Meibing Jin

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.

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.

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.

Ecosystem model intercomparison of under-ice and total primary production in the Arctic Ocean

Previous observational studies have found increasing primary production (PP) in response to declining sea ice cover in the Arctic Ocean. In this study, under-ice PP was assessed based on three coupled ice-ocean-ecosystem models participating in the Forum for Arctic Modeling and Observational Synthesis (FAMOS) project. All models showed good agreement with under-ice measurements of surface chlorophyll-a concentration and vertically integrated PP rates during the main under-ice production period, from mid-May to September. Further, modeled 30-year (1980–2009) mean values and spatial patterns of sea ice concentration compared well with remote sensing data. Under-ice PP was higher in the Arctic shelf seas than in the Arctic Basin, but ratios of under-ice PP over total PP were spatially correlated with annual mean sea ice concentration, with higher ratios in higher ice concentration regions. Decreases in sea ice from 1980 to 2009 were correlated significantly with increases in total PP and decreases in the under-ice PP/total PP ratio for most of the Arctic, but nonsignificantly related to under-ice PP, especially in marginal ice zones. Total PP within the Arctic Circle increased at an annual rate of between 3.2 and 8.0 Tg C/yr from 1980 to 2009. This increase in total PP was due mainly to a PP increase in open water, including increases in both open water area and PP rate per unit area, and therefore much stronger than the changes in under-ice PP. All models suggested that, on a pan-Arctic scale, the fraction of under-ice PP declined with declining sea ice cover over the last three decades.

A modeling study of coastal circulation and landfast ice in the nearshore Beaufort and Chukchi seas using CIOM

This study investigates sea ice and ocean circulation using a 3-D, 3.8 km CIOM (Coupled Ice-Ocean Model) under daily atmospheric forcing for the period 1990–2008. The CIOM was validated using both in situ observations and satellite measurements. The CIOM successfully reproduces some observed dynamical processes in the region, including the Bering-inflow-originated coastal current that splits into three branches: Alaska Coastal Water (ACW), Central Channel branch, and Herald Valley branch. In addition, the Beaufort Slope Current (BSC), the Beaufort Gyre, the East Siberian Current (ESC), mesoscale eddies, and seasonal landfast ice are well simulated. The CIOM also reproduces reasonable interannual variability in sea ice, such as landfast ice, and anomalous open water (less sea ice) during the positive Dipole Anomaly (DA) years, vice versa during the negative DA years. Sensitivity experiments were conducted with regard to the impacts of the Bering Strait inflow (heat transport), onshore wind stress, and sea ice advection on sea ice change, in particular on the landfast ice. It is found that coastal landfast ice is controlled by the following processes: wind forcing, Bering Strait inflow, and sea ice dynamics.

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.

Ocean mixing with lead-dependent subgrid scale brine rejection parameterization in a climate model

Sea ice thickness is highly spatially variable and can cause uneven ocean heat and salt flux on subgrid scales in climate models. Previous studies have demonstrated improvements in ocean mixing simulation using parameterization schemes that distribute brine rejection directly in the upper ocean mixed layer. In this study, idealized ocean model experiments were conducted to examine modeled ocean mixing errors as a function of the lead fraction in a climate model grid. When the lead is resolved by the grid, the added salt at the sea surface will sink to the base of the mixed layer and then spread horizontally. When averaged at a climate-model grid size, this vertical distribution of added salt is lead-fraction dependent. When the lead is unresolved, the model errors were systematic leading to greater surface salinity and deeper mixed-layer depth (MLD). An empirical function was developed to revise the added-salt-related parameter n from being fixed to lead-fraction dependent. Application of this new scheme in a climate model showed significant improvement in modeled wintertime salinity and MLD as compared to series of CTD data sets in 1997/1998 and 2006/2007. The results showed the most evident improvement in modeled MLD in the Arctic Basin, similar to that using a fixed n=5, as recommended by the previous Arctic regional model study, in which the parameter n obtained is close to 5 due to the small lead fraction in the Arctic Basin in winter.

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Synchronicity between Ice Retreat and Phytoplankton Bloom in Circum‐Antarctic Polynyas

Phytoplankton in Antarctic coastal polynyas has a temporally short yet spatially variant growth window constrained by ice cover and day length. Using 18-year satellite measurements (1997–2015) of sea ice and chlorophyll concentrations, we assessed the synchronicity between the spring phytoplankton bloom and light availability, taking into account the ice cover and the incident solar irradiance, for 50 circum-Antarctic coastal polynyas. The synchronicity was strong (i.e., earlier ice-adjusted light onset leads to earlier bloom and vice versa) in most of the western Antarctic polynyas but weak in a majority of the eastern Antarctic polynyas. The west-east asymmetry is related to sea ice production rate: the formation ofmany eastern Antarctic polynyas is associated with strong katabatic wind and high sea ice production rate, leading to stronger water column mixing that could damp phytoplankton blooms and weaken the synchronicity.