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Climate models as a test bed for climate reconstruction methods: pseudoproxy experiments
Author(s) -
Smerdon Jason E.
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: climate change
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.678
H-Index - 75
eISSN - 1757-7799
pISSN - 1757-7780
DOI - 10.1002/wcc.149
Subject(s) - climate change , computer science , climate model , general circulation model , test (biology) , climatology , data science , machine learning , artificial intelligence , econometrics , data mining , geology , mathematics , paleontology , oceanography
Abstract Millennium‐length, forced transient simulations with fully coupled general circulation models have become important new tools for addressing uncertainties in global and hemispheric temperature reconstructions targeting the Common Era (the last two millennia). These model simulations are used as test beds on which to evaluate the performance of paleoclimate reconstruction methods using controlled and systematic investigations known as pseudoproxy experiments (PPEs). Such experiments are motivated by the fact that any given real‐world reconstruction is the product of multiple uncontrolled factors, making it difficult to isolate the impact of one factor in reconstruction assessments and comparisons. PPEs have established a common experimental framework that can be systematically altered and evaluated, and thus test reconstruction methods and their dependencies. Although the translation of PPE results into real‐world implications must be done cautiously, their experimental design attributes allow researchers to test reconstruction techniques beyond what was previously possible with real‐world data alone. This review summarizes the development of PPEs and their findings over the last decade. The state of the science and its implications for global and hemispheric temperature reconstructions is also reviewed, as well as near‐term design improvements that will expand the utility of PPEs. WIREs Clim Change 2012, 3:63–77. doi: 10.1002/wcc.149 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models