Sea Surface Temperature Sensitivity to Water Turbidity from Simulations of the Turbid Black Sea Using HYCOM

This paper examines the sensitivity of sea surface temperature (SST) to water turbidity in the Black Sea using the eddy-resolving (;3.2-km resolution) Hybrid Coordinate Ocean Model (HYCOM), which includes a nonslab K-profile parameterization (KPP) mixed layer model. The KPP model uses a diffusive attenuation coefficient of photosynthetically active radiation ( kPAR) processed from a remotely sensed dataset to take water turbidity into account. Six model experiments (expt) are performed with no assimilation of any ocean data and wind/thermal forcing from two sources: 1) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA) and 2) Fleet Numerical Meteorology and Oceanography Center (FNMOC) Navy Operational Global Atmospheric Prediction System (NOGAPS). Forced with ECMWF, experiment 1 uses spatially and monthly varying kPAR values over the Black Sea, experiment 2 assumes all of the solar radiation is absorbed at the sea surface, and experiment 3 uses a constant kPAR value of 0.06 m21, representing clear-water constant solar attenuation depth of 16.7 m. Experiments 4, 5, and 6 are twins of 1, 2, and 3 but forced with NOGAPS. The monthly averaged model SSTs resulting from all experiments are then compared with a fine-resolution (;9 km) satellite-based monthly SST climatology (the Pathfinder climatology). Because of the high turbidity in the Black Sea, it is found that a clear-water constant attenuation depth (i.e., expts 3 and 6) results in SST bias as large as 3 8 Ci n comparison with standard simulations (expts 1 and 4) over most of the Black Sea in summer. In particular, when using the clear-water constant attenuation depth as opposed to using spatial and temporal kPAR, basin-averaged rms SST difference with respect to the Pathfinder SST climatology increases ;46% (from 1.418C in expt 1 to 2.068C in expt 3) in the ECMWF forcing case. Similarly, basin-averaged rms SST difference increases ;36% (from 1.398C in expt 4 to 1.898C in expt 6) in the NOGAPS forcing case. The standard HYCOM simulations (expts 1 and 4) have a very high basin-averaged skill score of 0.95, showing overall model success in predicting climatological SST, even with no assimilation of any SST data. In general, the use of spatially and temporally varying turbidity fields is necessary for the Black Sea OGCM studies because there is strong seasonal cycle and large spatial variation in the solar attenuation coefficient, and an additional simulation using a constant kPAR value of 0.19 m21, the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) space‐time mean for the Black Sea, did not yield as accurate SST results as experiments 1 and 4. Model‐data comparisons also revealed that relatively large HYCOM SST errors close to the coastal boundaries can be attributed to the misrepresentation of land‐ sea mask in the ECMWF and NOGAPS products. With the relatively accurate mask used in NOGAPS, HYCOM demonstrated the ability to simulate accurate SSTs in shallow water over the broad northwest shelf in the Black Sea, a region of large errors using the inaccurate mask in ECMWF. A linear relationship is found between changes in SST and changes in heat flux below the mixed layer. Specifically, a change of ; 50 Wm 22 in submixed-layer heat flux results in a SST change of ;3.08C, a value that occurs when using clear-water constant attenuation depth rather than monthly varying kPAR in the model simulations, clearly demonstrating potential impact of penetrating solar radiation on SST simulations.

[1]  Jean-Marie Beckers,et al.  Modelling the Danube-influenced North-western continental shelf of the Black Sea. I: Hydrodynamical processes simulated by 3-D and box models , 2002 .

[2]  E. Özsoy,et al.  Air–sea exchange in the Black Sea estimated from atmospheric analysis for the period 1979–1993 , 2001 .

[3]  Pierre-Marie Poulain,et al.  Observations of Black Sea mesoscale eddies and associated horizontal mixing , 2003 .

[4]  J. Beckers,et al.  Numerical simulations of seasonal and interannual variability of the Black Sea thermohaline circulation , 1999 .

[5]  N. Rayner,et al.  Version 2.2 of the Global sea-Ice and Sea Surface Temperature Data Set , 1996 .

[6]  Temel Oguz,et al.  Modeling the response of top‐down control exerted by gelatinous carnivores on the Black Sea pelagic food web , 2001 .

[7]  A. H. Murphy,et al.  Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient , 1988 .

[8]  E. Schneider,et al.  Sensitivity of the Simulated Annual Cycle of Sea Surface Temperature in the Equatorial Pacific to Sunlight Penetration , 1998 .

[9]  G. Mellor,et al.  Development of a turbulence closure model for geophysical fluid problems , 1982 .

[10]  Robert A. Arnone,et al.  Importance of solar subsurface heating in ocean general circulation models , 2001 .

[11]  P. Malanotte‐Rizzoli,et al.  Interdisciplinary Studies Integrating The Black Sea Biogeochemistry and Circulation Dynamics , 2002 .

[12]  Charlie N. Barron,et al.  The Modular Ocean Data Assimilation System (MODAS) , 2002 .

[13]  N. Schneider,et al.  The Indonesian Throughflow and the Global Climate System , 1998 .

[14]  K. Buesseler,et al.  The application of radiotracers to a study of Black Sea circulation: Validation of numerical simulations against observed weapons testing and Chernobyl 137Cs data , 1999 .

[15]  J. Holden,et al.  The storage and aging of continental runoff in large reservoir systems of the world , 1997 .

[16]  Thomas M. Smith,et al.  A High-Resolution Global Sea Surface Temperature Climatology for the 1961–90 Base Period , 1998 .

[17]  Timothy R. Parsons,et al.  Biological oceanography: an introduction. Second edition , 1997 .

[18]  E. Stanev,et al.  Oceanic response to atmospheric forcing derived from different climatic data sets. Intercomparison study for the Black Sea , 1998 .

[19]  L. Mee.  The Black Sea in crisis: a need for concerted international action , 1992 .

[20]  P. Malanotte‐Rizzoli,et al.  Wind and thermohaline circulation of the Black Sea driven by yearly mean climatological forcing , 1995 .

[21]  W. Large,et al.  Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization , 1994 .

[22]  Peter Cornillon,et al.  A Comparison of Satellite and In Situ–Based Sea Surface Temperature Climatologies , 1999 .

[23]  K. West,et al.  FORECAST EVALUATION , 2022 .

[24]  E. Stanev,et al.  Eddy Processes in Semienclosed Seas: A Case Study for the Black Sea , 1997 .

[25]  J. Cherniawsky,et al.  An upper ocean general circulation model for climate studies: global simulation with seasonal cycle , 1992 .

[26]  Malcolm J. Bowman,et al.  Rim current and coastal eddy mechanisms in an eddy-resolving Black Sea general circulation model , 2001 .

[27]  Joanna Staneva,et al.  The impact of the baroclinic eddies and basin oscillations on the transitions between different quasi-stable states of the Black Sea circulation , 2000 .

[28]  W. L. Darnell,et al.  A parameterization for longwave surface radiation from satellite data - Recent improvements , 1992 .

[29]  E. Stanev,et al.  The sensitivity of the heat exchange at sea surface to meso and sub-basin scale eddies: Model study for the Black Sea , 2001 .

[30]  K. Bryan.  A Numerical Method for the Study of the Circulation of the World Ocean , 1997 .

[31]  O. Saenko,et al.  Satellite Altimetry Observations of the Black Sea , 1999 .

[32]  H. Hurlburt,et al.  How Does Solar Attenuation Depth Affect the Ocean Mixed Layer? Water Turbidity and Atmospheric Forcing Impacts on the Simulation of Seasonal Mixed Layer Variability in the Turbid Black Sea* , 2005 .

[33]  Margaret J. Yelland,et al.  A new formula for determining the atmospheric longwave flux at the ocean surface at mid-high latitudes , 2003 .

[34]  Antonio J. Busalacchi,et al.  Effects of Penetrative Radiation on the Upper Tropical Ocean Circulation , 2002 .

[35]  A. Gücü.  Can overfishing be responsible for the successful establishment of Mnemiopsis leidyi in the Black Sea , 2002 .

[36]  W. Timothy Liu,et al.  Evaporation and solar irradiance as regulators of sea surface temperature in annual and interannual changes , 1994 .

[37]  O. Saenko,et al.  Satellite altimetry observations of the Black Sea level , 2001 .

[38]  J. Beckers,et al.  Reconnaissance of the main Black Sea's ecohydrodynamics by means of a 3D interdisciplinary model , 1998 .

[39]  Thomas M. Smith,et al.  An Improved In Situ and Satellite SST Analysis for Climate , 2002 .

[40]  Peter A. Rochford,et al.  The NRL Layered Global Ocean Model (NLOM) with an Embedded Mixed Layer Submodel: Formulation and Tuning , 2003 .

[41]  J. Beckers.  Application of the GHER 3D general circulation model to the Western Mediterranean , 1991 .

[42]  Melinda S. Peng,et al.  Navy Operational Global Atmospheric Prediction System (NOGAPS): Forcing for Ocean Models , 2002 .

[43]  Robert Frouin,et al.  Response of the equatorial Pacific to chlorophyll pigment in a mixed layer isopycnal ocean general circulation model , 2001 .

[44]  James C. McWilliams,et al.  Sensitivity to Surface Forcing and Boundary Layer Mixing in a Global Ocean Model: Annual-Mean Climatology , 1997 .

[45]  Rainer Bleck,et al.  An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates , 2002 .

[46]  G. Halliwell,et al.  Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM) , 2004 .

[47]  H. Hurlburt,et al.  Air–Sea Flux Estimates And The 1997–1998 Enso Event , 2002 .

[48]  Alan J. Wallcraft,et al.  A New Solar Radiation Penetration Scheme for Use in Ocean Mixed Layer Studies: An Application to the Black Sea Using a Fine-Resolution Hybrid Coordinate Ocean Model (HYCOM) , 2005 .

[49]  E. Stanev,et al.  Sea response to atmospheric variability. Model study for the Black Sea , 1995 .

[50]  E. Stanev,et al.  Numerical study on the planetary Rossby modes in the Black Sea , 1999 .

[51]  S. Maritorena,et al.  Bio-optical properties of oceanic waters: A reappraisal , 2001 .

[52]  E. Stanev,et al.  Modelling the impact of atmospheric and terrestrial inputs on the Black Sea coastal dynamics , 2001 .

[53]  Karline Soetaert,et al.  A model for early diagenetic processes in sediments of the continental shelf of the Black sea , 2002 .

[54]  Peter A. Rochford,et al.  An optimal definition for ocean mixed layer depth , 2000 .

[55]  E. Özsoy,et al.  Boundary current instabilities, upwelling, shelf mixing and eutrophication processes in the Black Sea , 1994 .

[56]  J. Murray,et al.  Variations in the chemistry of the Black Sea on a time scale of decades (1960–1995) , 2001 .

[57]  Peter A. Rochford,et al.  The Impact of Water Turbidity on Interannual Sea Surface Temperature Simulations in a Layered Global Ocean Model , 2004 .

[58]  J. Nihoul,et al.  Modelling the general circulation of shelf seas by 3Dk-ε models , 1989 .

[59]  J. M. Martín,et al.  The Level and Fate of Metals in the Danube River Plume , 2002 .

[60]  M. Grégoire,et al.  Study of the oxygen budget of the Black Sea waters using a 3D coupled hydrodynamical–biogeochemical model , 2001 .

[61]  Peter A. Rochford,et al.  Mixed layer depth variability over the global ocean , 2003 .

[62]  David E. Dietrich,et al.  Application of a modified Arakawa ‘a’ grid ocean model having reduced numerical dispersion to the Gulf of Mexico circulation , 1998 .

[63]  E. Özsoy,et al.  Coastal/deep ocean interactions in the Black Sea and their ecological/environmental impacts , 1996 .

[64]  P. Malanotte‐Rizzoli,et al.  Seasonal variability of wind and thermohaline‐driven circulation in the Black Sea: Modeling studies , 1996 .

[65]  A. H. Murphy.  Climatology, Persistence, and Their Linear Combination as Standards of Reference in Skill Scores , 1992 .