ALBERT

Description: The ability to precisely date climate proxies is central to the reconstruction of past climate variations. To a degree, all climate proxies are affected by age uncertainties, which are seldom quantified. This article proposes a probabilistic age model for proxies based on layer-counted chronologies, and explores its use for annually banded coral archives. The model considers both missing and doubly counted growth increments (represented as independent processes), accommodates various assumptions about error rates, and allows one to quantify the impact of chronological uncertainties on different diagnostics of variability. In the case of a single coral record, we find that time uncertainties primarily affect high-frequency signals but also significantly bias the estimate of decadal signals. We further explore tuning to an independent, tree-ring-based chronology as a way to identify an optimal age model. A synthetic pseudocoral network is used as testing ground to quantify uncertainties in the estimation of spatiotemporal patterns of variability. Even for small error rates, the amplitude of multidecadal variability is systematically overestimated at the expense of interannual variability (El Niño–Southern Oscillation, or ENSO, in this case), artificially flattening its spectrum at periods longer than 10 years. An optimization approach to correct chronological errors in coherent multivariate records is presented and validated in idealized cases, though it is found difficult to apply in practice due to the large number of solutions. We close with a discussion of possible extensions of this model and connections to existing strategies for modeling age uncertainties.

Description: The El Ninõ/Southern Oscillation (ENSO) is the leading mode of interannual climate variability. However, it is unclear how ENSO has responded to external forcing, particularly orbitally induced changes in the amplitude of the seasonal cycle during the Holocene. Here we present a reconstruction of seasonal and interannual surface conditions in the tropical Pacific Ocean from a network of high-resolution coral and mollusc records that span discrete intervals of the Holocene. We identify several intervals of reduced variance in the 2 to 7 yr ENSO band that are not in phase with orbital changes in equatorial insolation, with a notable 64% reduction between 5,000 and 3,000 years ago. We compare the reconstructed ENSO variance and seasonal cycle with that simulated by nine climate models that include orbital forcing, and find that the models do not capture the timing or amplitude of ENSO variability, nor the mid-Holocene increase in seasonality seen in the observations; moreover, a simulated inverse relationship between the amplitude of the seasonal cycle and ENSO-related variance in sea surface temperatures is not found in our reconstructions.We conclude that the tropical Pacific climate is highly variable and subject to millennial scale quiescent periods. These periods harbour no simple link to orbital forcing, and are not adequately simulated by the current generation of models. © 2016 Macmillan Publishers Limited.

Description: Pseudoproxy experiments (PPEs) have become an important framework for evaluating paleoclimate reconstruction methods. Most existing PPE studies assume constant proxy availability through time and uniform proxy quality across the pseudoproxy network. Real multiproxy networks are, however, marked by pronounced disparities in proxy quality, and a steep decline in proxy availability back in time, either of which may have large effects on reconstruction skill. A suite of PPEs constructed from a millennium-length general circulation model (GCM) simulation is thus designed to mimic these various real-world characteristics. The new pseudoproxy network is used to evaluate four climate field reconstruction (CFR) techniques: truncated total least squares embedded within the regularized EM (expectation-maximization) algorithm (RegEM-TTLS), the Mann et al. (2009) implementation of RegEM-TTLS (M09), canonical correlation analysis (CCA), and Gaussian graphical models embedded within RegEM (GraphEM). Each method's risk properties are also assessed via a 100-member noise ensemble. Contrary to expectation, it is found that reconstruction skill does not vary monotonically with proxy availability, but also is a function of the type and amplitude of climate variability (forced events vs. internal variability). The use of realistic spatiotemporal pseudoproxy characteristics also exposes large inter-method differences. Despite the comparable fidelity in reconstructing the global mean temperature, spatial skill varies considerably between CFR techniques. Both GraphEM and CCA efficiently exploit teleconnections, and produce consistent reconstructions across the ensemble. RegEM-TTLS and M09 appear advantageous for reconstructions on highly noisy data, but are subject to larger stochastic variations across different realizations of pseudoproxy noise. Results collectively highlight the importance of designing realistic pseudoproxy networks and implementing multiple noise realizations of PPEs. The results also underscore the difficulty in finding the proper bias-variance tradeoff for jointly optimizing the spatial skill of CFRs and the fidelity of the global mean reconstructions.

Description: Paleoclimatology is a highly collaborative scientific endeavor, increasingly reliant on online databases for data sharing. Yet, there is currently no universal way to describe, store and share paleoclimate data: in other words, no standard. Data standards are often regarded by scientists as mere technicalities, though they underlie much scientific and technological innovation, as well as facilitating collaborations between research groups. In this article, we propose a preliminary data standard for paleoclimate data, general enough to accommodate all the proxy and measurement types encountered in a large international collaboration (PAGES2K). We also introduce a vehicle for such structured data (Linked Paleo Data, or LiPD), leveraging recent advances in knowledge representations (Linked Open Data). The LiPD framework enables quick querying and extraction, and we expect that it will facilitate the writing of open-source, community codes to access, analyze, model and visualize paleoclimate observations. We welcome community feedback on this standard, and encourage paleoclimatologists to experiment with the format for their own purposes.

Description: The ability to precisely date climate proxies is central to the reconstruction of past climate variations. To a degree, all climate proxies are affected by age uncertainties, which are seldom quantified. This article proposes a probabilistic age model for proxies based on layer-counted chronologies, and explores its use for annually-banded coral archives. The model considers both missing and doubly-counted growth increments (represented as independent processes), accommodates various assumptions about error rates, and allows to quantify the impact of chronological uncertainties on different diagnostics of variability. In one dimension, we find that time uncertainties primarily affect high-frequency signals but also significantly bias the estimate of decadal signals. We further explore tuning to an independent, tree-ring based chronology as a way to identify an optimal age model. In the multivariate case, a synthetic pseudocoral network is used as testing ground to quantify uncertainties in the estimation of spatiotemporal patterns of variability. Even for small error rates, the amplitude of multidecadal variability is systematically overestimated at the expense of interannual variability (ENSO, in this case), artificially flattening its spectrum at periods longer than 10 yr. An approach to correct chronological errors in coherent multivariate records is presented and validated in idealized cases, though it is found difficult to apply in practice due to the large size of the solution space. We end with a discussion of possible extensions of this model and connections to existing strategies for modeling age uncertainties.

Description: Pseudoproxy experiments (PPEs) have become an essential framework for evaluating paleoclimate reconstruction methods. Most existing PPE studies assume constant proxy availability through time and uniform proxy quality across the pseudoproxy network. Real multi-proxy networks are, however, marked by pronounced disparities in proxy quality, and a steep decline in proxy availability back in time, either of which may have large effects on reconstruction skill. Additionally, an investigation of a real-world global multi-proxy network suggests that proxies are not exclusively indicators of local climate; rather, many are indicative of large-scale teleconnections. A suite of PPEs constructed from a millennium-length general circulation model simulation is thus designed to mimic these various real-world characteristics. The new pseudoproxy network is used to evaluate four climate field reconstruction (CFR) techniques: truncated total least square embedded within the regularized EM algorithm (RegEM-TTLS), the Mann et al. (2009) implementation of RegEM-TTLS (M09), canonical correlation analysis (CCA), and Gaussian graphical models embedded within RegEM (GraphEM). Each method's risk properties are also assessed via a 100-member noise ensemble. Contrary to expectation, it is found that reconstruction skill does not vary monotonically with proxy availability, but rather is a function of the type of climate variability (forced events vs. internal variability). The use of realistic spatiotemporal pseudoproxy characteristics also exposes large inter-method differences. Despite the comparable fidelity in reconstructing the global mean temperature, spatial skill varies considerably between CFR techniques. Both GraphEM and CCA efficiently exploit teleconnections, and produce consistent reconstructions across the ensemble. RegEM-TTLS and M09 appear advantageous for reconstructions on highly noisy data, but are subject to larger stochastic variations across different realizations of pseudoproxy noise. Results collectively highlight the importance of designing realistic pseudoproxy networks and implementing multiple noise realizations of PPEs. The results also underscore the difficulty in finding the proper bias-variance tradeoff for jointly optimizing the spatial skill of CFRs and the fidelity of the global mean reconstructions.

Description: Inferring climate from paleodata 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 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 paleoclimate literature, without consideration of nonlinearities, (ii) the same methods, after empirically transforming the data to normality to account for nonlinearities, (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 paleoclimate settings. Accounting for nonlinearities in the classical sedimentary record from Laguna Pallcacocha leads to quantitative departures from the results of the original study, and 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 nonlinear recorders of normally distributed climate variables are interpreted, compared to other proxy records, and incorporated into multiproxy reconstructions.

Description: 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.