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

How to cite this Dataset

Cleator, Sean, Harrison, Sandy, Nichols, Nancy, Prentice, Iain Colin and Roulstone, Ian (2019): A new multi-variable benchmark for Last Glacial Maximum climate simulations. University of Reading. Dataset. https://doi.org/10.17864/1947.206

There is a more recent version of this item available.

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/10.17864/1947.197, where the method of accounting for CO2 change was being applied incorrectly.

Resource Type:

Dataset

Creators:

Cleator, Sean

, Harrison, Sandy

, Nichols, Nancy, Prentice, Iain Colin 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:

5 November 2024

DOI:

https://doi.org/10.17864/1947.206

Metadata Record URL:

https://researchdata.reading.ac.uk/id/eprint/206

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

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:

02 May 2019 13:12

Last Modified:

10 Jun 2024 10:47