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        <gco:CharacterString>STUDY NOTES: The data provided are for the LMR version 2 reconstructions in netcdf format. Data from two versions are provided, both described in Tardif et al. (2019) Common aspects are: CCSM4 Last Millennium simulation as the source of prior, with states from 100 randomly drawn years as the prior ensemble in each Monte-Carlo realization Regression-based Proxy System Models, formulated using the seasonal responses of individual records, with bivariate models w.r.t. temperature and precipitation for tree-ring width proxies, and univariate w.r.t. temperature for all other proxy archives. Covariance localization applied with a cut-off length scale of 25000 km Reconstructions generated at an annual resolution, on a 2ox2o grid. Differences are related to the set of assimilated proxies: LMR v2.1: Proxies from the PAGES2k (2017) data set*. Corresponds to results presented in Tardif et al. (2019), section 3, figures 2-5. LMR v2.0: Proxies from PAGES2k (2017) + Anderson et al. (2019) [see figure 8 from Tardif et al. (2019)]. Reconstruction results are discussed in Tardif et al. (2019), section 4.3, and shown in figures 9-10, (e) and (f), and in figure 11. * with the exception of the Palmyra coral record: we used the more recent version from Emile-Geay et al (2013), instead of the version from Cobb et al. (2003) as in PAGES2k (2017); and the Kiritimati coral record: the longer record from the Anderson et al (2019) dataset, taken from Cobb et al (2013), replaces a slightly shorter record included in PAGES2k (2017). Data Notes File and variable naming conventions follow as closely as possible those for the NOAA 20th Century Reanalysis. Gridded fields have the format: (time, MCrun, lat, lon) where time is the year, lat is the latitude index, lon is the longitude index, and MCrun indicate the Monte Carlo iteration index. There are in fact 20 LMR reconstructions contained in these arrays. They differ in the climate model ensemble prior to assimilation (random draws from the CCSM4 Last Millennium simulation) and the proxies that were drawn randomly for the reconstruction (75% of all available proxies). You probably want to start with the "grand mean," which is an average over the MCrun dimension. NOTE: the variance on the MCrun index does NOT represent the uncertainty (error) in the reconstructions! It only represents the piece from the prior and proxies. The major portion of the error is in the full ensemble, which is summarized by the spread field for each iteration. All fields are anomalies from the 1951--1980 time-mean. netCDF File Descriptions ------------------------ Global and hemispheric-mean two-meter air temperature [format: (time, MCrun, members); this has all ensemble members]: global-mean temperature v2.0: gmt_MCruns_ensemble_full_LMRv2.0.nc; v2.1: gmt_MCruns_ensemble_full_LMRv2.1.nc NH-mean temperature v2.0: nhmt_MCruns_ensemble_full_LMRv2.0.nc ; v2.1: nhmt_MCruns_ensemble_full_LMRv2.1.nc SH-mean temperature: v2.0: shmt_MCruns_ensemble_full_LMRv2.0.nc ; v2.1: shmt_MCruns_ensemble_full_LMRv2.1.nc Two-meter air temperature: full grid ensemble mean v2.0: air_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: air_MCruns_ensemble_mean_LMRv2.1.nc full grid ensemble spread v2.0: air_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: air_MCruns_ensemble_spread_LMRv2.1.nc Sea-surface temperature: full grid ensemble mean v2.0: sst_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: sst_MCruns_ensemble_mean_LMRv2.0.nc full grid ensemble spread v2.0: sst_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: sst_MCruns_ensemble_spread_LMRv2.0.nc 500 hPa geopotential height: full grid ensemble mean v2.0: hgt500_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: hgt500_MCruns_ensemble_mean_LMRv2.0.nc full grid ensemble spread v2.0: hgt500_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: hgt500_MCruns_ensemble_spread_LMRv2.0.nc Mean-sea-level pressure: full grid ensemble mean v2.0: prmsl_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: prmsl_MCruns_ensemble_mean_LMRv2.0.nc full grid ensemble spread v2.0: prmsl_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: prmsl_MCruns_ensemble_spread_LMRv2.0.nc Precipitation: full grid ensemble mean v2.0: prate_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: prate_MCruns_ensemble_mean_LMRv2.0.nc full grid ensemble spread v2.0: prate_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: prate_MCruns_ensemble_spread_LMRv2.0.nc Precipitable water: full grid ensemble mean v2.0: pr_wtr_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: pr_wtr_MCruns_ensemble_mean_LMRv2.0.nc full grid ensemble spread v2.0: pr_wtr_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: pr_wtr_MCruns_ensemble_spread_LMRv2.0.nc Palmer drought severity index (PDSI): full grid ensemble mean v2.0: pdsi_MCruns_ensemble_mean_LMRv2.0.nc ; v2.1: pdsi_MCruns_ensemble_mean_LMRv2.1.nc full grid ensemble spread v2.0: pdsi_MCruns_ensemble_spread_LMRv2.0.nc ; v2.1: pdsi_MCruns_ensemble_spread_LMRv2.1.nc Climate indices (Nino3.4, PDO, AMO etc.) [format: (time, MCrun, members); this has all ensemble members]: climate indices v2.0: posterior_climate_indices_MCruns_ensemble_full_LMRv2.0.nc ; v2.1: posterior_climate_indices_MCruns_ensemble_full_LMRv2.1.nc 
ABSTRACT SUPPLIED BY ORIGINATOR: The Last Millennium Reanalysis (LMR) utilizes an ensemble methodology to assimilate paleoclimate data for the production of annually resolved climate field reconstructions of the Common Era. Two key elements are the focus of this work: the set of assimilated proxy records and the forward models that map climate variables to proxy measurements. Results based on an updated proxy database and seasonal regression-based forward models are compared to the LMR prototype, which was based on a smaller set of proxy records and simpler proxy models formulated as univariate linear regressions against annual temperature. Validation against various instrumental-era gridded analyses shows that the new reconstructions of surface air temperature and 500 hPa geopotential height are significantly improved (from 10% to more than 100%), while improvements in reconstruction of the Palmer Drought Severity Index are more modest. Additional experiments designed to isolate the sources of improvement reveal the importance of the updated proxy records, including coral records for improving tropical reconstructions, and tree-ring density records for temperature reconstructions, particularly in high northern latitudes. Proxy forward models that account for seasonal responses, and dependence on both temperature and moisture for tree-ring width, also contribute to improvements in reconstructed thermodynamic and hydroclimate variables in midlatitudes. The variability of temperature at multidecadal to centennial scales is also shown to be sensitive to the set of assimilated proxies, especially to the inclusion of primarily moisture-sensitive tree-ring-width records.</gco:CharacterString>
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