资源环境科技发展态势分析平台

Paleoclimate reconstruction relies on estimates of spatiotemporal relationships among climate variables to interpolate between proxy data. This work quantifies how structural uncertainties in those relationships translate to uncertainties in reconstructed values. 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 paleoclimate reconstructions of 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 less 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.