Projection of temperature and precipitation under SSPs-RCPs Scenarios over northwest China

Qin, Jiancheng; Su, Buda; Tao, Hui; Wang, Yanjun; Huang, Jinlong; Jiang, Tong

doi:10.1007/s11707-020-0847-8

Cited by 29 publications

(11 citation statements)

References 29 publications

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“…Furthermore, at the annual scale, the decreasing trend was observed in the low forcing scenario, no significant trend in medium forcing scenario, and an increasing trend was observed under medium and high forcing scenarios, which indicated that the study area will likely experience high (low) precipitation phases under SSP3-7.0 and SSP5-8.5 (SSP1-2.6 and SSP2-4.5). Qin et al (2021) found an increase in annual precipitation under SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5 in the 21st century over the northwestern region of China. Mondal et al (2021) found that precipitation is expected to increase by 4.8% under seven SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5) during 2015-2100 across the Indus basin.…”

Section: Discussionmentioning

confidence: 89%

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Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Abbas

Ullah

et al. 2022

Intl Journal of Climatology

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This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management.

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Section: Discussionmentioning

confidence: 89%

“…Qin et al . (2021) found an increase in annual precipitation under SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0, and SSP5‐8.5 in the 21st century over the northwestern region of China. Mondal et al .…”

Section: Discussionmentioning

confidence: 99%

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Abbas

Ullah

et al. 2022

Intl Journal of Climatology

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“…Although global climate models have improved in the recent decade (Kim et al, 2020;Ayugi et al, 2021), it seems not to appear to be the case for arid and semi-arid regions such as CA (Mehran et al, 2014;Guo et al, 2021;Qin et al, 2021). The uncertainties for CMIP6 in assessing precipitation extremes arise from our limited knowledge of the key physical processes for circulation changes.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, some results suggest that CMIP6 models, which generally reflect observed patterns of global and regional extreme events, show limited improvement over the CMIP5 model (Kim et al, 2020). However, most model evaluation studies focused on global or monsoon regions (e.g., You et al, 2008;Akinsanola et al, 2020;Dong and Dong, 2021;Tang et al, 2021;Vicente-Serrano et al, 2021), while efforts addressing precipitation extremes in arid and semi-arid regions are limited (Qin et al, 2021), especially in the Central Asia (hereafter CA) region (Figure 1). Guo et al (2021) addressed the ability of CMIP6 models to simulate annual precipitation patterns and suggested that the simple ensemble mean based on all models may not be a wise choice for climate change studies in the CA region.…”

Section: Introductionmentioning

confidence: 99%

Biases of the Mean and Shape Properties in CMIP6 Extreme Precipitation Over Central Asia

Zhang²,

Ding³

et al. 2022

Front. Earth Sci.

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The global climate models (GCMs) are indispensable for accurately simulating the climate variability and change, and numerous studies have assessed climatic extreme events globally and regionally. However, the shape properties of GCM precipitation extreme simulations, such as measures of asymmetry (e.g., skewness coefficient) and measures of tail heaviness (e.g., kurtosis coefficient), have received far less attention. Here, we address this issue by comparing the performance of 22 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing the statistical properties of ground observations for the period 2001–2014 over typical arid and semiarid Central Asia. We evaluated the performance of the CMIP6 models using novel methodologies to assess biases not only in mean and variation but also in higher order L-moments which involved less bias and variance than the conventional moment approach, including 1) summary statistics as expressed by univariate analysis of L-moments and 2) the bivariate kernel densities of (mean, L-variation) and (L-skewness, L-kurtosis) using the application of the highest probability region (HPR) and applying the Hellinger distance as a measure of agreement. The results show that CMIP6 simulations can reproduce the shape properties of precipitation extremes with the observational datasets and that biases are observed when the mean and variation are examined bivariate. An ensemble mean of the CMIP6 models does not improve the performance of the variation and skewness of the simulated precipitation extremes, while it only slightly constrains the mean and kurtosis error of most metrics. Our results could provide guidance for climate research and improve the statistical properties of CMIP6 models in relation to ground observations.

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“…Using the JRA55 reanalysis dataset and ground-level mean precipitation observations, Peng & Zhou, (2017) suggested that enhanced evaporation induced by increased downward longwave radiation acted as the dominant contributor to the wetting trend during 1961-2010. While several mechanisms have been proposed for explaining the wetting trend, the knowledge gap remains between climate-driven impacts on precipitation across Northwest China (Qin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

Yang

Zhang

et al. 2023

Front. Earth Sci.

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The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

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Projection of temperature and precipitation under SSPs-RCPs Scenarios over northwest China

Cited by 29 publications

References 29 publications

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Biases of the Mean and Shape Properties in CMIP6 Extreme Precipitation Over Central Asia

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

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