Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, H.; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

doi:10.1007/s00382-015-2703-1

Cited by 41 publications

(41 citation statements)

References 70 publications

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“…In the perspective of the Indian land region (in a zoomed figure), however SNR first increases over the central Indian region as the lead decreases and attains maximum value at lead‐2 then decreases at lead‐1 followed by increase at lead‐0 (Table ). However, this does not explain the found maximum ISMR prediction skill at lead‐3 ( Pokhrel et al , ) shown in Table . The SNR

\geq

1.966 (1.69) is significant at 95 (90%) level based on F‐ratio statistics, which suggests inherent low predictability all over the tropical region except the equatorial Pacific and is consistent with previous studies [e.g., Kang and Shukla , ].…”

Section: Potential Predictabilitymentioning

confidence: 99%

Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2

Saha

Pokhrel

Salunke

et al. 2016

J Adv Model Earth Syst

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The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information-based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead-4 to lead-2 and then decreases at lead-1 followed by again increase at lead-0. While the actual ISMR prediction skill is highest at lead-3 forecast (second highest at lead-1), the potential predictability is highest at lead-2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño-Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required.

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Section: Potential Predictabilitymentioning

confidence: 99%

Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2

Saha

Pokhrel

Salunke

et al. 2016

J Adv Model Earth Syst

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“…Therefore, this study explores the crucial role of cloud microphysics on the ISM precipitating clouds, planetary‐scale Hadley and Walker circulation, and surface radiation fluxes using the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). CFSv2 is one of the leading coupled climate models used in global climate forecast by NCEP [ S. Saha et al ., ] as well as Indian summer monsoon rainfall (ISMR) forecast on seasonal [ Pokhrel et al ., ; Saha et al ., ] and extended range [ Sahai et al ., ] timescale. This study is organized in the following manner: model description, design of numerical experiments, and data sets used are elaborated in section 2.…”

Section: Introductionmentioning

confidence: 99%

Effect of cloud microphysics on Indian summer monsoon precipitating clouds: A coupled climate modeling study

et al. 2017

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The quest for one of the most dominant processes controlling the large‐scale circulations in the tropics is unraveled. The impact of cloud microphysical processes is known to have effects on rainfall and local atmospheric thermodynamics; however, its effect on the prevailing mean circulations is not yet studied. Two sets of coupled global climate model experiments (ICE and NO ICE microphysics) reveal that ice microphysics improves the strength of the Hadley circulation with respect to observation. Results pinpoint that ICE simulation enhances high cloud fraction (global tropics: ~59%, India: ~51%) and stratiform rain (global tropics: ~5%, India: ~15%) contribution. ICE and NO ICE cloud microphysics impacts differently on the outgoing longwave radiation (OLR), tropospheric temperature, and surface shortwave and longwave radiation. The effect of ice microphysics reduces OLR, which signifies deeper convection in the ICE run. The global annual average of the net radiation flux (shortwave and longwave) at the surface in ICE run (108.1 W/m2) is close to the observation (106 W/m2), which is overestimated in NO ICE run (112 W/m2). The result of apparent heat source term over the land and ocean surface eventually modifies regional Hadley circulation. Thus, the effect of ice microphysics in the global coupled model is important not only because of microphysics but also due to the radiation feedbacks. Therefore, better ice‐phase microphysics is required in the new generation of climate forecast model, which may lead to improvements in the simulation of monsoon.

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“…MM‐Rain provides better predictability over most of Colombia during NDJ. In case of Indian summer monsoon rainfall (June–September), the prediction skill is generally controlled by the Indian and the Pacific Ocean basins generated SST variability [ Pokhrel et al , ]. In our analysis, MM‐Rain offers lower MSE in comparison to POM‐Rain over southern and central India in MJJ and western and southern part of India during ASO.…”

Section: Resultsmentioning

confidence: 99%

Global seasonal precipitation forecasts using improved sea surface temperature predictions

2017

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Climate models driven by observed or modeled sea surface temperature (SST) or SST anomalies (SSTA) are used as a standard tool in seasonal climate predictions. This study investigates the merit of seasonal rainfall predictions obtained by using multimodel SSTA to drive a climate model over a single model case. The multimodel SSTA predictions are obtained by combining the predictions of five climate models, whereas the stand‐alone model predictions come from the Predictive Ocean Atmosphere Model for Australia. The climate model used is the Australian Community Climate and Earth System Simulator (ACCESS). The use of multimodel SSTA over a single model shows marginal improvements in seasonal rainfall predictions across the entire globe. Further improvements in rainfall forecasts are possible either through improved parameterizations in the ACCESS model or by using another climate model. As a final step, the rainfall forecasts from the multimodel and the single model SSTA (two‐member hierarchical rainfall forecasts, denoted as HIER) are combined and to gain further improvements over the individual multimodel or single model rainfall predictions for most of the grid cells in all seasons.

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Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

Cited by 41 publications

References 70 publications

Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2

Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2

Effect of cloud microphysics on Indian summer monsoon precipitating clouds: A coupled climate modeling study

Global seasonal precipitation forecasts using improved sea surface temperature predictions

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