The Statistical DownScaling Model: insights from one decade of application

Abstract

ABSTRACT:The Statistical DownScaling Model (SDSM) is a freely available tool that produces high resolution climate change scenarios. The first public version of the software was released in 2001 and since then there have been over 170 documented studies worldwide. This article recounts the underlining conceptual and technical evolution of SDSM, drawing upon independent assessments of model capabilities. These studies show that SDSM yields reliable estimates of extreme temperatures, seasonal precipitation totals, areal and inter-site precipitation behaviour. Frequency estimation of extreme precipitation amounts in dry seasons is less reliable. A meta-analysis of SDSM outputs shows a preponderance of research in Canada, China and the UK, whereas the United States and Australasia are under-represented. In line with the wider downscaling community, the most favoured sector of analysis is water and flood risk management which accounts for nearly half of all output; research in other sectors such as agriculture, built environment and human health is less prominent but growing. Over 50% of the studies are concerned with production of climate scenarios, comparison or technical refinement of downscaling methodologies. In contrast, there is relatively little evidence of application to adaptation planning and climate risk management. We assert that further attention to physically meaningful quantities such as wind speeds, wave heights, phenological and hazard metrics could improve uptake of downscaled products. Chronic uncertainty in boundary forcing continues to undermine confidence in downscaled scenarios so these tools are best used for sensitivity testing and adaptation options appraisal.

The Minimum and Maximum Temperature Forecast Using Statistical Downscaling Techniques for Port-Harcourt Metropolis, Nigeria

“…To select the period of calibration the [26] which stipulates that the calibration period be taken within half of the observation period but not more than the NCEP predictor period of 2001 (for HadCM 3). Since there were about 26 large scale predictors they needed to be first screened using Pearson's product moment correlation (PPMC) in the SDSM environment, so that the predictors that performed significantly with the predictor were selected for cali- Table 1).…”

“…Statistical downscaling method lies in the ability to use the properties of the free atmosphere as predictors of the local climate elements (which are called the predictand). Information on the large-scale predictors may be generated from direct weather observation or from some climate model output, yet downscaling as a technique is yet termed very important [13] [14], because of the usability of findings for climate impact and risks assessments.…”

Evaluation of Statistical-Downscaling/Bias-Correction Methods to Predict Hydrologic Responses to Climate Change in the Zarrine River Basin, Iran

“…and max. temperatures than for the precipitation, for which the biases are still considerable (a well-known common deficiency of the SDSM method [12]). For that reason, the latter is corrected in an additional step using the trend-preserving bias correction method [25], somewhat similar to the QM, but with different CP j correction factors in Equations (3) and (4).…”

“…That contradicts obviously the SDSM-predicted precipitation, but is more consistent the QM precipitation in a sensible manner. As mentioned earlier, the classical SDSM method is often deficient in the downscaling of the precipitation [12], and for this reason a trend-preserving bias correction of the SDSM downscaled precipitation has been included here. However, Figure 7 shows that, although this add-on improves the predicted precipitation slightly, the observed precipitation is still significantly overestimated.…”

Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China

“…There are some well-known weather generators like long Ashton research station weather generator (LARS-WG) (Racsko et al, 1991), climate generator (CLI-GEN) (Nicks and Gander, 1994), and statistical downscaling model (SDSM) (Wilby and Dawson, 2013). They all have advantages, but are single-site based.…”