A new multi-variable benchmark for Last Glacial Maximum climate simulations

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Description

Reconstruction of climate anomalies for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. This reconstruction is designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.

This dataset supersedes the version published at http://dx.doi.org/10.17864/1947.206, where the spatial and temporal correlations used with the PMIP data were based on a Gaussian function, instead of a Bessel function.

Resource Type:	Dataset
Creators:	Cleator, Sean ORCID: https://orcid.org/0000-0001-9602-1989, Harrison, Sandy ORCID: https://orcid.org/0000-0001-5687-1903, Nichols, Nancy, Prentice, Iain Colin ORCID: https://orcid.org/0000-0002-1296-6764 and Roulstone, Ian
Rights-holders:	Sean F. Cleator, University of Surrey, University of Reading, Imperial College London
Data Publisher:	University of Reading
Publication Year:	2019
Data last accessed:	14 November 2024
DOI:	https://doi.org/10.17864/1947.229
Metadata Record URL:	https://researchdata.reading.ac.uk/id/eprint/229
Organisational units:	Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Science > School of Mathematical, Computational and Physical Sciences > Department of Mathematics and Statistics
Participating Organisations:	University of Surrey, University of Reading, Imperial College London
Keywords:	Last Glacial Maximum, reconstruction, pollen, PMIP3, 3D-Var
Rights:	Creative Commons Attribution 4.0 International
Data Availability:	OPEN
Additional details Additional details Project Name: Glacial and Holocene Climates Reconstructed by Vegetation-Model Inversion Funders: Natural Environment Research Council (1859127) European Research Council (694481) Resource Language: English Geographic coverage: Global land surface with gaps Bounding Area: N. Lat. E. Long. S. Lat. W. Long. 90 180 -90 -180 Data processing and preparation activities: This dataset is made by combining site-based pollen reconstructions with aggregated outputs of PMIP3. The possible climates are varied and the most likely climate is found under a 3D-Variational data assimilation scheme. The uncertainty from this process is then used to screen out areas where the pollen reconstructions have had a minimal effect. By using a vegetation model, the effect of changing CO_2 concentrations on pollen reconstructions is also accounted for. The error of the resulting reconstruction is also calculated and given as 1 standard deviation from the variables. The individual fields are: grid cell centre latitude grid cell centre longitude moisture index mean annual precipitation (mm) mean annual temperature (degrees C) mean temperature of the coldest month (degrees C) mean temperature of the warmest month (degrees C) growing degree days above 5 degrees C (days degrees C) moisture index standard deviation mean annual precipitation standard deviation (mm) mean annual temperature standard deviation (degrees C) mean temperature of the coldest month standard deviation (degrees C) mean temperature of the warmest month standard deviation (degrees C) growing degree days above 5 degrees C standard deviation (days degrees C). Depositing User: Sean Cleator Date Deposited: 24 Sep 2019 14:42 Last Modified: 10 Jun 2024 10:47

Available Versions of this Item

• A new multi-variable benchmark for Last Glacial Maximum climate simulations. (deposited 28 Mar 2019 09:26)
  • A new multi-variable benchmark for Last Glacial Maximum climate simulations. (deposited 02 May 2019 13:12)
    • A new multi-variable benchmark for Last Glacial Maximum climate simulations. (deposited 24 Sep 2019 14:42) [Currently Displayed]
      • A new multi-variable benchmark for Last Glacial Maximum climate simulations. (deposited 20 Mar 2020 18:40)