Downscaling of Short-Term Precipitation Time Series for Climate Change Impact Assessment

Olsson, Jonas; Gidhagen, Lars; Kawamura, Akira

doi:10.1007/978-3-642-22285-6_67

Cited by 2 publications

(1 citation statement)

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“…Great uncertainties exist in GCMs and downscaling processes, leading to inevitable biases for regional climate. The widely used method for dealing with these biases is delta change method (Hay et al ., ; Gao et al ., ; Olsson et al ., ), which, to some extent, could eliminate simulation biases of future climate because of global climate models’ systematic deviation. The algorithm is described as follows.…”

Section: Model and Methodsmentioning

confidence: 99%

Assessing the effect of climate natural variability in water resources evaluation impacted by climate change

Liu

Zhang

Wang

et al. 2012

Hydrological Processes

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Water resource assessment on climate change is crucial in water resource planning and management. This issue is becoming more urgent with climate change intensifying. In the current research of climate change impact, climate natural variability (fluctuation) has seldom been studied separately. Many studies keep attributing all changes (e.g. runoff) to climate change, which may lead to wrong understanding of climate change impact assessment. Because of lack of long enough historical series, impacts of climate variability have been always avoided deliberately. Based on Latin hypercube sampling technique, a block sampling approach was proposed for climate variability simulation in this study. The widely used time horizon was defined as baseline period, and the runoff variation probability affected by climate natural variability was analysed. Allowing for seven future climate projections in total of three GCMs (CSIRO, NCAR, and MPI) and three emission scenarios (A1B, A2, and B1), the impact of future climate change on water resources was estimated in terms of separating the contribution from climate natural variability. Based on the analysis of baseline period, for the future period from 2021 to 2051, the impact of climate natural variability may play a major part, whereas for the period from 2061 to 2091, climate change attributed to greenhouse gases may dominate the changing process. The results show that changes from climate variability possess a comparable magnitude, which highlights the importance to separate impacts of climate variability in assessing climate change, instead of attributing all changes to climate change solely.

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Section: Model and Methodsmentioning

confidence: 99%