Review and assessment of latent and sensible heat flux accuracy over the global oceans

For over a decade, several research groups have been developing air-sea heat flux information over the global ocean, including latent (LHF) and sensible (SHF) heat fluxes over the global ocean. This paper aims to provide new insight into the quality and error characteristics of turbulent heat flux estimates at various spatial and temporal scales (from daily upwards). The study is performed within the European Space Agency (ESA) Ocean Heat Flux (OHF) project. One of the main objectives of the OHF project is to meet the recommendations and requirements expressed by various international programs such as the World Research Climate Program (WCRP) and Climate and Ocean Variability, Predictability, and Change (CLIVAR), recognizing the need for better characterization of existing flux errors with respect to the input bulk variables (e.g. surface wind, air and sea surface temperatures, air and surface specific humidities), and to the atmospheric and oceanic conditions (e.g. wind conditions and sea state). The analysis is based on the use of daily averaged LHF and SHF and the associated bulk variables derived from major satellite-based and atmospheric reanalysis products. Inter-comparisons of heat flux products indicate that all of them exhibit similar space and time patterns. However, they also reveal significant differences in magnitude in some specific regions such as the western ocean boundaries during the Northern Hemisphere winter season, and the high southern latitudes. The differences tend to be closely related to large differences in surface wind speed and/or specific air humidity (for LHF) and to air and sea temperature differences (for SHF). Further quality investigations are performed through comprehensive comparisons with daily-averaged LHF and SHF estimated from moorings. The resulting statistics are used to assess the error of each OHF product. Consideration of error correlation between products and observations (e.g., by their assimilation) is also given. This reveals generally high noise variance in all products and a weak signal in common with in situ observations, with some products only slightly better than others. The OHF LHF and SHF products, and their associated error characteristics, are used to compute daily OHF multiproduct-ensemble (OHF/MPE) estimates of LHF and SHF over the ice-free global ocean on a 0.25° × 0.25° grid. The accuracy of this heat multiproduct, determined from comparisons with mooring data, is greater than for any individual product. It is used as a reference for the anomaly characterization of each individual OHF product.

[1]  AkimaHiroshi A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures , 1970 .

[2]  B. Chapron,et al.  Homogenization of scatterometer wind retrievals , 2017 .

[3]  Michael G. Bosilovich,et al.  NASA’s modern era retrospective-analysis for research and applications: integrating Earth observations , 2008 .

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

[5]  Hiroshi Akima,et al.  A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures , 1970, JACM.

[6]  A. Stoffelen Toward the true near-surface wind speed: Error modeling and calibration using triple collocation , 1998 .

[7]  Peter Schlüssel,et al.  Retrieval of latent heat flux and longwave irradiance at the sea surface from SSM/I and AVHRR measurements , 1995 .

[8]  J. Carton,et al.  Intraseasonal Latent Heat Flux Based on Satellite Observations , 2009 .

[9]  F. Wentz A well‐calibrated ocean algorithm for special sensor microwave / imager , 1997 .

[10]  Elizabeth C. Kent,et al.  A NEW AIR―SEA INTERACTION GRIDDED DATASET FROM ICOADS WITH UNCERTAINTY ESTIMATES , 2009 .

[11]  Elizabeth C. Kent,et al.  New Insights into the Ocean Heat Budget Closure Problem from Analysis of the SOC Air–Sea Flux Climatology , 1999 .

[12]  S. Gulev Influence of Space-Time Averaging on the Ocean-Atmosphere Exchange Estimates in the North Atlantic Midlatitudes , 1994 .

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

[14]  Alberto M. Mestas-Nuñez,et al.  The ENSO footprint in monthly satellite evaporation over the global ocean during 1993–2007 , 2013 .

[15]  Peter K. Taylor,et al.  Intercomparison and validation of ocean–atmosphere energy flux fields. Final report of the Joint WCRP/SCOR Working Group on Air–Sea Fluxes (SCOR Working Group 110) , 2000 .

[16]  K. Taylor Summarizing multiple aspects of model performance in a single diagram , 2001 .

[17]  Christian Kummerow,et al.  Toward an Intercalibrated Fundamental Climate Data Record of the SSM/I Sensors , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Abderrahim Bentamy,et al.  Satellite Estimates of Wind Speed and Latent Heat Flux over the Global Oceans , 2003 .

[19]  G. Martin,et al.  A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests , 2000 .

[20]  Elizabeth C. Kent,et al.  ICOADS Release 2.5: extensions and enhancements to the surface marine meteorological archive , 2011 .

[21]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[22]  Alberto M. Mestas-Nuñez,et al.  The Impact of Satellite Winds and Latent Heat Fluxes in a Numerical Simulation of the Tropical Pacific Ocean , 2006 .

[23]  Zhaomin Wang,et al.  Impact of Synoptic Atmospheric Forcing on the Mean Ocean Circulation , 2016 .

[24]  M. Hubert,et al.  A Deterministic Algorithm for Robust Location and Scatter , 2012 .

[25]  Peter Cornillon,et al.  The Past, Present, and Future of the AVHRR Pathfinder SST Program , 2010 .

[26]  Darren L. Jackson,et al.  Predicting near-surface atmospheric variables from Special Sensor Microwave/Imager using neural networks with a first-guess approach , 2010 .

[27]  M. Kubota,et al.  An analysis of the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J‐OFURO) satellite‐derived latent heat flux using moored buoy data , 2006 .

[28]  S. Gulev,et al.  Climatologically Significant Effects of Some Approximations in the Bulk Parameterizations of Turbulent Air–Sea Fluxes , 2017 .

[29]  H. Charnock Wind stress on a water surface , 1955 .

[30]  S. Gille,et al.  New Approaches for Air-Sea Fluxes in the Southern Ocean , 2016 .

[31]  Wade T. Crow,et al.  Beyond triple collocation: Applications to soil moisture monitoring , 2014 .

[32]  Elizabeth C. Kent,et al.  ICOADS Release 3.0: a major update to the historical marine climate record , 2017 .

[33]  E. F. Bradley,et al.  A guide to making climate quality meteorological and flux measurements at sea , 2007 .

[34]  Carol Anne Clayson,et al.  Version 1 : a New Satellite-Based Ocean-Atmosphere Turbulent Flux Dataset , 2013 .

[35]  Axel Andersson,et al.  The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3 , 2010 .

[36]  Robert A. Weller,et al.  Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and Sensible Heat Fluxes, Ocean Evaporation, and Related Surface Meteorological Variables , 2008 .

[37]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

[38]  Unexpected impacts of the Tropical Pacific array on reanalysis surface meteorology and heat fluxes , 2014 .

[39]  C. Donlon,et al.  Exploitation of error correlation in a large analysis validation: GlobCurrent case study , 2018, Remote Sensing of Environment.

[40]  Alberto M. Mestas-Nuñez,et al.  Seasonal and El Niño Variability in Weekly Satellite Evaporation over the Global Ocean during 1996–98 , 2006 .

[41]  James Hansen,et al.  An imperative to monitor Earth's energy imbalance , 2016 .

[42]  Abderrahim Bentamy,et al.  Gridded surface wind fields from Metop/ASCAT measurements , 2012 .

[43]  S. Gulev,et al.  Estimation of the Impact of Sampling Errors in the VOS Observations on Air–Sea Fluxes. Part I: Uncertainties in Climate Means , 2007 .

[44]  Ad Stoffelen,et al.  Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target , 2014 .

[45]  Mark A. Bourassa,et al.  A comparison of nine monthly air–sea flux products , 2011 .

[46]  Deborah K. Smith,et al.  A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications , 2011 .

[47]  D. Holdstock Past, present--and future? , 2005, Medicine, conflict, and survival.

[48]  Axel Andersson,et al.  Evaluation of HOAPS-3 Ocean Surface Freshwater Flux Components , 2011 .

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

[50]  M. Latif,et al.  North Atlantic Ocean control on surface heat flux on multidecadal timescales , 2013, Nature.

[51]  E. F. Bradley,et al.  Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE Algorithm , 2003 .

[52]  Robert Atlas,et al.  A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products , 2004 .

[53]  Heikki Järvinen,et al.  Variational quality control , 1999 .

[54]  Abderrahim Bentamy,et al.  Improvement in air–sea flux estimates derived from satellite observations , 2013 .

[55]  A. Beljaars The parametrization of surface fluxes in large-scale models under free convection , 1995 .

[56]  Estimation of the impact of sampling errors in the VOS observations on air-sea fluxes. Part II: Impact on trends and interannual variability , 2007 .

[57]  R. Atlas,et al.  Surface Turbulent Heat and Momentum Fluxes over Global Oceans Based on the Goddard Satellite Retrievals, Version 2 (GSSTF2) , 2003 .

[58]  Frank J. Wentz,et al.  SSM/I Version-7 Calibration Report , 2012 .

[59]  S. Gulev,et al.  Probability Distribution Characteristics for Surface Air–Sea Turbulent Heat Fluxes over the Global Ocean , 2012 .

[60]  Wade T. Crow,et al.  Recent advances in (soil moisture) triple collocation analysis , 2016, Int. J. Appl. Earth Obs. Geoinformation.