A Performance Evaluation of <scp>CMIP6</scp> Wind Fields for Robust Forcing in Indian Ocean Wave Climate Studies

ABSTRACT The Coupled Model Intercomparison Project phase Six (CMIP6) lacks wave climate projections, emphasising the critical need to select the most accurate CMIP6 model winds for projecting wave climate. This study focuses on evaluating and selecting the optimal CMIP6 model wind fields for the Indian Ocean wave climate projections. A 35‐year (1980–2014) wind‐wave climate simulation of the Indian Ocean (IO) using the third‐generation wave model WAVEWATCH‐III (WW3), forced with seven CMIP6 Global Climate Models (BCC‐CSM2‐HR, EC‐Earth3, CMCC‐CM2‐SR, GFDL‐ESM4, CNRM‐CM6‐1‐HR, HadGEM3‐GC31‐MM and MPI‐ESM1‐2‐HR), is generated and validated against in situ buoy observations and ERA5 reanalysis data. Statistical analyses revealed that MPI, BCC and EC are the most accurate in representing wave characteristics in the IO, exhibiting strong correlations with observations and effectively capturing inter‐annual variability. Extreme wave analysis shows that MPI, BCC and EC model wind‐forced wave simulations match well with ERA5 data. The top three models (MPI, BCC and EC) are then selected for the composite analysis to assess their capability to reproduce the climate mode impacts on IO wave climate. EC performs best in capturing wave fields under El‐Nino Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole influences, followed by BCC and MPI. Thus, the study identifies BCC, MPI and EC as the optimal CMIP6 models for the Indian Ocean wave projections.

Authors

Institutions

Publication Details

Journal
International Journal of Climatology
Published
2024-12-30
DOI
https://doi.org/10.1002/joc.8744
Citations
2
Primary Topic
Ocean Waves and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Performance Evaluation of CMIP6 Wind Fields for Robust Forcing in Indian Ocean Wave Climate Studies

T. M. Balakrishnan Nair, T. Srinivasa Kumar, S. Malavika, Meenakshi Sreejith et al.
2 citations
International Journal of Climatology
Ocean Waves and Remote Sensing
0.96
article

A Performance Evaluation of CMIP6 Wind Fields for Robust Forcing in Indian Ocean Wave Climate Studies

T. M. Balakrishnan Nair, T. Srinivasa Kumar, S. Malavika, Meenakshi Sreejith, P. G. Remya, S. Sreelakshmi, Bipin Kumar
article en
2 citations

Abstract

ABSTRACT The Coupled Model Intercomparison Project phase Six (CMIP6) lacks wave climate projections, emphasising the critical need to select the most accurate CMIP6 model winds for projecting wave climate. This study focuses on evaluating and selecting the optimal CMIP6 model wind fields for the Indian Ocean wave climate projections. A 35‐year (1980–2014) wind‐wave climate simulation of the Indian Ocean (IO) using the third‐generation wave model WAVEWATCH‐III (WW3), forced with seven CMIP6 Global Climate Models (BCC‐CSM2‐HR, EC‐Earth3, CMCC‐CM2‐SR, GFDL‐ESM4, CNRM‐CM6‐1‐HR, HadGEM3‐GC31‐MM and MPI‐ESM1‐2‐HR), is generated and validated against in situ buoy observations and ERA5 reanalysis data. Statistical analyses revealed that MPI, BCC and EC are the most accurate in representing wave characteristics in the IO, exhibiting strong correlations with observations and effectively capturing inter‐annual variability. Extreme wave analysis shows that MPI, BCC and EC model wind‐forced wave simulations match well with ERA5 data. The top three models (MPI, BCC and EC) are then selected for the composite analysis to assess their capability to reproduce the climate mode impacts on IO wave climate. EC performs best in capturing wave fields under El‐Nino Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole influences, followed by BCC and MPI. Thus, the study identifies BCC, MPI and EC as the optimal CMIP6 models for the Indian Ocean wave projections.

International Journal of ClimatologyVol. 45(4)
Indian National Centre for Ocean Information Services (IN), Kerala University of Fisheries and Ocean Studies (IN)
Ministry of Earth Sciences
Climate action
Openalex Percentile: Top 23%
Ocean Waves and Remote Sensing
0.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.