TY - JOUR
T1 - Piecing together the past
T2 - Statistical insights into paleoclimatic reconstructions
AU - Tingley, Martin P.
AU - Craigmile, Peter F.
AU - Haran, Murali
AU - Li, Bo
AU - Mannshardt, Elizabeth
AU - Rajaratnam, Bala
N1 - Funding Information:
We thank SAMSI and the organizers of the 2010–2011 Program on Space-time modeling for Epidemiology, Climate Change, and Environmental Mapping for making this collaboration possible. The manuscript benefited from conversations with Bo Christiansen, John Haslett, Cindy Greenwood, Michael Evans, Matthew Schofield, and the participation of MPT, PFC, and EM-S in the 11th International Meeting for Statistical Climatology. We thank Noel Cressie, Julien Emile-Geay, Michael Mann, Douglas Nychka, Tapio Schneider, Eugene Wahl, and five referees for comments that improved both the content and presentation of this manuscript. MPT is supported by the National Science Foundation under award number CMG-0724828. PFC is supported by the National Science Foundation under award numbers DMS-0604963 and DMS-0906864. MH is partially supported by the US Geological Survey (USGS-CDI) and the National Science Foundation (NSF-HSD). BL is partially supported by the National Science Foundation under award number DMS-1007686. BR is partially supported by the National Science Foundation under awards numbers DMS-0906392, AGS-1003823, SU-WI-EVP10, and SUFSC08-SUFSC10-SMSCVISG0906.
PY - 2012/3/5
Y1 - 2012/3/5
N2 - Reconstructing a climate process in both space and time from incomplete instrumental and climate proxy time series is a problem with clear societal relevance that poses both scientific and statistical challenges. These challenges, along with the interdisciplinary nature of the reconstruction problem, point to the need for greater cooperation between the earth science and statistics communities - a sentiment echoed in recent parliamentary reports. As a step in this direction, it is prudent to formalize what is meant by the paleoclimate reconstruction problem using the language and tools of modern statistics. This article considers the challenge of inferring, with uncertainties, a climate process through space and time from overlapping instrumental and climate sensitive proxy time series that are assumed to be well dated - an assumption that is likely only reasonable for certain proxies over at most the last few millennia. Within a unifying, hierarchical space-time modeling framework for this problem, the modeling assumptions made by a number of published methods can be understood as special cases, and the distinction between modeling assumptions and analysis or inference choices becomes more transparent. The key aims of this article are to 1) establish a unifying modeling and notational framework for the paleoclimate reconstruction problem that is transparent to both the climate science and statistics communities; 2) describe how currently favored methods fit within this framework; 3) outline and distinguish between scientific and statistical challenges; 4) indicate how recent advances in the statistical modeling of large space-time data sets, as well as advances in statistical computation, can be brought to bear upon the problem; 5) offer, in broad strokes, some suggestions for model construction and how to perform the required statistical inference; and 6) identify issues that are important to both the climate science and applied statistics communities, and encourage greater collaboration between the two.
AB - Reconstructing a climate process in both space and time from incomplete instrumental and climate proxy time series is a problem with clear societal relevance that poses both scientific and statistical challenges. These challenges, along with the interdisciplinary nature of the reconstruction problem, point to the need for greater cooperation between the earth science and statistics communities - a sentiment echoed in recent parliamentary reports. As a step in this direction, it is prudent to formalize what is meant by the paleoclimate reconstruction problem using the language and tools of modern statistics. This article considers the challenge of inferring, with uncertainties, a climate process through space and time from overlapping instrumental and climate sensitive proxy time series that are assumed to be well dated - an assumption that is likely only reasonable for certain proxies over at most the last few millennia. Within a unifying, hierarchical space-time modeling framework for this problem, the modeling assumptions made by a number of published methods can be understood as special cases, and the distinction between modeling assumptions and analysis or inference choices becomes more transparent. The key aims of this article are to 1) establish a unifying modeling and notational framework for the paleoclimate reconstruction problem that is transparent to both the climate science and statistics communities; 2) describe how currently favored methods fit within this framework; 3) outline and distinguish between scientific and statistical challenges; 4) indicate how recent advances in the statistical modeling of large space-time data sets, as well as advances in statistical computation, can be brought to bear upon the problem; 5) offer, in broad strokes, some suggestions for model construction and how to perform the required statistical inference; and 6) identify issues that are important to both the climate science and applied statistics communities, and encourage greater collaboration between the two.
KW - Bayesian methods
KW - Hierarchical modeling
KW - Paleoclimate
KW - Space-time modeling
KW - Spatial modeling
UR - http://www.scopus.com/inward/record.url?scp=84862777051&partnerID=8YFLogxK
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U2 - 10.1016/j.quascirev.2012.01.012
DO - 10.1016/j.quascirev.2012.01.012
M3 - Review article
AN - SCOPUS:84862777051
SN - 0277-3791
VL - 35
SP - 1
EP - 22
JO - Quaternary Science Reviews
JF - Quaternary Science Reviews
ER -