EGU2020-5553
https://doi.org/10.5194/egusphere-egu2020-5553
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Global reconstruction of surface temperature fields for past equilibrium climates

Julia Hargreaves and James Annan
Julia Hargreaves and James Annan
  • BlueSkiesResearch.org.uk, SETTLE, United Kingdom of Great Britain and Northern Ireland (jules@blueskiesresearch.org.uk)

Paleoclimate simulations are widely used as a test of the ability of climate models to simulate climate states that are substantially different to the present day, and quantitative reconstructions of these climate states is an essential component of model evaluation.  With there being no large network of instrumental observations from these periods, we must rely on inferences from a relatively modest number of unevenly distributed proxy records which are believed to be quantitatively indicative of the climate state.  In order to robustly establish climatic conditions over global scales, we require methods for smoothing and interpolating between these sparse and imperfect estimates.  In recent years, we have worked on this problem and created a global reconstruction of the Last Glacial Maximum [Annan and Hargreaves, 2013, Climate of the Past] using the data and models which were available at that time.  The method uses scaled patterns from the PMIP ensemble of structurally diverse climate simulations, combined with sparse sets of proxy data, to produce spatially coherent and complete  data  fields  for  surface  air  and  sea  temperatures  (potentially  including  the  seasonal cycle)  along  with  uncertainty  estimates  over  the  whole  field.   This  approach  is  more  robust than alternative methods, which either perform a purely statistical interpolation of the data or at best combine the data with a single climate model. Here, we aim to improve the method, update the inputs, and apply the same technique to both Last Glacial Maximum and mid Pliocene climate intervals. As well as generating spatially complete and coherent maps of climate variables, our approach also generates well-calibrated uncertainty estimates.

How to cite: Hargreaves, J. and Annan, J.: Global reconstruction of surface temperature fields for past equilibrium climates, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5553, https://doi.org/10.5194/egusphere-egu2020-5553, 2020

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Presentation version 1 – uploaded on 28 Apr 2020
  • CC1: Comment on EGU2020-5553, Chris Brierley, 06 May 2020

    I'll take on the "Name these EOFs" challenge...

    • EOF1 = a pattern-scaled cooling - I'd expect the PC loading to scale with global mean temperature change
    • EOF2 = a Laurentide Ice sheet impact on the Jet Stream
    • EOF3 = possibly a PMIP2 vs PMIP3 ice sheet reconstruction pattern
    • EOF4 = Atlantic sea ice extent
    • EOF5 & EOF6 = AMOC uncertainty/variability

    You didn't present how much variance each EOF explains - I guess upwards of 2/3rds in the first one, which is why your old reconstruction with PMIP2 performance suprised you. I also think that looking at the PC loadings for EOF3 might help you identify if this a PMIP2/PMIP3 thing.

    p.s. there are now four lgm PMIP4 runs, if you want fields for them as well.