Impact of increased grid resolution on global marine biogeochemistry

Abstract Here we examine the impact of mesoscale processes on the global marine biogeochemical system by performing simulations at two different resolutions, 2° (LO-res) and 1/4° resolution (HI-res) using the PELAGOS model. Both the LO-res and HI-res simulations are set up with the same forcings and biogeochemical parameterizations, while the initial conditions are provided by a spinup of the LO-res simulation. This allows us to perform a direct inter-comparison of the two cases with a view to understanding how the introduction of mesoscale features affects the biogeochemical system, specifically how differences in the resolved horizontal and vertical motions are reflected in the plankton biomass and the nutrient availability. While the global large-scale oceanographic features (fronts, gyres, etc.) are captured in both the LO-res and HI-res simulations, differences in the mesoscale flow structures, and in particular the resolved vertical physics in the HI-res simulation generate very different behavior in the biogeochemical system. These differences in the physics drive what is a spun-up biogeochemical system in the LO-res simulation into a new regime in the HI-res simulation with significant reduction of typical low resolution biases. Coastal features are well reproduced due to stronger Ekman upwelling at the continental margins and increased eddy kinetic energy in the Southern Ocean significantly reduces the winter overestimation. These biases in the LO-res model are a result of inadequate vertical dynamics. The enhancement of surface chlorophyll can be attributed to improvements in the winter mixed layer in some regions such as the North Atlantic, while it is overall the difference in the Ekman vertical velocity which improves surface production allowing to simulate more realistic deep chlorophyll maxima as well. While the HI-res is better than the LO-res at capturing the timing of the spring bloom in the Southern Ocean, it still overestimates the peak of the bloom, hinting at the need to better understand the driving forces of the seasonal cycle of sub-Antarctic plankton dynamics.

[1]  P. Gent,et al.  Isopycnal mixing in ocean circulation models , 1990 .

[2]  S. Doney,et al.  An intermediate complexity marine ecosystem model for the global domain , 2001 .

[3]  G. Madec,et al.  On the role of the mesoscale circulation on an idealized coastal upwelling ecosystem , 2010 .

[4]  S. Speich,et al.  Anticyclonic and cyclonic eddies of subtropical origin in the subantarctic zone south of Africa , 2011 .

[5]  Gurvan Madec,et al.  A global ocean mesh to overcome the North Pole singularity , 1996 .

[6]  M. Lozier,et al.  On the source of Gulf Stream nutrients , 2008 .

[7]  S. Swart,et al.  Mesoscale features and phytoplankton biomass at the GoodHope line in the Southern Ocean during austral summer , 2012 .

[8]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[9]  Shigeki Hosoda,et al.  Improved description of global mixed-layer depth using Argo profiling floats , 2010 .

[10]  P. Delecluse,et al.  OPA 8.1 Ocean General Circulation Model reference manual , 1998 .

[11]  M. Lévy,et al.  The Modulation of Biological Production by Oceanic Mesoscale Turbulence , 2008 .

[12]  J. Beckers,et al.  Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (Diva) , 2012 .

[13]  E. Woodward,et al.  Seasonal and spatial variability in plankton production and respiration in the Subtropical Gyres of the Atlantic Ocean , 2009 .

[14]  J. Dunne,et al.  Physical drivers of interannual chlorophyll variability in the eastern subtropical North Atlantic , 2013 .

[15]  James C. McWilliams,et al.  Eddy-induced reduction of biological production in eastern boundary upwelling systems , 2011 .

[16]  Corinne Le Quéré,et al.  Simulating dimethylsulphide seasonality with the Dynamic Green Ocean Model PlankTOM5 , 2010 .

[17]  Marcello Vichi,et al.  A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory , 2007 .

[18]  Amit Tandon,et al.  Rapid changes in mixed layer stratification driven by submesoscale instabilities and winds , 2010 .

[19]  Peter R. Oke,et al.  Evaluation of a near-global eddy-resolving ocean model , 2012 .

[20]  Michele Scardi,et al.  Challenges of modeling depth‐integrated marine primary productivity over multiple decades: A case study at BATS and HOT , 2010 .

[21]  F. Chavez,et al.  Primary production in the eastern tropical Pacific: A review , 2006 .

[22]  Taro Takahashi,et al.  Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data , 2009 .

[23]  Watson W. Gregg,et al.  Ocean primary production and climate: Global decadal changes , 2003 .

[24]  J. Pelegrí,et al.  Nutrient transport and mixing in the Gulf Stream , 1991 .

[25]  Rüdiger Gerdes,et al.  Formulation of an ocean model for global climate simulations , 2005 .

[26]  James V. Gardner,et al.  Mapping U.S. continental shelves , 1998 .

[27]  M. Lévy,et al.  Grid degradation of submesoscale resolving ocean models: Benefits for offline passive tracer transport , 2012 .

[28]  A. Oschlies Can eddies make ocean deserts bloom? , 2002 .

[29]  M. Maqueda,et al.  An elastic-viscous-plastic sea ice model formulated on Arakawa B and C grids , 2009 .

[30]  A. Orsi,et al.  On the meridional extent and fronts of the Antarctic Circumpolar Current , 1995 .

[31]  A. Navarra,et al.  A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: numerical simulations. , 2007 .

[32]  Marcello Vichi,et al.  Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980-2000 , 2009 .

[33]  J. Pelegrí,et al.  Nutrient irrigation of the North Atlantic , 2006 .

[34]  Gurvan Madec,et al.  Large-scale impacts of submesoscale dynamics on phytoplankton: Local and remote effects , 2012 .

[35]  M. Vichi,et al.  The emergence of ocean biogeochemical provinces: A quantitative assessment and a diagnostic for model evaluation , 2011 .

[36]  Amit Tandon,et al.  An analysis of mechanisms for submesoscale vertical motion at ocean fronts , 2006 .

[37]  M. Maqueda,et al.  Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics , 1997 .

[38]  Vagn Walfrid Ekman,et al.  On the influence of the earth's rotation on ocean-currents. , 1905 .

[39]  M. Lévy,et al.  Modifications of mode water properties by sub-mesoscales in a bio-physical model of the Northeast Atlantic , 2011 .

[40]  Craig M. Lee,et al.  Eddy-Driven Stratification Initiates North Atlantic Spring Phytoplankton Blooms , 2012, Science.

[41]  Pablo Sangrà,et al.  Coupling between the open ocean and the coastal upwelling region off northwest Africa: water recirculation and offshore pumping of organic matter , 2005 .

[42]  J. Noh,et al.  Distribution of plankton related to the mesoscale physical structure within the surface mixed layer in the southwestern East Sea, Korea , 2004 .

[43]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[44]  Michele Scardi,et al.  Assessing the Uncertainties of Model Estimates of Primary Productivity in the Tropical Pacific Ocean Revised , 2008 .

[45]  Scott C. Doney,et al.  Eddy‐driven sources and sinks of nutrients in the upper ocean: Results from a 0.1° resolution model of the North Atlantic , 2003 .

[46]  E. Buitenhuis,et al.  Potential impact of changes in river nutrient supply on global ocean biogeochemistry , 2007 .

[47]  F. L. Moigne,et al.  Description of the biogeochemical features of the subtropical southeastern Atlantic and the Southern Ocean south of South Africa during the austral summer of the International Polar Year , 2012 .

[48]  Impact of eddy-driven vertical fluxes on phytoplankton abundance in the euphotic layer , 2011 .

[49]  Marina Lévy,et al.  The influence of mesoscale and submesoscale heterogeneity on ocean biogeochemical reactions , 2013 .

[50]  Bruno Blanke,et al.  Variability of the Tropical Atlantic Ocean Simulated by a General Circulation Model with Two Different Mixed-Layer Physics , 1993 .

[51]  Thierry Penduff,et al.  Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution , 2006 .

[52]  C. McClain A decade of satellite ocean color observations. , 2009, Annual review of marine science.