Projecting future local precipitation and its extremes for sweden

Abstract

ABSTRACT. A procedure to obtain future local precipitation characteristics focused on extreme conditions has been developed based on a weather generator. The method involves six major steps: (1) the weather generator was calibrated using observed daily precipitation at 220 Swedish stations during 1961-2004; (2) present and future daily precipitation characteristics for the Swedish stations from two global climate models, namely ECHAM5 and HadCM3, were used to calculate weather generator parameters for the present and future climates at global climate model spatial scales; (3) the ratio of the weather generator parameters for the present climate simulated by the global climate models to those calculated for each station falling into the global climate model grid box were computed for all the stations; (4) these ratios were also assumed to be valid in the future climate, that way the future parameters for each station for the global climate model projected future climate could be calculated; (5) using the estimated future parameters of the weather generator, the future daily precipitation at each station could be simulated by the weather generator; (6) the simulated daily precipitation was used to compute eight indices describing mean and extreme precipitation climates. The future mean and extreme precipitation characteristics at the stations under the Second Report on Emission Scenarios A2 scenario were obtained and presented. An overall increasing trend for frequency and intensity of the indices are identified for the majority of the stations studied. The developed downscaling methodology is relatively simple but useful in deriving local precipitation changes, including changes in the precipitation extremes.

Local‐ and landscape‐scale impacts of clear‐cuts and climate change on surface water dissolved organic carbon in boreal forests

“…This is consistent with other climate change impact studies in the boreal forest [ Holmberg et al , ; Oni et al , ; Thorne , ; Woo et al , ]. The general trend toward slight increases in future precipitation is also consistent with other studies [ Chen et al , ].…”

No long‐term trends in pCO2 despite increasing organic carbon concentrations in boreal lakes, streams, and rivers

“…Since DOC concentrations are often used to predict p CO 2 and thereby CO 2 emissions from inland waters [e.g., Raymond et al ., ; Sobek et al ., ], predictions of future CO 2 emissions from inland waters need to consider the findings of this study. Although precipitation is predicted to further increase in the boreal region as a response to climate change [ Chen et al ., ; Teutschbein et al ., ], presumably resulting in more DOC being flushed into inland waters, it is unlikely that p CO 2 will follow this increase.…”

Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

“…Statistical downscaling methods include applying transfer functions, weather generators, weather typing, or quantile-mapping to the gridded, downscaled model output (Wood et al 2004). Weather generators use empirical relationships calculated from observations to simulate synthetic time series for rainfall data (Andréasson et al 2004;Chen et al 2015). Weather typing, or resampling, involves relating the weather patterns of the larger scale climate model to observed patterns in the local area (Prudhomme et al 2002;Onof and ArnbjergNielsen 2009).…”

“…The change factor, or delta change, approach adjusts an observed statistic (usually at the point scale) to a future date using a ratio or percentage that is calculated from the gridded, climate model output (Forsee and Ahmad 2011;Zhu 2012). Bias correction modifies the future, gridded value from the downscaled climate model based on the difference between the observed statistics (point scale) and past model simulation statistics (grid scale) or hindcast simulation statistics (grid-scale) (Arnbjerg-Nielsen et al 2013; Boé et al 2007;Chen et al 2015;Wilks and Wilby 1999;Wood et al 2000). Quantile mapping may be employed as a bias-correction technique (Boé et al 2007).…”