Climate Downscaling as a Source of Uncertainty in Projecting Local Climate Change Impacts

Abstract

This study assessed the sensitivity of the simulated future impact on forage yield over Japan to the precipitation change in growing season derived from the multiple downscaling models, taking the regional climate projection ensemble dataset for Japan as an example. Three regional climate models (RCMs: NHRCM, NRAMS, and TWRF), and one statistical model (CDFDM) provided the fine-resolution (20-km) climate data over Japan from the climate projection performed by a global climate model (GCM: MIROCHI) under A1B scenario. With the common boundaries for the RCMs (and predictor for the statistical model), there is a consistency for the increased summer precipitation. However, discrepancies were found for the degree of precipitation increase and change in the mean summer maximum number of consecutive dry days. These discrepancies caused the spread of the simulated future change in forage yield over Japan by 3.3-11.4% (2081-2100), relative to the present-day one (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). These results showed that the direction (increase or decrease) and amplitude of the simulated future impact di¤er between the climate scenarios from the downscaling models and those from the parent GCM, indicating that climate downscaling is a source of uncertainty in simulating future impact.

Reconsidering the Quality and Utility of Downscaling

“…Other more specialized ensembles have been set up as well to highlight special national needs (see Iizumi et al 2012; UKCIP 09 [http://ukclimateprojections. defra.gov.uk]; Kjellström et al, 2011).…”

Projection of Future Climate Change over Japan in Ensemble Simulations with a High-Resolution Regional Climate Model

“…Sasaki et al (2012; hereafter S12) achieved all-season downscaling results around Japan by integrating an NHRCM (5-km grid spacing) over 20 years at the end of the 21st century. Issues remaining unresolved include uncertainty in future projection of local climate change in Japan for climate downscaling (e.g., Iizumi et al 2012). Therefore, the next step is to perform high-resolution ensemble simulations and to estimate the uncertainty in projected future climate change.…”

Evaluating the utility of dynamical downscaling in agricultural impacts projections

“…It has been shown to improve weather and climate variability, especially over complex terrain (see ref. 16 For agricultural impacts, initial studies suggested that dynamical downscaling may improve projections, because the use of RCMs altered modeled crop yields by up to 20% (22)(23)(24)(25)(26)(27)(28)(29). These studies were not definitive, however; they covered limited areas and times, which can increase GCM-RCM differences, and most used the delta method to remove climate model bias, which can reduce differences.…”