Seasonal prediction skill of Indian summer monsoon rainfall in NMME models and monsoon mission CFSv2

Pillai, Prasanth A.; Rao, Suryachandra A.; Ramu, Dandi A.; Pradhan, Maheswar; George, Gibies

doi:10.1002/joc.5413

Cited by 42 publications

(43 citation statements)

References 52 publications

(93 reference statements)

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“…Even though CFSv2‐T126 well represents the convective centers over the ocean, a dry mean bias still persists same as in many coupled model Intercomparison Project Phase 5 and U.S. nation multimodel ensemble models over the central Indian region (George et al., 2016; B. B. Goswami et al., 2014; Pokhrel et al., 2013; Pillai et al., 2018; Ramu et al., 2016; Sabeerali et al., 2013; S. Saha et al., 2014; S. K. Saha et al., 2016). The dry bias over India is primarily attributed to the cold SST bias in the Indian Ocean (Figure 2b) and the presence of an anomalous anti‐cyclone over the central Indian region, thereby inhibiting the propagation of rain‐bearing monsoon low‐pressure systems over India (George et al., 2016; Srivastava et al., 2017).…”

Section: Resultsmentioning

confidence: 85%

“…Generally, many global coupled models show wet bias in seasonal mean precipitation over oceans and dry bias over land regions (e.g., George et al., 2016; B. B. Goswami et al., 2014; Pillai et al., 2018; Ramu et al., 2016; S. K. Saha et al., 2013). Hence, it is imperative to develop techniques to reduce the systematic biases of CGCMs and provide information at smaller spatial scales.…”

Section: Introductionmentioning

confidence: 99%

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Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Prasad

Ramu

Rao

et al. 2021

Earth and Space Science

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The usefulness of dynamical downscaling of seasonal reforecasts of Indian Monsoon is explored to address the seasonal mean biases in the reforecasts. Almost all the current generation global coupled models, including the Climate Forecast System version 2 (CFSv2, T126 ∼110 km), exhibit systematic mean dry bias over the central Indian region during the summer monsoon season. Cold sea surface temperature (SST) biases in the Indian Ocean and a weak monsoon circulation due to a colder tropospheric temperature contribute to this dry bias. Such systematic biases restrict the use of skillful forecasts from these models in climate applications (such as agriculture or hydrology). Dynamical downscaling of seasonal forecasts (∼110 km resolution) using the Weather Research and Forecasting (WRF) model coupled to a simple ocean mixed layer model (OML; WRFOML) at 38 km resolution significantly reduces the majority of the systematic biases reported earlier. The seasonal mean dry bias reduces to 16% in WRFOML as compared to 44% (33%) in the CFSv2‐T126 (WRFCTL) over the Indian land region. Warmer SSTs in the Indian Ocean and a more robust monsoon circulation emanating from a realistic simulation of the tropospheric temperature reduced the systematic biases in WRFOML compared to CFSv2‐T126 and WRFCTL. Additionally, category‐wise rainfall distributions are also improved drastically in the downscaled simulations (WRFOML). Downscaled reforecasts with reduced systematic biases have better suitability for climate applications.

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Section: Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Prasad

Ramu

Rao

et al. 2021

Earth and Space Science

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“…Compared with observations, the ENSO–precipitation link is moderately (though weaker than in observations) reproduced in SEAS5 at Lead‐1 (Figure 10b), however, the teleconnection pattern weakens with an increase in lead time, which could be a reason for the decrease in forecast skill with an increase in lead time. Recent studies have reported an overly strong relationship between ENSO and the summer monsoon precipitation in different seasonal prediction systems (Pillai, Rao, Ramu, Pradhan, & George, 2018, among others). The ENSO–precipitation relationship in these studies may be exaggerated by use of the ensemble mean (not shown) instead of individual ensemble members.…”

Section: Resultsmentioning

confidence: 99%

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Ehsan

Tippett

Kucharski

et al. 2020

Intl Journal of Climatology

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The potential and actual forecast skill of peak summer monsoon precipitation (July–August [JA]) over the core summer monsoon region of Pakistan (CSMRP: 68°–76°E, 30°–36°N) is investigated. The predictions from the latest version of the European Centre for Medium‐Range Weather Forecast System5 (SEAS5) and the North American Multimodel Ensemble (NMME) initialized in the beginning of June, May, and April are utilized. The potential skill is estimated by signal‐to‐noise ratio and perfect model correlation. The forecast skill is evaluated using the temporal anomaly correlation as well as the ranked probability skill score for probabilistic forecasts. The SEAS5 generally captures the observed climatological mean and variability patterns of peak summer monsoon precipitation over the region, with some biases mainly located over the complex topography zones. Observed SST anomalies in the El Niño–Southern Oscillation (ENSO) region are negatively correlated with observed peak summer monsoon precipitation over the region. The ENSO–precipitation connection is weakly reproduced in SEAS5 at Lead‐1 and fades gradually with increasing lead time. The potentially predictable component (signal) is small compared to the unpredictable part (noise). Nonetheless, statistically significant potential skill is found over the CSMRP. However, the forecast skill of peak summer monsoon precipitation over the region is lower than the potential skill in SEAS5. A multimodel ensemble average of five models shows little benefit in predicting peak monsoon precipitation over Pakistan. Extended spatial averaging has some impact on the skill; however, temporal averaging has no effect on the skill of the summer monsoon precipitation in SEAS5. Together these results show limitations of the current seasonal prediction models and demonstrate their inadequate performance in predicting peak summer monsoon precipitation over Pakistan.

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“…The predictability of the coupled model at longer time scales sensibly lies in the fact that Ocean has better memory in virtue of its higher specific heat capacity. Nevertheless, the skill of the present coupled models is far less than the potential predictability limit of ISMR (Pillai et al, 2018). The National Centers for Environmental Prediction CFSv2 has been adapted for operational seasonal prediction of ISMR under National Monsoon Mission by the Government of India.…”

Section: Discussionmentioning

confidence: 99%

Impact of horizontal resolution on sea surface temperature bias and air–sea interactions over the tropical Indian Ocean in CFSv2 coupled model

Kbrr

Sreenivas

Rao

et al. 2020

Intl Journal of Climatology

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The cold bias in sea surface temperature (SST) over the Tropical Indian Ocean (TIO) in Climate Forecast System version 2 (CFSv2) has been a limiting hurdle in predicting the Indian summer monsoon rainfall (ISMR). The present study addresses the impact of increasing horizontal resolution of the atmospheric model of CFSv2 from a low resolution (T126, ~100 km) to a high resolution (T382, ~38 km) in improving the SST and Ocean mixed layer simulation. The high resolution run has reasonably reduced the SST cold bias (−0.8 to −0.2°C) over southern TIO and (−0.69 to 0.07°C) over northern TIO, however, a moderate warm bias of 0.37°C is observed over the tropical Pacific Ocean in T382 run. We have conducted a mixed layer heat budget analysis to understand the causative factors responsible for improved SST simulation. The improvement in net heat fluxes resulted in reduced biases in SST over TIO in T382 simulation. Better simulation of the entrainment process has further improved SST over Southern TIO. The high‐resolution atmospheric model is able to resolve convection centres reasonably well, therefore resulted in better simulation of winds, which enabled better prediction of Ocean mixed layer. The low and middle clouds are also better resolved in high resolution run, the total cloud cover is well predicted by the T382 run, hence the biases in radiative and heat fluxes are decreased significantly compared with its lower resolution model, which improved the radiation fluxes and responsible for the observed improvement in SST and mixed layer prediction. The dominance of biases in net shortwave radiation resulting in moderate positive SST biases over the Northern tropical Pacific Ocean.

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Seasonal prediction skill of Indian summer monsoon rainfall in NMME models and monsoon mission CFSv2

Cited by 42 publications

References 52 publications

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Impact of horizontal resolution on sea surface temperature bias and air–sea interactions over the tropical Indian Ocean in CFSv2 coupled model

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