Assessing the performance of the BARCAST climate field reconstruction technique for a climate with long-range memory

Abstract. The skill of the state-of-the-art climate field reconstruction technique BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time) to reconstruct temperature with pronounced long-range memory (LRM) characteristics is tested. A novel technique for generating fields of target data has been developed and is used to provide ensembles of LRM stochastic processes with a prescribed spatial covariance structure. Based on different parameter setups, hypothesis testing in the spectral domain is used to investigate if the field and spatial mean reconstructions are consistent with either the fractional Gaussian noise (fGn) process null hypothesis used for generating the target data, or the autoregressive model of order 1 (AR(1)) process null hypothesis which is the assumed temporal evolution model for the reconstruction technique. The study reveals that the resulting field and spatial mean reconstructions are consistent with the fGn process hypothesis for some of the tested parameter configurations, while others are in better agreement with the AR(1) model. There are local differences in reconstruction skill and reconstructed scaling characteristics between individual grid cells, and the agreement with the fGn model is generally better for the spatial mean reconstruction than at individual locations. Our results demonstrate that the use of target data with a different spatiotemporal covariance structure than the BARCAST model assumption can lead to a potentially biased climate field reconstruction (CFR) and associated confidence intervals.

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How wrong are climate field reconstruction techniques in reconstructing a climate with long-range memory?

10.5194/cp-2018-17 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tine Nilsen ◽

Johannes P. Werner ◽

Dmitry V. Divine

Keyword(s):

Confidence Intervals ◽

Long Range ◽

Null Hypothesis ◽

General Circulation ◽

Input Data ◽

Reconstruction Technique ◽

Field Reconstruction ◽

Reconstructed Signal ◽

Climate Field Reconstruction ◽

Target Data

Abstract. The Bayesian hierarchical model BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time) climate field reconstruction (CFR) technique, and idealized input data are used in the pseudoproxy experiments of this study. Ensembles of targets are generated from fields of long-range memory stochastic processes using a novel approach. The range of experiment setups include input data with different levels of persistence and levels of proxy noise, but without any form of external forcing. The input data are thereby a simplistic alternative to standard target data extracted from general circulation model (GCM) simulations. Ensemble-based temperature reconstructions are generated, representing the European landmass for a millennial time period. Hypothesis testing in the spectral domain is then used to investigate if the field and spatial mean reconstructions are consistent with either the fractional Gaussian noise (fGn) null hypothesis used for generating the target data, or the autoregressive model of order one (AR(1)) null hypothesis which is the assumed temperature model for this reconstruction technique. The study reveals that the resulting field and spatial mean reconstructions are consistent with the fGn hypothesis for most of the parameter configurations. There are local differences in reconstructed scaling characteristics between individual grid cells, and a generally better agreement with the fGn model for the spatial mean reconstruction than at individual locations. The discrepancy from an fGn is most evident for the high-frequency part of the reconstructed signal, while the long-range memory is better preserved at frequencies corresponding to decadal time scales and longer. Selected experiment setups were found to give reconstructions consistent with the AR(1) model. Reconstruction skill is measured on an ensemble member basis using selected validation metrics. Despite the mismatch between the BARCAST temporal covariance model and the model of the target, the ensemble mean was in general found to be consistent with the target data, while the estimated confidence intervals are more affected by this discrepancy. Our results show that the use of target data with a different spatiotemporal covariance structure than the BARCAST model assumption can lead to a potentially biased CFR reconstruction and associated confidence intervals, because of the wrong model assumptions.

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A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part I: Development and Applications to Paleoclimate Reconstruction Problems

Journal of Climate ◽

10.1175/2009jcli3015.1 ◽

2010 ◽

Vol 23 (10) ◽

pp. 2759-2781 ◽

Cited By ~ 95

Author(s):

Martin P. Tingley ◽

Peter Huybers

Keyword(s):

Time Series ◽

Covariance Matrix ◽

Expectation Maximization Algorithm ◽

Covariance Structure ◽

Model Parameters ◽

Data Types ◽

Spatial Covariance ◽

Bayesian Algorithm ◽

Climate Anomalies ◽

Instrumental Values

Abstract Reconstructing the spatial pattern of a climate field through time from a dataset of overlapping instrumental and climate proxy time series is a nontrivial statistical problem. The need to transform the proxy observations into estimates of the climate field, and the fact that the observed time series are not uniformly distributed in space, further complicate the analysis. Current leading approaches to this problem are based on estimating the full covariance matrix between the proxy time series and instrumental time series over a “calibration” interval and then using this covariance matrix in the context of a linear regression to predict the missing instrumental values from the proxy observations for years prior to instrumental coverage. A fundamentally different approach to this problem is formulated by specifying parametric forms for the spatial covariance and temporal evolution of the climate field, as well as “observation equations” describing the relationship between the data types and the corresponding true values of the climate field. A hierarchical Bayesian model is used to assimilate both proxy and instrumental datasets and to estimate the probability distribution of all model parameters and the climate field through time on a regular spatial grid. The output from this approach includes an estimate of the full covariance structure of the climate field and model parameters as well as diagnostics that estimate the utility of the different proxy time series. This methodology is demonstrated using an instrumental surface temperature dataset after corrupting a number of the time series to mimic proxy observations. The results are compared to those achieved using the regularized expectation–maximization algorithm, and in these experiments the Bayesian algorithm produces reconstructions with greater skill. The assumptions underlying these two methodologies and the results of applying each to simple surrogate datasets are explored in greater detail in Part II.

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Constraining two climate field reconstruction methodologies over the North Atlantic realm using pseudo-proxy experiments

Quaternary Science Reviews ◽

10.1016/j.quascirev.2021.107009 ◽

2021 ◽

Vol 265 ◽

pp. 107009

Author(s):

Tine Nilsen ◽

Stefanie Talento ◽

Johannes P. Werner

Keyword(s):

North Atlantic ◽

Field Reconstruction ◽

The North ◽

The North Atlantic ◽

Climate Field Reconstruction

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Evaluating climate field reconstruction techniques using improved emulations of real-world conditions

Climate of the Past ◽

10.5194/cp-10-1-2014 ◽

2014 ◽

Vol 10 (1) ◽

pp. 1-19 ◽

Cited By ~ 50

Author(s):

J. Wang ◽

J. Emile-Geay ◽

D. Guillot ◽

J. E. Smerdon ◽

B. Rajaratnam

Keyword(s):

Graphical Models ◽

Real World ◽

General Circulation ◽

Circulation Model ◽

Internal Variability ◽

Field Reconstruction ◽

Spatial Skill ◽

Reconstruction Methods ◽

Climate Field Reconstruction ◽

Global Mean

Abstract. 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.

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Robustness of proxy-based climate field reconstruction methods

Journal of Geophysical Research Atmospheres ◽

10.1029/2006jd008272 ◽

2007 ◽

Vol 112 (D12) ◽

Cited By ~ 99

Author(s):

Michael E. Mann ◽

Scott Rutherford ◽

Eugene Wahl ◽

Caspar Ammann

Keyword(s):

Field Reconstruction ◽

Reconstruction Methods ◽

Climate Field Reconstruction

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Tropical Atlantic Influence on European Heat Waves

Journal of Climate ◽

10.1175/jcli3506.1 ◽

2005 ◽

Vol 18 (15) ◽

pp. 2805-2811 ◽

Cited By ~ 257

Author(s):

Christophe Cassou ◽

Laurent Terray ◽

Adam S. Phillips

Keyword(s):

Atmospheric Circulation ◽

Long Range ◽

Heat Waves ◽

Extreme Weather ◽

Temperature Data ◽

Tropical Atlantic ◽

Caribbean Basin ◽

Climate Anomalies ◽

Warm Summer ◽

The Caribbean

Abstract Diagnostics combining atmospheric reanalysis and station-based temperature data for 1950–2003 indicate that European heat waves can be associated with the occurrence of two specific summertime atmospheric circulation regimes. Evidence is presented that during the record warm summer of 2003, the excitation of these two regimes was significantly favored by the anomalous tropical Atlantic heating related to wetter-than-average conditions in both the Caribbean basin and the Sahel. Given the persistence of tropical Atlantic climate anomalies, their seasonality, and their associated predictability, the suggested tropical–extratropical Atlantic connection is encouraging for the prospects of long-range forecasting of extreme weather in Europe.

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Characterization of a contaminated wellfield using 3D electrical resistivity tomography implemented with geostatistical, discontinuous boundary, and known conductivity constraints

Geophysics ◽

10.1190/geo2012-0121.1 ◽

2012 ◽

Vol 77 (6) ◽

pp. EN85-EN96 ◽

Cited By ~ 27

Author(s):

Timothy C. Johnson ◽

Roelof J. Versteeg ◽

Mark Rockhold ◽

Lee D. Slater ◽

Dimitrios Ntarlagiannis ◽

...

Keyword(s):

Electrical Resistivity ◽

Electrical Resistivity Tomography ◽

Covariance Structure ◽

Imaging Data ◽

Spatial Covariance ◽

Hanford Site ◽

Resistivity Tomography ◽

Shallow Subsurface ◽

Subsurface Structures ◽

Regularized Inversion

Continuing advancements in subsurface electrical resistivity tomography (ERT) are increasing its capabilities for understanding shallow subsurface properties and processes. The inability of ERT imaging data to resolve unique subsurface structures and the corresponding need to include constraining information remains one of the greatest limitations, yet provides one of the greatest opportunities for further advancing the utility of the method. We propose a new method of incorporating constraining information into an ERT imaging algorithm in the form of discontinuous boundaries, known values, and spatial covariance information. We demonstrated the approach by imaging a uranium-contaminated wellfield at the Hanford Site in southeastern Washington State, USA. We incorporate into the algorithm known boundary information and spatial covariance structures derived from the highly resolved near-borehole regions of a regularized ERT inversion. The resulting inversion provides a solution which fits the ERT data (given the estimated noise level), honors the spatial covariance structure throughout the model, and is consistent with known bulk-conductivity discontinuities. The results are validated with core-scale measurements, indicating a significant improvement in accuracy over the standard regularized inversion and revealing important subsurface structure known to influence flow and transport at the site.

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