de.dkrz.wdcc.iso3879249 series EKF400_v2.0 Dr. Jörg Franke Institute of Geography and Oeschger Centre for Climate Change Research author Dr. Veronika Valler Institute of Geography and Oeschger Centre for Climate Change Research author Yuri Brugnara Institute of Geography and Oeschger Centre for Climate Change Research author Stefan Brönnimann Institute of Geography and Oeschger Centre for Climate Change Research author Dr. Jörg Franke Institute of Geography and Oeschger Centre for Climate Change Research pointOfContact Dr. Jörg Franke Institute of Geography and Oeschger Centre for Climate Change Research originator 2021-10-23T04:39:24 ISO 19115 ISO 19139 / DKRZ ISO Simple Profile V1.0 http://doi.org/doi:10.26050/WDCC/EKF400_v2.0 Ensemble Kalman Fitting Paleo-Reanalysis Version 2.0 (EKF400_v2.0) Ensemble Kalman Fitting Paleo-Reanalysis Version 2.0 (EKF400_v2.0) 2020-06-23 creation None Ensemble Kalman Fitting Paleo-Reanalysis Version 2 (EKF400_v2) doi:10.26050/WDCC/EKF400_v2.0 Dr. Jörg Franke Institute of Geography and Oeschger Centre for Climate Change Research originator doi:10.26050/WDCC/EKF400_v2.0 EKF400 version 2 is a monthly resolved paleo-reanalysis covering the period 1603 to 2003. Early instrumental temperature, surface pressure and precipitation (new in version 2) observations, temperature indices derived from historical documents and temperature and moisture sensitive tree-ring measurements (largely increased number in version 2) were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the global state of the atmosphere at 2x2 degree resolution for various variables and at all points in time. While version 1 was using time-dependent covariances from the 30-member ensemble, version 2 uses a blended covariance matrix. It consists of 50% time-dependent and 50% climatological covariances. This leads to a better covariance estimation, not only between various locations in space but in between various variables, too. Dr. Jörg Franke Institute of Geography and Oeschger Centre for Climate Change Research author Dr. Veronika Valler Institute of Geography and Oeschger Centre for Climate Change Research author Yuri Brugnara Institute of Geography and Oeschger Centre for Climate Change Research author Stefan Brönnimann Institute of Geography and Oeschger Centre for Climate Change Research author Dr. Jörg Franke Institute of Geography and Oeschger Centre for Climate Change Research pointOfContact data assimilation theme tree ring proxy data theme eng -180.0 180.0 -90.0 90.0 1602-01-15 2003-12-15 402 air_pressure_at_sea_level modelResult Pa air_temperature modelResult K eastward_wind modelResult m s-1 geopotential_height modelResult m lagrangian_tendency_of_air_pressure modelResult Pa s-1 latitude modelResult degrees_north level_temp modelResult m and Pa longitude modelResult degrees_east northward_wind modelResult m s-1 precipitation_amount modelResult kg m-2 pressure_level_gph modelResult Pa pressure_level_wind modelResult Pa time modelResult not filled NetCDF network Common Data Format distributor Mb 110330 http://doi.org/doi:10.26050/WDCC/EKF400_v2.0 series