Browsing by Author "McKay, Nicholas P."

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    A global Data Assimilation of Moisture Patterns from 21 000–0 BP (DAMP-21ka) using lake level proxy records
    (Copernicus Publications, 2024) Hancock, Christopher L.; Erb, Michael P.; McKay, Nicholas P.; Dee, Sylvia G.; Ivanovic, Ruza F.
    Global hydroclimate significantly differed from modern climate during the mid-Holocene (6 ka) and Last Glacial Maximum (21 ka). Consequently, both periods have been described as either a partial or reverse analogue for current climate change. To reconstruct past hydroclimate, an offline paleoclimate data assimilation methodology is applied to a dataset of 216 lake status records which provide relative estimates of water level change. The proxy observations are integrated with the climate dynamics of two transient simulations (TraCE-21ka and HadCM3) using a multivariate proxy system model (PSM) which estimates relative lake status from available climate simulation variables. The resulting DAMP-21ka (Data Assimilation of Moisture Patterns 21 000–0 BP) reanalysis reconstructs annual lake status and precipitation values at 500-year resolution and represents the first application of the methodology to global hydroclimate on timescales spanning the Holocene and longer. Validation using Pearson's correlation coefficients indicates that the reconstruction (0.24) is more skillful, on average, than model simulations (0.09), particularly in portions of North America and east Africa, where data density is high and proxy–model disagreement is prominent during the Holocene. Results of the PSM and assimilation are used to evaluate climatic controls on lake status, spatiotemporal patterns of moisture variability, and proxy–model disagreement. During the mid-Holocene, wetter conditions are reconstructed for northern and eastern Africa, Asia, and southern Australia, but in contrast to the model prior, negative anomalies are observed in North America, resulting in drier-than-modern conditions throughout the Northern Hemisphere midlatitudes. Proxy–model disagreement in western North America may reflect a bias in model simulations to stronger sea level pressure gradients in the North Pacific during the mid-Holocene. The data assimilation framework is able to reconcile these differences by integrating the constraints of proxy observations with the dynamics of the model prior to produce a more robust estimation of hydroclimate variability during the past 21 000 years.
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    Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation
    (Copernicus Publications, 2022) Erb, Michael P.; McKay, Nicholas P.; Steiger, Nathan; Dee, Sylvia; Hancock, Chris; Ivanovic, Ruza F.; Gregoire, Lauren J.; Valdes, Paul
    Paleoclimatic records provide valuable information about Holocene climate, revealing aspects of climate variability for a multitude of sites around the world. However, such data also possess limitations. Proxy networks are spatially uneven, seasonally biased, uncertain in time, and present a variety of challenges when used in concert to illustrate the complex variations of past climate. Paleoclimatic data assimilation provides one approach to reconstructing past climate that can account for the diverse nature of proxy records while maintaining the physics-based covariance structures simulated by climate models. Here, we use paleoclimate data assimilation to create a spatially complete reconstruction of temperature over the past 12 000 years using proxy data from the Temperature 12k database and output from transient climate model simulations. Following the last glacial period, the reconstruction shows Holocene temperatures warming to a peak near 6400 years ago followed by a slow cooling toward the present day, supporting a mid-Holocene which is at least as warm as the preindustrial. Sensitivity tests show that if proxies have an overlooked summer bias, some apparent mid-Holocene warmth could actually represent summer trends rather than annual mean trends. Regardless, the potential effects of proxy seasonal biases are insufficient to align the reconstructed global mean temperature with the warming trends seen in transient model simulations.