[1] This study investigates the potential of predicting local precipitation over northern Taiwan using statistical downscaling of large-scale circulation variables from global climate models (GCMs). Historical hindcast data of 500 hPa geopotential height (Z500) and sea level pressure (SLP) from six different GCMs, with the target season of being that of June, July, and August (JJA), are used as predictors for downscaling. Singular value decomposition analysis (SVDA) using observational data reveals that the rainfall over northern Taiwan is strongly coupled with a prominent tripole pattern of Z500 (SLP) field over the western North Pacific/East Asian coast. SVDA using model SLP or height field and station rainfall as input also gives similar results, indicating that most models can capture this mode of covariability. SLP and Z500 from models are then used for local rainfall prediction based on their relationship, which is drawn from the SVDA. For every station considered in this study, downscaled prediction shows considerable improvement when compared with model output. In particular, downscaling is able to correct the erroneous sign of model rainfall prediction. However, a few models show very low skill in their downscaled precipitation. For these models, the correlation between observed rainfall and simulated Z500 (SLP) leading SVD patterns is found to be weak. The performance based on the average of downscaled prediction using Z500 and SLP is also evaluated. In general, the average prediction is more stable and skillful when compared with results based on one predictor. Overall, this study demonstrates that useful regional climate information can be obtained from downscaling using large-scale variables from coarse-resolution GCM products.
[1] This study investigates the potential of projecting regional precipitation over Yunlin County using a singular-value-decomposition (SVD) based statistical downscaling scheme to cope with climate change; the change rate of local precipitation and its connection with the variability of large-scale circulation are also discussed. It is found that rainfall over Yunlin County is closely tied to the large-scale circulation over the East Asian monsoon region, and that general circulation models (GCMs) perform reasonably in simulating the mean states of sea level pressure (SLP) and meridional wind field at 850 hPa (V850) over this region. Consequently, the two large-scale variables, SLP and V850, both of which are taken from seven GCMs involving the 20C3M, A1B, and B1 scenarios, are used as predictors for downscaling. The result shows that the downscaling schemes based on these predictors show a skillful and stable performance in reproducing historical seasonal rainfall anomalies over Yunlin County, especially when the strategies of using two predictors and multiple model ensemble (MME) are considered. It also shows that comparing the mean states of 20C3M-downscaled and scenario-projected seasonal local rainfall in the coming 36 years with respect to the period of 1975-2000 reveals a suppressive pattern in the wet season and an increasing pattern in the dry season; the finding may be associated with the strength of large-scale circulation under different scenarios. Overall, this study demonstrates that useful information concerning the impact of climate change on regional precipitation can be obtained by downscaling schemes using outputs of GCMs as predictors.
In this study, an automated synoptic weather typing was employed to identity the weather types most likely associated with daily typhoon/typhoon-related heavy rainfall events for Chiayi, Taiwan. The synoptic weather typing was developed using principal components analysis, an average linkage clustering procedure, and discriminant function analysis. The classification results showed that the synoptic weather typing was successful at identifying typhoon-related weather types. Five synoptic weather types (Weather Types 1-5) were identified over the past 11-year period as the primary typhoon-related weather types. These five typhoon-related weather types can capture 34 out of 36 total typhoon-related heavy rainfall days ([50 mm/d) and all nine cases with typhoon-related daily rainfall [200 mm during the period March 1998-December 2008. This result suggests that synoptic weather typing can be useful to identify historical typhoon/typhoon-related heavy rainfall events. Moreover, the method has potential to assess climate change impacts on the frequency/intensity of future typhoon/typhoon-related heavy rainfall events using future downscaled GCM climate data.
This study investigated the streamflow impacts in wet and dry spells using a statistical downscaling projection method to obtain 5 km girds under four Representative Concentration Pathway (RCP) scenarios. Two upstream catchments, the Dahan and Laonong Rivers were selected as the study areas. A water balance hydrological model, also known as the Generalized Watershed Loading Function model, was used to simulate the streamflow impacts. There are 126 projections from 41 general circulation models (GCMs) and 4 RCPs used in this analysis. The analytical results indicate that the streamflow impacts in different RCP scenarios are significant but vary with individual GCM's projection. The variance of 20 selected GCMs is close to that of all other GCMs. Typically, more than 60% of GCMs project that in the early 21 st century, streamflow in each RCP increases by 0 -40% in wet spells and decrease by -40 ~ 0% for the Dahan River, but the streamflow increases during both wet and dry spells for the Laonong River. In the late 21 st century, the streamflow decreases by 60% during dry spells in the Laonong River. Various predictions for the early and late 21 st century show high variance of streamflow impacts. As such, decision makers must plan for reservoir operation and flexible water deployment adaptations during future dry spells.
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