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Quantifying Structural Uncertainty in Paleoclimate Data Assimilation With an Application to the Last Millennium
Author(s) -
Amrhein Daniel E.,
Hakim Gregory J.,
Parsons Luke A.
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl090485
Subject(s) - paleoclimatology , data assimilation , covariance , climatology , proxy (statistics) , uncertainty quantification , environmental science , climate change , propagation of uncertainty , econometrics , climate model , geology , meteorology , statistics , mathematics , geography , oceanography
Paleoclimate reconstruction relies on estimates of spatiotemporal relationships among climate quantities to interpolate between proxy data. This work quantifies how structural uncertainties in those relationships translate to uncertainties in reconstructions of past climate. We develop and apply a data assimilation uncertainty quantification approach to paleoclimate networks and observational uncertainties representative of data for the last millennium. We find that structural uncertainties arising from uncertain spatial covariance relationships typically contribute 10% of the total uncertainty in reconstructed temperature variability at small ( ∼ 200 km), continental, and hemispheric length scales, with larger errors (50% or larger) in regions where long‐range climate covariances are least certain. These structural uncertainties contribute far more to errors in uncertainty quantification, sometimes by a factor of 5 or higher. Accounting for and reducing uncertainties in climate model dynamics and resulting covariance relationships will improve paleoclimate reconstruction accuracy.