Quantifying Structural Uncertainty in Paleoclimate Data Assimilation With an Application to the Last Millennium.

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. Plain Language Summary: Reconstructing past climate conditions is important to characterize climate variability and extremes, with implications for understanding future variability and change. An important source of information is data from proxy sources like tree rings, ice cores, ocean sediment cores, and other sources, but those data are not available everywhere on the globe. Extracting climate information from these data requires information about how climate properties covary in space and time, and that information is inevitably flawed because we lack a complete picture of how the climate behaves. This work seeks to address how much those flaws matter, how they propagate to reconstructions of past climate, and how we can reduce them. We use different physically plausible representations of climate physics from three different coupled climate models in a series of reconstruction experiments and suggest that uncertain physics typically accounts for 10% of reconstruction errors in the particular case of reconstructing surface air temperature over the Last Millennium, with regionally higher values (50% or larger). We can reduce these errors by improving climate models and by improving how we represent uncertain physics in climate reconstructions. Key Points: Perfect model experiments show how uncertain climate covariance relationships lead to uncertainties in paleoclimate reconstructionsThese uncertainties typically account for 10% of total error in reconstructed Last Millennium air temperature, with large regional variabilityImproving climate model physics and accounting for its uncertainty will improve reconstruction accuracy [ABSTRACT FROM AUTHOR]