Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

Jiang, Zhihong; Li, Wei; Xu, Jianjun; Li, Laurent

doi:10.1175/jcli-d-15-0099.1

Cited by 213 publications

(153 citation statements)

References 47 publications

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“…Over East Asia, most models in the high-resolution group perform better in terms of 75th, 95th, and 99th percentile precipitation than the low-resolution group, and especially for the 99th percentile. These results are consistent with those of Jiang et al (2015), who shows that models with higher resolution produce relatively smaller errors in extreme precipitation. The low-resolution group reasonably well reproduces low percentile (i.e., 75th percentile) precipitation, as shown by the correlation, standard deviation, and RMSE.…”

Section: Precipitation Biasessupporting

confidence: 92%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Kim

Woo

et al. 2018

Clim Dyn

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In this study, a comparison in the precipitation extremes as exhibited by the seven reference datasets is made to ascertain whether the inferences based on these datasets agree or they differ. These seven datasets, roughly grouped in three categories i.e. rain-gauge based (APHRODITE, CPC-UNI), satellite-based (TRMM, GPCP1DD) and reanalysis based (ERA-Interim, MERRA, and JRA55), having a common data period 1998-2007 are considered. Focus is to examine precipitation extremes in the summer monsoon rainfall over South Asia, East Asia and Southeast Asia. Measures of extreme precipitation include the percentile thresholds, frequency of extreme precipitation events and other quantities. Results reveal that the differences in displaying extremes among the datasets are small over South Asia and East Asia but large differences among the datasets are displayed over the Southeast Asian region including the maritime continent. Furthermore, precipitation data appear to be more consistent over East Asia among the seven datasets. Decadal trends in extreme precipitation are consistent with known results over South and East Asia. No trends in extreme precipitation events are exhibited over Southeast Asia. Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulation data are categorized as high, medium and lowresolution models. The regions displaying maximum intensity of extreme precipitation appear to be dependent on model resolution. High-resolution models simulate maximum intensity of extreme precipitation over the Indian sub-continent, medium-resolution models over northeast India and South China and the low-resolution models over Bangladesh, Myanmar and Thailand. In summary, there are differences in displaying extreme precipitation statistics among the seven datasets considered here and among the 29 CMIP5 model data outputs.

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Section: Precipitation Biasessupporting

confidence: 92%

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Kim

Woo

et al. 2018

Clim Dyn

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“…As used in previous studies (Zhang et al, 2011;Li et al, 2013;Jiang et al, 2015), three common extreme precipitation indices calculated from daily data were selected for studying extreme precipitation (Table 1). The simple daily intensity index (SDII) describes the mean rainfall amounts of daily precipitation larger than 1 mm, and the annual account of days with daily precipitation greater than 10 mm (R10) represents the frequency of heavy precipitation.…”

Section: Extreme Precipitation Indicesmentioning

confidence: 99%

Impact of different cumulus convective parameterization schemes on the simulation of precipitation over China

Huang

Gao

2017

Tellus A: Dynamic Meteorology and Oceanography

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The impact of two cumulus convective schemes on the simulation of the precipitation over China is investigated using the Weather Research and Forecasting (WRF) model. Simulations for the period of 1982-2004 are performed at a horizontal resolution of 30 km and forced by NCEP Reanalysis II data. Results show precipitation simulated with the WRF model is quite sensitive to the choice of Kain-Fritsch and Grell cumulus schemes. Both the schemes have distinct skills in predicting the seasonal mean pattern, annual cycle and interannual variation in precipitation. The results show that the Kain-Fritsch scheme tends to overestimate the magnitude of the summer and annual mean precipitation over the main rain-belts, while the Grell scheme tends to underestimate these effects, particularly the simulation of the summer extreme precipitation. However, the Kain-Fritsch scheme is more skilful in capturing the seasonal mean pattern and annual cycle with higher spatial correlations in the main rain-belts. The Grell scheme shows some advantages for northern China and the Tibetan Plateau, especially in representing the interannual variation. The optimal ensemble approach is used to determine the best combination of the two schemes, with the results giving a better overall performance than the individual schemes alone in predicting summer precipitation. The temporal correlation coefficient of precipitation for the ensemble is significantly higher, while the root mean square error of extreme precipitation is reduced compared with the Kain-Fritsch and Grell results. This shows that the ensemble approach based on the optimal ensemble weight combines the advantages of the two cumulus schemes efficiently.

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“…2016, 8,13 3 associated with orbit drifts [22,23], change of the vertical weighting functions due to atmospheric CO2 changes [24], and long-term drift in the local time of measurements. So, errors associated with trend estimates are due to the uncertainties in the successive SSU adjustments and time continuity.…”

Section: Ssu/msu Data Setsmentioning

confidence: 99%

“…We use the IPCC models at the Program for Climate Model Diagnosis and Intercomparison (PCMDI) [8]. The datasets used for this study are the historical run of 35 available models with 11 high-top models (model tops above 1 hPa), enabling the comparisons with the highest altitude SSU data for the period of 1979-2005.…”

Section: Cmip5 Simulationsmentioning

confidence: 99%

“…As an important aspect of climate change, the vertical structure of temperature trends from the troposphere to stratosphere has received a great deal of attention in the climate change research community [1][2][3][4][5][6][7][8][9]. The World Climate Research Program (WCRP) made an incredible effort to understand the variability in the stratosphere based on climate model simulations [10].…”

Section: Introductionmentioning

confidence: 99%

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Use of SSU/MSU Satellite Observations to Validate Upper Atmospheric Temperature Trends in CMIP5 Simulations

Zhao

Jiang

et al. 2015

Remote Sensing

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Abstract:The tropospheric and stratospheric temperature trends and uncertainties in the fifth Coupled Model Intercomparison Project (CMIP5) model simulations in the period of 1979-2005 have been compared with satellite observations. The satellite data include those from the Stratospheric Sounding Units (SSU), Microwave Sounding Units (MSU), and the Advanced Microwave Sounding Unit-A (AMSU). The results show that the CMIP5 model simulations reproduced the common stratospheric cooling (´0.46-´0.95 K/decade) and tropospheric warming (0.05-0.19 K/decade) features although a significant discrepancy was found among the individual models being selected. The changes of global mean temperature in CMIP5 simulations are highly consistent with the SSU measurements in the stratosphere, and the temporal correlation coefficients between observation and model simulations vary from 0.6-0.99 at the 99% confidence level. At the same time, the spread of temperature mean in CMIP5 simulations increased from stratosphere to troposphere. Multiple linear regression analysis indicates that the temperature variability in the stratosphere is dominated by radioactive gases, volcanic events and solar forcing. Generally, the high-top models show better agreement with observations than the low-top model, especially in the lower stratosphere. The CMIP5 simulations underestimated the stratospheric cooling in the tropics and overestimated the cooling over the Antarctic compared to the satellite observations. The largest spread of temperature trends in CMIP5 simulations is seen in both the Arctic and Antarctic areas, especially in the stratospheric Antarctic.

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Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

Cited by 213 publications

References 47 publications

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Evaluation of precipitation extremes over the Asian domain: observation and modelling studies

Impact of different cumulus convective parameterization schemes on the simulation of precipitation over China

Use of SSU/MSU Satellite Observations to Validate Upper Atmospheric Temperature Trends in CMIP5 Simulations

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