Examining the Consistency of Sea Surface Temperature and Sea Ice Concentration in Arctic Satellite Products

Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a specified SIC and spatial resolution is first examined within collocated satellite-derived products contained within existing Level 4 SST analyses distributed using the data specification from the Group for High Resolution Sea Surface Temperature. The shape of the interdependence is further validated with manually quality-controlled buoy SST and SIC collocations. A parametric equation for the limiting SST value is derived from simulations of a mixed ocean/ice pixel with specified ice fraction and a linear SST gradient extending away from the ice edge. The exponential curve matching the observed interdependence suggests a maximum 5 km pixel-averaged SST at SIC values approaching zero between 6 and 8 °C. This maximum value is significantly greater than the previously assumed limiting values of ~3 °C and the corresponding SST gradient is larger than those typically observed with satellite SST products, but agrees well with recent Saildrone SST observations near ice. The curve provides a conservative limit with which inconsistent SST/SIC pairings can be identified, not only near the ice edge but at intermediate ice concentrations. Application of the filter improves the agreement between the SST/SIC relationship in satellite products and available Saildrone observations as well as the internal consistency of the different satellite products.

[1]  G. Dybkjær,et al.  A combined sea and sea-ice surface temperature climate dataset of the Arctic, 1982–2021 , 2023, Remote Sensing of Environment.

[2]  C. Gentemann,et al.  Comparison of GHRSST SST Analysis in the Arctic Ocean and Alaskan Coastal Waters Using Saildrones , 2022, Remote. Sens..

[3]  D. E. Harrison,et al.  Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs , 2021, Frontiers in Marine Science.

[4]  Thomas M. Smith,et al.  Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1 , 2020, Journal of Climate.

[5]  B. Fox‐Kemper,et al.  Saildrone: Adaptively Sampling the Marine Environment , 2020, Bulletin of the American Meteorological Society.

[6]  Jennifer Waters,et al.  The Current Configuration of the Ostia System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses , 2020, Remote. Sens..

[7]  Thomas M. Smith,et al.  Improved Estimation of Proxy Sea Surface Temperature in the Arctic , 2020, Journal of Atmospheric and Oceanic Technology.

[8]  Steinar Eastwood,et al.  Satellite-based time-series of sea-surface temperature since 1981 for climate applications , 2019, Scientific Data.

[9]  M. Martin,et al.  Improvements to feature resolution in the OSTIA sea surface temperature analysis using the NEMOVAR assimilation scheme , 2019, Quarterly Journal of the Royal Meteorological Society.

[10]  S. Kern,et al.  Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records , 2018, The Cryosphere.

[11]  Georg Heygster,et al.  Atmospheric Correction of Sea Ice Concentration Retrieval for 89 GHz AMSR-E Observations , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Jonathan P. D. Mittaz,et al.  A New High-Resolution Sea Surface Temperature Blended Analysis , 2017 .

[13]  M. Steele,et al.  Validation of satellite sea surface temperature analyses in the Beaufort Sea using UpTempO buoys , 2016 .

[14]  T. M. Chin,et al.  A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies , 2016 .

[15]  Keir Bovis,et al.  Estimating background error covariance parameters and assessing their impact in the OSTIA system , 2016 .

[16]  M. Bell,et al.  A multiple length scale correlation operator for ocean data assimilation , 2016 .

[17]  B. Brasnett,et al.  Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2 , 2016 .

[18]  S. Kern,et al.  Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations , 2015 .

[19]  Timothy F. R. Burgess,et al.  Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch , 2014, Remote. Sens..

[20]  Claire E. Bulgin,et al.  Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI) , 2014 .

[21]  Mark Buehner,et al.  A New Environment Canada Regional Ice Analysis System , 2013 .

[22]  M. Buehner,et al.  The Stratospheric Extension of the Canadian Global Deterministic Medium-Range Weather Forecasting System and Its Impact on Tropospheric Forecasts , 2012 .

[23]  C. Donlon,et al.  The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system , 2012 .

[24]  M. Buehner,et al.  Analysis and Forecasting of Sea Ice Conditions with Three-Dimensional Variational Data Assimilation and a Coupled Ice–Ocean Model , 2010 .

[25]  Lars Kaleschke,et al.  Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea , 2010 .

[26]  Thorsten Markus,et al.  The AMSR-E NT2 sea ice concentration algorithm: Its basis and implementation , 2009 .

[27]  James J. Hack,et al.  A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model , 2008 .

[28]  B. Brasnett The impact of satellite retrievals in a global sea‐surface‐temperature analysis , 2008 .

[29]  Thomas M. Smith,et al.  Daily High-Resolution-Blended Analyses for Sea Surface Temperature , 2007 .

[30]  Rasmus T. Tonboe,et al.  Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms , 2006 .

[31]  D. Barber,et al.  Pixel‐scale evaluation of SSM/I sea‐ice algorithms in the marginal ice zone during early fall freeze‐up , 2006 .

[32]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[33]  Wanqiu Wang,et al.  A New High-Resolution Blended Real-Time Global Sea Surface Temperature Analysis , 2003 .

[34]  Walter B. Tucker,et al.  Aerial observations of the evolution of ice surface conditions during summer , 2002 .

[35]  Bruce D. McKenzie,et al.  Operational Processing of Satellite Sea Surface Temperature Retrievals at the Naval Oceanographic Office , 1998 .

[36]  Donald J. Cavalieri,et al.  NASA Sea Ice Validation Program for the Defense Meteorological Satellite Program Special Sensor Microwave Imager , 1991 .

[37]  D. Perovich,et al.  Solar heating of a stratified ocean in the presence of a static ice cover , 1990 .

[38]  Donald J. Cavalieri,et al.  Reduction of weather effects in the calculation of sea ice concentration from microwave radiances , 1986 .

[39]  S. Kern,et al.  Simulated Geophysical Noise in Sea Ice Concentration Estimates of Open Water and Snow-Covered Sea Ice , 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  Kristian Mogensen,et al.  The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4 , 2012 .

[41]  Donald K. Perovich,et al.  Summer ice dynamics during SHEBA and its effect on the ocean heat content , 2001, Annals of Glaciology.

[42]  J. S. Dowker,et al.  Fundamentals of Physics , 1970, Nature.