RADIOLARIAN-BASED TRANSFER-FUNCTIONS FOR ESTIMATING MEAN SURFACE OCEAN TEMPERATURES AND SEASONAL RANGE

New radiolarian-based transfer functions to estimate sea surface tempe rature (SST) and seasonal range are presented. The transfer functions are based on the approach originated by Imbrie and Kipp [1971]. The tr ansfer functions differ from previous studies in the following three i mportant ways: (1) extensions to Q-mode factor analysis provide an obj ective method to cull species in the very diverse radiolarian populati on; (2) a log transform of the relative abundance data is used to norm alize the species percent data; and, (3) rather than writing equations for specific seasons, which are not independent data sets, statistica lly independent equations are developed to predict mean annual sea sur face temperatures as well as seasonal temperature range. One hundred a nd seventy surface sediment samples from the Pacific Ocean are used to generate the SST and season temperature range transfer functions. All samples were counted using a standardized radiolarian taxonomy. Forty one radiolarian species were used in the final regression equation. Q -mode factor analysis of this data set identified seven assemblages. T hese assemblages, the tropical, transitional, Antarctic, Bering Sea, w estern Pacific, central gyre, and eastern boundary current, are named for the oceanographic regions where these assemblages are important. T he seven assemblages are used in a regression analysis to predict SST and seasonal temperature range. The standard error of estimates for bo th mean SST and seasonal temperature range is 1.6 degrees C. Compariso n between radiolarian-based SSTs and SST estimates from alkenone U-k(3 7) in a 20,000 year long record from the northeast Pacific shows excel lent agreement. Comparison of mean SST estimates for the last glacial maximum (LGM) based on radiolarian and foraminifera in 10 eastern equa torial Pacific also show excellent concordance. These new LGM estimate s suggest that the original Climate: Long-Range Investigation Mapping, and Prediction (CLIMAP) reconstruction for this region underestimated surface ocean cooling.