Comparison of <scp>MMCFS</scp> and <scp>SINTEX‐F2</scp> for seasonal prediction of Indian summer monsoon rainfall

Pradhan, Maheswar; Rao, Suryachandra A.; Doi, Takeshi; Pillai, Prasanth A.; Srivastava, Ankur; Behera, Swadhin K.

doi:10.1002/joc.7169

Cited by 4 publications

(4 citation statements)

References 150 publications

(198 reference statements)

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“…As discussed earlier, the regions with enhanced prediction skills over Africa and Indian landmass coincide with areas with reduced dry bias. In our previous studies, Pillai et al (2018Pillai et al ( , 2021, Krishna et al (2019), andPradhan et al (2021), it was found that the global models with cooler SST bias predict a higher mean value of monsoon rainfall. Still, the prediction skill of SST and rainfall anomalies are less in those models.…”

Section: Impact On Seasonal Prediction Skillmentioning

confidence: 87%

“…Most of the general circulation models, including atmosphere standalone and ocean-atmosphere coupled models, have various problems in simulating seasonal mean rainfall patterns over the globe (Sperber et al, 2013;Goswami et al, 2014;Pillai et al, 2021;Pradhan et al, 2021;Reeves Eyre et al, 2021). Earlier studies reported that the standalone models overestimate the rainfall over the land region, whereas the coupled models underestimate (overestimate) the rainfall over landmass (ocean).…”

Section: Impact On Rainfallmentioning

confidence: 99%

“…However weaker negative correlations over the western Indian Ocean are observed in recent times, unlike the positive correlations reported in earlier studies (Pradhan et al, 2017) for a different study period . In most climate models, the ENSO-monsoon relation is reasonably well-simulated with some over-estimation of the ENSO impact on monsoons (Chattopadhyay et al, 2016;Li et al, 2017;Pradhan et al, 2017Pradhan et al, , 2021. However, the present-day climate models still have inherent problems in simulating realistic IOD-ISMR correlations (Wu et al, 2007;Li et al, 2017;Pradhan et al, 2017).…”

Section: Impact On Global Teleconnectionsmentioning

confidence: 99%

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Improvements in Diurnal Cycle and Its Impact on Seasonal Mean by Incorporating COARE Flux Algorithm in CFS

et al. 2022

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The variability of predicted variables at daily to seasonal scales in coupled models is primarily governed by surface boundary conditions between the ocean and atmosphere, namely, sea surface temperature (SST), turbulent heat, and momentum fluxes. Although efforts have been made to achieve good accuracy in surface fluxes and SST in observation and reanalysis products, less attention has been paid toward achieving improved accuracy in coupled model simulations. Improper diurnal phase and amplitude in intra-daily SST and precipitation are well-known problems in most global coupled general circulation models, including the Climate Forecast System v2 (CFSv2) model. The present study attempts to improve the representation of ocean-atmosphere surface boundary conditions in CFSv2, primarily used for India's operational forecasts at different temporal/spatial scales. In this direction, the diurnal warm layer and cool skin temperature correction scheme are implemented along with the surface flux parameterization scheme following Coupled Ocean-Atmosphere Response Experiment (COARE) v 3.0. The coupled model re-forecasts with a revised flux scheme showed better characteristics in various ocean-atmosphere parameters and processes at diurnal and seasonal time scales. At the diurnal scale, the phase and amplitude of intra-daily SST and mixed layer depth variabilities are improved over most tropical oceans. Improved diurnal SSTs helped in enhancing the diurnal range of precipitation by triggering stronger intra-daily convection. The corrected diurnal ocean-atmospheric boundary state translated into a reduction in seasonal mean dry bias over Indian landmass and the wet bias over tropical oceans. Better simulation of non-linearity associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), ENSO-Indian Summer Monsoon Rainfall (ISMR), and IOD-ISMR relation is among the most critical improvements achieved by revising the turbulent flux parameterization. The revised flux scheme showed enhanced prediction skills for tropical SST indices and ISMR.

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Section: Impact On Seasonal Prediction Skillmentioning

confidence: 87%

Section: Impact On Rainfallmentioning

confidence: 99%

Section: Impact On Global Teleconnectionsmentioning

confidence: 99%

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Improvements in Diurnal Cycle and Its Impact on Seasonal Mean by Incorporating COARE Flux Algorithm in CFS

et al. 2022

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“…High resolution (horizontal resolution of ~38 km) coupled model developed under the Monsoon Mission program (Monsoon Mission Climate Forecast system, MMCFS, Rao et al ., 2020), which was initially adopted from NCEP (Saha et al ., 2014), shows better skill compared with other models (Pillai et al ., 2018) with February (3‐month lead) initial conditions (IC) (Pillai et al ., 2017) for a hindcast period of 1981–2008. Successive studies (Pillai et al ., 2018, 2021; Pradhan et al ., 2021, etc.) have shown that the skill of high‐resolution MMCFS is due to its ability to simulate the tropical Pacific mean state and the ISMR teleconnection with El Nino Southern Oscillation (ENSO) better.…”

Section: Introductionmentioning

confidence: 99%

Impact of reduced ENSO variability and amplitude on ISMR prediction in the long‐lead forecasts of monsoon mission CFS

Pillai

Rao

Kiran

et al. 2022

Intl Journal of Climatology

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Despite the reduction in interannual variability and amplitude of El Nino Southern Oscillation (ENSO) after 2000, its influence on Indian summer monsoon rainfall (ISMR) is stronger than in the previous decades. Meanwhile, our analysis with high-resolution Monsoon Mission Climate Forecast system (MMCFS, 38 km horizontal resolution) indicates a sharp decrease in ISMR skill and ENSO-ISMR teleconnection since 2000 at long lead (3 months, Feb IC) hindcasts. At the same time, ISMR skill is intensified for a short lead (1 month, Apr IC). The persistence skill of ENSO decreased after 2000, but the decrease is more severe for January-March initial months. So the ENSO skill will be lower for models initialized during these months compared with other months. Also, the Feb IC hindcast has a similar ENSO teleconnection pattern during both periods and hence it failed to capture the modified influence of ENSO after 2000, such as the absence of dipole anomalies in the Indian Ocean and stronger subsidence over India. Thus, the model hindcasts that depend heavily on ENSO teleconnection for ISMR simulation have reduced skill in recent years. The role of other predictors along with ENSO for Apr IC hindcasts is hence evident in the current period. It is shown that after 2000, tropical Atlantic SST anomalies contributed significantly to ISMR interannual variability in observations. In these, Atlantic Meriodnal Mode (AMM) influences ISMR through off-equatorial circulation in absence of ENSO during the second period. Atlantic zonal mode has a weaker influence in absence of ENSO during both periods. Atlantic SST influence on ISMR is captured by April IC hindcasts during period 2, while Feb IC has the opposite relationship. Thus, the presence of another predictor in the form of tropical Atlantic SST along with strong ENSO-ISMR teleconnection makes the short lead hindcast of ISMR more skilful in the recent period for MMCFS.

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Seasonal predictability of the extreme Pakistani rainfall of 2022 possible contributions from the northern coastal Arabian Sea temperature

Doi,

Behera,

Yamagata

2024

npj Clim Atmos Sci

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During the summer of 2022, Pakistan and northwestern India were hit by an unprecedented spell of heavy rainfall. Despite capturing major tropical climate anomalies, almost all seasonal prediction systems failed to predict the extreme event, far surpassing the 2010 event. While trying to find possible causes of such a failure through an analysis of inter-member anomalies and numerical experiments, we have discovered that the positive sea surface temperature anomalies in the northern coastal Arabian Sea were one of the key factors behind the event. Although the ensemble mean prediction by the original system captured only 7% of the observed precipitation anomalies over Pakistan, the sensitivity experiments, in which the model sea surface temperature in the northern Arabian Sea was nudged to observation, captured about 25% of the observed anomalies. Improving our ability to predict the conditions of the northern Arabian Sea will contribute to better forecasting of extreme rainfall events such as the one experienced in Pakistan in 2022. Further analysis and possible coordinated sensitivity experiments with multi-models are necessary for a better understanding and improving the prediction.

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Comparison of MMCFS and SINTEX‐F2 for seasonal prediction of Indian summer monsoon rainfall

Cited by 4 publications

References 150 publications

Improvements in Diurnal Cycle and Its Impact on Seasonal Mean by Incorporating COARE Flux Algorithm in CFS

Improvements in Diurnal Cycle and Its Impact on Seasonal Mean by Incorporating COARE Flux Algorithm in CFS

Impact of reduced ENSO variability and amplitude on ISMR prediction in the long‐lead forecasts of monsoon mission CFS

Seasonal predictability of the extreme Pakistani rainfall of 2022 possible contributions from the northern coastal Arabian Sea temperature

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