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[1] Projections of precipitation and temperature from Global Climate Models (GCMs) are generally the basis for assessment of the impact of climate change on water resources. The reliability of such assessments, however, is questionable, since GCM projections are subject to uncertainties arising from inaccuracies in the models, greenhouse gas emission scenarios, and initial conditions (or ensemble runs) used. The purpose of the present study is to quantify these sources of uncertainties in future precipitation and temperature projections from GCMs. To this end, we propose a method to estimate a measure of the associated uncertainty (or error), the square root of error variance (SREV), that varies with space and time as a function of the GCM being assessed. The method is applied to estimate uncertainty in monthly precipitation and temperature outputs from six GCMs for the period 2001-2099. The results indicate that, for both precipitation and temperature, uncertainty due to model structure is the largest source of uncertainty. Scenario uncertainly increases, especially for temperature, in future due to divergence of the three emission scenarios analyzed. It is also found that ensemble run uncertainty is more important in precipitation simulation than in temperature simulation. Estimation of uncertainty in both space and time sheds lights on the spatial and temporal patterns of uncertainties in GCM outputs. The generality of this error estimation method also allows its use for uncertainty estimation in any other output from GCMs, providing an effective platform for risk-based assessments of any alternate plans or decisions that may be formulated using GCM simulations.

Assessment of climate change impacts on water resources is extremely challenging, due to the inherent uncertainties in climate projections using global climate models (GCMs). Three main sources of uncertainties can be identified in GCMs, i.e., model structure, emission scenario, and natural variability. The recently released fifth phase of the Coupled Model Intercomparison Project (CMIP5) includes a number of advances relative to its predecessor (CMIP3), in terms of the spatial resolution of models, list of variables, and concept of specifying future radiative forcing, among others. The question, however, is do these modifications indeed reduce the uncertainty in the projected climate at global and/or regional scales? We address this question by quantifying and comparing uncertainty in precipitation and temperature from 6 CMIP3 and 13 CMIP5 models. Uncertainty is quantified using the square root of error variance, which specifies uncertainty as a function of time and space, and decomposes the total uncertainty into its three constituents. The results indicate a visible reduction in the uncertainty of CMIP5 precipitation relative to CMIP3 but no significant change for temperature. For precipitation, the GCM uncertainty is found to be larger in regions of the world that receive heavy rainfall, as well as mountainous and coastal areas. For temperature, however, uncertainty is larger in extratropical cold regions and lower elevation areas.

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.

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