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

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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.






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Amrhein, DE, GJ Hakim and LA Parsons (2020). Quantifying Structural Uncertainty in Paleoclimate Data Assimilation With an Application to the Last Millennium. Geophysical Research Letters, 47(22). 10.1029/2020GL090485 Retrieved from https://hdl.handle.net/10161/26194.

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Luke Parsons


Luke Parsons is a climate researcher and lecturer. He teaches about climate change and climate impacts and studies climate dynamics, drought, and climate change + deforestation + emissions impacts on the environment, human health, well-being, and the economy. In addition to his work as a researcher, Luke is also a Wilderness First Responder and former NOLS instructor who enjoys backpacking, climbing, and taking panoramic landscape photographs.

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