000

PCP (ECHO-G)

-1.93 +286.352*hus.100000 -0.056*ua.40000 +0.004*hfls

and expectedly higher than that of the pure MLR-daily and this holds for both the calibration- and verification periods. The NS-values are particularly high (NS>0.5) for the minimum temperature time-series and are similar to those obtained with the LARS-WG previously.