Clara Deal

What controls primary production in the Arctic Ocean? Results from an intercomparison of five general circulation models with biogeochemistry

As a part of Arctic Ocean Intercomparison Project, results from five coupled physical and biological ocean models were compared for the Arctic domain, defined here as north of 66.6°N. The global and regional (Arctic Ocean (AO)–only) models included in the intercomparison show similar features in terms of the distribution of present-day water column–integrated primary production and are broadly in agreement with in situ and satellite-derived data. However, the physical factors controlling this distribution differ between the models. The intercomparison between models finds substantial variation in the depth of winter mixing, one of the main mechanisms supplying inorganic nutrients over the majority of the AO. Although all models manifest similar level of light limitation owing to general agreement on the ice distribution, the amount of nutrients available for plankton utilization is different between models. Thus the participating models disagree on a fundamental question: which factor, light or nutrients, controls present-day Arctic productivity. These differences between models may not be detrimental in determining present-day AO primary production since both light and nutrient limitation are tightly coupled to the presence of sea ice. Essentially, as long as at least one of the two limiting factors is reproduced correctly, simulated total primary production will be close to that observed. However, if the retreat of Arctic sea ice continues into the future as expected, a decoupling between sea ice and nutrient limitation will occur, and the predictive capabilities of the models may potentially diminish unless more effort is spent on verifying the mechanisms of nutrient supply. Our study once again emphasizes the importance of a realistic representation of ocean physics, in particular vertical mixing, as a necessary foundation for ecosystem modeling and predictions.

Trajectory of the Arctic as an integrated system

Although much remains to be learned about the Arctic and its component processes, many of the most urgent scientific, engineering, and social questions can only be approached through a broader system perspective. Here, we address interactions between components of the Arctic system and assess feedbacks and the extent to which feedbacks (1) are now underway in the Arctic and (2) will shape the future trajectory of the Arctic system. We examine interdependent connections among atmospheric processes, oceanic processes, sea-ice dynamics, marine and terrestrial ecosystems, land surface stocks of carbon and water, glaciers and ice caps, and the Greenland ice sheet. Our emphasis on the interactions between components, both historical and anticipated, is targeted on the feedbacks, pathways, and processes that link these different components of the Arctic system. We present evidence that the physical components of the Arctic climate system are currently in extreme states, and that there is no indication that the system will deviate from this anomalous trajectory in the foreseeable future. The feedback for which the evidence of ongoing changes is most compelling is the surface albedo-temperature feedback, which is amplifying temperature changes over land (primarily in spring) and ocean (primarily in autumn-winter). Other feedbacks likely to emerge are those in which key processes include surface fluxes of trace gases, changes in the distribution of vegetation, changes in surface soil moisture, changes in atmospheric water vapor arising from higher temperatures and greater areas of open ocean, impacts of Arctic freshwater fluxes on the meridional overturning circulation of the ocean, and changes in Arctic clouds resulting from changes in water vapor content.

Controls of the landfast ice-ocean ecosystem offshore Barrow, Alaska

Abstract Based on biophysical ice-core data collected in the landfast ice off Barrow, Alaska, USA, in 2002 and 2003, a one-dimensional ice–ocean ecosystem model was developed to determine the factors controlling the bottom-ice algal community. The data and model results revealed a three-stage ice-algal bloom: (1) onset and early slow growth stage before mid-March, when growth is limited by light; (2) fast growth stage with increased light and sufficient nutrients; and (3) decline stage after late May as ice algae are flushed out of the ice bottom. Stages 2 and 3 are either separated by a transition period as in 2002 or directly connected by ice melting as in 2003, when in situ light and nutrient enrichment experiments showed only light limitations. The modeled net primary production of ice algae (NPPAi) from March to June is 1.2 and 1.7 g Cm–2 for 2002 and 2003, respectively, within the range of previous observations. Model sensitivity studies found that overall NPPAi increased almost proportionally to the initial nutrient concentrations in the water column. A phytoplankton bloom (if it occurs as in 2002) would compete with ice algae for nutrients and lead to reduced NPPAi. About 45% of the NPPAi was exported to the shallow benthos.

A first appraisal of prognostic ocean DMS models and prospects for their use in climate models

Ocean dimethylsulfide (DMS) produced by marine biota is the largest natural source of atmospheric sulfur, playing a major role in the formation and evolution of aerosols, and consequently affecting climate. Several dynamic process-based DMS models have been developed over the last decade, and work is progressing integrating them into climate models. Here we report on the first international comparison exercise of both 1D and 3D prognostic ocean DMS models. Four global 3D models were compared to global sea surface chlorophyll and DMS concentrations. Three local 1D models were compared to three different oceanic stations (BATS, DYFAMED, OSP) where available time series data offer seasonal coverage of chlorophyll and DMS variability. Two other 1D models were run at one site only. The major point of divergence among models, both within 3D and 1D models, relates to their ability to reproduce the summer peak in surface DMS concentrations usually observed at low to mid- latitudes. This significantly affects estimates of global DMS emissions predicted by the models. The inability of most models to capture this summer DMS maximum appears to be constrained by the basic structure of prognostic DMS models: dynamics of DMS and dimethylsulfoniopropionate (DMSP), the precursor of DMS, are slaved to the parent ecosystem models. Only the models which include environmental effects on DMS fluxes independently of ecological dynamics can reproduce this summer mismatch between chlorophyll and DMS. A major conclusion of this exercise is that prognostic DMS models need to give more weight to the direct impact of environmental forcing (e.g., irradiance) on DMS dynamics to decouple them from ecological processes.

Prospects for simulating macromolecular surfactant chemistry at the ocean–atmosphere boundary

Biogenic lipids and polymers are surveyed for their ability to adsorb at the water–air interfaces associated with bubbles, marine microlayers and particles in the overlying boundary layer. Representative ocean biogeochemical regimes are defined in order to estimate local concentrations for the major macromolecular classes. Surfactant equilibria and maximum excess are then derived based on a network of model compounds. Relative local coverage and upward mass transport follow directly, and specific chemical structures can be placed into regional rank order. Lipids and denatured protein-like polymers dominate at the selected locations. The assigned monolayer phase states are variable, whether assessed along bubbles or at the atmospheric spray droplet perimeter. Since oceanic film compositions prove to be irregular, effects on gas and organic transfer are expected to exhibit geographic dependence as well. Moreover, the core arguments extend across the sea–air interface into aerosol–cloud systems. Fundamental nascent chemical properties including mass to carbon ratio and density depend strongly on the geochemical state of source waters. High surface pressures may suppress the Kelvin effect, and marine organic hygroscopicities are almost entirely unconstrained. While bubble adsorption provides a well-known means for transporting lipidic or proteinaceous material into sea spray, the same cannot be said of polysaccharides. Carbohydrates tend to be strongly hydrophilic so that their excess carbon mass is low despite stacked polymeric geometries. Since sugars are abundant in the marine aerosol, gel-based mechanisms may be required to achieve uplift. Uncertainties distill to a global scale dearth of information regarding two dimensional kinetics and equilibria. Nonetheless simulations are recommended, to initiate the process of systems level quantification.

Pan-Arctic simulation of coupled nutrient-sulfur cycling due to sea ice biology : Preliminary results

A dynamic model is constructed for interactive silicon, nitrogen, sulfur processing in and below Arctic sea ice, by ecosystems residing in the lower few centimeters of the distributed pack. A biogeochemically active bottom layer supporting sources/sinks for the pennate diatoms is appended to thickness categories of a global sea ice code. Nutrients transfer from the ocean mixed layer to drive algal growth, while sulfur metabolites are reinjected from the ice interface. Freeze, flux, flush and melt processes are linked to multielement geocycling for the entire high-latitude regime. Major element kinetics are optimized initially to reproduce chlorophyll observations, which extend across the seasons. Principal influences on biomass are solute exchange velocity at the solid interface, optical averaging in active ice and cell retention against ablation. The sulfur mechanism encompasses open water features such as accumulation of particulate dimethyl sulfoniopropionate, grazing and other disruptive releases, plus bacterial/enzymatic conversion to volatile dimethyl sulfide. For baseline settings, the mixed layer trace gas distribution matches sparging measurements where they are available. However, concentrations rise to well over 10 nM in remote, unsampled locations. Peak contributions are supported by ice grazing, mortality and fractional melting. The model bottom layer adds substantially to a ring maximum of reduced sulfur chemistry that may be dominant across the marginal Arctic environment. Sensitivity tests on this scenario include variation of cell sulfur composition and remineralization, routings/chemical time scales, and the physical dimension of water layers. An alternate possibility that peripheral additions are small cannot be excluded from the outcomes. It is concluded that seagoing dimethyl sulfide data are far too sparse at the present time to distinguish sulfur-ice production levels. Citation: Elliott, S., C. Deal, G. Humphries, E. Hunke, N. Jeffery, M. Jin, M. Levasseur, and J. Stefels (2012), Pan-Arctic simulation of coupled nutrient-sulfur cycling due to sea ice biology: Preliminary results, J. Geophys. Res., 117, G01016, doi:10.1029/2011JG001649.

The future of the subsurface chlorophyll-a maximum in the Canada Basin-A model intercomparison

Six Earth system models and three ocean-ice-ecosystem models are analyzed to evaluate magnitude and depth of the subsurface Chl-a maximum (SCM) in the Canada Basin and ratio of surface to subsurface Chl-a in a future climate scenario. Differences in simulated Chl-a are caused by large intermodel differences in available nitrate in the Arctic Ocean and to some extent by ecosystem complexity. Most models reproduce the observed SCM and nitracline deepening and indicate a continued deepening in the future until the models reach a new state with seasonal ice-free waters. Models not representing a SCM show either too much nitrate and hence no surface limitation or too little nitrate with limited surface growth only. The models suggest that suppression of the nitracline and deepening of the SCM are caused by enhanced stratification, likely driven by enhanced Ekman convergence and freshwater contributions with primarily large-scale atmospheric driving mechanisms. The simulated ratio of near-surface Chl-a to depth-integrated Chl-a is slightly decreasing in most areas of the Arctic Ocean due to enhanced contributions of subsurface Chl-a. Exceptions are some shelf areas and regions where the continued ice thinning leaves winter ice too thin to provide a barrier to momentum fluxes, allowing winter mixing to break up the strong stratification. Results confirm that algorithms determining vertically integrated Chl-a from surface Chl-a need to be tuned to Arctic conditions, but likely require little or no adjustments in the future.

Changing polar environments: Interdisciplinary challenges

In the past few decades, there has been enormous growth in scientific studies of physical, chemical, and biological interactions among reservoirs in polar regions. This has come, in part, as a result of a few significant discoveries: There is dramatic halogen chemistry that occurs on and above the sea ice in the springtime that destroys lower tropospheric ozone and mercury [Simpson et al., 2007; Steffen et al., 2008], the sunlit snowpack is very photochemically active [Grannas et al., 2007], biology as a source of organic compounds plays a pivotal role in these processes, and these processes are occurring in the context of rapidly changing polar regions under climate feedbacks that are as of yet not fully understood [Serreze and Barry, 2011]. Stimulated by the opportunities of the International Polar Year (IPY, 2007-2009), a number of large-scale field studies in both polar environments have been undertaken, aimed at the study of the complex biotic and abiotic processes occurring in all phases (see Figure 1). Sea ice plays a critical role in polar environments: It is a highly reflective surface that interacts with radiation; it provides a habitat for mammals and micro-organisms alike, thus playing a key role in polar trophic processes and elemental cycles; and it creates a saline environment for chemical processes that facilitate release of halogenated gases that contribute to the atmospheres ability to photochemically cleanse itself in an otherwise low-radiation environment. Ocean-air and sea ice-air interfaces also produce aerosol particles that provide cloud condensation nuclei.

Species distribution modeling of storm-petrels (Oceanodroma furcata and O. leucorhoa) in the North Pacific and the role of dimethyl sulfide

Storm-petrels have been shown to use dimethyl sulfide (DMS) as a foraging cue, suggesting that this compound may be used to predict their distribution. We describe a new distribution model that employs machine learning software and geographic information systems to model storm-petrel distribution. We used environmental predictor variables that included newly available climatologies of sea surface DMS concentrations to construct distribution maps of fork-tailed storm-petrel (Oceanodroma furcata) and Leach’s storm-petrel (O. leucorhoa) in the North Pacific and Bering Sea. Model accuracy was assessed by (1) using the area under the receiver operating characteristic curve (AUC) values and (2) comparing predicted distributions to presence and non-detection data from two opportunistic pelagic surveys performed in summer 2008. Models using all predictor variables gave AUC values of 0.89 and 0.75, sensitivity values of 0.73 and 0.61, and specificity values of 0.83 and 0.73 for fork-tailed and Leach’s storm-petrel, respectively. Models using all predictor variables except DMS gave AUC values of 0.87 and 0.74, sensitivity values of 0.81 and 0.60, and specificity values of 0.77 for fork-tailed and Leach’s storm-petrel, respectively. The large-scale link between DMS and how storm-petrels use it to locate foraging areas was reinforced by the partial dependence of DMS on the relative index of occurrence (RIO) of storm-petrels, and by a decrease in AUC values when removing DMS as a predictor. This work is a preliminary step toward linking seabird distribution to globally important infochemicals and should be a basis for further study.

Progress and Challenges in Biogeochemical Modeling of the Pacific Arctic Region

At this early stage of modeling marine ecosystems and biogeochemical cycles in the Pacific Arctic Region (PAR), numerous challenges lie ahead. Observational data used for model development and validation remain sparse, especially across seasons and under a variety of environmental conditions. Field data are becoming more available, but at the same time PAR is rapidly changing. Biogeochemical models can provide the means to capture some of these changes. This study introduces and synthesizes ecosystem modeling in PAR by discussing differences in complexity and application of one-dimensional, regional, and global earth system models. Topics include the general structure of ecosystem models and specifics of the combined benthic, pelagic, and ice PAR ecosystems, the importance of model validation, model responses to climate influences (e.g. diminishing sea ice, ocean acidification), and the impacts of circulation and stratification changes on PAR ecosystems and biogeochemical cycling. Examples of modeling studies that help place the region within the context of the Pan-Arctic System are also discussed. We synthesize past and ongoing PAR biogeochemical modeling efforts and briefly touch on decision makers’ use of ecosystem models and on necessary future developments.