Dataset Information

Integrated ocean management as a strategy to meet rapid climate change: the Norwegian case.

ABSTRACT: The prospects of rapid climate change and the potential existence of tipping points in marine ecosystems where nonlinear change may result from them being overstepped, raises the question of strategies for coping with ecosystem change. There is broad agreement that the combined forces of climate change, pollution and increasing economic activities necessitates more comprehensive approaches to oceans management, centering on the concept of ecosystem-based oceans management. This article addresses the Norwegian experience in introducing integrated, ecosystem-based oceans management, emphasizing how climate change, seen as a major long-term driver of change in ecosystems, is addressed in management plans. Understanding the direct effects of climate variability and change on ecosystems and indirect effects on human activities is essential for adaptive planning to be useful in the long-term management of the marine environment.

SUBMITTER: Hoel AH

PROVIDER: S-EPMC3357825 | biostudies-other | 2012 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Integrated ocean management as a strategy to meet rapid climate change: the Norwegian case.

Hoel Alf Håkon AH Olsen Erik E

Ambio 20120201 1

The prospects of rapid climate change and the potential existence of tipping points in marine ecosystems where nonlinear change may result from them being overstepped, raises the question of strategies for coping with ecosystem change. There is broad agreement that the combined forces of climate change, pollution and increasing economic activities necessitates more comprehensive approaches to oceans management, centering on the concept of ecosystem-based oceans management. This article addresses ...[more]

PMID: 22270708

Similar Datasets

Project 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.

Dataset Information

Integrated ocean management as a strategy to meet rapid climate change: the Norwegian case.

Publications

Integrated ocean management as a strategy to meet rapid climate change: the Norwegian case.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure