Chlorophyll bloom in response to tropical cyclone Hudhud in the Bay of Bengal: Bio-Argo subsurface observations

Chacko, Neethu

doi:10.1016/j.dsr.2017.04.010

Cited by 74 publications

(52 citation statements)

References 25 publications

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“…In addition, expanding coverage of Biogeochemical Argo float observations also offer opportunities for evaluating phytoplankton response to mixing forced by TCs and ECs (e.g., Chacko, 2017) and studying the role that these extreme weather events play in ventilating subsurface waters in oxygen minimum zones. These new applications of profiling floats will enable detailed investigations of the upper ocean processes involved in EC intensification and the role that these extreme weather events play in the ocean biogeochemistry within their main formation basins, such as the Northwest Pacific and North Atlantic ocean.…”

Section: Profiling Floatsmentioning

confidence: 99%

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

et al. 2019

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Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs, as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.

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Section: Profiling Floatsmentioning

confidence: 99%

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

et al. 2019

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“…For example, the aggregated direct impacts of subseasonal atmospheric, oceanic, and sea ice processes on subseasonal MLD variability are not well known on a global scale nor are the indirect rectified impacts of subseasonal atmospheric, oceanic, and sea ice variability on the annual mean and seasonal cycle of the MLD. In addition to building understanding of the mean and seasonal cycle of the MLD, it is necessary to understand subseasonal variability of the MLD because this variability may have important implications for biogeochemistry (Carranza et al, 2018;Carranza and Gille, 2015;Castro de la Guardia et al, 2019;Chacko, 2017;Duteil, 2019;Follows & Dutkiewicz, 2001;Fauchereau et al, 2011;Girishkumar et al, 2019;Jin et al, 2013;Lévy et al, 2009;Monteiro et al, 2015;Nicholson et al, 2016;Resplandy et al, 2009;Rodgers et al, 2014;Rumyantseva et al, 2015;Thomalla et al, 2011;Waniek, 2003;Waliser et al, 2005;Ye et al, 2013) as well as coupled atmosphere-ocean subseasonal Observations provide an important but incomplete picture of subseasonal MLD variability and its potential drivers. For example, in the atmosphere (der Van, 1957;Foltz and McPhaden, 2004;Gulev et al, 2002;Goubanova et al, 2013;Illig et al, 2014), ocean (Ferrari and Wunsch, 2009;Wunsch, 2002), and sea ice (Heil & Hibler, 2002;Kwok et al, 2003;Martini et al, 2014), subseasonal variability represents a substantial fraction of the kinetic energy near the sea surface.…”

Section: Introductionmentioning

confidence: 99%

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

Whitt

Nicholson²,

Carranza

2019

JGR Oceans

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Subseasonal surface wind variability strongly impacts the annual mean and subseasonal turbulent atmospheric surface fluxes. However, the impacts of subseasonal wind variability on the ocean are not fully understood. Here, we quantify the sensitivity of the ocean surface stress ( ), buoyancy flux (B), and mixed layer depth (MLD) to subseasonal wind variability in both a one-dimensional (1-D) vertical column model and a three-dimensional (3-D) global mesoscale-resolving ocean/sea ice model. The winds are smoothed by time filtering the pseudo-stresses, so the mean stress is approximately unchanged, and some important surface flux feedbacks are retained. The 1-D results quantify the sensitivities to wind variability at different time scales from 120 days to 1 day at a few sites. The 3-D results quantify the sensitivities to wind variability shorter than 60 days at all locations, and comparisons between 1-D and 3-D results highlight the importance of 3-D ocean dynamics. Globally, subseasonal winds explain virtually all of subseasonal variance, about half of subseasonal B variance but only a quarter of subseasonal MLD variance. Subseasonal winds also explain about a fifth of the annual mean MLD and a similar and spatially correlated fraction of the mean friction velocity, u * = √ | |∕ sw where sw is the density of seawater. Hence, the subseasonal MLD variance is relatively insensitive to subseasonal winds despite their strong impact on local B and variability, but the mean MLD is relatively sensitive to subseasonal winds to the extent that they modify the mean u * , and both of these sensitivities are modified by 3-D ocean dynamics. Key Points: • We quantify the global impact of subseasonal winds on ocean surface fluxes and mixed-layer depth (MLD) in a mesoscale-resolving model • Globally, subseasonal winds are responsible for about a fifth of the annual average MLD and about a quarter of the subseasonal MLD variance • The increase in the mean MLD with subseasonal winds is caused by a higher friction velocity, but other factors modify the sensitivity Supporting Information: • Supporting Information S1A few previous studies have looked into the sensitivity of the climatological MLD to subseasonal atmospheric variability in 3-D global ocean circulation models by explicitly modifying the wind forcing in ocean models using grids that do not fully resolve mesoscales. For example, Lee and Liu (2005) explore the impact of diurnal winds on the MLD and sea surface temperature using an eddy-parameterizing (nominal 1 • resolution) global ocean model. Their model is forced with daily averaged fluxes, except for the wind stresses, which are averaged over 12 hr or subsampled every 24 hr. The annual and zonal mean MLD increases by up to a maximum of about 10 m with 12-hr winds compared to subsampled 24-hr winds, and the response of the MLD is largely attributable to enhanced vertical mixing by high-frequency winds at middle-to-high latitudes. More recently, Condron and Renfrew (2013) parameterize the effects of unresolved mesoscale a...

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“…The smoothed Chl-a values are lower than Chl-a bloom peaks. The maximum Chl-a increase in Table 2 (for the SCS) is below 1.5 mg·m −3 , whereas the peak of the surface Chl-a bloom could be as high as~3.0 mg·m −3 [41]. Although the Chl-a data used to develop the multi-parameter quantification model are below 1.5 mg·m −3 for the SCS, this data set may represent most of Chl-a bloom cases in the area.…”

Section: Discussionmentioning

confidence: 99%

“…Strong typhoon winds may induce massive entrainment of air bubbles into the water column through the wave breaking; these bubbles have a spectral effect on ocean reflectance, and therefore, the bubble entrainment may affect the estimation accuracy of surface Chl-a concentration from remote sensing observations [40]. In situ measurements from a Bio-Argo float with fluorescence method to detect the Chl-a concentration located at the central Bay of Bengal indicate that there was substantial Chl-a increase (to as high as 4.5 mg·m −3 ) induced by a tropical cyclone, Hudhud, and the Bio-Argo observations on the Chl-a profiles suggest that the nutrient inject is a major reason for the Chl-a increase under the storm condition, leading to the chlorophyll bloom [41]. Nevertheless, the effect of the air bubbles on the Chl-retrieval under the storm condition does need further investigations to improve Chl-a algorithms for satellite ocean-color observations.…”

Section: Discussionmentioning

confidence: 99%

Quantification of Typhoon-Induced Phytoplankton Blooms Using Satellite Multi-Sensor Data

et al. 2018

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Using satellite-based multi-sensor observations, this study investigates Chl-a blooms induced by typhoons in the Northwest Pacific (NWP) and the South China Sea (SCS), and quantifies the blooms via wind-induced mixing and Ekman pumping parameters, as well as pre-typhoon mixed-layer depth (MLD). In the NWP, the Chl-a bloom is more correlated with the Ekman pumping than with the other two parameters, with an R 2 value of 0.56. In the SCS, the wind-induced mixing and Ekman pumping have comparable correlations with the Chl-a increase, showing R 2 values of 0.4~0.6. However, the MLD exhibits a negative correlation with the Chl-a increase. A multi-parameter quantification model of the Chl-a bloom strength achieves better results than the single-parameter regressions, yielding a more significant R 2 value of 0.80, and a lower regression rms of 0.18 mg·m −3 in the SCS, and the R 2 value in the NWP is also improved compared with the single-parameter regressions. The multi-parameter quantification model of Chl-a blooms is more accurate in the SCS than in the NWP, due to the fact that nutrient profiles in the NWP are uniform from surface to a deep depth (300 m). Thus, the Chl-a blooms are more correlated with the upper ocean dynamical processes in the SCS where a shallower nutricline is found.

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Chlorophyll bloom in response to tropical cyclone Hudhud in the Bay of Bengal: Bio-Argo subsurface observations

Cited by 74 publications

References 25 publications

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

Quantification of Typhoon-Induced Phytoplankton Blooms Using Satellite Multi-Sensor Data

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