Identification

Title

Piecing together the past: Statistical insights into paleoclimatic reconstructions

Abstract

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.

Resource type

document

Resource locator

Unique resource identifier

code

http://n2t.net/ark:/85065/d7j103n6

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2012-03-05T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

NOTICE: This is the author's version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact