The Ocean Colour Climate Change Initiative: II. Spatial and temporal homogeneity of satellite data retrieval due to systematic effects in atmospheric correction processors

Abstract. Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs.

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The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Remote Sensing of Environment ◽

10.1016/j.rse.2013.09.016 ◽

2015 ◽

Vol 162 ◽

pp. 271-294 ◽

Cited By ~ 89

Author(s):

Robert J.W. Brewin ◽

Shubha Sathyendranath ◽

Dagmar Müller ◽

Carsten Brockmann ◽

Pierre-Yves Deschamps ◽

...

Keyword(s):

Climate Change ◽

Round Robin ◽

Change Initiative ◽

Ocean Colour

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Quantifying uncertainty in the ESA Ocean Colour – Climate Change Initiative dataset for assimilation of total chlorophyll and phytoplankton functional types

10.5194/egusphere-egu2020-18684 ◽

2020 ◽

Author(s):

Alison Fowler ◽

Jozef Skákala ◽

Stefano Ciavatta

Keyword(s):

Climate Change ◽

Data Uncertainty ◽

Water Type ◽

Total Chlorophyll ◽

Change Initiative ◽

Ocean Colour ◽

North West ◽

In Situ Data ◽

Mean Square Errors ◽

Climate Studies

Monitoring biogeochemistry in shelf seas is of great significance for the economy, ecosystems understanding and climate studies. Data assimilation can aid the realism of marine biogeochemistry models by incorporating information from observations. An important source of information about phytoplankton groups and total chlorophyll is available from the ESA OC-CCI (ocean colour - climate change initiative) dataset.For any assimilation system to be successful it is important to accurately represent all sources of data uncertainty. For the ocean colour product, the propagation of errors throughout the ocean colour algorithm makes the characterisation of the uncertainty challenging. However, the problem can be simplified by assuming that the uncertainty is a function of optical water type (OWT), which characterises the water column of each observed pixel in terms of their reflectance properties.Within this work we apply the well-known Desroziers et al. (2005) consistency diagnostics to the Met Office&#8217;s NEMOVAR 3D-VAR DA system used to create daily biogeochemistry forecasts on the North-West European Shelf. The derived estimates of monthly ocean colour error covariances stratified by OWT are compared to previously derived estimates of the root mean square errors and biases using in-situ data match ups (Brewin et al. 2017). It is found that the agreement between the two estimates of the error variances have a strong seasonal and OWT dependence. The error correlations (which can only be estimated with the Desroziers&#8217; method) in some instances are found to be significant out to a few 100km particularly for more turbid waters during the spring bloom. The reliability and limitation of these two estimates of the ocean colour uncertainty are discussed along with the implications for the future assimilation of ocean colour products and for ecosystem and climate studies.

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Ocean Colour Climate Change Initiative — Approach and initial results

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6350979 ◽

2012 ◽

Cited By ~ 9

Author(s):

Shubha Sathyendranath ◽

Bob Brewin ◽

Dagmar Mueller ◽

Roland Doerffer ◽

Hajo Krasemann ◽

...

Keyword(s):

Climate Change ◽

Change Initiative ◽

Ocean Colour ◽

Initial Results

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Global maps of Forel–Ule index, hue angle and Secchi disk depth derived from 21 years of monthly ESA Ocean Colour Climate Change Initiative data

Earth System Science Data ◽

10.5194/essd-13-481-2021 ◽

2021 ◽

Vol 13 (2) ◽

pp. 481-490

Author(s):

Jaime Pitarch ◽

Marco Bellacicco ◽

Salvatore Marullo ◽

Hendrik J. van der Woerd

Keyword(s):

Climate Change ◽

Time Series ◽

Spatial Resolution ◽

Sensor Data ◽

Secchi Disk ◽

Secchi Disk Depth ◽

Change Initiative ◽

Ocean Colour ◽

Free Data ◽

Hue Angle

Abstract. We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel–Ule index, hue angle and Secchi disk depth. Source data come from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data-merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ∼4 km at the Equator and are available at PANGAEA (https://doi.org/10.1594/PANGAEA.904266; Pitarch et al., 2019a). Two smaller and easier-to-handle test datasets have been produced from the former: a global dataset at 1∘ spatial resolution and another one for the North Atlantic at 0.25∘ resolution. The computer code for the generation of the Forel–Ule index, hue angle and Secchi disk depth from a given remote-sensing reflectance is also shared at https://doi.org/10.5281/zenodo.4439646 (Pitarch et al., 2021) and can be easily set in loop mode for batch calculations.

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Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative Project

Ocean Science Discussions ◽

10.5194/osd-11-2029-2014 ◽

2014 ◽

Vol 11 (4) ◽

pp. 2029-2071 ◽

Cited By ~ 5

Author(s):

M. Ablain ◽

A. Cazenave ◽

G. Larnicol ◽

M. Balmaseda ◽

P. Cipollini ◽

...

Keyword(s):

Climate Change ◽

Sea Level Rise ◽

Sea Level ◽

Satellite Altimetry ◽

Climate Model ◽

Atmospheric Correction ◽

European Space Agency ◽

Processing System ◽

Change Initiative ◽

Global Mean

Abstract. Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the last two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as: "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently of 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various respective data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, inter-calibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and sea level budget closure approach, as well as comparisons with ocean re-analyses and climate model outputs.

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Accuracy Assessment of Primary Production Models with and without Photoinhibition Using Ocean-Colour Climate Change Initiative Data in the North East Atlantic Ocean

Remote Sensing ◽

10.3390/rs10071116 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1116 ◽

Cited By ~ 2

Author(s):

Polina Lobanova ◽

Gavin H. Tilstone ◽

Igor Bashmachnikov ◽

Vanda Brotas

Keyword(s):

Climate Change ◽

Atlantic Ocean ◽

Primary Production ◽

North Atlantic ◽

Production Model ◽

Change Initiative ◽

Ocean Colour ◽

East Atlantic ◽

North East ◽

The North

The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ 14C uptake measurements from the North East Atlantic Ocean (NEA). The models were run using the European Space Agency (ESA), Ocean Colour Climate Change Initiative (OC-CCI) version 3.0 data. The objectives of the study were to determine which is the most accurate PP model for the region in different provinces and seasons, what is the accuracy of the models using both high (daily) and low (weekly) temporal resolution OC-CCI data, and whether the performance of the models is improved by implementing a photoinhibition function? The Platt-Sathyendranath primary production model (PPPSM) was the most accurate over all NEA provinces and, specifically, in the Atlantic Arctic province (ARCT) and North Atlantic Drift (NADR) provinces. The implementation of a photoinhibition function in the PPPSM reduced its accuracy, especially at lower range PP. The Vertical Generalized Production Model-VGPM (PPVGPM) tended to over-estimate PP, especially in summer and in the NADR. The accuracy of PPVGPM improved with the implementation of a photoinhibition function in summer. The absorption model of primary production (PPAph), with and without photoinhibition, was the least accurate model for the NEA. Mapped images of each model showed that the PPVGPM was 150% higher in the NADR compared to PPPSM. In the North Atlantic Subtropical Gyre (NAST) province, PPAph was 355% higher than PPPSM, whereas PPVGPM was 215% higher. A sensitivity analysis indicated that chlorophyll-a (Chl a), or the absorption of phytoplankton, at 443 nm (aph (443)) caused the largest error in the estimation of PP, followed by the photosynthetic rate terms and then the irradiance functions used for each model.

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