An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000 - 1849
We present 50-member ensemble reconstruction of 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-resoluti...
| Main Authors: | , , , , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
figshare
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.6084/m9.figshare.c.5369309.v1 https://springernature.figshare.com/collections/An_ensemble_reconstruction_of_global_monthly_sea_surface_temperature_and_sea_ice_concentration_1000_-_1849/5369309/1 |
| Summary: | We present 50-member ensemble reconstruction of 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. For this, the intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based multiple linear regressions with partitioned variance, 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. |
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