Abstract
A new oceanic climatology to calculate ocean heat content (OHC) was developed for application year-round in the Atlantic Ocean basin. The Systematically Merged Atlantic Regional Temperature and Salinity (SMARTS) Climatology blends temperature and salinity fields from the World Ocean Atlas 2001 (WOA) and Generalized Digital Environmental Model v.3.0 (GDEM) at 1/4° resolution. This higher resolution climatology better resolves features in the Gulf of Mexico (GOM), including the Loop Current and eddy structures, than the previous coarser 1/2° products. Daily mean isotherm depths of the 20° C (D20) and 26° C (D26) (and their mean ratio), reduced gravity (e.g., 2-layer model), mixed layer depth (MLD), and OHC were estimated from the blended climatology. Using SMARTS with satellite-derived surface height anomaly and SST fields, daily values of D20, D26, MLD, and OHC were calculated from 1998 to 2010 using a two-layer model approach. Airborne and ship-deployed eXpendable BathyThermographs (XBT), long-term moorings, and Argo profiling floats provided the in-situ data to blend and assess the SMARTS Climatology. A clear, direct relationship emerged from the detailed analysis between satellite-derived and in-situ measurements of isotherm depths and OHC. This new climatological approach created a more accurate estimation of isotherm depths and OHC from satellite radar altimetry measurements, which can be used in hurricane intensity forecasts from the Statistical Hurricane Intensity Prediction Scheme (SHIPS). The Mainelli (2000) technique of calculating OHC was reexamined to most accurately project sea surface height anomalies (SSHA) into changes in depths of D20, D26, and MLD. SSHA surface features were tracked to determine realistic drift velocities ingested into the objective analysis algorithm. The former OHC algorithm assumed a climatological MLD, however observations show large temporal variability of MLD. Using a SSHA-dependent MLD for the OHC estimation improves the two-layer model by 5%. Upper ocean thermal structure estimations improved by 25% using the SMARTS Climatology as compared to that of Mainelli (2000).