Progress in Data Merging/Gridding & CDR and Differences Between Global SST Averages in Gridded Data Sets Alexey Kaplan Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA

Recommendations from WHITE PAPER Data Merging, Gridding, and Analysis: 1. Begin accounting for correlated errors in implementations of data gridding procedures. 2. Research into misspecification of a priori OI parameters on OA fields. 3. Research into non-stationary structures. 4. Research into optimizing inter-sensor bias correction.

Other relevant activities: Dataset development/improvement/p roduction Dataset development/improvement/p roduction User guidance (BAMS paper) User guidance (BAMS paper) CDR CDR

GHRSST and SST products Global Multi-Product Ensemble (GMPE): Met Office OSTIA SST analysis OSTIA NCEP RTG_SST_HR SST analysis RTG_SST_HR NAVOCEANO NAVO K10 SST observations NAVO K10NAVO K10 JMA MGDSST SST analysis MGDSST RSS RSS MW Fusion SST analysis RSS MW FusionRSS MW Fusion RSS RSS MW+IR Fusion SST analysis RSS MW+IR FusionRSS MW+IR Fusion FNMOCFNMOC GHRSST SST and sea Ice analysis FNMOC MERSEA ODYSSEA SST analysis ODYSSEA NOAA AVHRR OINOAA AVHRR OI (Reynolds) NOAA AVHRR OI Meterological Service of Canada (CMC) BMRC GAMSSA SST analysis

Data Merging, Gridding, and Analysis: 1. Begin accounting for correlated errors in implementations of data gridding procedures. OSTIA development 3. Research into non-stationary structures: MUR (1km resolution) 4. Research into optimizing inter-sensor bias correction: OSTIA, ODYSSEA New operational products in the U.S.: 5km geostationary analysis, Reynolds 2-step HR OI

Two seminal papers published, documenting the utility of GMPE and SQUAM: Martin, M., P. Dash, A. Ignatov, V. Banzon, H. Beggs, B. Brasnett, J.-F. Cayula, J. Cummings, C. Donlon, C. Gentemann, R. Grumbine, S. Ishizaki, E. Maturi, R. W. Reynolds, J. Roberts-Jones, Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE), Deep Sea Research Part II: Topical Studies in Oceanography, Available online 2 May 2012, ISSN , /j.dsr Dash, P., A. Ignatov, M. Martin, C. Donlon, B. Brasnett, R. W. Reynolds, V. Banzon, H. Beggs, J.-F. Cayula, Y. Chao, R. Grumbine, E. Maturi, A. Harris, J. Mittaz, J. Sapper, T. M. Chin, J. Vazquez-Cuervo, E. M. Armstrong, C. Gentemann, J. Cummings, J.-F. Piollé, E. Autret, J. Roberts-Jones, S. Ishizaki, J. L. Høyer, D. Poulter, Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 2: Near real time web-based level 4 SST Quality Monitor (L4-SQUAM), Deep Sea Research Part II: Topical Studies in Oceanography, Available online 17 April 2012, ISSN , /j.dsr Special Thanks to Jorge Vazquez, a Special Editor of this DSR-II Special Issue!

Recommendations from WHITE PAPER Data Merging, Gridding, and Analysis: 1. Begin accounting for correlated errors in implementations of data gridding procedures. 2. Research into misspecification of a priori OI parameters on OA fields. Reynolds et al synthetic data test for L4 methodology

GMPE ensemble median is more accurate than individual members (left); GMPE ensemble spread can be used as a proxy for the error in its median (up). Suppose we’ve reduced the number of L4 products to 1 or 2: GMPE ensemble will disappear!

Impacts of AATSR loss on the L4 products Sasha noticed the recent increase in the spread between some L4 products and independent data in SQUAM. It might be a manifestation of the the AATSR data loss. Sasha noticed the recent increase in the spread between some L4 products and independent data in SQUAM. It might be a manifestation of the the AATSR data loss. Prasanjit agreed to investigate further and to try to document this using SQUAM (action item). If successful, this might become a useful illustration of the importance of the AATSR data source. Prasanjit agreed to investigate further and to try to document this using SQUAM (action item). If successful, this might become a useful illustration of the importance of the AATSR data source.

Synthetic data tests for L4 products (Chelton and Reynolds method) HR ECCO model SST is sub-sampled/corrupted as if it came as the satellite data stream. Obj. An. procedure for a given L4 product is applied, the results are compared with the true model values. First (by G12) was done for the NCDC OI HR ECCO model SST is sub-sampled/corrupted as if it came as the satellite data stream. Obj. An. procedure for a given L4 product is applied, the results are compared with the true model values. First (by G12) was done for the NCDC OI Now has been done for OSTIA too (J.R.-J & M.M.) Now has been done for OSTIA too (J.R.-J & M.M.) L4 producers at GHRSST were enthusiastic to do this too (Helen, Jacob, Viva, Mike, Jean-Francois) and have a joint paper about it. L4 producers at GHRSST were enthusiastic to do this too (Helen, Jacob, Viva, Mike, Jean-Francois) and have a joint paper about it. We’ll offer this to all L4 producers We’ll offer this to all L4 producers

Transition to the modern Ocean Observing System From Woodruff et al. [2008], In Climate Variability and Extremes during the Past 100 Years, Bronniman et al. (eds.)

Merchant et al., JGR, 2012

Despite the efforts to correct inter- platform biases in the SST data used for producing gridded data sets, the remaining biases are significant enough to create easily discernible differences between global means estimated from such gridded data sets.

Global means from annually averaged OSTIA SST is systematically colder than that from the NCDC Daily 0.25 o AVHRR-only OI data set by about 0.1 o C, while the latter is colder than the same estimated from the (older) NCEP monthly 1 o OI by approximately the same amount. Global means from annually averaged OSTIA SST is systematically colder than that from the NCDC Daily 0.25 o AVHRR-only OI data set by about 0.1 o C, while the latter is colder than the same estimated from the (older) NCEP monthly 1 o OI by approximately the same amount. While historical SST data sets that make use of the AVHRR data (HadISST1 and COBE SST) show very good consistency with the NCEP monthly 1 o OI, they are colder than the products that use only in situ data (ERSST v3b, HadSST2, HadSST3, ICOADS). While historical SST data sets that make use of the AVHRR data (HadISST1 and COBE SST) show very good consistency with the NCEP monthly 1 o OI, they are colder than the products that use only in situ data (ERSST v3b, HadSST2, HadSST3, ICOADS).

The global mean difference between these two groups of gridded historical data sets becomes especially prominent after 2000, exceeding 0.1 o C in some years. All these differences are not due to differences in the domains of the data sets (they appear in co-located calculations as well) or can be reasonably explained by random error effects on global annual SST averages. The global mean difference between these two groups of gridded historical data sets becomes especially prominent after 2000, exceeding 0.1 o C in some years. All these differences are not due to differences in the domains of the data sets (they appear in co-located calculations as well) or can be reasonably explained by random error effects on global annual SST averages.

Systematic differences between ship and buoy data and remaining cold biases in the AVHRR data seem responsible for the global mean differences between historical data sets during the satellite period. Global mean differences between individual L4 products have to be traced to their input data sets and their inter- platform bias removal procedures. Systematic differences between ship and buoy data and remaining cold biases in the AVHRR data seem responsible for the global mean differences between historical data sets during the satellite period. Global mean differences between individual L4 products have to be traced to their input data sets and their inter- platform bias removal procedures. Homogenization of historical data sets in terms of a common reference across satellite and pre- satellite periods is yet to be satisfactorily resolved in the community, even with regards to the annual global SST means Homogenization of historical data sets in terms of a common reference across satellite and pre- satellite periods is yet to be satisfactorily resolved in the community, even with regards to the annual global SST means