Quantifying the sources of uncertainty in upper air climate variables

Abstract

Future estimates of precipitation and streamflow are of utmost interest in hydrological climate change impact assessments. Just as important as the estimate itself, is the variance around the ensemble mean of the projections, this variance being defined as uncertainty in the context of this study. This uncertainty in the hydrological variables of interest is affected by uncertainty in upper air climate variables which are used in statistical downscaling of precipitation or streamflow. Here the extent of uncertainty in upper air climate variables has been assessed for a selection of commonly used atmospheric variables for downscaling, namely, geopotential height and its difference in the north-south direction, specific humidity, and eastward and northward wind speeds. Generally, in statistical downscaling, no consideration is usually given to the uncertainty of different individual variables, which can result in biases in future climate simulations. The approach of quantifying uncertainty presented here has the potential to enable modelers to better formulate downscaling approaches, leading to more accurate characterization of future precipitation and its associated uncertainty. Based on the spread of multiple-model outputs, an uncertainty measure called square root of error variance has been used to quantify the contribution of different sources of uncertainty (i.e., models, scenarios, and ensembles) in monthly future climate projections in the 21st century at the 500 hPa and 850 hPa pressure levels. It has been shown that the different climate variables and levels of the atmosphere have distinct patterns in terms of their total future uncertainty and the contributions from the three sources. Scenario and model uncertainties in general contribute reasonably evenly to total uncertainty, with smaller contributions from the initial condition ensembles.