Downscaling the contribution to uncertainty in climate‐change assessments: representative concentration pathway (<scp>RCP)</scp> scenarios for the <scp>S</scp>outh <scp>A</scp>lborz <scp>R</scp>ange, <scp>I</scp>ran

Abstract

ABSTRACT One of the most important stages in climate‐change‐impact studies is uncertainty analysis, due to its great effect on both predictions and decision‐making. This study presents a procedure that characterizes the changes of climatic variables for the period 2011–2040 under representative concentration pathway (RCP) scenarios and then quantifies the uncertainty linked with the downscaling process using a bootstrapping method at 95% confidence intervals in one of the most vulnerable basins, the “Karaj‐Jajrud” located in the South Alborz Range, Iran. The results show that there is a consistent warming in mean air temperature time‐series with different magnitudes for all the RCP scenarios in the region for 2011–2040, whereas the results indicate decreasing precipitation compared with the baseline period for all RCP scenarios in the study area. Analysing the impacts of the downscaling process uncertainty on the prediction results shows that the contribution of this uncertainty source to the prediction uncertainty is relatively high, as about 30% of the downscaled temperature and precipitation data fall inside the 95% simulation confidence intervals. Furthermore, precipitation‐series uncertainty is more than the air temperature series. Climate change assessments and their uncertainty analysis can help managers to enhance preparedness and adaptation strategies in order to mitigate the consequences of natural hazards. More investigations can be done by adopting more general circulation models and other downscaling methods to compare the uncertainty that arises from each uncertainty source.