Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-254
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
08 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Comparison of spatial downscaling methods of general circulation models to study climate variability during the Last Glacial Maximum
Guillaume Latombe1,2, Ariane Burke3, Mathieu Vrac4, Guillaume Levavasseur5, Christophe Dumas4, Masa Kageyama4, and Gilles Ramstein4 1Department of Mathematical Sciences, Stellenbosch University, Matieland 7602, South Africa
2School of Biological Sciences, Monash University, Melbourne 3800, Australia
3Département d'anthropologie, Université de Montréal, Montréal, QC, Canada
4Laboratoire des Sciences du Climat et de l'Environnement/Institut Pierre-Simon Laplace, Université Paris-Saclay, CE Saclay, l'Orme des Merisiers, Bât. 701, Gif-sur-Yvette, France
5Institut Pierre Simon Laplace (IPSL), Pôle de modélisation du climat, UPMC, Paris, France
Abstract. The extent to which climate conditions influenced the spatial distribution of hominin populations in the past is highly debated. General Circulation Models (GCMs) and archaeological data have been used to address this issue. Most GCMs are not currently capable of simulating past surface climate conditions with sufficiently detailed spatial resolution to distinguish areas of potential hominin habitat, however. In this paper we propose a Statistical Downscaling Methods (SDM) for increasing the resolution of climate model outputs in a computationally efficient way. Our method uses a generalized additive model (GAM), calibrated over present-day data, to statistically downscale temperature and precipitation from the outputs of a GCM simulating the climate of the Last Glacial Maximum (19–23 000 BP) over Western Europe. Once the SDM is calibrated, we first interpolate the coarse-scale GCM outputs to the final resolution and then use the GAM to compute surface air temperature and precipitation levels using these interpolated GCM outputs and fine resolution geographical variables such as topography and distance from an ocean. The GAM acts as a transfer function, capturing non-linear relationships between variables at different spatial scales. We tested three different techniques for the first interpolation of GCM output: bilinear, bicubic, and kriging. The results were evaluated by comparing downscaled temperature and precipitation at local sites with paleoclimate reconstructions based on paleoclimate archives (archaeozoological and palynological data). Our results show that the simulated, downscaled temperature and precipitation values are in good agreement with paleoclimate reconstructions at local sites confirming that our method for producing fine-grained paleoclimate simulations suitable for conducting paleo-anthropological research is sound. In addition, the bilinear and bicubic interpolation techniques were shown to distort either the temporal variability or the values of the response variables, while the kriging method offers the best compromise. Since climate variability is an aspect of their environment to which human populations may have responded in the past this is an important distinction.

Citation: Latombe, G., Burke, A., Vrac, M., Levavasseur, G., Dumas, C., Kageyama, M., and Ramstein, G.: Comparison of spatial downscaling methods of general circulation models to study climate variability during the Last Glacial Maximum, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-254, in review, 2017.
Guillaume Latombe et al.
Guillaume Latombe et al.

Data sets

Latombe et al. scripts and data
G. Latombe, A. Burke, M. Vrac, G. Levavasseur, C. Dumas, M. Kageyama, and G. Ramstein
https://doi.org/10.6084/m9.figshare.5487145

Model code and software

Latombe et al. scripts and data
G. Latombe, A. Burke, M. Vrac, G. Levavasseur, C. Dumas, M. Kageyama, and G. Ramstein
https://doi.org/10.6084/m9.figshare.5487145
Guillaume Latombe et al.

Viewed

Total article views: 149 (including HTML, PDF, and XML)

HTML PDF XML Total Supplement BibTeX EndNote
114 34 1 149 7 0 1

Views and downloads (calculated since 08 Nov 2017)

Cumulative views and downloads (calculated since 08 Nov 2017)

Viewed (geographical distribution)

Total article views: 149 (including HTML, PDF, and XML)

Thereof 149 with geography defined and 0 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 20 Nov 2017
Publications Copernicus
Download
Short summary
It is still unclear how climate conditions, and especially climate variability, influenced the spatial distribution of past human populations. Global climate models (GCM) cannot simulate climate at sufficiently fine scale for this purpose. We propose a statistical method to obtain fine-scale climate projections for 15 000 years ago from coarse-scale GCM outputs. Our method agrees with local reconstructions from fossil and pollen data, and generates sensible climate variability maps over Europe.
It is still unclear how climate conditions, and especially climate variability, influenced the...
Share