0.0 2.5 5.0
DJF MAM JJA SON
K Hours 00 06 12 18
T2 Areal Mean over the IP
Figure 3.28: Left: Areal mean over the IP of the analysis minus background of the tem- perature at 2 m according to the season (winter DJF, spring MAM, summer JJA, autumn SON) and hour (00, 06, 12 and 18 UTC in blue, red, green and orange respectively). Right: Spatial distribution of the analysis minus background during winter at 00 and 12 UTC (top), and during summer at those same hours (bottom).
3.6 Description of the main features of the hydrological cycle
The D experiment accurately simulates the structure of the hydrological cycle over the IP. This section focuses on the main features of this cycle described by this experiment. The accumulated annual values of evaporation did not show a clear pattern over the IP. However, checking the mean daily values of evaporation for winter and summer (Figure 3.29), some interesting features can be observed. The evaporation of the IP is really small during winter (0.67 mm/day on average). The largest values are located in the south of Portugal for that season. During summer, conversely, remarkable values (above 4 mm/day) are observed in the north and north- western IP, the areas where the soil has the highest moisture content in that season. No special structures were observed for the evaporation during spring and autumn.
The influence of the Atlantic sources of moisture was recognizable in the precipi- tation (Figure 3.30). The D experiment obtained a similar pattern to that obtained
by AEMET for period 1971-2000 (AEMET,2011). In both cases, the smallest values
appeared in the southeastern IP and the largest in the northwestern region and near the mountain ranges. This pattern was not so recognizable for the EOBS dataset, as the mountain precipitation is not captured by its coarse spatial resolution. A good agreement between the D experiment and ECA&D dataset was observed. The precipitation is underestimated by the D experiment only in some stations of the southwestern IP, probably related to the difficulties simulating the convective pre-
Figure 3.29: Mean daily evaporation (mm/day) simulated by the D experiment over the IP for winter (left) and summer (right). The mean value of the evaporation over the IP is presented in the right bottom corner of the maps.
cipitation regimes that dominate this region.
The seasonal variability of the PW was also analysed in Figure 3.31. The PW showed higher values in the Mediterranean coast and the Ebro basin during sum- mer. Remarkable values were also observed near the Bay of Biscay. During the other seasons, especially spring and autumn, the PW values were higher in the most impor- tant basins of the IP: Guadalquivir, Guadiana, Tagus and Ebro basins. The smallest values were located near the mountain ranges during winter, as could be expected
Figure 3.30: Accumulated annual precipitation (mm) over the IP taken from the Spanish Meteorological Agency atlas (AEMET,2011) for 1971-2000 (left), compared to those from the D experiment (top right) and from ECA&D and EOBS (bottom right).
3.6 Description of the main features of the hydrological cycle
Figure 3.31: Mean daily PW (mm) simulated by the D experiment over the IP for winter (top left), spring (top right), summer (bottom left) and autumn (bottom right).
from the lower temperatures at low levels of the atmosphere and the smaller height of the troposphere at those places.
An advantage of using a model such as WRF is that new fields that cannot be estimated from observations alone can be studied. This is the case of the moisture that crosses the boundaries of the IP. Figure 3.32 shows the transboundary moisture fluxes across the boundaries of the IP during winter as simulated by the D experiment. According to the net values of each studied hour (00 UTC, 06 UTC, 12 UTC and 18 UTC), the IP imports a great amount of moisture during this season. The overall net flux is always negative, in the range of -34.35 Pg/mon and -12.7 Pg/mon for day and night respectively. The moisture enters the IP mainly through the Portuguese coast and exits the peninsula through the Mediterranean coast. During spring, the IP exports moisture at night (30 Pg/month), but imports it during the day (-27 Pg/mon).
During summer (Figure 3.33), the IP exports moisture during the whole day (net flux always positive ranging from 48 to 68 Pg/mon) except at 12 UTC and 15 UTC that the IP moisture flux is negative (-0.34 and -8 Pg/mon respectively). At those times, moisture is imported specially because of the breezes in the Alboran Sea (southern Spain) (see 06 UTC versus 18 UTC plots). This breeze reaches its maxi- mum intensity slightly later than 12 UTC. During Autumn, the IP mainly imports moisture, with the net flux ranging from -31 Pg/mon to 4 Pg/mon. As stated by
DJF 00 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 20.41 Pg/mon DJF 06 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 34.35 Pg/mon DJF 12 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 30.74 Pg/mon DJF 18 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 12.70 Pg/mon
Figure 3.32: Transboundary moisture fluxes analysis at 00 UTC (top left), 06 UTC (top right), 12 UTC (bottom left) and 18 UTC (bottom right) during winter. The reference bar is labelled at 6.5 Pg/month. The green bars represent an inland flux, while the blue bars refer to an outward flux.
in the United States.
The discussion of these results will take place in chapter 7. The discussion will focus mainly on different topics such as the length of the spin-up in our experiments, the effect of the data assimilation on the results, the representativeness problem, the coarse resolution of the validation datasets, the urban or built-up grid points discovered during the validation of evaporation and the breezes.
JJA 00 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 68.24 Pg/m on JJA 06 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 48.60 Pg/m on JJA 12 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 0.34 P g/mon JJA 18 10 10 5 5 0 0 5 5 35 35 40 40 45 45 Ref:6.5 Pg/mon NET: 53.61 Pg/m on
4
Comparison of DownscalingPrecipitation Models
T
his chapter focuses on the optimal calibration of the Statistical DownScalingModel (SDSM) and the comparison of the daily precipitation downscaled by this model and both WRF simulations (N and D experiments).
As different strategies were used to calibrate the SDSM, some comparisons be- tween SDSM experiments were carried out in order to study the predictor suites created for each of them, the effect of reducing the calibration period, changing the source of the predictors or enhancing the resolution of the reanalysis used as source of the predictors on the downscaled precipitation. These results are presented in sec- tions 4.1 and 4.2. Finally, the most similar SDSM experiment to the configuration used in WRF was compared to both WRF simulations in section 4.3, with our main objective here being to determine the best downscaling model.
4.1 Analysis of the predictor suites
4.1 Analysis of the predictor suites
As previously stated in Table 2.1 from chapter 2, four SDSM experiments were car- ried out with SDSM. In order to create a fair comparison between them, four different
predictor suites were created1. Taking into account that 21 stations were selected
over the IP and that there are 16 permutations of experiment-predictor suite, 336 downscaled precipitation time series would be created using SDSM. Hence, 672 com- parisons would be needed to carry out with both WRF simulations. The potential dimensionality of an exhaustive analysis would be very large so, in order to manage the degrees of freedom, two representative predictor suites were selected.
The four predictor suites were evaluated as follows: the N48 suite was compared to that from N79. The number of selected input predictors reduces by 6% from 125 predictors in N48 to 118 in N79. Fourteen stations retain the same predictors sets in both suites. In the remaining cases, differences between suites are due to reductions in the number of predictors used for calibration (e.g. Murcia, Albacete and Castellón de la Plana) or changes between predictors at different vertical levels. For example, Tarifa uses the wind strength at 850 hPa in the N48 predictor suite instead of 500 hPa in N79.
There is also a reduction in the number of input predictors when comparing the E79 and E79r suites. In this case, 132 predictors are used in E79 predictor suite and 113 in E79r suite (-14%). Nine stations have the same predictors in both suites. In the other stations (e.g. A Coruña, Santander, Tarifa, Cáceres and Albacete), there are fewer input predictors. Only Barcelona station has a new predictor in E79r compared to E79. This suggests that improving the resolution of the reanalysis, reduces the number of predictors needed to calibrate SDSM.
There is more than 85% overlap between the various predictor suites. In order
to maximise the parsimony of the input predictors (Wilby and Wigley, 2000) and
consistency in the reanalysis resolution, suites N48 and E79 were selected. Hereafter, both suites are named after the reanalysis used on their corresponding experiments: the predictor suite based on the N48 experiment refers to the ‘NCEP predictor suite’ and E79 to the ‘ERA predictor suite’.