Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5

Zhu, Yuyao; Yang, Saini

doi:10.1016/j.accre.2020.08.001

Cited by 211 publications

(122 citation statements)

References 37 publications

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“…However, the model on average overestimates the T2 about 1.1°C, which is mainly occurred on summer. A warm temperature bias in summer over the TP is also generally found in most other CMIP6 models (Zhu and Yang, 2020). The simulated RH values at 500 hPa are higher than those of the ERA5 of about 5.0% in all months.…”

Section: Resultssupporting

confidence: 70%

Modelling study on the source contribution to aerosol over the Tibetan Plateau

Zhao

Dai

Wang

et al. 2021

Intl Journal of Climatology

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Understanding the source of atmospheric aerosol is an essential step in determining the aerosol radiative effect over the Tibetan Plateau (TP). This study aims to clarify the contributions of anthropogenic and natural emission to the aerosols (carbonaceous and sulphate) characteristics inside the TP. An aerosol model named Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) recently coupled to the climate model named Chinese Academy of Sciences Flexible Global Ocean Atmosphere Land System (CAS‐FGOALS‐f3‐L) is used to simulate meteorological conditions and aerosols from 1985 to 2013, given emissions from the sixth phase of the Coupled Models Intercomparison Project (CMIP6). Comparisons between the simulations and ERA5 reanalysis data present that the model can capture the key spatial and temporal features of meteorological elements (2‐m temperature, relative humidity, and wind field at 500 hPa) over the Asia. Subsequently, we conduct six sensitivity studies to quantify the contributions of sources to aerosol surface concentration, burden and aerosol optical depth (AOD) over the TP. Our results illustrate that the outside and local anthropogenic sources contribute about 75.2% (78.9% in summer and 66.6% in winter) and 13.5% (7.4% in summer and 24.0% in winter) to the annual mean aerosols surface concentrations over the TP. The outside anthropogenic sources contribute to AODs in TP up to 87.3% (93.9% in autumn and 80.6% in winter). The ascending air over the TP is conducive to the transportation of the outsource pollutions to the TP. The outside natural sources (refer to biomass burning sources) contribute about 10% to aerosols over TP, and the inside natural sources play minor roles. The black carbon, organic carbon and sulphate (BOCS) induce average radiative forcing of −0.7 W m−2 at the near surface over the TP.

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Section: Resultssupporting

confidence: 70%

Modelling study on the source contribution to aerosol over the Tibetan Plateau

Zhao

Dai

Wang

et al. 2021

Intl Journal of Climatology

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“…NICAM shows similar bias as compared with gauge observation data (See Supplementary Figure S1). Wet bias over Tibetan Plateau in the NICAM is also seen in both CMIP5 and CMIP6 model simulations (e.g., Mehran et al., 2014; Mueller & Seneviratne, 2014; Su et al., 2013; Zhu & Yang, 2020). The average summer precipitation bias over Tibetan Plateau in NICAM is about 50%, while the bias in the CMIP5 (CMIP6) multimodel ensemble mean is about 80% (70%) (Su et al., 2013; Zhu & Yang, 2020) and these studies do not focus on the southern slope.…”

Section: Historical Precipitation From Observation and Modelmentioning

confidence: 71%

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High‐Resolution Global Nonhydrostatic Model

et al. 2021

JGR Atmospheres

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Current climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14‐km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30‐years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by ∼35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low‐level SSTP but increase over high‐level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ∼50%/°C and ∼8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.

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“…We use reanalysis data to place the AWS observations in context, but caution that the former are known to exhibit positive biases for precipitation over the Himalayas 67 and the adjacent Tibetan Plateau. 68,69 Our specific objectives of this paper are to: (1) place the first complete year of precipitation observations from the new AWS network in a regional and longer-term context; (2) characterize the temporal patterns, intensity, and phase of precipitation across the region; (3) categorize moisture source regions and synoptic-scale circulation associated with precipitation; and (4) identify characteristics of heavy precipitation events.…”

Section: And Avalanchesmentioning

confidence: 99%