Thermodynamic Response of a High-Resolution Tropical Indian Ocean Model to TOGA COARE Bulk Air–Sea Flux Parameterization: Case Study for the Bay of Bengal (BoB)

Mallick, Subrat Kumar; Agarwal, Neeraj; Sharma, Rashmi; Prasad, K. Venkatesh; Ramakrishna, S. S. V. S.

doi:10.1007/s00024-020-02448-6

Cited by 14 publications

(6 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This demonstrates that the overestimated Q lat in ATM is primarily due to too‐strong near‐surface wind speeds, not boundary‐layer humidity or temperature biases. Turbulent heat fluxes within the BoB are also sensitive to the choice of bulk parametrization schemes (e.g., Mallick et al ., 2020). OAFlux and ATM use different bulk formulae, so differences in the turbulent flux algorithm may also contribute to the differences in turbulent fluxes shown here.…”

Section: Resultsmentioning

confidence: 99%

The influence of air–sea coupling on forecasts of the 2016 Indian summer monsoon and its intraseasonal variability

Valdivieso

Peatman

Klingaman

2020

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Daily initialized coupled and uncoupled numerical weather prediction (NWP) forecasts from the global Met Office Unified Model (MetUM) are compared for the 2016 Indian summer monsoon. Three MetUM configurations are used: atmosphere only (ATM), coupled to a mixed-layer ocean model (KPP), and coupled to a dynamical ocean model (NEMO). The analysis focuses on the impact of air-sea coupling, particularly in the Bay of Bengal (BoB), on NWP for monsoon rainfall. Seasonal-mean biases in all three configurations are highly consistent and driven by errors in atmospheric processes. Rainfall is initially overestimated over India, but underestimated over the BoB, the latter associated with too much shortwave radiation and too little cloud cover in MetUM. The excess shortwave radiation (>40 W⋅m −2 over the northwest BoB) is partially compensated by additional latent cooling, primarily due to overestimated surface wind speeds. In NEMO and KPP, coupling improves the timing of intraseasonal active and break phases over India, primarily the end of these phases, which are systematically too late in ATM. NEMO and KPP show a more realistic intraseasonal local phase relationship between sea surface temperature (SST) and rainfall throughout the BoB, but no configuration reproduces the observed significant lagged relationship between BoB SST and Indian rainfall. The lack of this relationship may be partly attributed to weak heat flux feedbacks to northern BoB SST, with the forecast shortwave feedback having systematically the wrong sign (positive) compared to satellite radiation, and thus contributing to SST warming at all lead times. Based on these MetUM forecasts, there is a limited impact of coupling on NWP for monsoon rainfall, both for the mean rainfall and intraseasonal variability. Further research to improve NWP for monsoon rainfall should focus on reducing MetUM atmospheric systematical biases. K E Y W O R D S air-sea coupling, atmospheric convection, Bay of Bengal, Indian monsoon, intraseasonal variability, weather forecasting This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Resultsmentioning

confidence: 99%

The influence of air–sea coupling on forecasts of the 2016 Indian summer monsoon and its intraseasonal variability

Valdivieso

Peatman

Klingaman

2020

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

show abstract

“…For example, reduced southward export of OMHT over AS increases the surface warming by ~4%, reducing the RMSE by ~0.2 to ~0.6°C and becoming closer to observation. This is in good agreement that the reduction of basin‐average OMHT increases surface warming and improves the SST simulation in the north TIO (Mallick et al ., 2020). The error in EEIO may be due to a strong equatorial mixing and weaker export/import of the heat out of the region.…”

Section: Discussionmentioning

confidence: 99%

“…The oceans' meridional heat transport (OMHT; see Equation ()) are computed using the formulation of Mallick et al . (2020) and Zheng and Giese (2009).

Added value ((), AV) goodbreak= ([], \frac{((), {RMSE}_{MPI - ESM - LR} goodbreak- {RMSE}_{ROM})}{{RMSE}_{MPI - ESM - LR}}) goodbreak\times 100

OMHT = \int_{west}^{east} \int_{- H}^{0} ρ C_{p} υ T_{((), z)} dzdx

where RMSE is the root mean square error of simulated SST with respect to Reynolds OISST,

x

is the coordinate in the east–west direction,

z

is the vertical coordinate,

T_{((), z)}

is the simulated oceans' water temperature (WT),

υ

is the meridional current velocity,

C_{p}

is the seawater thermal capacity under constant pressure,

ρ

is the oceans' water density, and

H

is the depth of the ocean.…”

Section: Data Methodologymentioning

confidence: 99%

“…The models' accuracy in the representation of the thermal structure is evaluated by calculating bias, mean, standard deviation (STD), root-mean-square-error (RMSE), correlation, and the percentage change in the RMSE (see Equation ( 1)) between the runs with respect to observations is addressed in the study. The oceans' meridional heat transport (OMHT; see Equation ( 2)) are computed using the formulation of Mallick et al (2020) and Zheng and Giese (2009).…”

Section: Data Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Regional earth system model for CORDEX‐South Asia: A comparative assessment of RESM and ESM over the tropical Indian Ocean

Kumar

Mallick

Mishra

et al. 2022

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

Understanding climate variability requires good quality high resolution spatially and temporally varying ocean fields due to its decisive role in regulating the region's climate, including the Indian summer monsoon. In this regard, we employed a new high-resolution regional earth system model (RESM), namely ROM over CORDEX South Asia. We demonstrated the performance of the RESM and its added value over the global earth system model, namely MPI-ESM, in simulating the hydrographic characteristics and associated mechanism over the tropical Indian Ocean (TIO). ROM shows better skill than MPI-ESM in simulating the near-surface and subsurface characteristics of the ocean. However, larger added values are noticed for subsurface (>350 m) thermal structure. MPI-ESM's cold sea surface temperature (SST) bias is reduced in ROM which is associated with weaker winds, anomalous cyclonic wind stress curl, enhanced stratifications, and a shallower mixed layer, resulting in greater upper-ocean heating. The reduced SST bias is also consistent with improved ocean meridional heat transport (OMHT) in ROM. For example, reduced southward export of OMHT over the Arabian Sea increases the surface warming by 4%, reducing the RMSE by 0.2-0.6 C and becoming closer to observation. The anomalous cyclonic wind stress curl, in turn, caused mixed layer stratifications in the western Indian Ocean. The advective heat transfer from the south-eastern Indian Ocean to the western Indian Ocean reduced oceanic cooling by vertical processes, overcame the cooling by the net loss of surface heat fluxes, and favoured the TIO's surface warming.

show abstract

“…Zeng et al (1998) and Brunke et al (2002) have identified that the COARE3.0 algorithm is recognized as one of the least problematic algorithms for computing turbulent fluxes at the ocean surface. Mallick et al (2020) have shown that implementing the COARE3.0 flux algorithm over the Indian Ocean in Modular Ocean Model v3.0 had demonstrated significant improvements in SST representation by reducing the errors in simulated SSTs by 5-40% in the Bay of Bengal. In addition, they have also shown that the upper ocean temperature profiles have also improved by reducing the biases by 10-40%.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Thermodynamic Response of a High-Resolution Tropical Indian Ocean Model to TOGA COARE Bulk Air–Sea Flux Parameterization: Case Study for the Bay of Bengal (BoB)

Cited by 14 publications

References 73 publications

The influence of air–sea coupling on forecasts of the 2016 Indian summer monsoon and its intraseasonal variability

The influence of air–sea coupling on forecasts of the 2016 Indian summer monsoon and its intraseasonal variability

Regional earth system model for CORDEX‐South Asia: A comparative assessment of RESM and ESM over the tropical Indian Ocean

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

Contact Info

Product

Resources

About