Community review of Southern Ocean satellite data needs

Pope, Allen; Wagner, P.; Johnson, R. W.; Shutler, J. D.; Baeseman, Jenny; Newman, Louise

doi:10.1017/s0954102016000390

Cited by 45 publications

(23 citation statements)

References 245 publications

(310 reference statements)

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“…Snow density was measured at 18 sites, well distributed across the area, and the mean of these sites is used for this analysis unless stated otherwise. A full overview of the measurement procedure is provided in Price et al (2014). Additional in situ measurements of sea ice thickness are included in the analysis: two measurements taken at one location in McMurdo Sound in July and November.…”

Section: Mcmurdo Sound and Field Datamentioning

confidence: 99%

“…The range in p w during this period is less than 1 kg m −3 . The ρ i value used here is in the middle of the measured range in McMurdo Sound, the use of which is discussed in Price et al (2014). ρ s is the mean value taken from 18 of the 39 in situ sites where snow density was measured.…”

Section: Cryosat-2mentioning

confidence: 99%

“…Sea ice thickness inferred from altimetry in McMurdo Sound will be influenced by the buoyant sub-ice platelet layer (Price et al, 2014). The Fb measurement used to infer thickness is representative of the solid sea ice and the layer of sub-ice platelets attached below.…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

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Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

Price

Soltanzadeh²,

Rack

et al. 2019

The Cryosphere

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Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

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Section: Mcmurdo Sound and Field Datamentioning

confidence: 99%

Section: Cryosat-2mentioning

confidence: 99%

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Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

Price

Soltanzadeh²,

Rack

et al. 2019

The Cryosphere

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“…However, these approaches are limited to the upper layer of the ocean and might miss important features of phytoplankton vertical distribution (Erickson et al, ; Holm‐Hansen & Hewes, ; Parslow et al, ). In addition, the observations of ocean colour in the SO in winter are impaired by the presence of sea‐ice and persistent cloud cover (Pope et al, ). The recent development of autonomous observation platforms equipped with bio‐optical sensors such as gliders and especially the so‐called Biogeochemical‐Argo (BGC‐Argo) floats (Johnson & Claustre, ) offers the opportunity to study phytoplankton geographical, vertical and temporal distribution in the critically under‐sampled SO waters.…”

Section: Introductionmentioning

confidence: 99%

Plankton Assemblage Estimated with BGC‐Argo Floats in the Southern Ocean: Implications for Seasonal Successions and Particle Export

et al. 2017

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The Southern Ocean (SO) hosts plankton communities that impact the biogeochemical cycles of the global ocean. However, weather conditions in the SO restrict mainly in situ observations of plankton communities to spring and summer, preventing the description of biological successions at an annual scale. Here, we use shipboard observations collected in the Indian sector of the SO to develop a multivariate relationship between physical and bio‐optical data, and, the composition and carbon content of the plankton community. Then we apply this multivariate relationship to five biogeochemical Argo (BGC‐Argo) floats deployed within the same bio‐geographical zone as the ship‐board observations to describe spatial and seasonal changes in plankton assemblage. The floats reveal a high contribution of bacteria below the mixed layer, an overall low abundance of picoplankton and a seasonal succession from nano‐ to microplankton during the spring bloom. Both naturally iron‐fertilized waters downstream of the Crozet and Kerguelen Plateaus show elevated phytoplankton biomass in spring and summer but they differ by a nano‐ or microplankton dominance at Crozet and Kerguelen, respectively. The estimated plankton group successions appear consistent with independent estimations of particle diameter based on the optical signals. Furthermore, the comparison of the plankton community composition in the surface layer with the presence of large mesopelagic particles diagnosed by spikes of optical signals provides insight into the nature and temporal changes of ecological vectors that drive particle export. This study emphasizes the power of BGC‐Argo floats for investigating important biogeochemical processes at high temporal and spatial resolution.

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“…Due to the extreme climate and the difficulties of conducting field research above 60 • south, the Southern Ocean (SO), especially around Antarctica, lacks a systematic in situ sampling program of its peculiar bio-optics and micro-organism community structure [1][2][3]. As a result, satellite measurements from space still have large errors in estimating phytoplankton biomass [4,5] and global chlorophyll-a satellite algorithms typically underestimate chlorophyll-a in the Southern Ocean [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Fluorescence-Based Approach to Estimate the Chlorophyll-A Concentration of a Phytoplankton Bloom in Ardley Cove (Antarctica)

Zeng

Fischer

et al. 2017

Remote Sensing

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Abstract:A phytoplankton bloom occurred in Ardley Cove, King George Island in January 2016, during which maximum chlorophyll-a reached 9.87 mg/m 3 . Records show that blooms have previously not occurred in this area prior to 2010 and the average chlorophyll-a concentration between 1991 and 2009 was less than 2 mg/m 3 . Given the lack of in situ measurements and the poor performance of satellite algorithms in the Southern Ocean and Antarctic waters, we validate and assess several chlorophyll-a algorithms and apply an improved baseline fluorescence approach to examine this bloom event. In situ water properties including in vivo fluorescence, water leaving radiance, and solar irradiance were collected to evaluate satellite algorithms and characterize chlorophyll-a concentration, as well as dominant phytoplankton groups. The results validated the nFLH fluorescence baseline approach, resulting in a good agreement at this high latitude, high chlorophyll-a region with correlation at 59.46%. The dominant phytoplankton group within the bloom was micro-phytoplankton, occupying 79.58% of the total phytoplankton community. Increasing sea ice coverage and sea ice concentration are likely responsible for increasing phytoplankton blooms in the recent decade. Given the profound influence of climate change on sea-ice and phytoplankton dynamics in the region, it is imperative to develop accurate methods of estimating the spatial distribution and concentrations of the increasing occurrence of bloom events.

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Community review of Southern Ocean satellite data needs

Cited by 45 publications

References 245 publications

Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

Plankton Assemblage Estimated with BGC‐Argo Floats in the Southern Ocean: Implications for Seasonal Successions and Particle Export

Fluorescence-Based Approach to Estimate the Chlorophyll-A Concentration of a Phytoplankton Bloom in Ardley Cove (Antarctica)

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