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This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment. [ABSTRACT FROM AUTHOR]

Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments. Plain Language Summary: Drought is affected by both rainfall and evapotranspiration. Drought indices represent drought severity compared to normal conditions as a function of time. Some drought indices are based on rainfall alone and some use both rainfall and evapotranspiration in the calculations. To understand future drought risk, simulations from climate models are needed. Unfortunately, different climate models often disagree on amounts and patterns of rainfall in the future, the disagreement being considerably more on rainfall than for evapotranspiration. In this study, we attempt to reduce the impact of these differences by developing a new method to estimate future drought. We used a mathematical method known as wavelets to estimate drought indices based on rainfall. Evapotranspiration is then used directly from the climate model and combined with the rainfall based drought index to create one overall drought index. We used projections from multiple climate models to understand if our new method led to a greater agreement in how often and severe future droughts may be. Our results confirm that the new method offers greater consistency in drought projections for the future. Key Points: Current methods for projecting drought ignore differences in uncertainty between P and ET simulationsA new basis for projecting drought is proposed that explicitly accounts for relative uncertainty between P and ETOur results show that the new method offers greater consistency in drought projections for the future [ABSTRACT FROM AUTHOR]

Broadacre livestock production is a major but highly diverse component of agriculture in Australia that will be significantly exposed to predicted changes in climate over coming decades. We used the GRAZPLAN simulation models to assess the impacts of climate change under the SRES A2 scenario across southern Australia. Climate change impacts were examined across space (25 representative locations) and time (1970-99, 2030, 2050 and 2070 climate) for each of five livestock enterprises. Climate projection uncertainty was considered by analysing projections from four global circulation models ( GCMs). Livestock production scenarios were compared at their profit-maximizing stocking rate, constrained to ensure that risks of soil erosion were acceptable. Impacts on net primary productivity ( ANPP) varied widely between GCM projections; the average declines from historical climate were 9% in 2030, 7% in 2050 and 14% in 2070. Declines in ANPP were larger at lower-rainfall locations. Sensitivity of ANPP to changes in rainfall ranged from 0.4 to 1.7, to temperature increase from −0.15 to +0.07 °C−1 and to CO2 increase from 0.11 to 0.32. At most locations the dry summer period lengthened, exacerbating the greater erosion risk due to lower ANPP. Transpiration efficiency of pastures increased by 6-25%, but the proportion of ANPP that could safely be consumed by livestock fell sharply so that operating profit (at constant prices) fell by an average of 27% in 2030, 32% in 2050 and 48% in 2070. This amplification of ANPP reductions into larger profitability declines is likely to generalize to other extensive livestock systems. Profit declines were most marked at drier locations, with operating losses expected at 9 of the 25 locations by 2070. Differences between livestock enterprises were smaller than differences between locations and dates. Future research into climate change impacts on Australian livestock production needs to emphasise the dry margin of the cereal-livestock zone. [ABSTRACT FROM AUTHOR]

Three formulations of a non-parametric stochastic downscaling framework for simulation of daily rainfall at multiple point locations for catchment-scale climate change impact assessment under enhanced greenhouse conditions are evaluated. In the first case, rainfall is downscaled using a system of variables that combine atmospheric circulation indicators (geo-potential height, pressure and derived variables) with time-lagged wetness indicators reflecting a continuous rainfall state. In the second and third cases, the above system of variables is augmented using indicators of atmospheric moisture found relevant in defining the downscaling relationship. The downscaling framework is specified using 43 years of daily rainfall observations at 30 locations near Sydney, Australia, with reanalysis data being used to represent observed atmospheric variables. A single ensemble member of the CSIRO Mark 3 general circulation model (GCM) (corresponding to the SRES A2 emission scenario) is used for comparison of downscaled rainfall for current and future (year 2070) conditions. While all the downscaling formulations show an overall similarity in the downscaled rainfall for the current climate, variations for some rainfall attributes are observed for year 2070 conditions. A variable convergence score (VCS) is employed to assess the relative skill of atmospheric predictors used in the different formulations. The VCS indicates use of temperature depression (difference of dew point and atmospheric temperatures) as an indicator of atmospheric moisture to obtain more realistic estimates of rainfall for year 2070. In general, a decrease in winter and spring and an increase in summer in the frequency of wet days with a decrease in the winter rainfall amount in the year 2070 is projected. Also, inland stations are found to be more sensitive to the expected future climate changes in comparison to the coastal ones. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

A climate change resulting from recent increases in the atmospheric greenhouse gases (GHGs) is supposed to occur sometime in the middle of the next century. This climate change induced by GHGs would be caused by radiative forcing through the greenhouse effect and would lead to global warming and eventually to sea-level rise. This paper presents a climate change scenario for the greater Caribbean as simulated by the Canadian Climate Centre (CCC) general circulation model (GCM). The CCC GCM projects a GHG induced temperature increase of about 2 0Cand more extreme rainfall conditions for the greater Caribbean. The climate records of temperature, rainfall and cloudiness for several stations on the island of Trinidad are then examined in an attempt to detect GHG climate change signals. The temperature change to date, close to 1 0C, and the fluctuations in rainfall patterns, over the lastfive decades, seem to be indicative of early greenhouse signals. ) 1997 by the Royal Meteorological Society. [ABSTRACT FROM AUTHOR]