Climate field reconstruction uncertainty arising from multivariate and nonlinear properties of predictors

Evans, M. N.; Smerdon, Jason E.; Kaplan, Aleksey; Tolwinski-Ward, S. E.; González-Rouco, J. Fidel

doi:10.1002/2014gl062063

Cited by 39 publications

(62 citation statements)

References 55 publications

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“…temperature and soil moisture controls on tree-ring width (e.g. Anchukaitis et al, 2006;Vaganov et al, 2006;Tolwinski-Ward et al, 2011;Evans et al, 2014) or temperature and seawater composition controls on the oxygen isotopic composition of biocarbonates (e.g. Thompson et al, 2011;Russon et al, 2013;Dee et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies

Emile‐Geay

Tingley

2016

Clim. Past

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Abstract. Inferring climate from palaeodata frequently assumes a direct, linear relationship between the two, which is seldom met in practice. Here we simulate an idealized proxy characterized by a nonlinear, thresholded relationship with surface temperature, and we demonstrate the pitfalls of ignoring nonlinearities in the proxy-climate relationship. We explore three approaches to using this idealized proxy to infer past climate: (i) methods commonly used in the palaeoclimate literature, without consideration of nonlinearities; (ii) the same methods, after empirically transforming the data to normality to account for nonlinearities; and (iii) using a Bayesian model to invert the mechanistic relationship between the climate and the proxy. We find that neglecting nonlinearity often exaggerates changes in climate variability between different time intervals and leads to reconstructions with poorly quantified uncertainties. In contrast, explicit recognition of the nonlinear relationship, using either a mechanistic model or an empirical transform, yields significantly better estimates of past climate variations, with more accurate uncertainty quantification. We apply these insights to two palaeoclimate settings. Accounting for nonlinearities in the classical sedimentary record from Laguna Pallcacocha leads to quantitative departures from the results of the original study, and it markedly affects the detection of variance changes over time. A comparison with the Lake Challa record, also a nonlinear proxy for El Niño-Southern Oscillation, illustrates how inter-proxy comparisons may be altered when accounting for nonlinearity. The results hold implications for how univariate, nonlinear recorders of normally distributed climate variables are interpreted, compared to other proxy records, and incorporated into multiproxy reconstructions.

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Section: Discussionmentioning

confidence: 99%

Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies

Emile‐Geay

Tingley

2016

Clim. Past

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“…PSMs translate relevant dynamical and isotope variables to modeled proxy space for a direct comparison between GCM output and observations, thereby alleviating the need for a calibration. Thus far, only Evans et al [2014] have used PSM-generated proxies in pseudoproxy reconstructions; using a tree ring width model only, the authors found that the PSM-based reconstructions differed substantially from a standard ''temperature plus noise'' pseudoproxy model. Our study builds on this work by including PSMs for oxygen isotopes in corals and ice cores in addition to tree ring width.…”

Section: Introductionmentioning

confidence: 99%

“…Our study builds on this work by including PSMs for oxygen isotopes in corals and ice cores in addition to tree ring width. Importantly, this study uses PSMs for both the production of the pseudoproxies and their estimation in the DA reconstruction process; this differs from Evans et al [2014] where a PSM for tree ring width was only used for the production of the pseudoproxies. Through a series of experiments reconstructing both surface temperature and geopotential height at 500 hPa, the PSM-pseudoproxy framework serves as a test bed for investigating assumptions concerning our understanding of proxy-temperature relationships, and how errors in these assumptions trickle down to the reconstructed fields.…”

Section: Introductionmentioning

confidence: 99%

On the utility of proxy system models for estimating climate states over the common era

Dee

Steiger

Emile‐Geay

et al. 2016

J Adv Model Earth Syst

138

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Paleoclimate data assimilation has recently emerged as a promising technique to estimate past climate states. Here we test two of the underlying assumptions of paleoclimate data assimilation as applied so far: (1) climate proxies can be modeled as linear, univariate recorders of temperature and (2) structural errors in GCMs can be neglected. To investigate these two points and related uncertainties, we perform a series of synthetic, paleoclimate data assimilation‐based reconstructions where “pseudo” proxies are generated with physically based proxy system models (PSMs) for coral δ18O, tree ring width, and ice core δ18O using two isotope‐enabled atmospheric general circulation models. For (1), we find that linear‐univariate models efficiently capture the GCM's climate in ice cores and corals and do not lead to large losses in reconstruction skill. However, this does not hold for tree ring width, especially in regions where the trees' response is dominated by moisture supply; we quantify how the breakdown of this assumption lowers reconstruction skill for each proxy class. For (2), we find that climate model biases can introduce errors that greatly reduce reconstruction skill, with or without perfect proxy system models. We explore possible strategies for mitigating structural modeling errors in GCMs and discuss implications for paleoclimate reanalyses.

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“…Conversion of the proxy into a climatic variable can be achieved through regression or traditional inverse methods as is done with the widely used regional drought atlases (Cook et al, , 2010aPalmer et al, 2015). Nevertheless, in many cases the multivariate nature of proxy data, the presence of large uncertainties and limited spatiotemporal coverage in a calibration proxy network or nonstationary behavior between the proxy predictor and the climate predictand render regression and inversion challenging (e.g., Wilson et al, 2010;Lehner et al, 2012;Smerdon, 2012;Tingley et al, 2012;Coats et al, 2013a;Gallant et al, 2013;Evans et al, 2014;Konecky et al, 2014;Raible et al, 2014;Wang et al, 2014bWang et al, , 2015Konecky et al, 2016).…”

Section: Expectations Of Temporal or Spatial Consistency Betweenmentioning

confidence: 99%

“…PSMs are forward models and therefore do not require assumptions of local or regional stationarity and linearity that are typically applied in inverse approaches, and they can characterize multivariate influences (Evans et al, 2013;Phipps et al, 2013). Paleoclimate observations in their "native" units may be directly compared to models by using the output variables of simulations or other climate models that have been processed through a PSM to generate simulated paleoclimate observations -sometimes called "pseudoproxies" in the literature (Thompson et al, 2011;Tolwinski-Ward et al, 2011;Smerdon, 2012;Phipps et al, 2013;Evans et al, 2014). PSMs may therefore provide more appropriately analogous comparisons for models and proxies; they also allow for the propagation of uncertainties because PSM-derived estimates of parameter errors and observational random error do not need to be inverted.…”

Section: Expectations Of Temporal or Spatial Consistency Betweenmentioning

confidence: 99%

Comparing proxy and model estimates of hydroclimate variability and change over the Common Era

Smerdon¹,

Luterbacher²,

Phipps³

et al. 2017

Clim. Past

104

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Abstract. Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited because of a paucity of modern instrumental observations that are distributed unevenly across the globe and only span parts of the 20th and 21st centuries. Such data coverage is insufficient for characterizing hydroclimate and its associated dynamics because of its multidecadal to centennial variability and highly regionalized spatial signature. High-resolution (seasonal to decadal) hydroclimatic proxies that span all or parts of the Common Era (CE) and paleoclimate simulations from climate models are therefore important tools for augmenting our understanding of hydroclimate variability. In particular, the comparison of the two sources of information is critical for addressing the uncertainties and limitations of both while enriching each of their interpretations. We review the principal proxy data available for hydroclimatic reconstructions over the CE and highlight the contemporary understanding of how these proxies are interpreted as hydroclimate indicators. We also review the available last-millennium simulations from fully coupled climate models and discuss several outstanding challenges associated with simulating hydroclimate variability and change over the CE. A specific review of simulated hydroclimatic changes forced by volcanic events is provided, as is a discussion of expected improvements in estimated radiative forcings, models, and their implementation in the future. Our review of hydroclimatic proxies and last-millennium model simulations is used as the basis for articulating a variety of considerations and best practices for how to perform proxy-model comparisons of CE hydroclimate. This discussion provides a framework for how best to evaluate hydroclimate variability and its associated dynamics using these comparisons and how they can better inform interpretations of both proxy data and model simulations. We subsequently explore means of using proxy-model comparisons to better constrain and characterize future hydroclimate risks. This is explored specifically in the context of several examples that demonstrate how proxy-model comparisons can be used to quantitatively constrain future hydroclimatic risks as estimated from climate model projections.

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Climate field reconstruction uncertainty arising from multivariate and nonlinear properties of predictors

Cited by 39 publications

References 55 publications

Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies

Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies

On the utility of proxy system models for estimating climate states over the common era

Comparing proxy and model estimates of hydroclimate variability and change over the Common Era

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