ALBERT

Description: The MAPPS global database of photosynthesis-irradiance (P-E) parameters consists of over 5000 P-E experiments that provides information on the spatio-temporal variability in the two P-E parameters (the assimilation number, and the initial slope) that are fundamental inputs for models of marine primary production that use chlorophyll as the state variable. The experiments were carried out by an international group of research scientists to examine the basin-scale variability in the photophysiological response of marine phytoplankton over a range of oceanic regimes (from the oligotrophic gyres to productive shelf systems) and covers several decades. These data can be used to improve the assignment of P-E parameters in the estimation of marine primary production using satellite data.

Description: We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters. This article is protected by copyright. All rights reserved.

Description: Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 113-133, doi:10.1016/j.jmarsys.2008.05.010.

Description: Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (〈 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.

Description: This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G), as well as by numerous other grants to the various participating investigators

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Melin, F., Moore, T. S., Muller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zuhlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., & Platt, T. An ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI). Sensors, 19(19), (2019): 4285, doi: 10.3390/s19194285.

Description: Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Description: This work was funded by the Ocean Colour Climate Change initiative of the European Space Agency (Grant Number 4000101437/10/I-LG). We acknowledge additional funding support by NERC through the National Centre for Earth Observation (Grant Number PR140015). Additional funding from a Simons Foundation Grant (549947, SS) is also gratefully acknowledged. V.B. also acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme grant agreement N_ 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation – PORTWIMS.

Description: Author Posting. © American Geophysical Union, 2010. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 24 (2010): GB3020, doi:10.1029/2009GB003655.

Description: The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-a was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-a time series were available.

Description: This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G).

Description: Si listano le singole sezioni in cui S.Simoncelli ha contribuito. Ogni sezione puo' essere citata separatamente dal report 1.1 Ocean temperature and salinity S. Mulet, B. Buongiorno Nardelli, S. Good, A. Pisano, E. Greiner, M. Monier E. Autret, L. Axell, F. Boberg, S. Ciliberti, M. Drévillon, R. Droghei, O. Embury, J. Gourrion, J. Høyer, M. Juza, J. Kennedy, B. Lemieux-Dudon, E. Peneva, R. Reid, S. Simoncelli, A. Storto, J. Tinker, K. von Schuckmann, S. L. Wakelin. 2.1. Ocean heat content ..K. von Schuckmann, A. Storto, S. Simoncelli, R. P. Raj, A.Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T Szerkely, M. Mayer, K. A. Peterson, H. Zuo, G. Garric, M. Monier. 3.4 Water mass formation processes in the Mediterranean Sea over the past 30 years S. Simoncelli, Nadia Pinardi, C. Fratianni, C. Dubois, G. Notarstefano. 3.5 Ventilation of the Western Mediterranean Deep Water through the Strait of Gibraltar S. Sammartino, J. García Lafuente, C. Naranjo, S. Simoncelli. 4.4 Unusual salinity pattern in the South Adriatic Sea in 2016 Z. Kokkini, G. Notarstefano P-M Poulain, E. Mauri, R. Gerin, S. Simoncelli

Description: The oceans regulate our weather and climate from global to regional scales. They absorb over 90% of accumulated heat in the climate system (IPCC 2013 IPCC. 2013. Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change [Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors]. Cambridge: Cambridge University Press, 1535. doi: 10.1017/CBO9781107415324. [Crossref], , [Google Scholar]) and over a quarter of the anthropogenic carbon dioxide (Le Quéré et al. 2016 Le Quéré C, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, Peters GP, Manning AC, Boden TA, Tans PP, Houghton RA, et al. 2016. Global carbon budget 2016. Earth Syst Sci Data. 8( 2): 605– 649. doi: 10.5194/essd-8-605-2016 [Crossref], [Web of Science ®], , [Google Scholar]). They provide nearly half of the world’s oxygen. Most of our rain and drinking water is ultimately regulated by the sea. The oceans provide food and energy and are an important source of the planet's biodiversity and ecosystem services. They are vital conduits for trade and transportation and many economic activities depend on them (OECD 2016 OECD . 2016. The ocean economy in 2030. Paris : OECD Publishing. doi: 10.1787/9789264251724-en. [Crossref], , [Google Scholar]). Our oceans are, however, under threat due to climate change and other human induced activities and it is vital to develop much better, sustainable and science-based reporting and management approaches (UN 2017 UN . 2017. Report of the United Nations conference to support the implementation of sustainable development goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development (Advance unedited version). https://sustainabledevelopment.un.org/content/documents/15662FINAL_15_June_2017_RepoRe_Goal_14.pdf . [Google Scholar]). Better management of our oceans requires long-term, continuous and state-of-the art monitoring of the oceans from physics to ecosystems and global to local scales. The Copernicus Marine Environment Monitoring Service (CMEMS) has been set up to address these challenges at European level. Mercator Ocean was tasked in 2014 by the European Union under a delegation agreement to implement the operational phase of the service from 2015 to 2021 (CMEMS 2014 CMEMS . 2014. Technical annex to the delegation agreement with Mercator Ocean for the implementation of the Copernicus Marine Environment Monitoring Service (CMEMS). www.copernicus.eu/sites/default/files/library/CMEM_TechnicalAnnex_PUBLIC.docx.pdf . [Google Scholar]). The CMEMS now provides regular and systematic reference information on the physical state, variability and dynamics of the ocean, ice and marine ecosystems for the global ocean and the European regional seas (Figure 0.1; CMEMS 2016 CMEMS . 2016. High level service evolution strategy, a document prepared by Mercator Ocean with the support of the CMEMS STAC. [Google Scholar]). This capacity encompasses the description of the current situation (analysis), the prediction of the situation 10 days ahead (forecast), and the provision of consistent retrospective data records for recent years (reprocessing and reanalysis). CMEMS provides a sustainable response to European user needs in four areas of benefits: (i) maritime safety, (ii) marine resources, (iii) coastal and marine environment and (iv) weather, seasonal forecast and climate.

Description: Copernicus Marine Environment Monitoring Service

Description: Published

Description: S1-S142

Description: 4A. Oceanografia e clima

Description: JCR Journal