Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin

Su, Buda; Huang, Jinlong; Gemmer, Marco; Jian, Dongnan; Tao, Hui; Jiang, Tong; Zhao, Chengyi

doi:10.1016/j.atmosres.2016.03.023

Cited by 110 publications

(75 citation statements)

References 49 publications

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“…For the purpose of Statistical inference in the future, the statistical relationship between observed and simulated historical data is established. As a bias correction method, the Equidistant Cumulative Distribution Functions (EDCDF) method is used in this study [35,36]. The Cumulative Distribution Function (CDF) of observed, simulated and predicted data is established to calculate to cumulative frequency of a certain value in the future.…”

Section: Bias Correction Methodsmentioning

confidence: 99%

Study on Variations in Climatic Variables and Their Influence on Runoff in the Manas River Basin, China

Ren

Xue

Liu

et al. 2017

Water

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Climate change in Northwest China could lead to the change of the hydrological cycle and water resources. This paper assessed the influence of climate change on runoff in the Manas River basin as follows. First, the temporal trends and abrupt change points of runoff, precipitation, and mean, lowest and highest temperature in yearly scale during the period of 1961-2015 were analyzed using the Mann-Kendall (MK) test. Then the correlation between runoff and climatic variables was characterized in a monthly, seasonal and yearly scale using the partial correlation method. Furthermore, three global climate models (GCMs) from Coupled Model Inter-comparison Project Phase 5 (CMIP5) were bias-corrected using Equidistant Cumulative Distribution Functions (EDCDF) method to reveal the future climate change during the period from 2021 to 2060 compared with the baseline period of 1961-2000. The influence of climate change on runoff was studied by simulating the runoff with the GCMs using a modified TOPMODEL considering the future snowmelt during the period from 2021 to 2060. The results showed that the runoff, precipitation, and mean, lowest and highest temperature all presented an increasing trend in yearly scale during the period of 1961-2015, and their abrupt change points were at a similar time; the runoff series was more strongly related to temperature than to precipitation in the spring, autumn and yearly scales, and the opposite was true in winter. All GCMs projected precipitation and temperature, and the runoff simulated with these GCMs were predicted to increase in the period from 2021 to 2060 compared with the baseline period of 1961-2000. These findings provide valuable information for assessing the influence of climate change on water resources in the Manas River basin, and references for water management in such regions.

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Section: Bias Correction Methodsmentioning

confidence: 99%

Study on Variations in Climatic Variables and Their Influence on Runoff in the Manas River Basin, China

Ren

Xue

Liu

et al. 2017

Water

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“…The results of the hindcast analysis for the period 2006–2015 show a good representation of the trends, seasonality, and spatial correlations, while the absolute annual BIAS and temporal correlations differ quite strongly. Compared with the simulation results by Rajbhandari et al () and Su et al (), who used the RCM PRECIS driven by HadCM3 and the multi‐model ensemble of 21 CMIP5 GCMs over the IRB, respectively, CCLM performs better in reproducing the amounts and annual cycles of mean precipitation and annual precipitation, as well as the spatial patterns. Considering the biases and inaccuracies mentioned earlier, CCLM is a robust RCM and can be used to project the temperature and precipitation in the IRB.…”

Section: Conclusion and Discussionmentioning

confidence: 79%

“…strong increase over the Indus Delta and in the northern mountainous areas. Similar results of warming in the upper IRB are also projected by other RCMs and downscaled GCMs (Kazmi et al , ; Rajbhandari et al , ; Su et al , ). Based on the results by Ali et al () the GCM CCAM and the RCM RegCM also project a consistent increase in temperature over the northwestern mountainous part of the IRB under RCP8.5.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Simulation and projection of climatic changes in the Indus River Basin, using the regional climate model COSMO‐CLM

Huang

Wang

Fischer

et al. 2016

Intl Journal of Climatology

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In this article, the analysis of simulated and projected climatic changes in the Indus River Basin (IRB) is presented. The performance of the regional climate model COSMO‐CLM (CCLM) driven by the global circulation model MPI‐ESM‐LR is evaluated on the ability of reproducing temperature, precipitation, and wind pattern for the period 1961–2005. There exist quantitative biases, especially in precipitation pattern, which are associated to the diverse reproduction of the atmospheric circulation by the global circulation model. The overall results show that CCLM is able to satisfyingly capture the dominant spatial characteristics in annual temperature and precipitation time series, including the warming trend, and the seasonal variations. The changes in precipitation and temperature over the IRB are projected for the mid‐ (2046–2065) and late‐21st century (2081–2100). Relative to the baseline period (1986–2005), the average annual temperature will increase during the mid‐ and late‐21st century over the whole basin. Extreme temperature events, i.e. heat waves, are more likely to occur. An increase in summer temperature is also projected especially in the upper IRB, where the persistent increase is likely to cause further melting of glaciers. The annual precipitation will decrease in both the mid‐ and late‐21st century with significant spatial changes. A decrease in the monsoon precipitation is also projected, particularly in the central and southern plains of lower altitudes, which might be highly related to the reduction of wet air from the Indian Ocean and the increment in outflow to the east of the IRB. For winter and spring precipitation, except for the late‐21st century under the representative climate pathway (RCP) 2.6, a decreasing trend is projected, which might be caused by the reduction of water vapour from the west. This will further weaken the availability of water resources in these seasons, especially in the upper IRB of higher altitudes.

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“…Scenarios are the key tools used to describe possible climatic, societal, and environmental changes in the future (Moss et al, ; O'Neill et al, ). The current parallel scenario development process began with representative concentration pathways, which reflect the atmospheric concentration targets of greenhouse gases and have been widely used to understand potential changes to the future climate (Huang et al, ; Su et al, ; Taylor et al, ; Van Vuuren et al, ). Future socioeconomic conditions that connect the impact of, adaptation to, and mitigation of climate change are generating increasing concern among multiple stakeholders regarding mitigation targets and liability (Kriegler et al, ).…”

Section: Introductionmentioning

confidence: 99%

Effect of Fertility Policy Changes on the Population Structure and Economy of China: From the Perspective of the Shared Socioeconomic Pathways

et al. 2019

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Beginning in 2016, all couples in China were allowed to have two children without any restrictions. This paper provides population and economic projections under five shared socioeconomic pathways (SSPs) and three fertility policies. By replacing the one‐child policy with the two‐child policy, the population is predicted to continue growing until 2025–2035, with a peak of approximately 1.39–1.42 billion, and then to decline under four SSPs, with the exception of the fragmented world SSP3. As a result, the two‐child policy will lead to mitigation of the pressure from labor shortages and aging problems to a certain extent. In addition, an increase in working‐age people with higher education level relative to projections based on the one‐child policy will lead to an increase in gross domestic product by approximately 38.1–43.9% in the late 21st century. However, labor shortages and aging problems are inevitable, and the proportion of elderly in China will be greater than 14% and 21% by approximately 2025 and 2035, respectively. Full liberalization of fertility is expected to reduce the share of elderly people by 0.7–1.0% and to lead to an increase in gross domestic product by 5.3–6.7% relative to the two‐child policy in the late 21st century. The full liberalization of fertility policies is recommended, supplemented by increases in pension and child‐rearing funds, improvement in the quality of health services for females and children, and extension of compulsory education to meet the needs of an aged society.

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Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin

Cited by 110 publications

References 49 publications

Study on Variations in Climatic Variables and Their Influence on Runoff in the Manas River Basin, China

Study on Variations in Climatic Variables and Their Influence on Runoff in the Manas River Basin, China

Simulation and projection of climatic changes in the Indus River Basin, using the regional climate model COSMO‐CLM

Effect of Fertility Policy Changes on the Population Structure and Economy of China: From the Perspective of the Shared Socioeconomic Pathways

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