Evaluation and projections of surface air temperature over the Tibetan Plateau from CMIP6 and CMIP5: warming trend and uncertainty

Zhou, Minpei; Yu, Zhongbo; Gu, Huanghe; Ju, Qin; Gao, Yiyan; Li, Wen; Huang, Tangkai; Wang, Wei

doi:10.1007/s00382-022-06518-4

Cited by 14 publications

(6 citation statements)

References 86 publications

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“…In many uncertainty quantification studies, scenario uncertainty tends to be very high (or continues to increase) by the end of the 21st century (Hawkins & Sutton, 2011; You et al, 2021; Zhou et al, 2022). Typically, uncertainty analyses in these studies focus on single variables, such as temperature or precipitation.…”

Section: Discussionmentioning

confidence: 99%

“…These considerable uncertainties stem from three sources, namely, model uncertainty, scenario uncertainty, and internal variability of the climate (Hawkins & Sutton, 2011). Currently, previous studies utilizing CMIP6 models for future projections and quantifying their uncertainties have mainly focused on univariate analysis (Woldemeskel et al, 2012(Woldemeskel et al, , 2016You et al, 2021;Zhou et al, 2022). There is a lack of research on quantifying uncertainty in the prediction of CWPEs.…”

Section: Introductionmentioning

confidence: 99%

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Compound wind and precipitation extremes at a global scale based on CMIP6 models: Evaluation, projection and uncertainty

Zhu,

Yang,

Tang

2023

Intl Journal of Climatology

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Compound wind and precipitation extremes (CWPEs) amplify risk to human health, socio‐economic and ecological systems relative to their single extreme meteorological events. Given the rise in weather and climate extremes resulting from global warming, it is crucial to evaluate the ability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to capture this bivariate compound event and explore projected changes of CWPEs in the future under different climate‐change scenarios—the Shared Socioeconomic Pathway (SSP) scenarios. In this study, we first evaluate 14 CMIP6 models at a global scale using the ERA5 reanalysis data set spanning 1979–2014. Overall, some of the CMIP6 models, especially the multi‐model ensemble mean (MMEM), can reasonably capture CWPEs during the historical period, with more CWPEs in the northern and southern hemispheres during their respective cold seasons. However, the MMEM tends to overestimate CWPEs in some land areas and show underestimation in some oceanic regions. Then we compute projected changes of CWPEs in periods 1 (2041–2070) and 2 (2071–2100) under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios. Low emission scenarios effectively mitigate the long‐term increase in future CWPEs. The occurrence of CWPEs will change significantly with the increase of emissions during period 2, particularly in polar regions. Finally, we quantify the uncertainty for global future projections of CWPE changes. The main sources of uncertainty are internal variability and model uncertainty, but the contribution of scenario uncertainty will increase as time progresses. Overall, our results provide useful information to cope with CWPEs' global impact, emphasizing the importance of incorporating the compound nature of weather and climate extremes in future climate projections.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compound wind and precipitation extremes at a global scale based on CMIP6 models: Evaluation, projection and uncertainty

Zhu,

Yang,

Tang

2023

Intl Journal of Climatology

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“…Su et al, 2013), but consensus on its future warming magnitude remains lacking. The magnitude of temperature increase could be influenced by emission scenarios (Tian et al, 2015;You, Cai, Wu, et al, 2021;M. Zhou et al, 2022), with the projected difference between the very high and very low emission scenarios reaching as much as 6°C by the end of the 21st century (Y.…”

Section: Introductionmentioning

confidence: 99%

“…All models project that the TP surface temperature will continue to increase under future warming climate (e.g., Fan et al., 2022; Guo et al., 2016; F. Su et al., 2013), but consensus on its future warming magnitude remains lacking. The magnitude of temperature increase could be influenced by emission scenarios (Tian et al., 2015; You, Cai, Wu, et al., 2021; M. Zhou et al., 2022), with the projected difference between the very high and very low emission scenarios reaching as much as 6°C by the end of the 21st century (Y. Peng et al., 2022). Furthermore, the projected TP temperature increase shows significant inter‐model variation even under the same scenario, owing to model discrepancies in their dynamical framework, parameterization, and coupling schemes, etc.…”

Section: Introductionmentioning

confidence: 99%

Emergent Constraints on Future Projections of Tibetan Plateau Warming in Winter

Hu,

Wang,

Chen

et al. 2024

Geophysical Research Letters

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The Tibetan Plateau (TP) is an area highly sensitive to climate change and is warming faster than the global average. The TP temperature change has a significant impact on the local ecological environment and the downstream weather and climate. The TP will undoubtedly warm in the future, but the warming extent is uncertain. Using the Coupled Model Inter‐comparison Project Phase 6 multi‐model ensemble, we found that models simulating smaller TP temperature increases in recent decades tend to project weaker warming in the future. This relationship is driven by the simulation of snowmelt response to greenhouse gas increases, as snow‐related albedo feedback dominates the TP temperature changes in both historical and future periods. Based on a two‐step emergent constraint approach, the rectified TP warming magnitude increases by about 0.3°C compared to the unconstrained result under both the medium and high emission scenarios, and the inter‐model uncertainty is reduced by about 60%.

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“…Their results revealed that CMIP6 models better replicated the historical Tmax, Tmin, and precipitation, and CMIP5 models exhibited a higher bias than CMIP6. Zhou et al 11 also compared the CMIP6 and CMIP5 models to project the surface air temperature of the Tibetan Plateau. They found less uncertainty in the CMIP6 results compared to CMIP5.…”

Section: Introductionmentioning

confidence: 99%

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Maqsood,

Wang,

Farooque

et al. 2024

Sci Rep

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Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202–2050) and FP2 (2051–2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

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Evaluation and projections of surface air temperature over the Tibetan Plateau from CMIP6 and CMIP5: warming trend and uncertainty

Cited by 14 publications

References 86 publications

Compound wind and precipitation extremes at a global scale based on CMIP6 models: Evaluation, projection and uncertainty

Compound wind and precipitation extremes at a global scale based on CMIP6 models: Evaluation, projection and uncertainty

Emergent Constraints on Future Projections of Tibetan Plateau Warming in Winter

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

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