Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

Mapping Intimacies ◽

10.5194/bg-2020-152 ◽

2020 ◽

Author(s):

David Ford

Keyword(s):

Ocean Model ◽

Point Of View ◽

Global Ocean ◽

Ocean Colour ◽

Ocean Reanalysis ◽

Argo Data ◽

Argo Floats ◽

Variable Surface ◽

Added Benefit ◽

The Impact

Abstract. A set of observing system simulation experiments has been performed to explore the impact on global ocean biogeochemical reanalyses of assimilating chlorophyll from remotely sensed ocean colour, and assess the potential impact of assimilating in situ observations of chlorophyll, nitrate, oxygen, and pH from a proposed array of Biogeochemical-Argo (BGC-Argo) floats. Two different potential BGC-Argo array distributions were tested: one where biogeochemical sensors are placed on all current Argo floats, and one where biogeochemical sensors are placed on a quarter of current Argo floats. This latter approximately corresponds to the proposed BGC-Argo array of 1000 floats. Different strategies for updating model variables when assimilating ocean colour were assessed. All similarly improved the assimilated variable surface chlorophyll, but had a mixed impact on the wider ecosystem and carbon cycle, degrading some key variables of interest. Assimilating BGC-Argo data gave no added benefit over ocean colour in terms of simulating surface chlorophyll, but for most other variables, including sub-surface chlorophyll, adding BGC-Argo greatly improved results throughout the water column. This included surface partial pressure of carbon dioxide (pCO2), which was not assimilated but is an important output of reanalyses. Both BGC-Argo array distributions gave benefits, with greater improvements seen with more observations. From the point of view of ocean reanalysis, it is recommended to proceed with development of BGC-Argo as a priority. The proposed array of 1000 floats will lead to clear improvements in reanalyses, with a larger array likely to bring further benefits. The ocean colour satellite observing system should also be maintained, as ocean colour and BGC-Argo will provide complementary benefits. There is also much potential to improve the use of existing observations, particularly in terms of multivariate balancing, through improving assimilation methodologies.

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Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

Biogeosciences ◽

10.5194/bg-18-509-2021 ◽

2021 ◽

Vol 18 (2) ◽

pp. 509-534

Author(s):

David Ford

Keyword(s):

Global Scale ◽

Ocean Model ◽

Point Of View ◽

Global Ocean ◽

Ocean Colour ◽

Ocean Reanalysis ◽

Argo Data ◽

Argo Floats ◽

Improved Model ◽

The Impact

Abstract. A set of observing system simulation experiments was performed. This assessed the impact on global ocean biogeochemical reanalyses of assimilating chlorophyll from remotely sensed ocean colour and in situ observations of chlorophyll, nitrate, oxygen, and pH from a proposed array of Biogeochemical-Argo (BGC-Argo) floats. Two potential BGC-Argo array distributions were tested: one for which biogeochemical sensors are placed on all current Argo floats and one for which biogeochemical sensors are placed on a quarter of current Argo floats. Assimilating BGC-Argo data greatly improved model results throughout the water column. This included surface partial pressure of carbon dioxide (pCO2), which is an important output of reanalyses. In terms of surface chlorophyll, assimilating ocean colour effectively constrained the model, with BGC-Argo providing no added benefit at the global scale. The vertical distribution of chlorophyll was improved by assimilating BGC-Argo data. Both BGC-Argo array distributions gave benefits, with greater improvements seen with more observations. From the point of view of ocean reanalysis, it is recommended to proceed with development of BGC-Argo as a priority. The proposed array of 1000 floats will lead to clear improvements in reanalyses, with a larger array likely to bring further benefits. The ocean colour satellite observing system should also be maintained, as ocean colour and BGC-Argo will provide complementary benefits.

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Antarctic ice sheet fertilises the Southern Ocean

Biogeosciences ◽

10.5194/bg-11-2635-2014 ◽

2014 ◽

Vol 11 (10) ◽

pp. 2635-2643 ◽

Cited By ~ 53

Author(s):

R. Death ◽

J. L. Wadham ◽

F. Monteiro ◽

A. M. Le Brocq ◽

M. Tranter ◽

...

Keyword(s):

Southern Ocean ◽

Ice Sheet ◽

Ocean Model ◽

Coastal Sediments ◽

Global Ocean ◽

Iron Cycling ◽

Antarctic Ice Sheet ◽

Significant Control ◽

Subglacial Meltwater ◽

The Impact

Abstract. Southern Ocean (SO) marine primary productivity (PP) is strongly influenced by the availability of iron in surface waters, which is thought to exert a significant control upon atmospheric CO2 concentrations on glacial/interglacial timescales. The zone bordering the Antarctic Ice Sheet exhibits high PP and seasonal plankton blooms in response to light and variations in iron availability. The sources of iron stimulating elevated SO PP are in debate. Established contributors include dust, coastal sediments/upwelling, icebergs and sea ice. Subglacial meltwater exported at the ice margin is a more recent suggestion, arising from intense iron cycling beneath the ice sheet. Icebergs and subglacial meltwater may supply a large amount of bioavailable iron to the SO, estimated in this study at 0.07–0.2 Tg yr−1. Here we apply the MIT global ocean model (Follows et al., 2007) to determine the potential impact of this level of iron export from the ice sheet upon SO PP. The export of iron from the ice sheet raises modelled SO PP by up to 40%, and provides one plausible explanation for seasonally very high in situ measurements of PP in the near-coastal zone. The impact on SO PP is greatest in coastal regions, which are also areas of high measured marine PP. These results suggest that the export of Antarctic runoff and icebergs may have an important impact on SO PP and should be included in future biogeochemical modelling.

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Antarctic Ice Sheet fertilises the Southern Ocean

Biogeosciences Discussions ◽

10.5194/bgd-10-12551-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 12551-12570 ◽

Cited By ~ 8

Author(s):

R. Death ◽

J. L. Wadham ◽

F. Monteiro ◽

A. M. Le Brocq ◽

M. Tranter ◽

...

Keyword(s):

Southern Ocean ◽

Ice Sheet ◽

Ocean Model ◽

Coastal Sediments ◽

Global Ocean ◽

Iron Cycling ◽

Antarctic Ice Sheet ◽

Significant Control ◽

Subglacial Meltwater ◽

The Impact

Abstract. Southern Ocean (SO) marine primary productivity (PP) is strongly influenced by the availability of iron in surface waters, which is thought to exert a significant control upon atmospheric CO2 concentrations on glacial/interglacial timescales. The zone bordering the Antarctic Ice Sheet exhibits high PP and seasonal plankton blooms in response to light and variations in iron availability. The sources of iron stimulating elevated SO PP are in debate. Established contributors include dust, coastal sediments/upwelling, icebergs and sea ice. Subglacial meltwater exported at the ice margin is a more recent suggestion, arising from intense iron cycling beneath the ice sheet. Icebergs and subglacial meltwater may supply a large amount of bioavailable iron to the SO, estimated in this study at 0.07–1.0 Tg yr−1. Here we apply the MIT global ocean model (Follows et al., 2007) to determine the potential impact of this level of iron export from the ice sheet upon SO PP. The export of iron from the ice sheet raises modelled SO PP by up to 40%, and provides one plausible explanation for very high seasonally observed PP in the near-coastal zone. The impact on SO PP is greatest in coastal regions, which are also areas of high observed marine PP. These results suggest that the export of Antarctic runoff and icebergs may have an important impact on SO PP and should be included in future biogeochemical modelling.

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Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽

10.5194/os-5-403-2009 ◽

2009 ◽

Vol 5 (4) ◽

pp. 403-419 ◽

Cited By ~ 11

Author(s):

C. Skandrani ◽

J.-M. Brankart ◽

N. Ferry ◽

J. Verron ◽

P. Brasseur ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Reanalysis Data ◽

Ocean Model ◽

Sea Surface ◽

Global Ocean ◽

Atmospheric Forcing ◽

Ocean Reanalysis ◽

Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

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Salinity Assimilation Using S(T): Covariance Relationships

Monthly Weather Review ◽

10.1175/mwr3089.1 ◽

2006 ◽

Vol 134 (3) ◽

pp. 759-771 ◽

Cited By ~ 37

Author(s):

K. Haines ◽

J. D. Blower ◽

J-P. Drecourt ◽

C. Liu ◽

A. Vidard ◽

...

Keyword(s):

General Circulation ◽

Power Spectra ◽

General Circulation Models ◽

Temporal Variations ◽

Ocean Model ◽

Spatial Covariance ◽

Ocean Reanalysis ◽

Argo Data ◽

Ocean Reanalyses ◽

Salinity Data

Abstract Assimilation of salinity into ocean and climate general circulation models is a very important problem. Argo data now provide far more salinity observations than ever before. In addition, a good analysis of salinity over time in ocean reanalyses can give important results for understanding climate change. Here it is shown from the historical ocean database that over large regions of the globe (mainly midlatitudes and lower latitudes) variance of salinity on an isotherm S(T) is often less than variance measured at a particular depth S(z). It is also shown that the dominant temporal variations in S(T) occur more slowly than variations in S(z), based on power spectra from the Bermuda time series. From ocean models it is shown that the horizontal spatial covariance of S(T) often has larger scales than S(z). These observations suggest an assimilation method based on analyzing S(T). An algorithm for assimilating salinity data on isotherms is then presented, and it is shown how this algorithm produces orthogonal salinity increments to those produced during the assimilation of temperature profiles. It is argued that the larger space and time scales can be used for the S(T) assimilation, leading to better use of scarce salinity observations. Results of applying the salinity assimilation algorithm to a single analysis time within the ECMWF seasonal forecasting ocean model are also shown. The separate salinity increments coming from temperature and salinity data are identified, and the independence of these increments is demonstrated. Results of an ocean reanalysis with this method will appear in a future paper.

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Impact of biogeochemistry feedbacks on the projected climate change signal over the Indian Continent

10.5194/egusphere-egu2020-19402 ◽

2020 ◽

Author(s):

Dmitry Sein ◽

William Cabos ◽

Pankaj Kumar ◽

Vladimir Ryabchenko ◽

Stanislav Martyanov ◽

...

Keyword(s):

Climate Change ◽

Marine Ecosystem ◽

Regional Model ◽

Ocean Model ◽

Global Ocean ◽

Future Climate Change ◽

Climate Change Signal ◽

Water Type ◽

Substantial Impact ◽

The Impact

There are few studies dedicated to assessing the impact of biogeochemistry feedbacks on the climate change signal. In this study, we evaluate this impact in a future climate change scenario over the Indian subcontinent with the coupled regional model ROM in the Indian CORDEX area.In ROM a global ocean model (MPIOM) with regionally high horizontal resolution (up to 15 km resolution in the Bay of Bengal) is coupled to an atmospheric regional model (REMO, with 25 km resolution) and global terrestrial hydrology model. The ocean and the atmosphere are interacting within the region covered by the atmospheric domain. Outside this domain, the ocean model is not coupled to the atmosphere, being driven by prescribed atmospheric forcing, thus running in so-called stand-alone mode.To assess the impact of biogeochemical feedbacks on the climate change signal, we compare two simulations with ROM. In both simulations, the model is driven by data from a climate change simulation under the RCP 8.5 scenario with the MPI-ESM global model and differ only in the activation of the biochemistry module of MPIOM. In the first simulation, we use a light attenuation parameterization based on the Jerlov water types, when the attenuation coefficient varies spatially depending on the water type specified but does not vary in time. In the second simulation, we introduce the biochemical feedbacks as implemented in the global ocean biogeochemistry model HAMOCC. &#160;Both simulations capture the main features of the present time atmospheric and oceanic variability in the region and the model with HAMOCC reproduces well the intra-annual dynamics of the marine ecosystem in the northern Indian Ocean.A comparison of the simulated changes in atmospheric variables shows that the feedbacks have a substantial impact on the climate change signal for precipitation and air temperature, especially over the central Indian region.Acknowledgement: The work was supported by the Russian Science Foundation (Project 19-47-02015) and Indian project no. DST/INT/RUS/RSF/P-33/G.

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The Impact of Southern Ocean Sea Ice in a Global Ocean Model

Journal of Physical Oceanography ◽

10.1175/1520-0485(1998)028<1999:tiosos>2.0.co;2 ◽

1998 ◽

Vol 28 (10) ◽

pp. 1999-2018 ◽

Cited By ~ 37

Author(s):

Achim Stössel ◽

Seong-Joong Kim ◽

Sybren S. Drijfhout

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Ocean Model ◽

Global Ocean ◽

The Impact

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How essential are Argo observations to constrain a global ocean data assimilation system?

Ocean Science Discussions ◽

10.5194/osd-12-1145-2015 ◽

2015 ◽

Vol 12 (3) ◽

pp. 1145-1186 ◽

Cited By ~ 2

Author(s):

V. Turpin ◽

E. Remy ◽

P. Y. Le Traon

Keyword(s):

Data Assimilation ◽

Forecast Error ◽

Global Ocean ◽

Altimeter Data ◽

Western Boundary ◽

Argo Data ◽

Ocean Data Assimilation ◽

In Situ Observations ◽

The Impact

Abstract. Observing System Experiments (OSEs) are carried out over a one-year period to quantify the impact of Argo observations on the Mercator-Ocean 1/4° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of Argo data sets are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation-model forecast error is also significant from the surface down to a depth of 2000 m. Differences between independent observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. All Argo observations thus matter, even with a 1/4° model resolution. The main conclusion is that the performance of global data assimilation systems is heavily dependent on the availability of Argo data.

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An explicit estimate of the atmospheric nutrient impact on global oceanic productivity

Ocean Science ◽

10.5194/os-16-1183-2020 ◽

2020 ◽

Vol 16 (5) ◽

pp. 1183-1205

Author(s):

Stelios Myriokefalitakis ◽

Matthias Gröger ◽

Jenny Hieronymus ◽

Ralf Döscher

Keyword(s):

Ocean Model ◽

Global Ocean ◽

Earth Model ◽

Nutrient Inputs ◽

Deposition Fluxes ◽

Nutrient Deposition ◽

The North ◽

Atmospheric Forcings ◽

Substantial Decline ◽

The Impact

Abstract. State-of-the-art global nutrient deposition fields are coupled here to the Pelagic Interactions Scheme for Carbon and Ecosystem Studies (PISCES) biogeochemistry model to investigate their effect on ocean biogeochemistry in the context of atmospheric forcings for pre-industrial, present, and future periods. PISCES, as part of the European Community Earth system model (EC-Earth) model suite, runs in offline mode using prescribed dynamical fields as simulated by the Nucleus for European Modelling of the Ocean (NEMO) ocean model. Present-day atmospheric deposition fluxes of inorganic N, Fe, and P into the global ocean account for ∼ 40 Tg N yr−1, ∼ 0.28 Tg Fe yr−1, and ∼ 0.10 Tg P yr−1. Pre-industrial atmospheric nutrient deposition fluxes are lower compared to the present day (∼ 51 %, ∼ 36 %, and ∼ 40 % for N, Fe, and P, respectively). However, the overall impact on global productivity is low (∼ 3 %) since a large part of marine productivity is driven by nutrients recycled in the upper ocean layer or other local factors. Prominent changes are, nevertheless, found for regional productivity. Reductions of up to 20 % occur in oligotrophic regions such as the subtropical gyres in the Northern Hemisphere under pre-industrial conditions. In the subpolar Pacific, reduced pre-industrial Fe fluxes lead to a substantial decline of siliceous diatom production and subsequent accumulation of Si, P, and N, in the subpolar gyre. Transport of these nutrient-enriched waters leads to strongly elevated production of calcareous nanophytoplankton further south and southeast, where iron no longer limits productivity. The North Pacific is found to be the most sensitive to variations in depositional fluxes, mainly because the water exchange with nutrient-rich polar waters is hampered by land bridges. By contrast, large amounts of unutilized nutrients are advected equatorward in the Southern Ocean and North Atlantic, making these regions less sensitive to external nutrient inputs. Despite the lower aerosol N : P ratios with respect to the Redfield ratio during the pre-industrial period, the nitrogen fixation decreased in the subtropical gyres mainly due to diminished iron supply. Future changes in air pollutants under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario result in a modest decrease of the atmospheric nutrients inputs into the global ocean compared to the present day (∼ 13 %, ∼ 14 %, and ∼ 20 % for N, Fe, and P, respectively), without significantly affecting the projected primary production in the model. Sensitivity simulations further show that the impact of atmospheric organic nutrients on the global oceanic productivity has turned out roughly as high as the present-day productivity increase since the pre-industrial era when only the inorganic nutrients' supply is considered in the model. On the other hand, variations in atmospheric phosphorus supply have almost no effect on the calculated oceanic productivity.

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