Data-assimilation techniques for paleoclimate data

AbstractScientific understanding of low-frequency tropical Pacific variability, especially responses to perturbations in radiative forcing, suffers from short observational records, sparse proxy networks, and bias in model simulations. Here, we combine the strengths of proxies and models through coral-based paleoclimate data assimilation. We combine coral archives (δ18O, Sr/Ca) with the dynamics, spatial teleconnections, and intervariable relationships of the CMIP5/PMIP3 Past1000 experiments using the Last Millennium Reanalysis data assimilation framework. This analysis creates skillful reconstructions of tropical Pacific temperatures over the observational era. However, during the period of intense volcanism in the early 19th century, southwestern Pacific corals produce El Niño Southern Oscillation (ENSO) reconstructions that are of opposite sign from those from eastern Pacific corals and tree ring records. We systematically evaluate the source of this discrepancy using 1) single-proxy experiments, 2) varied proxy system models (PSMs), and 3) diverse covariance patterns from the Past1000 simulations. We find that individual proxy records and coral PSMs do not significantly contribute to the discrepancy. However, following major eruptions, the southwestern Pacific corals locally record more persistent cold anomalies than found in the Past1000 experiments and canonical ENSO teleconnections to the southwest Pacific strongly control the reconstruction response. Furthermore, using covariance patterns independent of ENSO yield reconstructions consistent with coral archives across the Pacific. These results show that model bias can strongly affect how proxy information is processed in paleoclimate data assimilation. As we illustrate here, model bias influences the magnitude and persistence of the response of the tropical Pacific to volcanic eruptions.

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

Geophysical Research Letters ◽

10.1029/2020gl090485 ◽

2020 ◽

Vol 47 (22) ◽

Author(s):

Daniel E. Amrhein ◽

Gregory J. Hakim ◽

Luke A. Parsons

Keyword(s):

Data Assimilation ◽

Last Millennium ◽

Structural Uncertainty ◽

Paleoclimate Data

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Supplementary material to "Assimilating monthly precipitation data in a paleoclimate data assimilation framework"

10.5194/cp-2019-137-supplement ◽

2019 ◽

Author(s):

Veronika Valler ◽

Yuri Brugnara ◽

Jörg Franke ◽

Stefan Brönnimann

Keyword(s):

Data Assimilation ◽

Monthly Precipitation ◽

Precipitation Data ◽

Supplementary Material ◽

Monthly Precipitation Data ◽

Paleoclimate Data

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Greenland temperature and precipitation over the last 20 000 years using data assimilation

Climate of the Past ◽

10.5194/cp-16-1325-2020 ◽

2020 ◽

Vol 16 (4) ◽

pp. 1325-1346

Author(s):

Jessica A. Badgeley ◽

Eric J. Steig ◽

Gregory J. Hakim ◽

Tyler J. Fudge

Keyword(s):

Data Assimilation ◽

Spatial Patterns ◽

Climate Model ◽

Ice Core ◽

Ice Sheet ◽

Proxy Data ◽

Model Simulations ◽

Temperature And Precipitation ◽

Climate Model Simulations ◽

Paleoclimate Data

Abstract. Reconstructions of past temperature and precipitation are fundamental to modeling the Greenland Ice Sheet and assessing its sensitivity to climate. Paleoclimate information is sourced from proxy records and climate-model simulations; however, the former are spatially incomplete while the latter are sensitive to model dynamics and boundary conditions. Efforts to combine these sources of information to reconstruct spatial patterns of Greenland climate over glacial–interglacial cycles have been limited by assumptions of fixed spatial patterns and a restricted use of proxy data. We avoid these limitations by using paleoclimate data assimilation to create independent reconstructions of mean-annual temperature and precipitation for the last 20 000 years. Our method uses oxygen isotope ratios of ice and accumulation rates from long ice-core records and extends this information to all locations across Greenland using spatial relationships derived from a transient climate-model simulation. Standard evaluation metrics for this method show that our results capture climate at locations without ice-core records. Our results differ from previous work in the reconstructed spatial pattern of temperature change during abrupt climate transitions; this indicates a need for additional proxy data and additional transient climate-model simulations. We investigate the relationship between precipitation and temperature, finding that it is frequency dependent and spatially variable, suggesting that thermodynamic scaling methods commonly used in ice-sheet modeling are overly simplistic. Our results demonstrate that paleoclimate data assimilation is a useful tool for reconstructing the spatial and temporal patterns of past climate on timescales relevant to ice sheets.

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