Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon

Salunke, Popat; Jain, Shipra; Mishra, Saroj K.

doi:10.1007/s00704-018-2644-9

Cited by 32 publications

(18 citation statements)

References 60 publications

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“…Using glacier mass balances to infer the high-altitude precipitation in the upper Indus basin, Immerzeel et al (2015) suggests an underestimation of precipitation reaching a factor of two to ten in observational datasets. Most studies comparing models and reanalyses with observational datasets do also account for large differences from about 100 % to 200 % (e.g., Palazzi et al, 2013;Su et al, 2013;Salunke et al, 2019). GPCP is in closer agreement with CMIP6 models, which could be explained by a better representation of solid precipitation.…”

Section: Discussionmentioning

confidence: 87%

“…Some models also show temperature biases in the troposphere at a global scale (not shown) that might amplify and/or even trigger surface biases in HMA. Salunke et al (2019) atmospheric circulation, as the position of jets, could also feed the observed biases in models over HMA. Further analyses in higher atmospheres and circulation must be done to quantify this impact on present biases.…”

Section: Discussionmentioning

confidence: 99%

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Climate change in the High Mountain Asia in CMIP6

Lalande

Ménégoz

Krinner

et al. 2021

Preprint

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Abstract. Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four shared socioeconomic pathways SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 CMIP6 models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of GCMs shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] °C (90 % confidence interval) in comparison with the CRU observational dataset, associated with a snow cover mean overestimation of 12 [−13 to 43] %, corresponding to a relative bias of 52 [−53 to 183] % in comparison with the NOAA CDR satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm day−1, corresponding to a relative bias of 143 [31 to 281] %, but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another one. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models performing the best for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends to the model biases is found for some variables and seasons, however, some highly biased models fall within the range of observed trends suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] °C for SSP1-2.6 to 6.5 [4.9 to 9.0] °C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 [−16.4 to −5.0] % to −32.2 [−49.1 to −25.0] % and a relative median precipitation increase from 8.5 [4.8 to 18.2] % to 24.9 [14.4 to 48.1] % by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the Global Surface Air Temperature (GSAT), except for summer snow cover that shows a slower decrease at strong levels of GSAT.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Climate change in the High Mountain Asia in CMIP6

Lalande

Ménégoz

Krinner

et al. 2021

Preprint

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“…The interpolation method used in this study do not have any significant impact on the spatial distribution of climatology, trends, biases or annual cycle. Several past studies have also used bi‐linear interpolation and obtained satisfactory results (New et al ., ; Hempel et al ., ; Zhao et al ., ; Li et al ., ; Fotso‐Nguemo et al ., ; Salunke et al ., ). Since this paper mainly focuses on the climatological seasonal means, the interpolation has negligible impact on the findings of this paper.…”

Section: Methodsmentioning

confidence: 99%

Assessment of CMIP5 multimodel mean for the historical climate of Africa

Zebaze

Jain

Salunke

et al. 2019

Atmospheric Science Letters

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The fidelity of 28 CMIP5 models and their multimodel mean (MMM) in simulating the historical climate of Africa is assessed in this study. For the historical period of 1975–2005, the spatial distribution of the seasonal precipitation is simulated with pattern correlation coefficients (PCCs) of 0.91, 0.95, 0.94, and 0.95 for March–May (MAM), June–August (JJA), September–November (SON), and December–February (DJF) seasons, respectively, when compared with the CRU data. For the surface temperature, the PCCs are 0.96, 0.98, 0.83, and 0.97, respectively, for the four seasons mentioned in the preceding. The root mean square error (RMSE) for the precipitation are 0.86, 0.77, 0.97, and 1.05 mm day−1 for the MAM, JJA, SON, and DJF seasons, respectively, whereas for the temperature, the respective values are 1.64, 1.28, 1.68, and 1.87°C. The study also assesses the performance of the models over four sub‐regions of Africa, viz. North, South, East, and West Africa. The observational trends show large spatial heterogeneity in warming for each season. The MMM does not show the strong warming over Africa and simulate generally a weak warming over most of the region. The MMM fails to capture the sign and magnitude of the observed precipitation trends.

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“…Further, the frequency distribution of precipitation rate (Figure 5b) shows that the frequency of light precipitation rate (1-10 mm/day) and moderate precipitation rate (10-20 mm/day) in DefCAM5 is overestimated, while the frequency of very heavy (extreme) precipitation rate (greater than 40 mm/day) is underestimated (also seen in CMIP5 models by Jain et al 2019 andSalunke et al 2019). StochCAM5 improves the frequency distribution of precipitation rate, as well as the contributions of light to extreme precipitation rates to total precipitation (Figure 5c).…”

Section: Precipitation Patternmentioning

confidence: 91%

Impact of Stochastic Entrainment in the NCAR CAM Deep Convection Parameterization on the Simulation of South Asian Summer Monsoon

2021

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Model simulations are highly sensitive to the formulation of the atmospheric mixing process or entrainment in the deep convective parameterizations used in their atmospheric component. In this paper, we have implemented stochastic entrainment in the deep convection scheme of NCAR CAM5 and analyzed the improvements in model simulation, focusing on the South Asian Summer Monsoon (SASM), as compared to the deterministic entrainment formulation in the default version of the model. Simulations using stochastic entrainment (StochCAM5) outperformed default model simulations (DefCAM5), as inferred from multiple metrics associated with the SASM. StochCAM5 significantly alleviated some of the longstanding SASM biases seen in DefCAM5, such as precipitation pattern and magnitude over the Arabian Sea and western Equatorial Indian ocean, early monsoon withdrawal, and the overestimation (underestimation) in the frequency of light (large-to-extreme) precipitation. Related SASM dynamical and thermodynamical features, such as Somali Jet, low-level westerly winds, and meridional tropospheric temperature gradient (MTTG), are improved in StochCAM5. Further, the simulation of monsoon intra-seasonal oscillation (MISO), Madden Julian Oscillation (MJO), and equatorial Kelvin waves are improved in StochCAM5. Many essential climate variables, such as shortwave and longwave cloud forcing, cloud cover, relative and specific humidity, and precipitable water, show significant improvement in StochCAM5.

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Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon

Cited by 32 publications

References 60 publications

Climate change in the High Mountain Asia in CMIP6

Climate change in the High Mountain Asia in CMIP6

Assessment of CMIP5 multimodel mean for the historical climate of Africa

Impact of Stochastic Entrainment in the NCAR CAM Deep Convection Parameterization on the Simulation of South Asian Summer Monsoon

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