Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Abstract

Abstract. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physicalbiogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO 2 , but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

“…This experiment is analogous to those of Natvik and Evensen (2003), Gregg (2008) and Ford et al (2012). This is a reasonable assumption in offshore waters, but OC3M is known to be unreliable in coastal waters where sediments (e.g.…”

“…A thorough review of these approaches in a statistical sense is presented in Dowd et al (2014). There are now examples of operational and pre-operational global systems that routinely assimilate Chl a products (Ford et al, 2012). Additionally, there has been further experimentation with assimilating alternative remotely sensed apparent optical properties (AOPs) such as the vertical attenuation coefficient at 443 nm, Kd 443 (Ciavatta et al, 2014) and inherent optical properties (IOPs) such as phytoplankton absorption (a ph ), as described in Shulman et al (2013).…”

Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3D ocean coupled physical–biogeochemical model

“…Here, the observations correspond to [chl a], which is linked through Eqs. 3 and 4 to the modelled P. Another issue with the use of real ocean colour data is the specification of the observation error matrix R, which is discussed for instance in Ford et al (2012). The different options considered to tackle these issues in the present study are discussed in Section 3.…”

“…A comprehensive review of data assimilation in CPBM is provided by Gregg (2008). A collection of studies have been performed quite recently using ocean colour data, which demonstrate the value of the assimilation concepts inherited from optimal control or estimation theory (Hemmings et al 2003, 2004, Losa et al 2004, Friedrichs 2002, Garcia-Gorriz et al 2003, Ishizaka 1990, Armstrong and Sarmiento 1995, Natvik and Evensen 2003a,b, Gregg 2008, Nerger and Gregg 2007, 2008, Fontana et al 2009, Ford et al 2012, Hu et al 2012, Korres et al 2012, Roy et al 2012. Very often, variational data assimilation methods are implemented with the purpose of parameter estimation.…”

“…Brasseur et al, 2009, Ford et al, 2012, short-term forecasts or longer-term simulations in relation with climate change (IPCC, 2007). However, the capacity of these CPBMs to realistically represent the observed variability of biogeochemical marine properties is still limited by the various sources of errors that occur in the simulations as a result of imperfect descriptions of the physical environment that drives the biology and empirical parameterizations of the biogeochemical interactions.…”