Spatio-temporal variability of surface chlorophyll and pCO2 over the tropical Indian Ocean and its long-term trend using CMIP6 models

Mohanty, Sachiko; Bhattacharya, Biswarup; Singh, Charu

doi:10.1016/j.scitotenv.2023.168285

Cited by 3 publications

(1 citation statement)

References 90 publications

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“…This gives the confidence that the model is a reliable tool for making future climate projections. As part of the World Climate Research Programme (WCRP), the Coupled Model Intercomparison Project Phase 6 (CMIP6) oversees the implementation of General Circulation Models (GCMs) by multiple modeling institutions, aiming to simulate the Earth's climate system behaviour to study a wide range of climaterelated phenomena such as climate variabilities and past & future climate projection ( Mohanty et al, 2024;Peng et al, 2021;Pereira et al, 2023;Petrik et al, 2022). This simulation aims to explore how Earth's climate responds to various climate forcing under distinct scenarios known as "Socioeconomic pathways (SSPs)".…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CMIP6 Models Performance in Simulating Historical Biogeochemistry across Southern South China Sea

Marshal,

Chung,

Mohd Akhir

2024

Preprint

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Abstract. This study evaluates the ability of Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate biogeochemical variables in the southern South China Sea (SCS). The analysis focuses on chlorophyll, phytoplankton, nitrate, and oxygen at annual and seasonal scales. The models' performance is assessed against Copernicus Marine Environment Monitoring Service (CMEMS) data using statistical metrics such as the Taylor diagram and Taylor skill score. The results show that the models generally capture the observed spatial patterns of biogeochemical variables but exhibit varying degrees of overestimation or underestimation. The performance of the models is also influenced by the season, with some models showing better performance during the southwest monsoon than the northeast monsoon. Overall, the top five best-performing models for biogeochemical variables are MIROC-ES2H, GFDL-ESM4, CanESM5-CanOE, MPI-ESM1-2-LR, and NorESM2-LM. The findings of this study have implications for researchers and end-users of the datasets, providing guidance for model improvement and understanding the impacts of climate change on the SCS ecosystem.

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Section: Introductionmentioning

confidence: 99%

Evaluation of CMIP6 Models Performance in Simulating Historical Biogeochemistry across Southern South China Sea

Marshal,

Chung,

Mohd Akhir

2024

Preprint

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Indian Ocean Acidification and Its Driving Mechanisms Over the Last Four Decades (1980–2019)

Chakraborty,

Joshi,

Ghoshal

et al. 2024

Global Biogeochemical Cycles

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This paper aims to study the changes in the Indian Ocean seawater pH in response to the changes in sea‐surface temperature, sea‐surface salinity, dissolved inorganic carbon (DIC), and total alkalinity (ALK) over the period 1980–2019 and its driving mechanisms using a high‐resolution regional model outputs. The analysis indicates that the rate of change of declining pH in the Arabian Sea (AS), the Bay of Bengal (BoB), and the Equatorial Indian Ocean (EIO) is −0.014 0.002, −0.014 0.001, and −0.015 0.001 unit dec−1, respectively. Both in AS and BoB (EIO), the highest (lowest) decadal DIC trend is found during 2000–2009. The surface acidification rate has accelerated throughout the IO region during 2010–2019 compared to the previous decades. Further, our analysis indicates that El Ninõ and positive Indian Ocean Dipole events lead to an enhancement of the Indian Ocean acidification. The increasing anthropogenic CO2 uptake by the ocean dominantly controls 80% (94.5% and 85.7%) of the net pH trend (1980–2019) in AS (BoB and EIO), whereas ocean warming controls 14.4% (13.4% and 7.0%) of pH trends in AS (BoB and EIO). The changes in ALK contribute to enhancing the pH trend of AS by 5.0%. ALK dominates after DIC in the EIO and, similar to the AS, contributes to increasing the negative pH trend by 10.7%. In contrast, it has a buffering effect in the BoB, suppressing the pH trend by −5.4%.

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Evaluation of CMIP6 model performance in simulating historical biogeochemistry across the southern South China Sea

Marshal,

Xiang Chung,

Roseli

et al. 2024

Biogeosciences

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Abstract. This study evaluates the ability of Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate biogeochemical variables in the southern South China Sea (SCS). The analysis focuses on key biogeochemical variables: chlorophyll, phytoplankton, nitrate, and oxygen based on their availability in the selected models at annual and seasonal scales. The models' performance is assessed against Copernicus Marine Environment Monitoring Service (CMEMS) data using statistical metrics such as the Taylor diagram and Taylor skill score. The results show that the models generally capture the observed spatial patterns of surface biogeochemical variables. However, they exhibit varying degrees of overestimation or underestimation in their quantitative measures. Specifically, their mean bias error ranges from −0.02 to +2.5 mg m−3 for chlorophyll, −0.5 to +1 mmol m−3 for phytoplankton, −0.1 to +1.3 mmol m−3 for nitrate, and −2 to +2.5 mmol m−3 for oxygen. The performance of the models is also influenced by the season, with some models showing better performance during June, July, and August than December, January, and February. Overall, the top five best-performing models for biogeochemical variables are MIROC-ES2H, GFDL-ESM4, CanESM5-CanOE, MPI-ESM1-2-LR, and NorESM2-LM. The findings of this study have implications for researchers and end users of the datasets, providing guidance for model improvement and understanding the impacts of climate change on the southern SCS ecosystem.

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Spatio-temporal variability of surface chlorophyll and pCO2 over the tropical Indian Ocean and its long-term trend using CMIP6 models

Cited by 3 publications

References 90 publications

Evaluation of CMIP6 Models Performance in Simulating Historical Biogeochemistry across Southern South China Sea

Evaluation of CMIP6 Models Performance in Simulating Historical Biogeochemistry across Southern South China Sea

Indian Ocean Acidification and Its Driving Mechanisms Over the Last Four Decades (1980–2019)

Evaluation of CMIP6 model performance in simulating historical biogeochemistry across the southern South China Sea

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