Building the capacity for forecasting marine biogeochemistry and ecosystems: recent advances and future developments

Gehlen, Marion; Barciela, Rosa; Bertino, Laurent; Brasseur, Pierre; Butenschön, Momme; Chai, Fei; Crise, A.; Drillet, Yann; Ford, David; Lavoie, Diane; Lehodey, Patrick; Perruche, Coralie; Samuelsen, Annette; Simon, Ehouarn

doi:10.1080/1755876x.2015.1022350

Cited by 62 publications

(55 citation statements)

References 105 publications

(21 reference statements)

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“…Ocean colour data assimilation is being increasingly utilized by the reanalysis and forecasting community (Gehlen et al, 2015), and has already been successfully demonstrated for ERSEM (Ciavatta et al, 2011(Ciavatta et al, , 2014. A suitable ocean colour assimilation scheme for operational purposes is being developed as a collaboration between the Met Office and PML, to be implemented in the SSB ERSEM version and run operationally as part of CMEMS.…”

Section: Summary and Discussionmentioning

confidence: 99%

Observing and modelling phytoplankton community structure in the North Sea

et al. 2017

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Abstract. Phytoplankton form the base of the marine food chain, and knowledge of phytoplankton community structure is fundamental when assessing marine biodiversity. Policy makers and other users require information on marine biodiversity and other aspects of the marine environment for the North Sea, a highly productive European shelf sea. This information must come from a combination of observations and models, but currently the coastal ocean is greatly under-sampled for phytoplankton data, and outputs of phytoplankton community structure from models are therefore not yet frequently validated. This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea using accessory pigment analysis. The observations allow a good understanding of the patterns of surface phytoplankton biomass and community structure in the North Sea for the observed months of August 2010 and 2011. Two physical-biogeochemical ocean models, the biogeochemical components of which are different variants of the widely used European Regional Seas Ecosystem Model (ERSEM), were then validated against these and other observations. Both models were a good match for sea surface temperature observations, and a reasonable match for remotely sensed ocean colour observations. However, the two models displayed very different phytoplankton community structures, with one better matching the in situ observations than the other. Nonetheless, both models shared some similarities with the observations in terms of spatial features and interannual variability. An initial comparison of the formulations and parameterizations of the two models suggests that diversity between the parameter settings of model phytoplankton functional types, along with formulations which promote a greater sensitivity to changes in light and nutrients, is key to capturing the observed phytoplankton community structure. These findings will help inform future model development, which should be coupled with detailed validation studies, in order to help facilitate the wider application of marine biogeochemical modelling to user and policy needs.

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Section: Summary and Discussionmentioning

confidence: 99%

Observing and modelling phytoplankton community structure in the North Sea

et al. 2017

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“…Analysis to date has suggested that skillful seasonal and, in some cases, multi-annual sea surface temperature predictions are possible for many regions despite coarse model resolution Tommasi et al, 2017b). Seasonal to multi-annual predictions of ocean productivity, oxygen and other potential factors important to marine resource distributions and abundance have provided some cause for optimism (Séférian et al, 2014;Gehlen et al, 2015), but have not been assessed with the same rigor as ocean temperatures, in part due to the sparsity of data with which to robustly characterize the model skill. The use of short-term forecasts in dynamic management approaches (Maxwell et al, 2015;Dunn et al, 2016;Tommasi et al, 2017b) may also contribute to building long-term resilience (Hobday et al, 2016;Tommasi et al, 2017a).…”

Section: Current Approaches For Predicting and Projecting Marine Mammmentioning

confidence: 99%

Projecting Marine Mammal Distribution in a Changing Climate

et al. 2017

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Climate-related shifts in marine mammal range and distribution have been observed in some populations; however, the nature and magnitude of future responses are uncertain in novel environments projected under climate change. This poses a challenge for agencies charged with management and conservation of these species. Specialized diets, restricted ranges, or reliance on specific substrates or sites (e.g., for pupping) make many marine mammal populations particularly vulnerable to climate change. High-latitude, predominantly ice-obligate, species have experienced some of the largest changes in habitat and distribution and these are expected to continue. Efforts to predict and project marine mammal distributions to date have emphasized data-driven statistical habitat models. These have proven successful for short time-scale (e.g., seasonal) management activities, but confidence that such relationships will hold for multi-decade projections and novel environments is limited. Recent advances in mechanistic modeling of marine mammals (i.e., models that rely on robust physiological and ecological principles expected to hold under climate change) may address this limitation. The success of such approaches rests on continued advances in marine mammal ecology, behavior, and physiology together with improved regional climate projections. The broad scope of this challenge suggests initial priorities be placed on vulnerable species or populations (those already experiencing declines or projected to undergo ecological shifts resulting from climate Silber et al.Projecting Marine Mammal Distributions changes that are consistent across climate projections) and species or populations for which ample data already exist (with the hope that these may inform climate change sensitivities in less well observed species or populations elsewhere). The sustained monitoring networks, novel observations, and modeling advances required to more confidently project marine mammal distributions in a changing climate will ultimately benefit management decisions across time-scales, further promoting the resilience of marine mammal populations.

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“…The annual increase also occurs against a background of large spatial variation of, for example, 140 µatm in different regions of the Southern Ocean in spring (Bakker et al, 2008; E. M. . Similarly, seasonal variation of 0.04 in surface pH in the subtropical North Atlantic Ocean (González-Dávila et al, 2007) is 20 times the mean annual decrease in surface ocean pH at a rate of −0.002 yr −1 Lauvset et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

Bakker

Pfeil

Landa

et al. 2016

Earth Syst. Sci. Data

5,780

378

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A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water f CO 2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. Highprofile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection Sabine et al., 2013;Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here:

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Building the capacity for forecasting marine biogeochemistry and ecosystems: recent advances and future developments

Cited by 62 publications

References 105 publications

Observing and modelling phytoplankton community structure in the North Sea

Observing and modelling phytoplankton community structure in the North Sea

Projecting Marine Mammal Distribution in a Changing Climate

A multi-decade record of high-quality <i>f</i>CO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT)

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Building the capacity for forecasting marine biogeochemistry and ecosystems: recent advances and future developments

Cited by 62 publications

References 105 publications

Observing and modelling phytoplankton community structure in the North Sea

Observing and modelling phytoplankton community structure in the North Sea

Projecting Marine Mammal Distribution in a Changing Climate

A multi-decade record of high-quality &lt;i&gt;f&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; data in version 3 of the Surface Ocean CO&lt;sub&gt;2&lt;/sub&gt; Atlas (SOCAT)

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A multi-decade record of high-quality <i>f</i>CO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT)