scholarly journals The role of phytoplankton dynamics in the seasonal and interannual variability of carbon in the subpolar North Atlantic – a modeling study

2012 ◽  
Vol 5 (3) ◽  
pp. 683-707 ◽  
Author(s):  
S. R. Signorini ◽  
S. Häkkinen ◽  
K. Gudmundsson ◽  
A. Olsen ◽  
A. M. Omar ◽  
...  
Keyword(s):  
Functional Groups ◽  
Mixed Layer Depth ◽  
Co2 Flux ◽  
Early Spring ◽  
Biogeochemical Model ◽  
Co2 Uptake ◽  
Biological Interactions ◽  
In Situ Data ◽  
Phytoplankton Functional Groups ◽  
The Impact

Abstract. We developed an ecosystem/biogeochemical model system, which includes multiple phytoplankton functional groups and carbon cycle dynamics, and applied it to investigate physical-biological interactions in Icelandic waters. Satellite and in situ data were used to evaluate the model. Surface seasonal cycle amplitudes and biases of key parameters (DIC, TA, pCO2, air-sea CO2 flux, and nutrients) are significantly improved when compared to surface observations by prescribing deep water values and trends, based on available data. The seasonality of the coccolithophore and "other phytoplankton" (diatoms and dinoflagellates) blooms is in general agreement with satellite ocean color products. Nutrient supply, biomass and calcite concentrations are modulated by light and mixed layer depth seasonal cycles. Diatoms are the most abundant phytoplankton, with a large bloom in early spring and a secondary bloom in fall. The diatom bloom is followed by blooms of dinoflagellates and coccolithophores. The effect of biological changes on the seasonal variability of the surface ocean pCO2 is nearly twice the temperature effect, in agreement with previous studies. The inclusion of multiple phytoplankton functional groups in the model played a major role in the accurate representation of CO2 uptake by biology. For instance, at the peak of the bloom, the exclusion of coccolithophores causes an increase in alkalinity of up to 4 μmol kg−1 with a corresponding increase in DIC of up to 16 μmol kg−1. During the peak of the bloom in summer, the net effect of the absence of the coccolithophores bloom is an increase in pCO2 of more than 20 μatm and a reduction of atmospheric CO2 uptake of more than 6 mmol m−2 d−1. On average, the impact of coccolithophores is an increase of air-sea CO2 flux of about 27%. Considering the areal extent of the bloom from satellite images within the Irminger and Icelandic Basins, this reduction translates into an annual mean of nearly 1500 tonnes C yr−1.

scholarly journals The role of phytoplankton dynamics in the seasonal variability of carbon in the subpolar North Atlantic – a modeling study

2011 ◽  
Vol 4 (1) ◽  
pp. 289-342 ◽  
Author(s):  
S. R. Signorini ◽  
S. Häkkinen ◽  
K. Gudmundsson ◽  
A. Olsen ◽  
A. M. Omar ◽  
...  
Keyword(s):  
Functional Groups ◽  
Seasonal Variability ◽  
Mixed Layer Depth ◽  
Early Spring ◽  
Biogeochemical Model ◽  
Co2 Uptake ◽  
Biological Interactions ◽  
Surface Ocean ◽  
In Situ Data ◽  
Phytoplankton Functional Groups

Abstract. We use an ecosystem/biogeochemical model, which includes multiple phytoplankton functional groups and carbon cycle dynamics, to investigate physical-biological interactions in Icelandic waters. Satellite and in situ data were used to validate the model. The seasonality of the coccolithophore and "other phytoplankton" (diatoms and dinoflagellates) blooms is in general agreement with satellite ocean color products. Nutrient supply, biomass and calcite concentrations are modulated by light and mixed layer depth seasonal cycles. Diatoms are the most abundant with a large bloom in early spring and a secondary bloom in fall. The diatom bloom is followed by blooms of dinoflagellates and coccolithophores. The effect of biological changes on the seasonal variability of the surface ocean pCO2 is nearly twice the temperature effect. The inclusion of multiple functional groups in the model played a major role in the accurate representation of CO2 uptake by biology. For instance, at the peak of the bloom, the exclusion of coccolithophores causes an increase in alkalinity of up to 4 μmol kg−1 with a corresponding increase in DIC of up to 16 μmol kg−1. The net effect of the absence of the coccolithophores bloom is an increase in pCO2 of more than 20 μatm and a reduction of atmospheric CO2 uptake of more than 6 mmol m−2 d−1.

scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2020 ◽  
Vol 20 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near-infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint-mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

scholarly journals Estimates of European uptake of CO<sub>2</sub> inferred from GOSAT X<sub>CO<sub>2</sub></sub> retrievals: sensitivity to measurement bias inside and outside Europe

2016 ◽  
Vol 16 (3) ◽  
pp. 1289-1302 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
R. J. Parker ◽  
N. M. Deutscher ◽  
D. G. Feist ◽  
...  
Keyword(s):  
Mole Fraction ◽  
Alternative Hypothesis ◽  
Co2 Flux ◽  
Total Carbon ◽  
Co2 Uptake ◽  
In Situ Data ◽  
Erroneous Estimate ◽  
Measurement Biases ◽  
Scanning Imaging

Abstract. Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

scholarly journals Defining BGC-Argo-based metrics of ocean health and biogeochemical functioning for the evaluation of global ocean models

2021 ◽  
Author(s):  
Alexandre Mignot ◽  
Hervé Claustre ◽  
Gianpiero Cossarini ◽  
Fabrizio D'Ortenzio ◽  
Elodie Gutknecht ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Dissolved Inorganic Carbon ◽  
Numerical Models ◽  
Co2 Flux ◽  
Global Scale ◽  
Global Ocean ◽  
Argo Floats ◽  
In Situ Data ◽  
Biogeochemical Models ◽  
The Impact

Abstract. Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and ocean health. Classically, validation of such models relies on comparison with surface quantities from satellite (such as chlorophyll-a concentrations), climatologies, or sparse in situ data (such as cruises observations, and permanent fixed oceanic stations). However, these datasets are not fully suitable to assess how models represent many climate-relevant biogeochemical processes.  These limitations now begin to be overcome with the availability of a large number of vertical profiles of light, pH, oxygen, nitrate, chlorophyll-a concentrations and particulate backscattering acquired by the Biogeochemical-Argo (BGC-Argo) floats network. Additionally, other key biogeochemical variables such as dissolved inorganic carbon and alkalinity, not measured by floats, can be predicted by machine learning-based methods applied to float oxygen concentrations. Here, we demonstrate the use of the global array of BGC-Argo floats for the validation of biogeochemical models at the global scale. We first present 18 key metrics of ocean health and biogeochemical functioning to quantify the success of BGC model simulations. These metrics are associated with the air-sea CO2 flux, the biological carbon pump, oceanic pH, oxygen levels and Oxygen Minimum Zones (OMZs). The metrics are either a depth-averaged quantity or correspond to the depth of a particular feature. We also suggest four diagnostic plots for displaying such metrics.

scholarly journals Elevated uptake of CO<sub>2</sub> over Europe inferred from GOSAT X<sub>CO<sub>2</sub></sub> retrievals: a real phenomenon or an artefact of the analysis?

2015 ◽  
Vol 15 (2) ◽  
pp. 1989-2011 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
R. J. Parker ◽  
N. M. Deutscher ◽  
D. G. Feist ◽  
...  
Keyword(s):  
Co2 Flux ◽  
Total Carbon ◽  
Co2 Uptake ◽  
In Situ Data ◽  
A Value ◽  
Co2 Balance ◽  
Real Phenomenon ◽  
Retrieval Bias ◽  
Annual Flux

Abstract. Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Recent work has shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) have a larger seasonal amplitude and a more negative annual net CO2 balance than those inferred from the in situ data. The causes of this enhanced European CO2 uptake have since become the focus of recent studies. We show this elevated uptake over Europe could largely be explained by mis-fitting data due to regional biases. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate surface flask data and the XCO2 data from the surface-based Total Carbon Column Observing Network (TCCON). The same EnKF system is also used to assimilate two, independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.18 ± 0.1 GtC a−1 compared to a value of 0.56 ± 0.1 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we have performed a series of numerical experiments that assimilate observations with biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that 50–80% of the elevated European uptake in 2010 inferred from GOSAT data is due to retrievals outside the immediate European region, while most of the remainder can be explained by a sub-ppm retrieval bias over Europe. We have used data assimilation techniques to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. We find a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.18 GtC a−1.

scholarly journals Quantifying the model structural error in carbon cycle data assimilation systems

2012 ◽  
Vol 5 (3) ◽  
pp. 2259-2288 ◽  
Author(s):  
S. Kuppel ◽  
F. Chevallier ◽  
P. Peylin
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Structural Model ◽  
Co2 Flux ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Simple Method ◽  
Total Uncertainty ◽  
Structural Error ◽  
The Impact

Abstract. This study explores the impact of the structural error of biosphere models when assimilating net ecosystem exchange (NEE) measurements or CO2 concentration measurements to optimize uncertain model parameters within carbon cycle data assimilation systems (CCDASs). This error has been proven difficult to identify and is often neglected in the total uncertainty budget. We propose a simple method which derives it from the model-minus-observation mismatch statistics. This diagnosis is applied to a state-of-the-art biogeochemical model using measurements of the net surface CO2 flux at twelve sites located in temperate deciduous broadleaf forests. We find that the structural model error in the NEE space has a standard deviation of 1.7 g C m−2 d−1, without a significant correlation structure beyond lags of a few days, and a large spatial structure that can be approximated with an exponential decay of e-folding length 500 km. In the space of concentrations, its characteristics are commensurate with the transport errors, both for surface air sample measurements and total column measurements. The inferred characteristics are confirmed by complementary optimality diagnostics performed after site-scale parameter optimizations.

scholarly journals The impact of improved satellite retrievals on estimates of biospheric carbon balance

2019 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Carbon Balance ◽  
Near Infrared ◽  
Co2 Flux ◽  
Retrieval Algorithm ◽  
Co2 Uptake ◽  
Near Infrared Radiation ◽  
Sources And Sinks ◽  
Satellite Retrievals ◽  
The Impact ◽  
Insight Into

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first satellite dedicated to monitoring CO2 from space and could provide novel insight into CO2 fluxes across the globe. However, one continuing challenge is the development of a robust retrieval algorithm: an estimate of atmospheric CO2 from satellite observations of near infrared radiation. The OCO-2 retrievals have undergone multiple updates since the satellite's launch, and the retrieval algorithm is now on its ninth version. Some of these retrieval updates, particularly version 8, led to marked changes in the CO2 observations, changes of 0.5 ppm or more. In this study, we evaluate the extent to which current OCO-2 observations can constrain monthly CO2 sources and sinks from the biosphere, and we particularly focus on how this constraint has evolved with improvements to the OCO-2 retrieval algorithm. We find that improvements in the CO2 retrieval are having a potentially transformative effect on satellite-based estimates of the global biospheric carbon balance. The version 7 OCO-2 retrievals formed the basis of early inverse modeling studies using OCO-2 data; these observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions. By contrast, newer versions of the retrieval algorithm yield a far more detailed constraint, and we are able to constrain CO2 budgets for seven global biome-based regions, particularly during the Northern Hemisphere summer when biospheric CO2 uptake is greatest. Improvements to the OCO-2 observations have had the largest impact on glint mode observations, and we also find the largest improvements in the terrestrial CO2 flux constraint when we include both nadir and glint data.

Observations Inform Improvements in Model Silicon Cycling in a Semi-enclosed Coastal Sea

2021 ◽  
Author(s):  
Elise Olson ◽  
Nina Nemcek ◽  
Susan Allen
Keyword(s):  
Functional Groups ◽  
Nutrient Dynamics ◽  
Biological Model ◽  
Model Parameters ◽  
Dissolved Silica ◽  
Strait Of Georgia ◽  
Silicon Cycle ◽  
Phytoplankton Functional Groups ◽  
Coastal Sea ◽  
The Impact

&lt;p&gt;We have developed a coupled physical-biological model representing plankton and nutrient dynamics of the Strait of Georgia, a fjord-like semi-enclosed coastal sea on the west coast of Canada. The nutrient-phytoplankton-zooplankton-detritus (NPZD)-type biological model is based on nitrogen uptake and remineralization with a coupled silicon cycle and includes both diatom and non-siliceous phytoplankton functional groups. The Strait of Georgia exhibits an estuarine circulation driven by input from the Fraser River as well as many smaller rivers and streams. It has high levels of dissolved silica (can be &gt;50 &amp;#956;M even at the surface). Silicon-replete conditions shape key characteristics of the local ecosystem, which include heavily silicified glass sponge reefs as well as frequent diatom and occasional silicoflagellate blooms. We therefore consider the ability of the model to match observed silicon levels an indicator of the fidelity of its representation of local biogeochemistry. Silicon in the model may be in the form of dissolved silica, living diatoms, or particulate biogenic silica, and model diatom growth may be limited by nitrogen, light, or dissolved silica availability. We will discuss the challenges involved in accurately representing important drivers of the regional silicon cycle. These include accurately capturing the division of primary productivity between diatoms and non-siliceous phytoplankton functional groups, as well as uncertainties in the magnitude of terrestrial inputs and sediment fluxes. We will show how evaluating the model functional groups by comparison with phytoplankton community composition determined by high performance liquid chromatography (HPLC) has informed our interpretation of model results and provided direction for efforts at improving model performance. We will discuss the impact of targeted adjustments to model parameters on the model silicon cycle in light of comparisons to observations.&lt;/p&gt;

scholarly journals Water level variation at a beaver pond significantly impacts net CO<sub>2</sub> uptake of a continental bog

2021 ◽  
Author(s):  
Hongxing He ◽  
Tim Moore ◽  
Elyn R. Humphreys ◽  
Peter M. Lafleur ◽  
Nigel T. Roulet
Keyword(s):  
Water Level ◽  
Co2 Flux ◽  
Water Levels ◽  
Co2 Uptake ◽  
Beaver Pond ◽  
Human Disturbances ◽  
Flux Dynamics ◽  
Net Co2 Uptake ◽  
Northern Peatlands ◽  
The Impact

Abstract. The carbon (C) dynamics of northern peatlands are sensitive to hydrological changes owing to ecohydrological feedback. We quantified and evaluated the impact of water level variations in a beaver pond (BP) on the CO2 flux dynamics of an adjacent, raised Sphagnum – shrub-dominated bog in southern Canada. We applied the CoupModel to the Mer Bleue bog, where the hydrological, energy and CO2 fluxes have been measured continuously for over 20 years. The lateral flow from the bog to the BP was estimated by the hydraulic gradient between the peatland and the BP's water level and the vertical profile of peat hydraulic conductivity. The model outputs were compared with the measured hydrological components, CO2 flux and energy flux data (1998–2019). CoupModel was able to reproduce the measured data well. The simulation shows that variation in the BP water level (naturally occurring or due to management) influenced the bog net ecosystem exchange of CO2 (NEE). Over 1998–2004, the BP water level was 0.75 to 1.0 m lower than during 2017–2019. Simulated net CO2 uptake was 55 g C m−2 yr−1 lower during 1998–2004 compared to 2017–2019 when there was no BP disturbance, which was similar to the differences in measured NEE between those periods. Peatland annual NEE was well correlated with water table depth within the bog, and NEE also shows a linear relation with the water level at the BP, with a slope of −120 g CO2-C m−2 yr−1 m−1. The current modelling predicts the bog may switch from CO2 sink to source when the BP water levels drop lower than ~ 1.7 m below the peat surface at the eddy covariance tower, 250 m from the BP. This study highlights the importance of natural and human disturbances to adjacent water bodies in regulating net CO2 uptake function of northern peatlands.

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