New developments in CLAMP: Calibration using global gridded meteorological data

Climate Leaf Analysis Multivariate Program (CLAMP) is a versatile technique for obtaining quantitative estimates for multiple terrestrial palaeoclimate variables from woody dicot leaf assemblages. To date it has been most widely applied to the Late Cretaceous and Tertiary of the mid- to high latitudes because of concerns over the relative dearth of calibration sites in modern low-latitude warm climates, and the loss of information associated with the lack of marginal teeth on leaves in paratropical to tropical vegetation. This limits CLAMP's ability to quantify reliably climates at low latitudes in greenhouse worlds of the past. One of the reasons for the lack of CLAMP calibration samples from warm environments is the paucity of climate stations close to potential calibration vegetation sites at low latitudes. Agriculture and urban development have destroyed most lowland sites and natural vegetation is now largely confined to mountainous areas where climate stations are few and climatic spatial variation is high due to topographic complexity. To attempt to overcome this we have utilised a 0.5° × 0.5° grid of global interpolated climate data based on the data set of New et al. (1999) supplemented by the ERA40 re-analysis data for atmospheric temperature at upper levels. For each location, the 3-D climatology of temperature from the ECMWF re-analysis project was used to calculate the mean lower tropospheric lapse rate for each month of the year. The gridded data were then corrected to the altitude of the plant site using the monthly lapse rates. Corrections for humidity were also made. From this the commonly returned CLAMP climate variables were calculated. A bilinear interpolation scheme was then used to calculate the climate parameters at the exact lat/long of the site. When CLAMP analyses using the PHYSG3BR physiognomic data calibrated with the climate station based MET3BR were compared to analyses using the gridded data at the same locations (GRIDMET3BR), the results were indistinguishable in that they fell within the range of statistical uncertainty determined for each analysis. This opens the way to including natural vegetation anywhere in the world irrespective of the proximity of a meteorological station.