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Journal ArticleDOI

Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: Implications for multi-decadal variability

15 Oct 2017-Earth and Planetary Science Letters (Elsevier)-Vol. 476, pp 34-46

AbstractThe spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence of significant low-frequency (multi-decadal to centennial scale) variability in the climate system. Since this low-frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent studies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns surrounding the ability of GCMs to predict long-term, high-magnitude variability under greenhouse forcing ( Laepple and Huybers, 2014a , 2014b ). However, efforts to ground climate model simulations directly in paleoclimate observations are impeded by fundamental differences between models and the proxy data: proxy systems often record a multivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper we bridge this gap via a forward proxy modeling approach, coupled to an isotope-enabled GCM. This allows us to disentangle the various contributions to signals embedded in ice cores, speleothem calcite, coral aragonite, tree-ring width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward-modeled “pseudoproxies” exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We apply our method to representative case studies, and broaden these insights with an analysis of the PAGES2k database ( PAGES2K Consortium, 2013 ). We find that current proxy system models (PSMs) can help resolve model-data discrepancies on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of incomplete physics and/or forcings.

Topics: Climate model (58%)

Summary (2 min read)

2.1. GCM & PSM-Generated Pseudoproxies

  • Each proxy type employs its own unique PSM.
  • The complicated nature of proxy data (e.g. chronological uncertainties and impacts on phasing) precludes point-to-point comparisons of time series, and thus there is a strong case for comparing simulated proxy to the observations in the frequency domain.

3. Case Studies

  • Various approaches including downscaling or bias correction can help to minimize such problems, or paleoclimate data can be aggregated to match GCM grid cell size.
  • For each proxy type, the authors attempt to answer whether the mismatch arises from a lack of low-frequency variability simulated by the GCM SPEEDY-IER, or from a data-model comparison strategy problem.
  • For completeness, the authors report absolute variance for all case studies and the PAGES2k data in SI Section S3.

3.1. Spectral Fingerprinting of Proxy Systems

  • As a first pass, the authors forced each PSM with white noise climate inputs to assess the impact of proxy system processes alone on the shape of the spectra.
  • For ice cores, speleothems, and tree ring widths, the white noise +.
  • For all proxy types, the spectra revert to the shape of the white input climate signal on decadal and longer timescales.
  • Under different PSM formulations these spectra could change significantly, and this non-unicity proves a large source of uncertainty.

3.2.1. Corals

  • Shows that the corals are generally strong SST proxies (or, possibly, that the GCM completely underplays salinity variability).
  • Testing the effects of parametric uncertainty for the corals provides an example of how PSMs can be used to inform data-model comparison.
  • More interestingly, discrepancies exist between the simulated and observed power spectrum on decadal to centennial timescales.
  • Further, if the authors instead evaluate both in terms of absolute variance, the Palmyra record exhibits larger σ at the decadal band as compared to the PSM-simulated data (SI Section S3).
  • While the PSM-generated pseudo-coral captures interannual SST variability similar to observations, the PSM seems not to account for the larger variance in the observations on longer timescales, and this discrepancy remains even when uncertainties in the coral's sensitivity to salinity and δ 18 O S W are taken into account.

3.2.2. Ice Cores

  • On decadal to centennial timescales, differences in the observed vs. simulated spectral slopes are more modest than for interannual, but three of the records tend to increasingly diverge at low frequencies (see Fig. 3 ).
  • 18 O PRECIP vs. the observed ice core values exhibit some agreement on multi-decadal frequencies, but the model does not simulate comparable variance in the observations on longer (>centennial) timescales (see Fig. 3 ).
  • This suggests that neither the GCM, the water isotope physics in the GCM, nor the PSM can account for observed low frequency variability.

3.2.3. Speleothems

  • The speleothem PSM highlights the fact that on interannual to decadal timescales, the authors can essentially obtain a β value in agreement with observations simply as a function of the karst parameters.
  • On longer timescales, the simulated spectra tend to flatten while the observed spectra continue to show increased lowfrequency variance, potentially indicative of climate processes resulting in a spectrum similar to what the authors would expect from a power law system (see Fig. 5 ).

3.2.5. Tree Ring Width

  • Aggressive detrending methods tend to remove low frequency variability (demonstrated by Table 2 ).
  • Table 2 also illustrates the RCS method is most conservative in maintaining low-frequency TRW variability.
  • In general, using the same detrending method for both proxy and pseudoproxy is essential.

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Content maybe subject to copyright    Report

Improved spectral comparisons of paleoclimate
models and observations via proxy system
modeling: Implications for multi-decadal variability
Item Type Article
Authors Dee, S. G.; Parsons, L. A.; Loope, G. R.; Overpeck, J. T.; Ault, T. R.;
Emile-Geay, J.
Citation Dee, S. G., Parsons, L. A., Loope, G. R., Overpeck, J. T., Ault,
T. R., & Emile-Geay, J. (2017). Improved spectral comparisons
of paleoclimate models and observations via proxy system
modeling: Implications for multi-decadal variability. Earth and
Planetary Science Letters, 476, 34-46.
DOI 10.1016/j.epsl.2017.07.036
Publisher ELSEVIER SCIENCE BV
Journal Earth and Planetary Science Letters
Rights © 2017 Elsevier B.V. All rights reserved.
Download date 10/08/2022 08:27:40
Item License http://rightsstatements.org/vocab/InC/1.0/
Version Final accepted manuscript
Link to Item http://hdl.handle.net/10150/632084

Improved spectral comparisons of paleoclimate models and observations1
via proxy system modeling: implications for multi-decadal variability2
S.G. Dee
a,
, L. Parsons
b
, G. Loope
b
, J. T. Overpeck
b
, T.R. Ault
c
, J. Emile-Geay
d
3
a
Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI 029124
b
Department of Geosciences, University of Arizona, Tucson, AZ 85721 USA5
c
Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA6
d
Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA7
Abstract8
The spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence
of significant low-frequency (multi-decadal to centennial) variability in the climate system. Since this low-
frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential
to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent stud-
ies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns
surrounding the ability of GCMs to predict long term, high-magnitude variability under greenhouse forc-
ing (??). However, eorts to ground climate model simulations directly in paleoclimate observations are
impeded by fundamental dierences between models and the proxy data: proxy systems often record a mul-
tivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper
we bridge this gap via a forward modeling approach, coupled to an isotope-enabled GCM. This allows us to
disentangle the various contributions to signals embedded in ice cores, speleothem calcite, corals, tree-ring
width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward modeled “pseu-
doproxies” exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the
spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We
apply our method to representative case studies, and parlay these insights into an analysis of the PAGES2k
database (?). We find that current proxy system models (PSMs) can help resolve model-data discrepancies
on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to
centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the pa-
leoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of
Corresponding author
Email addresses: sylvia 11 dee@brown.edu (S.G. Dee), lukeaparsons@email.arizona.edu (L. Parsons), (G. Loope),
jto@email.arizona.edu (J. T. Overpeck), toby.ault@cornell.edu (T.R. Ault), julieneg@usc.edu (J. Emile-Geay)
Preprint submitted to Elsevier April 18, 2019

incomplete physics and/or forcings.
Keywords: climate variability, general circulation models, data-model comparison, paleoclimatology9
1. Introduction10
Our understanding of the complex dynamics of climate response to anthropogenic forcing rests jointly11
upon observations over the instrumental period, general circulation models (GCMs), and paleoclimate data.12
GCMs provide a basis for exploring the mechanisms driving forced and stochastic climate variability; how-13
ever, improved predictions of decadal- to centennial-scale hydroclimatic variability from GCMs may de-14
pend crucially on constraints from high-resolution paleoclimate observations (e.g. ??). Such data provide15
much-needed statistics for climate variability and augment the relatively short instrumental record. Thus,16
combining data from both models and high-resolution paleoclimate records yields meaningful advances for17
understanding future climate.18
Constraining climate models with paleoclimate data requires a robust method for comparing the two. Re-19
cently, a number of studies have compared GCM simulations and paleoclimate data in the frequency domain,20
applying spectral analysis to both the simulated and observed climate record. For temperature, precipitation,21
or any other key indicator in a paleoclimate archive, comparing the power spectral densities (PSDs) across22
models and data allows one to assess the dominant modes of variability in both signals (???). Recently, ??23
showed that commonly employed proxies for Holocene sea surface temperature (SST) exhibit a spectrum24
of SST variability inconsistent with GCM simulations on millennial timescales. Similarly, ? found that25
last-millennium terrestrial records from western North American exhibit larger low-frequency variability26
(and larger spectral slopes) when compared to the suite of CMIP5 Last-Millennium GCM simulations (??).27
While the absolute variability simulated in climate models is dierent from the shape of the power spec-28
trum (which measures variability as a function of timescale), the two are closely related (we evaluate both29
via Supplementary Information, SI hereafter); the spectrum observed in these paleoclimate records implies30
scaling behavior originating from the climate system, and high variability on longer timescales. Scaling31
behavior can also imply longer climate-system memory of extreme events, such as megadrought (??). Thus,32
the mismatch in the shape of the spectrum simulated by GCMs vs. that observed in paleoclimate data has33
been invoked as deficiencies in the ability of GCMs to simulate climate with a level of realism required34
2

for predicting decadal to centennial variability (??). Such findings harbor important implications about risk35
prediction using climate models (e.g. future drought in the southwest U.S.(?)).36
The direct comparison of climate model output with paleoclimate observations involves three main chal-37
lenges (e.g. ?): (1) Internal variability in models is not directly comparable to paleoclimate data in time;38
(2) biases in climate models limit their ability to correctly simulate extremes in hydroclimate; (3) proxy39
archives naturally filter and distort the original climate signal, confounding direct comparisons of paleocli-40
mate data to climate model variables. To address the first two of these issues, comparing PSDs removes41
model biases while comparing time-scale dependent variances, and ignores phase relationships (which are42
not expected not match because of natural climate variability, inter alia), allowing a more robust analysis of43
the partitioning of variance across dierent timescales in models vs. data (?).44
In this study, we take additional measures to address the third challenge, which relates to the filtering of45
the initial climate signal by proxy systems. A conversion step is needed to translate between model output46
and the proxy signal. Accomplishing a major part of this conversion, recent advances in climate modeling47
have allowed for the explicit incorporation of stable water isotope tracers in both the atmosphere and the48
ocean (see Table S7, SI). For water isotope-based proxy systems, stable water isotopes translate between49
the dynamical climate model variables (e.g. temperature and precipitation) and the geochemical signal that50
the proxy data encode (e.g. δ
18
O of precipitation). Adding water isotope physics to GCMs provides crucial51
insight, helping to determine the drivers of isotopic variations observed in proxy data and associated climate52
patterns (?). Embedded water-isotope physics bring us closer to a direct comparison between models and53
data, but do not account for physical processes by which proxy systems alter and subsequently record the54
original climate signal. In an eort to avoid assumptions inherent to inverse approaches (e.g. inverse-method55
or calibration-based reconstructions in paleoclimate), we turn to proxy system modeling (for a review, see56
??), and employ a new approach using both water isotope physics and proxy system models (PSMs) as57
tools for simulating each individual process that alters the original climate signal (be it biological, physical,58
or geochemical). Dynamical and isotope variables are translated to proxy units for a direct comparison59
between GCM output and observations (a forward approach).60
Our study builds upon the analysis of ? and ??? by employing this forward approach for data-model61
comparison in the frequency domain. In general, there are two methods that allow for a meaningful com-62
3

parison of proxy and model spectra. One is the inverse-method correction of the proxy spectra accounting63
for the distortion applied by the recording processes (e.g. ?, which essentially applies an inverted forward64
model of the proxy), and one is the forward modeling employed in this manuscript, which in many cases65
eorts increased flexibility. In this study, we use forward modeling to disentangle the multivariate influences66
on proxy data using state-of-the-art PSMs for ice cores, corals, tree-ring cellulose, speleothem calcite (?)67
and tree-ring width (?). Within this novel framework, we address the following questions: (1) are there68
proxy system processes that alter the spectrum of simulated (hydro)climatic variability, and are the impacts69
of these processes distinguishable from climate in spectral space? (2) accounting for these processes, do70
GCM+PSM-driven pseudoproxies exhibit spectral characteristics comparable to proxy observations?71
Section 2 outlines our experimental design, and Section 3 gives results showing case studies for the72
piece-wise transformation of the climate signal down to proxy units. We extend this analysis to a global73
scale using the PAGES2k Phase 1 Network in Section 4. Finally, we discuss the limitations and caveats of74
our approach, and suggestions for future research, in Section 5.75
2. Methods76
2.1. GCM & PSM-Generated Pseudoproxies77
To provide climate model estimates of hydroclimate variability over the last millennium, as well as78
climate fields for the PSM-generated network, we use the water isotope enabled GCM SPEEDY-IER (?) (see79
SI Section S8 for details). We forced a transient simulation of SPEEDY-IER with sea surface temperatures80
from the last millennium simulation (?) of the CCSM4 coupled model (?), spanning 850-2005 (1000-200581
considered for this study). We generate synthetic proxy time series using ‘proxy system models’ (PSMs82
??). PSMs convert the simulated climate (e.g. temperature, precipitation) into a proxy time series. A given83
PSM includes three sub-models, each of which mimics a separate modification of the original input signal84
as it would occur in nature: (1) a sensor model, which describes any physical, geochemical or biological85
processes altering the climate signal; (2) an archive model, which accounts for any processes that aect86
the emplacement of the signal in the proxy medium, and (3) an observation model, which accounts for87
dating uncertainties and analytical errors in the final measurement made on the paleoclimate data (?). The88
4

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  • ...…interdecadal GMST variability (Brown et al., 2015, 2017; Parsons & Hakim, 2019), with both instrumental (Laepple & Huybers, 2014a) and paleoclimate (Dee et al., 2017; Laepple & Huybers, 2014b; Parsons et al., 2017) evidence suggesting that climate models may underestimate local, low‐frequency…...

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Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: implications for multi-decadal variability" ?

In this paper the authors bridge this gap via a forward modeling approach, coupled to an isotope-enabled GCM. The paper addresses the following questions: ( 1 ) do forward modeled “ pseudoproxies ” exhibit variability comparable to proxy data ? The authors apply their method to representative case studies, and parlay these insights into an analysis of the PAGES2k database ( ? ). The authors conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of ∗Corresponding author Email addresses: sylvia 11 dee @ brown. The authors find that current proxy system models ( PSMs ) can help resolve model-data discrepancies on interannual to decadal timescales, but can not account for the mismatch in variance on multi-decadal to centennial timescales.