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Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color-based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ∼ 1000 14C measurements spanning more than a decade (1983-1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (< 0.2 g C m- 2 d- 1) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison 6 years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill. © 2008 Elsevier B.V.

We present a generalized framework for assessing the skill of global upper ocean ecosystem-biogeochemical models against in-situ field data and satellite observations. We illustrate the approach utilizing a multi-decade (1979-2004) hindcast experiment conducted with the Community Climate System Model (CCSM-3) ocean carbon model. The CCSM-3 ocean carbon model incorporates a multi-nutrient, multi-phytoplankton functional group ecosystem module coupled with a carbon, oxygen, nitrogen, phosphorus, silicon, and iron biogeochemistry module embedded in a global, three-dimensional ocean general circulation model. The model is forced with physical climate forcing from atmospheric reanalysis and satellite data products and time-varying atmospheric dust deposition. Data-based skill metrics are used to evaluate the simulated time-mean spatial patterns, seasonal cycle amplitude and phase, and subannual to interannual variability. Evaluation data include: sea surface temperature and mixed layer depth; satellite-derived surface ocean chlorophyll, primary productivity, phytoplankton growth rate and carbon biomass; large-scale climatologies of surface nutrients, pCO2, and air-sea CO2 and O2 flux; and time-series data from the Joint Global Ocean Flux Study (JGOFS). Where the data is sufficient, we construct quantitative skill metrics using: model-data residuals, time-space correlation, root mean square error, and Taylor diagrams. © 2008 Elsevier B.V. All rights reserved.

To better understand the role of iron in driving marine ecosystems, the Southern Ocean Iron Experiment (SOFeX) fertilized two surface water patches with iron north and south of the Antarctic Polar Front Zone (APFZ). Using 1-D coupled biological-physical simulations, we examine the biogeochemical dynamics that occurred both inside and outside of the fertilized patches during and shortly after the SOFeX field campaign. We focus, in particular, on three main issues governing the biological response to deliberate iron fertilization: the interaction among phytoplankton, light, macronutrient and iron limitation; dilution and lateral mixing between the fertilized patch and external, unfertilized waters; and the effect of varying mixed layer depth on the light field. At the patch south of the APFZ, sensitivity simulations with no dilution results in the maximum bloom magnitude, whereas dilution with external water extends the development of the north patch bloom by relieving silicon limitation. In model sensitivity studies for both sites, maximum chlorophyll concentration and dissolved inorganic carbon depletion inside the fertilized patches are inversely related to mixed layer depth, similar to the patterns observed across a number of iron fertilization field experiments. Our results suggest that Southern Ocean phytoplankton blooms resulting from natural or deliberate iron fertilization will tend to become iron-light co-limited unless the mixed layer depth is quite shallow. © 2007 Elsevier B.V. All rights reserved.

Seasonal and interannual variability in atmospheric carbon dioxide (CO2) concentrations was simulated using fluxes from fossil fuel, ocean and terrestrial biogeochemical models, and a tracer transport model with time-varying winds. The atmospheric CO2 variability resulting from these surface fluxes was compared to observations from 89 GLOBALVIEW monitoring stations. At northern hemisphere stations, the model simulations captured most of the observed seasonal cycle in atmospheric CO2, with the land tracer accounting for the majority of the signal. The ocean tracer was 3–6 months out of phase with the observed cycle at these stations and had a seasonal amplitude only ∼10% on average of observed. Model and observed interannual CO2 growth anomalies were only moderately well correlated in the northern hemisphere (R ∼ 0.4–0.8), and more poorly correlated in the southern hemisphere (R < 0.6). Land dominated the interannual variability (IAV) in the northern hemisphere, and biomass burning in particular accounted for much of the strong positive CO2 growth anomaly observed during the 1997–1998 El Niño event. The signals in atmospheric CO2 from the terrestrial biosphere extended throughout the southern hemisphere, but oceanic fluxes also exerted a strong influence there, accounting for roughly half of the IAV at many extratropical stations. However, the modeled ocean tracer was generally uncorrelated with observations in either hemisphere from 1979–2004, except during the weak El Niño/post-Pinatubo period of the early 1990s. During that time, model results suggested that the ocean may have accounted for 20–25% of the observed slowdown in the atmospheric CO2 growth rate

Application of biogeochemical models to the study of marine ecosystems is pervasive, yet objective quantification of these models' performance is rare. Here, 12 lower trophic level models of varying complexity are objectively assessed in two distinct regions (equatorial Pacific and Arabian Sea). Each model was run within an identical one-dimensional physical framework. A consistent variational adjoint implementation assimilating chlorophyll-a, nitrate, export, and primary productivity was applied and the same metrics were used to assess model skill. Experiments were performed in which data were assimilated from each site individually and from both sites simultaneously. A cross-validation experiment was also conducted whereby data were assimilated from one site and the resulting optimal parameters were used to generate a simulation for the second site. When a single pelagic regime is considered, the simplest models fit the data as well as those with multiple phytoplankton functional groups. However, those with multiple phytoplankton functional groups produced lower misfits when the models are required to simulate both regimes using identical parameter values. The cross-validation experiments revealed that as long as only a few key biogeochemical parameters were optimized, the models with greater phytoplankton complexity were generally more portable. Furthermore, models with multiple zooplankton compartments did not necessarily outperform models with single zooplankton compartments, even when zooplankton biomass data are assimilated. Finally, even when different models produced similar least squares model-data misfits, they often did so via very different element flow pathways, highlighting the need for more comprehensive data sets that uniquely constrain these pathways.

Recent upward revisions in key sink/source terms for fixed nitrogen (N) in the oceans imply a short residence time and strong negative feedbacks involving denitrification and N fixation to prevent large swings in the ocean N inventory over timescales of a few centuries. We tested the strength of these feedbacks in a global biogeochemical elemental cycling (BEC) ocean model that includes water column denitrification and an explicit N fixing phytoplankton group. In the northern Indian Ocean and over longer timescales in the tropical Atlantic, we find strong stabilizing feedbacks that minimize changes in marine N inventory over timescales of ∼30–200 years. In these regions high atmospheric dust/iron inputs lead to phosphorus limitation of diazotrophs, and thus a tight link between N fixation and surface water N/P ratios. Maintenance of the oxygen minimum zones in these basins depends on N fixation driven export. The stabilizing feedbacks in other regions are significant but weaker owing to iron limitation of the diazotrophs. Thus Fe limitation appears to restrict the ability of N fixation to compensate for changes in denitrification in the current climate, perhaps leading the oceans to lose fixed N. We suggest that iron is the ultimate limiting nutrient leading to nitrogen being the proximate limiting nutrient over wide regions today. Iron stress was at least partially alleviated during more dusty, glacial times, leading to a higher marine N inventory, increased export production, and perhaps widespread phosphorus limitation of the phytoplankton community. The increased efficiency of the biological pump would have contributed to the glacial drawdown in atmospheric CO2.

Estimates of sources/sinks of carbon dioxide (CO2) at the Earth's surface are commonly made using atmospheric CO2 inverse modeling, terrestrial and oceanic biogeochemical modeling, and inventory-based studies. In this study, we compare sea-air CO2 fluxes from the Time-Dependent Inverse (TDI) atmosphere model and the marine Biogeochemical Elemental Cycling (BEC) model to study the processes involved in ocean carbon cycling at subbasin scales. A dust generation and transport model, based on analyzed meteorology and terrestrial vegetation cover, is also used to estimate the interannual variability in dust and iron deposition to different ocean basins. Overall, a fairly good agreement is established between the TDI and BEC model results for the net annual patterns and seasonal cycle of sea-air CO2 exchange. Sensitivity studies with the ocean biogeochemical model using increased or reduced atmospheric iron inputs indicate the relative sensitivity of air-sea CO2 exchange. The simulated responses to changes in iron inputs are not instantaneous (peak response after ∼2−3 years). The TDI model derived seasonal cycles for the Southern Ocean (South Atlantic) are better matched by the BEC model by increasing (decreasing) iron inputs through atmospheric aerosols. Our results suggest that some of the interannual variability in TDI model air-sea CO2 fluxes during the past decade may be explainable by dust variability that relaxes/increases iron limitation in high-nitrate, low-chlorophyll (HNLC) ocean regions.

Coastal landscapes are naturally shifting mosaics of distinct ecosystems that are rapidly migrating with sea-level rise. Previous work illustrates that transitions among individual ecosystems have disproportionate impacts on the global carbon cycle, but this cannot address nonlinear interactions between multiple ecosystems that potentially cascade across the coastal landscape. Here, we synthesize carbon stocks, accumulation rates, and regional land cover data over 36 years (1984 and 2020) for a variety of ecosystems across a large portion of the rapidly transgressing mid-Atlantic coast. The coastal landscape of the Virginia Eastern Shore consists of temperate forest, salt marsh, seagrass beds, barrier islands, and coastal lagoons. We found that rapid losses and gains within individual ecosystems largely offset each other, which resulted in relatively stable areas for the different ecosystems, and a 4% (196.9 Gg C) reduction in regional carbon storage. However, new metrics of carbon replacement times indicated that it would take only ~ 7 years of carbon accumulation in surviving ecosystems to compensate this loss. Our findings reveal unique compensatory mechanisms at the scale of entire landscapes that quickly absorb losses and facilitate increased regional carbon storage in the face of historical and contemporary sea-level rise. However, the strength of these compensatory mechanisms may diminish as climate change exacerbates the magnitude of carbon losses. [ABSTRACT FROM AUTHOR]

A global ocean biogeochemical model is used to quantify the sensitivity of marine biogeochemistry and air–sea CO2 exchange to variations in dust deposition over decadal timescales. Estimates of dust deposition generated under four climate states provide a large range in total deposition with spatially realistic patterns; transient ocean model experiments are conducted by applying a step-function change in deposition from a current climate control. Relative to current conditions, higher dust deposition increases diatom and export production, nitrogen fixation and oceanic net CO2 uptake from the atmosphere, while reduced dust deposition has the opposite effects. Over timescales less than a decade, dust modulation of marine productivity and export is dominated by direct effects in high-nutrient, low-chlorophyll regions, where iron is the primary limiting nutrient. On longer timescales, an indirect nitrogen fixation pathway has increased importance, significantly amplifying the ocean biogeochemical response. Because dust iron input decouples carbon cycling from subsurface macronutrient supply, the ratio of the change in net ocean CO2 uptake to change in export flux is large, 0.45–0.6. Decreasing dust deposition and reduced oceanic CO2 uptake over the next century could provide a positive feedback to global warming, distinct from feedbacks associated with changes in stratification and circulation.

In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochemical cycles in the ocean will respond to global warming. We define the term "biogeochemical functional group" to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, "functional groups" have no phylogenetic meaning-these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E. huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. One obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our tendency to model the organisms for which we have the most validation data (e.g., E. huxleyi and Trichodesmium) even when they may represent only a fraction of the biogeochemical functional group we are trying to represent. When we step back and look at the paleo-oceanographic record, it suggests that oxygen concentrations have played a central role in the evolution and emergence of many of the key functional groups that influence biogeochemical cycles in the present-day ocean. However, more subtle effects are likely to be important over the next century like changes in silicate supply or turbulence that can influence the relative success of diatoms versus dinoflagellates, coccolithophorids and diazotrophs. In general, inferences drawn from the paleo-oceanographic record and theoretical work suggest that global warming will tend to favor the latter because it will give rise to increased stratification. However, decreases in pH and Fe supply could adversely impact coccolithophorids and diazotrophs in the future. It may be necessary to include explicit dynamic representations of nitrogen fixation, denitrification, silicification and calcification in our models if our goal is predicting the oceanic carbon cycle in the future, because these processes appear to play a very significant role in the carbon cycle of the present-day ocean and they are sensitive to climate change. Observations and models suggest that it may also be necessary to include the DMS cycle to predict future climate, though the effects are still highly uncertain. We have learned a tremendous amount about the distributions and biogeochemical impact of bacteria in the ocean in recent years, yet this improved understanding has not yet been incorporated into many of our models. All of these considerations lead us toward the development of increasingly complex models. However, recent quantitative model intercomparison studies suggest that continuing to add complexity and more functional groups to our ecosystem models may lead to decreases in predictive ability if the models are not properly constrained with available data. We also caution that capturing the present-day variability tells us little about how well a particular model can predict the future. If our goal is to develop models that can be used to predict how the oceans will respond to global warming, then we need to make more rigorous assessments of predictive skill using the available data. © 2006 Elsevier Ltd. All rights reserved.

Satellite remote sensing estimates of surface chlorophyll, temperature, wind speed, and sea ice cover are examined in the region of the Southern Ocean Iron Experiment (SOFeX). Our objectives are to place SOFeX into a regional context and highlight regional mesoscale spatial and monthly temporal variability. SOFeX fertilized two patches with iron, one south of the Antarctic Polar Front (PF) and one north of the PF but south of the Subantarctic Front (SAF). Satellite-observable phytoplankton blooms developed in both patches. The spring sea ice retreat near the south patch site was delayed in the 2001–2002 season, in turn delaying the naturally occurring, modest spring bloom in this region. Ambient surface chlorophyll concentrations for the area surrounding the southern patch during January 2002 are low (mean 0.26 mg/m3) compared with climatological January values (0.42 mg/m3). Regions east and west at similar latitudes exhibited higher mean chlorophyll concentrations (0.79 and 0.74 mg/m3, respectively). These modest phytoplankton blooms were likely stimulated by melting sea ice via changes in the light-mixing regime and release of iron and were smaller in magnitude than the iron-induced bloom within the SOFeX southern patch (>3 mg/m3). Iron inputs from melting ice may drive much of the natural spatial and temporal variability within the seasonal ice zone. Mean chlorophyll concentrations surrounding the SOFeX northern patch site were similar to climatological values during the SOFeX season. The northern patch was stretched into a long, thin filament along the southern boundary of the SAF, likely increasing the mixing/dilution rate with surrounding waters.

A global three-dimensional marine ecosystem model with several key phytoplankton functional groups, multiple limiting nutrients, explicit iron cycling, and a mineral ballast/organic matter parameterization is run within a global ocean circulation model. The coupled biogeochemistry/ecosystem/circulation (BEC) model reproduces known basin-scale patterns of primary and export production, biogenic silica production, calcification, chlorophyll, macronutrient and dissolved iron concentrations. The model captures observed high nitrate, low chlorophyll (HNLC) conditions in the Southern Ocean, subarctic and equatorial Pacific. Spatial distributions of nitrogen fixation are in general agreement with field data, with total N-fixation of 55 Tg N. Diazotrophs directly account for a small fraction of primary production (0.5%) but indirectly support 10% of primary production and 8% of sinking particulate organic carbon (POC) export. Diatoms disproportionately contribute to export of POC out of surface waters, but CaCO3 from the coccolithophores is the key driver of POC flux to the deep ocean in the model. An iron source from shallow ocean sediments is found critical in preventing iron limitation in shelf regions, most notably in the Arctic Ocean, but has a relatively localized impact. In contrast, global-scale primary production, export production, and nitrogen fixation are all sensitive to variations in atmospheric mineral dust inputs. The residence time for dissolved iron in the upper ocean is estimated to be a few years to a decade. Most of the iron utilized by phytoplankton is from subsurface sources supplied by mixing, entrainment, and ocean circulation. However, owing to the short residence time of iron in the upper ocean, this subsurface iron pool is critically dependent on continual replenishment from atmospheric dust deposition and, to a lesser extent, lateral transport from shelf regions.

A global marine ecosystem mixed-layer model is used to study iron cycling and nutrient-limitation patterns in surface waters of the world ocean. The ecosystem model has a small phytoplankton size class whose growth can be limited by N, P, Fe, and/or light, a diatom class which can also be Si-limited, and a diazotroph phytoplankton class whose growth rates can be limited by P, Fe, and/or light levels. The model also includes a parameterization of calcification by phytoplankton and is described in detail by Moore et al. (Deep-Sea Res. II, 2002). The model reproduces the observed high nitrate, low chlorophyll (HNLC) conditions in the Southern Ocean, subarctic Northeast Pacific, and equatorial Pacific, and realistic global patterns of primary production, biogenic silica production, nitrogen fixation, particulate organic carbon export, calcium carbonate export, and surface chlorophyll concentrations. Phytoplankton cellular Fe/C ratios and surface layer dissolved iron concentrations are also in general agreement with the limited field data. Primary production, community structure, and the sinking carbon flux are quite sensitive to large variations in the atmospheric iron source, particularly in the HNLC regions, supporting the Iron Hypothesis of Martin (Paleoceanography 5 (1990) 1-13). Nitrogen fixation is also strongly influenced by atmospheric iron deposition. Nitrogen limits phyuoplankton growth rates over less than half of the world ocean during summer months. Export of biogenic carbon is dominated by the sinking particulate flux, but detrainment and turbulent mixing account for 30% of global carbon export. Our results, in conjunction with other recent studies, suggest the familiar paradigm that nitrate inputs to the surface layer can be equated with particulate carbon export needs to be expanded to include multiple limiting nutrients and modes of export. © 2001 Elsevier Science Ltd. All rights reserved.

A new marine ecosystem model designed for the global domain is presented, and model output is compared with field data from nine different locations. Field data were collected as part of the international Joint Global Ocean Flux Study (JGOFS) program, and from historical time series stations. The field data include a wide variety of marine ecosystem types, including nitrogen- and iron-limited systems, and different physical environments from high latitudes to the mid-ocean gyres. Model output is generally in good agreement with field data from these diverse ecosystems. These results imply that the ecosystem model presented here can be reliably applied over the global domain. The model includes multiple potentially limiting nutrients that regulate phytoplankton growth rates. There are three phytoplankton classes, diatoms, diazotrophs, and a generic small phytoplankton class. Growth rates can be limited by available nitrogen, phosphorus, iron, and/or light levels. The diatoms can also be limited by silicon. The diazotrophs are capable of nitrogen fixation of N2 gas and cannot be nitrogen-limited. Calcification by phytoplankton is parameterized as a variable fraction of primary production by the small phytoplankton group. There is one zooplankton class that grazes the three phytoplankton groups and a large detrital pool. The large detrital pool sinks out of the mixed layer, while a smaller detrital pool, representing dissolved organic matter and very small particulates, does not sink. Remineralization of the detrital pools is parameterized with a temperature-dependent function. We explicitly model the dissolved iron cycle in marine surface waters including inputs of iron from subsurface sources and from atmospheric dust deposition. © 2001 Elsevier Science Ltd. All rights reserved.

Processes driving changes in sea-ice seasonality and sea-ice thickness were explored for a 'warm-shelf' region along the West Antarctic Peninsula using vertically coupled sea-ice-ocean thermodynamic simulations, with and without assimilated satellite sea-ice observations and moored ocean temperature observations. Simulations with assimilated sea-ice observations permitted investigation of surface [thermodynamic and dynamic (e.g., wind-driven)] processes affecting sea-ice thickness and seasonality. Assimilation of quasi-weekly variability in the depth and temperature of the deep warm pycnocline permitted examination of subsurface processes affecting sea-ice. Simulations using assimilated sea-ice observations (and implied motion) always produced greater surface heat fluxes and overall thinner sea ice. Assimilating seasonal and quasi-weekly variability in the depth and temperature of the pycnocline modified the start of the sea-ice season by −23 to +1 d, and also modified the sea ice thickness/seasonality to be thinner/shorter or thicker/longer at sub-seasonal and seasonal timescales, highlighting a mechanism where a shoaling pycnocline enhanced upward deep-water heat fluxes as transient surface-induced turbulence had a greater effect on a reduced mixed layer volume. The observed interplay of surface, subsurface, and sea-ice modulation of ocean-atmosphere heat transfer underscores the importance of representing the interaction between sea-ice concentration and upper ocean variability in climate projections. [ABSTRACT FROM AUTHOR]

Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. Plain Language Summary: Water quality affects coastal ocean ecosystems and their ecological functions, including primary productivity, predation, and aquatic vegetation growth. An important component of water quality, water clarity, has historically been measured by tools such as Secchi disks. They are black and white painted disks that are lowered into the water column to estimate the depth at which they are no longer visible from the surface. They are a reliable, repeatable, and low‐cost tool; thus, they have contributed to extensive long‐term records of overall water quality. However, they produce gaps in space and time. Here, we created a remote sensing product to fill these gaps using open‐access high resolution satellite data and low‐cost in situ methods. Our product allows for the retrieval of water clarity data across an entire water body and at times field measurements are not available. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. Key Points: Coupled satellite estimates with in situ observations increased the spatiotemporal coverage of water clarity estimation in a coastal ocean lagoon systemDemonstrated an accessible approach for modifying bio‐optical algorithms in optically complex coastal watersModel improved water clarity estimates, reduced biases, and decreased errors associated with Landsat‐8/Sentinel‐2 differences [ABSTRACT FROM AUTHOR]