Uncertainty analysis of impacts of climate change on snow processes: Case study of interactions of GCM uncertainty and an impact model

Kudo, Ryoji; Yoshida, Takeo; Masumoto, T.

doi:10.1016/j.jhydrol.2017.03.007

Cited by 28 publications

(20 citation statements)

References 41 publications

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“…The GCM is currently the most feasible method for predicting large-scale climate changes. However, due to the differences in resolution, initial conditions, and mechanisms of each GCM, the results have significant uncertainty, and the accuracy of the simulated results is closely related to the simulated region and the simulated climate variables [36][37][38][39][40]. Therefore, it is necessary to conduct an adaptive assessment of each GCM in the study area before using GCM data to investigate regional climate change, and then to select the GCMs with better regional adaptability.…”

Section: Multi-model Adaptive Assessmentmentioning

confidence: 99%

Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Wang

Cheng

Liu

et al. 2019

Water

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Based on three IPCC (Intergovernmental Panel on Climate Change) Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), observed meteorological data, ERA-40 reanalysis data, and five preferred GCM (general circulation model) outputs selected from 23 GCMs of CMIP5 (Phase 5 of the Coupled Model Intercomparison Project), climate change scenarios including daily precipitation, maximum air temperature, and minimum air temperature from 2021 to 2050 in the Heihe River basin, which is the second largest inland river basin in Northwest China, were generated by constructing a statistical downscaling model (SDSM). Results showed that the SDSM had a good prediction capacity for the air temperature in the Heihe River basin. During the calibration and validation periods from 1961 to 1990 and from 1991 to 2000, respectively, the coefficient of determination (R2) and the Nash–Sutcliffe efficiency coefficient (NSE) were both larger than 0.9, while the root mean square error (RMSE) was within 20%. However, the SDSM showed a relative lower simulation efficiency for precipitation, with R2 and NSE values of most meteorological stations reaching 0.5, except for stations located in the downstream desert areas. Compared with the baseline period (1976–2005), changes in the annual mean precipitation simulated by different GCMs during 2021–2050 showed great difference in the three RCP scenarios, fluctuating from −10 to +10%, which became much more significant at seasonal and monthly time scales, except for the consistent decreasing trend in summer and increasing trend in spring. However, the maximum and minimum air temperature exhibited a similar increasing tendency during 2021–2050 in all RCP scenarios, with a higher increase in maximum air temperature, which increased as the CO2 concentration of the RCP scenarios increased. The results could provide scientific reference for sustainable agricultural production and water resources management in arid inland areas subject to climate change.

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Section: Multi-model Adaptive Assessmentmentioning

confidence: 99%

Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Wang

Cheng

Liu

et al. 2019

Water

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“…The conceptual models (e.g., lumped hydrological model coupled with a day‐degree approach) are easy‐to‐use tools for projecting future changes in snow hydrological regime (Barnhart et al, ). However, compare with physically based distributed snowmelt runoff models and land surface hydrological models coupled with improved snow physics (Islam & Déry, ; Kudo et al, ; Shrestha et al, ; Wang et al, ; Xue et al, ; L. Zhang et al, ), they are often limited in comprehensively representing the physical processes (e.g., snow accumulation and ablation) of snow hydrology. The land surface hydrological models are thus attractive in data‐sparse regions (e.g., the Tibetan Plateau) with the aid of gridded meteorological forcing and remote sensing data and convenient to be used for projections of future climate impact by coupling a suite of climate models under varied emission scenarios.…”

Section: Introductionmentioning

confidence: 99%

Snow Hydrology in the Upper Yellow River Basin Under Climate Change: A Land Surface Modeling Perspective

Liu

Sun

et al. 2018

JGR Atmospheres

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Snow has been widely recognized as a crucial component of water resources and is expected to be vulnerable to climate change in cold and mountainous regions. Here we projected climate change impacts on snow hydrology in the upper Yellow River (UYR) basin through a distributed biosphere hydrological model with improved snow physics, forced with Inter‐Sectoral Impact Model Intercomparison Project climate model outputs under two emission scenarios (representative concentration pathways, RCP4.5 and RCP8.5) during the period of 1996–2025. The results indicated that the climate in the UYR basin is turning warmer and wetter. In this context, more precipitation would occur as rain (snow‐to‐precipitation ratio would decrease accordingly). The total runoff generation would increase slightly with increased precipitation. The simulated snow depth and snow water equivalent would decrease by 33% and 19% per 1 °C warming under the RCP4.5. These declined rates would further enlarge under the RCP8.5. Accordingly, the multimodel ensemble mean snowmelt‐to‐runoff ratio decreases by 9% and 7% per 1 °C warming under the two emission scenarios. The projected responses of snow hydrology (e.g., snow depth, snow water equivalent, and snowmelt) to the changing climate in the UYR basin are consistent among different climate models and RCP scenarios, which would provide valuable insights for water resources management, environmental protection, and climate change adaption in high mountainous basins and their downstream regions.

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“…Moreover, ROS index integrate events with a very small contribution of snowmelt to the high flows while neglecting rainfall only events (Cohen et al, 2015;Il Jeong and Sushama, 2018;Pradhanang et al, 2013). The definition of ROS also introduces more uncertainties as it depends on the combination of simulated precipitation and temperature for several days (Kudo et al, 2017). Our heavy rain and warm index minimizes this uncertainty and take into consideration heavy rainfall whatever the amount of snow covering the ground.…”

Section: Relevance Of Rain and Warm Events To Explain Future Evolutiomentioning

confidence: 99%

“…CC BY 4.0 License. definition of ROS index is also subjected to high uncertainties (Kudo et al, 2017) and this index may not be relevant in regions affected by decrease of snowpack (Il Jeong and Sushama, 2018). These results emphasize the need of new compound climate indices to understand the impact of atmospheric circulation on hydrometeorological extreme events in the Great lake region.…”

Section: Introductionmentioning

confidence: 99%

Winter hydrometeorological extreme events modulated by large scale atmospheric circulation in southern Ontario

Champagne¹,

Leduc²,

Coulibaly³

et al. 2019

Preprint

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Abstract. Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in term of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. Rain on Snow index has been widely used but it neglects rain only events which are expected to be more frequent in the future. In this study we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model Large Ensemble (CRCM5-LE). These climate data were used as input in PRMS hydrological model to simulate the future evolution of high flows in three watersheds in Southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in northeastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10 mm and temperature higher than 5 °C were a necessary historical condition to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated to two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP) and the Atlantic Ocean (South). These hydrometeorological extreme events will be more frequent in the near future and will still be associated to the same atmospheric patterns. The future evolution of the index will be modulated by the internal variability of the climate system as higher Z500 in the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high flows generation. This study shows the values of CRCM5-LE dataset to simulate hydrometeorological extreme events in Eastern Canada and to better understand the uncertainties associated to internal variability of climate.

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Uncertainty analysis of impacts of climate change on snow processes: Case study of interactions of GCM uncertainty and an impact model

Cited by 28 publications

References 41 publications

Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Multi-Model Projections of Climate Change in Different RCP Scenarios in an Arid Inland Region, Northwest China

Snow Hydrology in the Upper Yellow River Basin Under Climate Change: A Land Surface Modeling Perspective

Winter hydrometeorological extreme events modulated by large scale atmospheric circulation in southern Ontario

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