Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

Krishna, R. Phani Murali; Rao, Suryachandra A.; Srivastava, Ankur; Kottu, Hari Prasad; Pradhan, Maheswar; Pillai, Prasanth A.; Dandi, Ramu; Sabeerali, C. T.

doi:10.1007/s00382-019-04921-y

Cited by 19 publications

(24 citation statements)

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“…6), though the interannual variability is slightly overestimated (1.09, ratio of standard deviation of simulated ISMR to the observed), which demonstrates that even a coarserresolution model with improved cloud, convection, and radiation physics parameterization can achieve enhanced skill. SN-MC (Saha et al 2019), SAS2sc (Krishna et al 2019), and MC (Hazra et al 2017) also simulate higher skill scores of 0.62, 0.65, and 0.71, respectively, which demonstrates an improvement over the control run, even though the interannual variance is underestimated. Some of the model development activities mentioned above are in the process of being incorporated in the high-resolution MM model, and we hope that their inclusion will further enhance the country-averaged ISMR prediction skill and the spatial skill of the model, although the possibility of compensating errors requires that this be tested carefully.…”

Section: Randd Toward Improving Ismr Prediction and Predictabilitymentioning

confidence: 92%

“…To test the impact of the progress made under MM, a series of hindcast experiments were carried out using seven versions of the MM model with mixed physics configurations, namely, 1) the standard CFSv2 at T126 resolution with standard physics (CTL; Saha et al 2014), 2) with the high-resolution (T382) MM model (Ramu et al 2016), 3) with revised SAS, improved cloud microphysics (WSM6) and radiation (Abhik et al 2017), 4) with old snow model but new cloud microphysics parameterization (MC; Hazra et al 2017), 5) with new snow model combined with new cloud microphysics (SN-MC; Saha et al 2019), 6) with the revised convection parameterization scheme (SAS2; Han and Pan 2011; Krishna et al 2019), and 7) the revised convection parameterization scheme and revised shallow convection scheme (SAS2sc). These hindcasts were carried out for the period 1981-2010.…”

Section: Randd Toward Improving Ismr Prediction and Predictabilitymentioning

confidence: 99%

“…High-resolution (T382) seasonal prediction system (Ramu et al 2016) Grand MME prediction system for extended-range prediction of monsoon (Sahai et al 2013(Sahai et al , 2015a A very high-resolution T574 and T1534 (semi-Lagrangian core) Global Ensemble Forecast System (GEFS) for short-range forecasts CFSv2 seasonal forecast model transformed into an Earth system model (IITM-ESM) suitable for longterm climate change studies (Swapna et al 2015(Swapna et al , 2018Krishnan et al 2019); although not formally funded under MM, the development of IITM-ESM is essentially a by-product of sustained in-house climate model development efforts at the CCCR, IITM Developmental activities to improve the original model performance New convection parameterization scheme (SAS2, Han and Pan 2011;Ganai et al 2015Ganai et al , 2016Krishna et al 2019) to replace the original SAS scheme…”

Section: Setup Of Cfsv2 Prediction Systemmentioning

confidence: 99%

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Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales

Rao¹,

Goswami²,

Sahai³

et al. 2019

Bulletin of the American Meteorological Society

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In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

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Section: Randd Toward Improving Ismr Prediction and Predictabilitymentioning

confidence: 92%

Section: Randd Toward Improving Ismr Prediction and Predictabilitymentioning

confidence: 99%

Section: Setup Of Cfsv2 Prediction Systemmentioning

confidence: 99%

See 1 more Smart Citation

Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales

Rao¹,

Goswami²,

Sahai³

et al. 2019

Bulletin of the American Meteorological Society

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“…During El Niños, the Walker circulation shifts eastward, inducing subsidence and dry conditions in the Indian sector and vice-versa during La Niñas (Walker 1924;Sikka 1980;Rasmusson and Carpenter 1983;Webster et al 1998;andNath 2000, 2012). It is therefore extremely important to examine if the ENSO-ISM relationships are well simulated in state-of-the art climate and seasonal forecasting models (Annamalai et al 2007;Terray et al 2012;Sperber et al 2013;Jourdain et al 2013;Sabeerali et al 2019;Krishna et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…So far, we have only a poor understanding of the relative roles of local ocean-atmosphere coupling (e.g. IO) and ENSO in the occurrence of extreme ISMs both in observations and coupled simulations (Gadgil et al 2005;Saha et al 2016;Krishna et al 2019). In particular, the way the IOD and IOB modes influence ISM and interact with ENSO remains unclear (Wu and Kirtman 2004ab;Achuthavarier et al 2012;, Annamalai et al 2017, and may limit drastically ISM seasonal predictability and the accuracy of ISM projections (Sabeerali et al 2019;Li et al 2017ac).…”

Section: Introductionmentioning

confidence: 99%

Anatomy of the Indian Summer Monsoon and ENSO relationships in state-of-the-art CGCMs: role of the tropical Indian Ocean

et al. 2020

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Indian Summer Monsoon (ISM) rainfall and El Niño-Southern Oscillation (ENSO) exhibit an inverse relationship during boreal summer, which is one of the roots of ISM interannual variability and its seasonal predictability. Here we document how current climate and seasonal prediction models simulate the timing and amplitude of this ISM-ENSO teleconnection. Many Coupled General Circulation Models (CGCMs) do simulate a simultaneous inverse relationship between ENSO and ISM, though with a large spread. However, most of them show significant negative correlations before ISM, which are at odd with observations. Consistent with this systematic error, simulated Niño-3.4 Sea Surface Temperature (SST) variability has erroneous high amplitude during boreal spring and ISM rainfall variability is also too strong during the first part of ISM. The role of the Indian Ocean (IO) in modulating the ISM-ENSO relationships is further investigated using dedicated experiments with the SINTEX-F2 CGCM. Decoupled tropical Pacific and IO experiments are conducted to assess the direct relationship between ISM and IO SSTs on one hand, and the specific role of IO feedback on ENSO on the other hand. The direct effect of IO SSTs on ISM is weak and insignificant at the interannual time scale in the Pacific decoupled experiment. On the other hand, IO decoupled experiments demonstrate that El Niño shifts rapidly to La Niña when ocean-atmosphere coupling is active in the whole IO or only in its western part. This IO negative feedback is mostly active during the decaying phase of El Niño, which is accompanied by a basin-wide warming in the IO, and significantly modulates the length of ENSO events in our simulations. This IO feedback operates through a modulation of the Walker circulation over the IO, which strengthens and shifts eastward an anomalous anticyclone centered on the Philippine Sea and associated easterly wind anomalies in the equatorial western Pacific during boreal winter. In turn, these atmospheric anomalies lead to a fast ENSO turnabout via oceanic adjustement processes mediated by eastward propagating upwelling Kelvin waves. An experiment in which only the SouthEast Indian Ocean (SEIO) is decoupled, demonstrates that the equatorial SST gradient in the IO during boreal winter plays a fundamental role in the efficiency of IO feedback. In this experiment, simulated ISM-ENSO lead-lag correlations match closely the observations. This success is associated with removal of erroneous SEIO SST variability during boreal winter in the SEIO decoupled experiment. Finally, it is illustrated that most CMIP5 CGCMs exhibit similar SST errors in the SEIO during boreal winter in addition to an exagerated SEIO SST variability during boreal fall.

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

Pradhan

Rao

Doi

et al. 2021

Intl Journal of Climatology

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A comparison of the Indian summer Monsoon Rainfall (ISMR) in two different coupled models (viz., Scale Interaction Experiment‐Frontier‐F2; SINTEX‐F2, and Monsoon Mission Climate Forecast System; MMCFS) is carried out to ascertain the predictability sources in these models and their strengths and weaknesses. SINTEX‐F2 has a stronger cold sea surface temperature (SST) bias in the central equatorial Pacific, and it simulates mean ISMR better while underestimating the interannual variability of ISMR. On the other hand, MMCFS has warmer SST bias in the tropical Pacific off the equator, simulates a drier mean monsoon, but has a more realistic ISMR interannual variability. Further, the cold SST bias in the central tropical Pacific adversely affects the ability of the SINTEX‐F2 to capture the El Niño–Southern Oscillation (ENSO) related interannual variability and teleconnection patterns with monsoon by shifting it further westward in the tropical Pacific Ocean. The models' skill in simulating various climate indices are compared by considering criteria such as anomaly correlation coefficient (ACC), spread to root mean square error (RMSE) ratio, and signal to noise ratio (SNR). The prediction skill for ISMR in terms of ACC in MMCFS (.53) and SINTEX‐F2 (.45) are comparable for hindcasts initialized in February month. However, RMSE for ISMR from February initial conditions in SINTEX‐F2 (1.43 mm/day) is small compared to MMCFS (2.34 mm/day). A simple multi‐model ensemble prediction system based on MMCFS and SINTEX‐F2 results in better prediction skill in terms of ACC for tropical SST and ISMR. The spread/RMSE ratio for ISMR is similar in both the models but is better for ENSO indices in MMCFS at a longer lead time than in SINTEX‐F2. Considering the SNR as performance criteria, MMCFS has an advantage due to better predictability of SST and vertical wind shear in several parts of the Indo‐Pacific domain.

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Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

Cited by 19 publications

References 50 publications

Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales

Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales

Anatomy of the Indian Summer Monsoon and ENSO relationships in state-of-the-art CGCMs: role of the tropical Indian Ocean

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

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