Abstract
This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.
Highlights
Background & SummaryThe oceans cover approximately 71% of the Earth’s surface and have a significant influence on atmospheric processes by supplying heat and moisture, driving the atmospheric circulation, from micro to macro scale
Forced Atmospheric General Circulation Models (AGCMs) setups are used in a broad variety of applications, e.g., the analysis of how Sea Surface Temperature (SST) patterns impact on the atmosphere, data assimilation appoaches, and so on
Prominent temperature reconstructions include one which investigates the origin of the Little Ice Age anomalies[4] and the Last Millenium Reanalysis products[5], which provides a framework for annual mean temperature reconstruction
Summary
The oceans cover approximately 71% of the Earth’s surface and have a significant influence on atmospheric processes by supplying heat and moisture, driving the atmospheric circulation, from micro to macro scale. Prominent temperature reconstructions include one which investigates the origin of the Little Ice Age anomalies[4] and the Last Millenium Reanalysis products[5], which provides a framework for annual mean temperature reconstruction The former has been used for AGCM simulations[9], but with the annual fields augmented with intra-annual and sub-grid scale variability. As in many climate field reconstructions, the augmentation technique assumes the same statistical relationship between the reconstruction and available observations during calibration and reconstruction periods Our approach fills this gap, by starting from an ensemble of annual reconstructions[8] and augmenting them with intra-annual and sub-grid scale variability from an ensemble of historical observations in a way that the annual means of the coarse resolution SST reconstructions are preserved.
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