Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes
- URL: http://arxiv.org/abs/2211.08160v1
- Date: Tue, 15 Nov 2022 14:15:04 GMT
- Title: Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes
- Authors: Seth D. Axen, Alexandra Gessner, Christian Sommer, Nils Weitzel,
\'Alvaro Tejero-Cantero
- Abstract summary: We build on recent scale sparsetemporal GPs to reduce the computational burden.
We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
- Score: 61.31361524229248
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Paleoclimatology -- the study of past climate -- is relevant beyond climate
science itself, such as in archaeology and anthropology for understanding past
human dispersal. Information about the Earth's paleoclimate comes from
simulations of physical and biogeochemical processes and from proxy records
found in naturally occurring archives. Climate-field reconstructions (CFRs)
combine these data into a statistical spatial or spatiotemporal model. To date,
there exists no consensus spatiotemporal paleoclimate model that is continuous
in space and time, produces predictions with uncertainty, and can include data
from various sources. A Gaussian process (GP) model would have these desired
properties; however, GPs scale unfavorably with data of the magnitude typical
for building CFRs. We propose to build on recent advances in sparse
spatiotemporal GPs that reduce the computational burden by combining
variational methods based on inducing variables with the state-space
formulation of GPs. We successfully employ such a doubly sparse GP to construct
a probabilistic model of European paleoclimate from the Last Glacial Maximum
(LGM) to the mid-Holocene (MH) that synthesizes paleoclimate simulations and
fossilized pollen proxy data.
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