Evaluation of CMIP5 and CORDEX derived wave climate in Indian Ocean

Chowdhury, Piyali; Behera, Manasa Ranjan

doi:10.1007/s00382-018-4391-0

Cited by 21 publications

(15 citation statements)

References 39 publications

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“…In this study, we used a third‐generation spectral wave model MIKE21 (DHI) to simulate the wave climate over the IO region (http://manuals.mikepoweredbydhi.help/2017/MIKE_21.htm). MIKE21 is an operational wave simulation model of the Danish Hydraulic Institute and has been extensively validated for wave simulation (Sørensen et al, ; Venugopal and Smith, ; Remya et al, ; Chowdhury and Behera, ). The numerical model simulates the time evolution of the wave spectrum over a spatial grid by solving the wave action conservation equation with an implicit, cell‐centred finite volume method.…”

Section: Methodsmentioning

confidence: 99%

“…() found that IPSL and GFDL show greater skill in simulating propagation characteristics of the Indian monsoon. Therefore, Chowdhury and Behera () performed an evaluation test of the GCMs listed in Table to analyse how they behave during wave simulation over the IO region. The authors concluded that the ensemble derived from these GCMs was efficient in capturing the seasonality in IO wave climate along with capturing the annual trends.…”

Section: Methodsmentioning

confidence: 99%

“…However, according to McSweeney et al () it is important to select appropriate climate model(s) and representative concentration pathway (RCP) scenario(s) in order to generate projections that are policy‐relevant and manageable to develop, analyse, and disseminate. It was concluded in Chowdhury and Behera () that RCMs fail in providing acceptable wind inputs for wave simulations over the IO. It is well known that the nearshore wave climate along the Indian coast is heavily influenced by the seasonal monsoon winds and the strong swells arriving from the southern IO (SIO; Aboobacker and Shanas, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wave climate projections along the Indian coast

Chowdhury

Behera

Reeve

2019

Intl Journal of Climatology

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Future changes in wave climate will influence the marine ecosystem, coastal erosion, design of coastal defences, operation of near-and off-shore structures, and coastal zone management policies and may further add to the potential vulnerabilities of coastal regions to projected sea level rise. Many studies have reported changes in the global wave characteristics under climate change scenarios, but it is important to project future changes in local/regional wave climate for smooth implementation of policies and preventing severe coastal erosion and flooding. In this study the regional wave climate along the Indian coast for two time slices, 2011-2040 and 2041-2070, is reported using an ensemble of near-surface winds generated by four different CMIP5 general circulation models (GCMs), under RCP4.5 scenario. Comparison of the wave climate for the two time slices shows an increase in wave heights and periods along much of the Indian coast, with the maximum wave heights increasing by more than 30% in some locations. An important finding is that at most locations along the east coast, wave periods are expected to increase by almost 20%, whereas along the west coast an increase of around 10% is expected. This will alter the distribution of wave energy at the shoreline through changes in wave refraction and diffraction, with potential implications for the performance and design of coastal structures and swash-aligned beaches. Furthermore, the computations show material changes in the directional distribution of waves. This is particularly important in determining the longshore transport of sediments and can lead to realignment of drift-aligned beaches, manifesting itself as erosion and/or siltation problems. This study is a preliminary contribution towards regional climate projections for the Indian Ocean region which are needed to plan and mitigate the impacts of future climate change. K E Y W O R D S climate change, coastal vulnerability, dynamical projections, Indian coast, surface ocean waves, wave climate variability

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wave climate projections along the Indian coast

Chowdhury

Behera

Reeve

2019

Intl Journal of Climatology

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“…An atmospheric LGM forcing for NorESM-OC was created by adding anomalies (relative to the pre-industrial 15 state) derived from PMIP3 models (Morée and Schwinger, 2019; version 1) to the CORE Normal Year Forcing (NYF; Large and Yeager, 2004). The use of mean PMIP/CMIP anomalies to force stand-alone models is a standard approach that has been tested before (Mitchell et al, 2017;Chowdhury and Behera, 2019;Muglia et al, 2015;Muglia et al, 2018). Through this approach, the effect of the presence of sea ice on the atmospheric state is included in the forcing, but the sea ice model handles the actual formation/melt of sea ice.…”

Section: Last Glacial Maximum Set-upmentioning

confidence: 99%

Evaluating the Biological Pump Efficiency of the Last Glacial Maximum Ocean using δ<sup>13</sup>C

Morée¹,

Schwinger²,

Ninneman³

et al. 2020

Preprint

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Abstract. Although both physical and biological marine changes are required to explain the 100 ppm lower atmospheric pCO2 of the Last Glacial Maximum (LGM, ~ 21 ka) as compared to pre-industrial (PI) times, their exact contributions are debated. Proxies of past marine carbon cycling (such as δ13C) document these changes, and thus provide constraints for quantifying the drivers of long-term carbon cycle variability. This modelling study explores the relative roles of physical and biological changes in the ocean needed to simulate an LGM ocean in satisfactory agreement with proxy data, and here especially δ13C. We prepared a PI and LGM ocean model state (NorESM-OC) with full biogeochemistry (including the carbon isotopes δ13C and radiocarbon) and dynamic sea ice. The modelled LGM-PI differences are evaluated against a wide range of physical and biogeochemical proxy data, and show agreement for key aspects of the physical ocean state within the data uncertainties. However, the lack of a simulated increase of regenerated nutrients for the LGM indicates that additional biogeochemical changes are required to simulate an LGM ocean in agreement with proxy data. In order to examine these changes, we explore the theoretical effects of different global mean biological pump efficiencies on the simulated marine biogeochemical tracer distributions. We estimate that (besides changes in ocean circulation) an approximate doubling of the global mean biological pump efficiency from 38 % (PI) to 75 % (LGM) reduces model-proxy biases the most. The remaining absolute model-proxy error in δ13C (which is 0.07 ‰ larger than the 0.19 ‰ data uncertainty) indicates that additional changes in ocean dynamics are needed to simulate an LGM ocean in agreement with proxy data, such as increased aging or volume of Southern Source Waters. Besides that, our theoretical approach of increasing the biological pump efficiency may be too simplified to capture the vertical redistribution of regenerated nutrients – also suggested by a too weak chemocline. Our results underline that only those coupled climate models that contain the processes and/or components that realistically change both ocean circulation and biogeochemistry will be able to simulate an LGM ocean in satisfactory agreement with proxy data – and hence be reliable for use in climate projections. Therefore, future research should aim to identify the exact physical and biogeochemical processes that could have doubled the global mean biological pump efficiency (i.e., the interior regenerated signature) between the PI and LGM, with a likely central role for Southern Source Waters.

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“…Hu et al [7] simulated wave parameters associated with typhoons in the Northwestern Pacific and investigated the wave climate by typhoons. Hindcast wave data were used to study the wave climate of various regions, including the Indian Ocean [8], North Sea [9], Hawaiian coast [10], Brazilian coast [11], Swedish coast [12], Black Sea [13,14], and Persian Gulf [15].…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of Assimilated Coastal Wave Data in the Northwestern Pacific Area

Hisaki

2020

JMSE

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The assimilated coastal wave data are useful for wave climate study, coastal engineering, and design for marine disaster protection. However, the assimilated coastal wave data are few. Here, wave analysis data produced by the JMA (Japan Meteorological Agency) and ERA5 wave data were compared with GPS (Global Positioning System) buoy-measured wave data. In addition, the accuracy of ERA5 wave data for various conditions was investigated. The accuracy of JMA analysis wave height was better than that of ERA5 wave height. The ERA5 wave height was underestimated as the wave height increased. The accuracy of the ERA5 wave height was significantly different in fetch-unlimited and fetch-limited conditions. The difference of the skill metrics between fetch-unlimited and fetch-limited conditions was due to the overestimation of the fetch in the ERA5 grid. This result also applied to the wave period.

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Evaluation of CMIP5 and CORDEX derived wave climate in Indian Ocean

Cited by 21 publications

References 39 publications

Wave climate projections along the Indian coast

Wave climate projections along the Indian coast

Evaluating the Biological Pump Efficiency of the Last Glacial Maximum Ocean using δ<sup>13</sup>C

Intercomparison of Assimilated Coastal Wave Data in the Northwestern Pacific Area

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Evaluation of CMIP5 and CORDEX derived wave climate in Indian Ocean

Cited by 21 publications

References 39 publications

Wave climate projections along the Indian coast

Wave climate projections along the Indian coast

Evaluating the Biological Pump Efficiency of the Last Glacial Maximum Ocean using δ&lt;sup&gt;13&lt;/sup&gt;C

Intercomparison of Assimilated Coastal Wave Data in the Northwestern Pacific Area

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Evaluating the Biological Pump Efficiency of the Last Glacial Maximum Ocean using δ<sup>13</sup>C