Articles | Volume 11, issue 3
https://doi.org/10.5194/cp-11-533-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/cp-11-533-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical Note: Probabilistically constraining proxy age–depth models within a Bayesian hierarchical reconstruction model
Department for Earth Science and Bjerknes Centre for Climate Research, University of Bergen, P.O. Box 7803, 5020 Bergen, Norway
M. P. Tingley
Department of Meteorology and Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA
Related authors
Tine Nilsen, Johannes P. Werner, Dmitry V. Divine, and Martin Rypdal
Clim. Past, 14, 947–967, https://doi.org/10.5194/cp-14-947-2018, https://doi.org/10.5194/cp-14-947-2018, 2018
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The BARCAST climate field reconstruction method is tested using synthetic data experiments. It is demonstrated that the output reconstructions have altered statistical properties compared with the input data, but they are also not necessarily consistent with the model assumption of the reconstruction method. The conclusion is that the statistical properties of a reconstruction not only reflect the statistics of the real climate, but they may very well be affected by the manipulation of the data.
Johannes P. Werner, Dmitry V. Divine, Fredrik Charpentier Ljungqvist, Tine Nilsen, and Pierre Francus
Clim. Past, 14, 527–557, https://doi.org/10.5194/cp-14-527-2018, https://doi.org/10.5194/cp-14-527-2018, 2018
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We present a new gridded Arctic summer temperature reconstruction back to the first millennium CE. Our method respects the age uncertainties of the data, which results in a more precise reconstruction.
The spatial average shows a millennium-scale cooling trend which is reversed in the mid-19th century. While temperatures in the 10th century were probably as warm as in the 20th century, the spatial coherence of the recent warm episodes seems unprecedented.
The spatial average shows a millennium-scale cooling trend which is reversed in the mid-19th century. While temperatures in the 10th century were probably as warm as in the 20th century, the spatial coherence of the recent warm episodes seems unprecedented.
Hans W. Linderholm, Marie Nicolle, Pierre Francus, Konrad Gajewski, Samuli Helama, Atte Korhola, Olga Solomina, Zicheng Yu, Peng Zhang, William J. D'Andrea, Maxime Debret, Dmitry V. Divine, Björn E. Gunnarson, Neil J. Loader, Nicolas Massei, Kristina Seftigen, Elizabeth K. Thomas, Johannes Werner, Sofia Andersson, Annika Berntsson, Tomi P. Luoto, Liisa Nevalainen, Saija Saarni, and Minna Väliranta
Clim. Past, 14, 473–514, https://doi.org/10.5194/cp-14-473-2018, https://doi.org/10.5194/cp-14-473-2018, 2018
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This paper reviews the current knowledge of Arctic hydroclimate variability during the past 2000 years. We discuss the current state, look into the future, and describe various archives and proxies used to infer past hydroclimate variability. We also provide regional overviews and discuss the potential of furthering our understanding of Arctic hydroclimate in the past. This paper summarises the hydroclimate-related activities of the Arctic 2k group.
Marie Nicolle, Maxime Debret, Nicolas Massei, Christophe Colin, Anne deVernal, Dmitry Divine, Johannes P. Werner, Anne Hormes, Atte Korhola, and Hans W. Linderholm
Clim. Past, 14, 101–116, https://doi.org/10.5194/cp-14-101-2018, https://doi.org/10.5194/cp-14-101-2018, 2018
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Arctic climate variability for the last 2 millennia has been investigated using statistical and signal analyses from North Atlantic, Siberia and Alaska regionally averaged records. A focus on the last 2 centuries shows a climate variability linked to anthropogenic forcing but also a multidecadal variability likely due to regional natural processes acting on the internal climate system. It is an important issue to understand multidecadal variabilities occurring in the instrumental data.
Jasper G. Franke, Johannes P. Werner, and Reik V. Donner
Clim. Past, 13, 1593–1608, https://doi.org/10.5194/cp-13-1593-2017, https://doi.org/10.5194/cp-13-1593-2017, 2017
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We apply evolving functional network analysis, a tool for studying temporal changes of the spatial co-variability structure, to a set of
Late Holocene paleoclimate proxy records covering the last two millennia. The emerging patterns obtained by our analysis are related to
long-term changes in the dominant mode of atmospheric circulation in the region, the North Atlantic Oscillation (NAO). We obtain a
qualitative reconstruction of the NAO long-term variability over the entire Common Era.
Tine Nilsen, Johannes P. Werner, Dmitry V. Divine, and Martin Rypdal
Clim. Past, 14, 947–967, https://doi.org/10.5194/cp-14-947-2018, https://doi.org/10.5194/cp-14-947-2018, 2018
Short summary
Short summary
The BARCAST climate field reconstruction method is tested using synthetic data experiments. It is demonstrated that the output reconstructions have altered statistical properties compared with the input data, but they are also not necessarily consistent with the model assumption of the reconstruction method. The conclusion is that the statistical properties of a reconstruction not only reflect the statistics of the real climate, but they may very well be affected by the manipulation of the data.
Johannes P. Werner, Dmitry V. Divine, Fredrik Charpentier Ljungqvist, Tine Nilsen, and Pierre Francus
Clim. Past, 14, 527–557, https://doi.org/10.5194/cp-14-527-2018, https://doi.org/10.5194/cp-14-527-2018, 2018
Short summary
Short summary
We present a new gridded Arctic summer temperature reconstruction back to the first millennium CE. Our method respects the age uncertainties of the data, which results in a more precise reconstruction.
The spatial average shows a millennium-scale cooling trend which is reversed in the mid-19th century. While temperatures in the 10th century were probably as warm as in the 20th century, the spatial coherence of the recent warm episodes seems unprecedented.
The spatial average shows a millennium-scale cooling trend which is reversed in the mid-19th century. While temperatures in the 10th century were probably as warm as in the 20th century, the spatial coherence of the recent warm episodes seems unprecedented.
Hans W. Linderholm, Marie Nicolle, Pierre Francus, Konrad Gajewski, Samuli Helama, Atte Korhola, Olga Solomina, Zicheng Yu, Peng Zhang, William J. D'Andrea, Maxime Debret, Dmitry V. Divine, Björn E. Gunnarson, Neil J. Loader, Nicolas Massei, Kristina Seftigen, Elizabeth K. Thomas, Johannes Werner, Sofia Andersson, Annika Berntsson, Tomi P. Luoto, Liisa Nevalainen, Saija Saarni, and Minna Väliranta
Clim. Past, 14, 473–514, https://doi.org/10.5194/cp-14-473-2018, https://doi.org/10.5194/cp-14-473-2018, 2018
Short summary
Short summary
This paper reviews the current knowledge of Arctic hydroclimate variability during the past 2000 years. We discuss the current state, look into the future, and describe various archives and proxies used to infer past hydroclimate variability. We also provide regional overviews and discuss the potential of furthering our understanding of Arctic hydroclimate in the past. This paper summarises the hydroclimate-related activities of the Arctic 2k group.
Marie Nicolle, Maxime Debret, Nicolas Massei, Christophe Colin, Anne deVernal, Dmitry Divine, Johannes P. Werner, Anne Hormes, Atte Korhola, and Hans W. Linderholm
Clim. Past, 14, 101–116, https://doi.org/10.5194/cp-14-101-2018, https://doi.org/10.5194/cp-14-101-2018, 2018
Short summary
Short summary
Arctic climate variability for the last 2 millennia has been investigated using statistical and signal analyses from North Atlantic, Siberia and Alaska regionally averaged records. A focus on the last 2 centuries shows a climate variability linked to anthropogenic forcing but also a multidecadal variability likely due to regional natural processes acting on the internal climate system. It is an important issue to understand multidecadal variabilities occurring in the instrumental data.
Jasper G. Franke, Johannes P. Werner, and Reik V. Donner
Clim. Past, 13, 1593–1608, https://doi.org/10.5194/cp-13-1593-2017, https://doi.org/10.5194/cp-13-1593-2017, 2017
Short summary
Short summary
We apply evolving functional network analysis, a tool for studying temporal changes of the spatial co-variability structure, to a set of
Late Holocene paleoclimate proxy records covering the last two millennia. The emerging patterns obtained by our analysis are related to
long-term changes in the dominant mode of atmospheric circulation in the region, the North Atlantic Oscillation (NAO). We obtain a
qualitative reconstruction of the NAO long-term variability over the entire Common Era.
Related subject area
Subject: Proxy Use-Development-Validation | Archive: Modelling only | Timescale: Decadal-Seasonal
Multi-timescale data assimilation for atmosphere–ocean state estimates
An ensemble-based approach to climate reconstructions
Nathan Steiger and Gregory Hakim
Clim. Past, 12, 1375–1388, https://doi.org/10.5194/cp-12-1375-2016, https://doi.org/10.5194/cp-12-1375-2016, 2016
Short summary
Short summary
We present a data assimilation algorithm that incorporates proxy data at arbitrary timescales. Within a synthetic-test framework, we find that atmosphere–ocean states are most skillfully reconstructed by incorporating proxies across multiple timescales compared to using them at short or long timescales alone. Additionally, reconstructions that incorporate long-timescale proxies improve the low-frequency components of the reconstructions relative to using only high-resolution proxies.
J. Bhend, J. Franke, D. Folini, M. Wild, and S. Brönnimann
Clim. Past, 8, 963–976, https://doi.org/10.5194/cp-8-963-2012, https://doi.org/10.5194/cp-8-963-2012, 2012
Cited articles
Altekar, G., Dwarkadas, S., Huelsenbeck, J. P., and Ronquist, F.: Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference, Bioinformatics, 20, 407–415, 2004.
Anchukaitis, K. J. and Tierney, J. E.: Identifying coherent spatiotemporal modes in time-uncertain proxy paleoclimate records, Clim. Dynam., 41, 1291–1306, 2013.
Banerjee, S., Carlin, B. P., and Gelfand, A. E.: Hierarchical Modeling and Analysis for Spatial Data, Chapman & Hall/CRC, New York, 2004.
Berliner, L., Wikle, C., and Cressie, N.: Long-lead prediction of Pacific SSTs via Bayesian dynamic modeling, J. Climate, 13, 3953–3968, 2000.
Blaauw, M. and Christen, J. A.: Flexible paleoclimate age-depth models using an autoregressive gamma process, Bayesian Analysis, 6, 457–474, 2011.
Blockley, S., Blaauw, M., Ramsey, C. B., and van der Plicht, J.: Building and testing age models for radiocarbon dates in lateglacial and early Holocene sediments, Quaternary Sci. Rev., 1915–1926, 2007.
Brüggemann, W.: A minimal cost function method for optimizing the age-depth relation of deep-sea sediment cores, Paleoceanography, 7, 467–487, 1992.
Comboul, M., Emile-Geay, J., Evans, M. N., Mirnateghi, N., Cobb, K. M., and Thompson, D. M.: A probabilistic model of chronological errors in layer-counted climate proxies: applications to annually banded coral archives, Clim. Past, 10, 825–841, https://doi.org/10.5194/cp-10-825-2014, 2014.
Cook, E. R., Briffa, K. R., and Jones, P. D.: Spatial regression methods in dendroclimatology – a review and comparison of 2 techniques, Int. J. Climatol., 14, 379–402, 1994.
Curry, W. and Oppo, D.: Glacial water mass geometry and the distribution of δ13C of \chem§igma CO_2 in the western Atlantic Ocean, Paleoceanography, PA1017, https://doi.org/10.1029/2004PA001021, 2005.
Earl, D. J. and Deem, M. W.: Parallel tempering: theory, applications, and new perspectives, Phys. Chem. Chem. Phys., 7, 3910–3916, https://doi.org/10.1039/B509983H, 2005.
Evans, M. N., Tolwinski-Ward, S., Thompson, D., and Anchukaitis, K. J.: Applications of proxy system modeling in high resolution paleoclimatology, Quaternary Sci. Rev., 76, 16–28, 2013.
Gelfand, A. E., Kim, H.-J., Sirmans, C., and Banerjee, S.: Spatial modeling with spatially varying coefficient processes, J. Am. Stat. Assoc., 98, 387–396, 2003.
Gelman, A., Carlin, J., Stern, H., and Rubin, D.: Bayesian Data Analysis, 2nd edn., Chapman & Hall, 2003.
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and estimation, J. Am. Stat. Assoc., 102, 359–378, 2007.
Guillot, D., Rajaratnam, B., and Emile-Geay, J.: Statistical paleoclimate reconstructions via Markov random fields, Annals of Applied Statisitcs, in review, 2014.
Haflidason, H., Eiriksson, J., and Kreveld, S. V.: The tephrochronology of Iceland and the North Atlantic region during the Middle and Late Quaternary: a review, J. Quaternary Sci., 15, 3–22, 2000.
Hendy, E. J., Gagan, M. K., and Lough, J. M.: Chronological control of coral records using luminescent lines and evidence for non-stationary ENSO teleconnections in northeast Australia, Holocene, 13, 187–199, 2003.
Herbach, H.: Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15, 559–570, 2000.
Hughes, A. L. C., Clark, C. D., and Jordan, C. J.: Flow-pattern evolution of the last British Ice Sheet, Quarternary Science Reviews, 89, 148–168, https://doi.org/10.1016/j.quascirev.2014.02.002, 2014.
Huybers, P. and Wunsch, C.: A depth-derived Pleistocene age model: uncertainty estimates, sedimentation variability, and nonlinear climate change, Paleoceanography, 19, PA1028, https://doi.org/10.1029/2002PA000857, 2004.
Imbrie, J., Hays, J., Martinson, D., McIntyre, A., Mix, A., Morley, J., Pisias, N., Prell, W., and Shackleton, N.: The orbital theory of Pleistocene climate: support from a revised chronology of the marine δ18O record, in: Milankovitch and Climate: Understanding the Response to Astronomical Forcing, vol. 1, Springer, New York, 269 pp., 1984.
Jones, P. D., Briffa, K. R., Osborn, T. J., Lough, J. M., van Ommen, T. D., Vinther, B. M., Luterbacher, J., Wahl, E. R., Zwiers, F. W., Mann, M. E., Schmidt, G. A., Ammann, C. M., Buckley, B. M., Cobb, K. M., Esper, J., Goosse, H., Graham, N., Jansen, E., Kiefer, T., Kull, C., Küttel, M., Mosley-Thompson, E., Overpeck, J. T., Riedwyl, N., Schulz, M., Tudhope, A. W., Villalba, R., Wanner, H., Wolff, E., and Xoplaki, E.: High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects, Holocene, 19, 3–49, 2009.
Kaufman, C. and Shaby, B.: The role of the range parameter for estimation and prediction in geostatistics, Biometrika, 100, 473–484, 2013.
Kaufman, D.: A community-driven framework for climate reconstructions, Eos, Transactions American Geophysical Union, 7 October 2014, 95, 361–362, 2014.
Kofke, D. A.: On the acceptance probability of replica-exchange Monte Carlo trials, J. Chem. Phys., 117, 6911–6914, 2002.
Li, B., Nychka, D., and Ammann, C.: The value of multiproxy reconstruction of past climate, J. Am. Stat. Assoc., 105, 883–895, 2010.
Li, Z., Protopopescu, V. A., Arnold, N., Zhang, X., and Gorin, A.: Hybrid parallel tempering and simulated annealing method, Appl. Math. Comput., 212, 216–228, 2009.
Lisiecki, L. and Raymo, M.: A Pliocene-Pleistocene stack of 57 globally distributed benthic \chem\delta^{18O} records, Paleoceanography, 20, PA1071, https://doi.org/10.1029/2004PA001071, 2005.
Lisiecki, L. and Raymo, M.: Plio-Pleistocene climate evolution: trends and transitions in glacial cycle dynamics, Quaternary Sci. Rev., 26, 56–69, 2007.
Lisiecki, L. E. and Lisiecki, P. A.: Application of dynamic programming to the correlation of paleoclimate records, Paleoceanography, 17, 1049, https://doi.org/10.1029/2001PA000733, 2002.
Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M., and Wanner, H.: European seasonal and annual temperature variability, trends, and extremes since 1500, Science, 303, 1499–1503, 2004.
Mann, M. E., Zhang, Z., Hughes, M. K., Bradley, R. S., Miller, S. K., Rutherford, S., and Ni, F.: Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, P. Natl. Acad. Sci. USA, 105, 13252–13257, 2008.
Mannshardt, E., Craigmile, P., and Tingley, M. P.: Statistical modeling of extreme value behavior in North American tree-ring density series, Clim. Change, 117, 843–858, 2013.
Martinson, D., Menke, W., and Stoffa, P.: An inverse approach to signal correlation, J. Geophys. Res., 87, 4807–4818, 1982.
McKay, N. P. and Kaufmann, D. S.: An extended Arctic proxy temperature database for the past 2000 years, Scientific Data, 1, 140026, https://doi.org/10.1038/sdata.2014.26, 2014.
Moberg, A., Sonechkin, D. M., Holmgren, K., Datsenko, N. M., and Karlen, W.: Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data, Nature, 433, 613–617, 2005.
NRC: Surface Temperature Reconstructions for the Last 2000 Years, The National Academies Press, Washington DC, 2006.
Osete, M.-L., Martín-Chivelet, J., Rossi, C., Edwards, R. L., Egli, R., Muñoz-García, M. B., Wang, X., Pavón-Carrasco, F. J., and Heller, F.: The Blake geomagnetic excursion recorded in a radiometrically dated speleothem, Earth Planet. Sc. Lett., 353, 173–181, 2012.
PAGES2k Consortium: Continental-scale temperature variability over the common era, Nat. Geosci., 6, 339–346, 2013.
PAGES2k Consortium: PAGES 2k Proxy Database, Tech. rep., IGBP Pages 2k, available at: http://www.pages-igbp.org/, 2014.
Pauling, A., Luterbacher, J., Casty, C., and Wanner, H.: 500 years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation, Clim. Dynam., 26, 387–405, 2006.
Ramsey, C.: Deposition models for chronological records, Quaternary Sci. Rev., 27, 42–60, 2008.
Schneider, T.: Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values, J. Climate, 14, 853–871, 2001.
Shackleton, N., Berger, A., and Peltier, W.: An alternative astronomical calibration of the lower Pleistocene timescale based on ODP Site 677, Trans. R. Soc. Edinburgh Earth Sci., 81, 251–261, 1990.
Shaw, A.: Time in Stratigraphy, McGraw-Hill, New York, 1964.
Smerdon, J. E.: Climate models as a test bed for climate reconstruction methods: pseudoproxy experiments, Wiley Interdisciplinary Reviews (WIREs) Clim. Change, 3, 63–77, https://doi.org/10.1002/wcc.149, 2012.
Smerdon, J. E., Kaplan, A., Chang, D., and Evans, M. N.: A pseudoproxy evaluation of the CCA and RegEM methods for reconstructing climate fields of the last millennium, J. Climate, 23, 4856–4880, 2010.
Smerdon, J. E., Kaplan, A., Zorita, E., González-Rouco, J. F., and Evans, M.: Spatial performance of four climate field reconstruction methods targeting the Common Era, Geophys. Res. Lett., 38, L11705, https://doi.org/10.1029/2011GL047372, 2011.
Tierney, J. E., Smerdon, J. E., Anchukaitis, K. J., and Seager, R.: Multidecadal variability in East African hydroclimate controlled by the Indian Ocean, Nature, 493, 389–392, 2013.
Tingley, M. P.: A bayesian ANOVA scheme for calculating climate anomalies, with applications to the instrumental temperature record, J. Climate, 25, 777–791, 2012.
Tingley, M. P. and Huybers, P.: A bayesian algorithm for reconstructing climate anomalies in space and time, Part I: Development and applications to paleoclimate reconstruction problems, J. Climate, 23, 2759–2781, 2010a.
Tingley, M. P. and Huybers, P.: A bayesian algorithm for reconstructing climate anomalies in space and time, Part II: Comparison with the regularized expectation-maximization algorithm, J. Climate, 23, 2782–2800, 2010b.
Tingley, M. P. and Huybers, P.: Recent temperature extremes at high northern latitudes unprecedented in the past 600 years, Nature, 496, 201–205, 2013.
Tingley, M. P., Craigmile, P. F., Haran, M., Li, B., Mannshardt-Shamseldin, E., and Rajaratnam, B.: Piecing together the past: statistical insights into paleoclimatic reconstructions, Quaternary Sci. Rev., 35, 1–22, 2012.
Tolwinski-Ward, S., Tingley, M., Evans, M., Hughes, M., and Nychka, D.: Probabilistic reconstructions of local temperature and soil moisture from tree-ring data with potentially time-varying climatic response, Clim. Dynam., 44, 1–16, 2014.
Tolwinski-Ward, S. E., Evans, M. N., Hughes, M. K., and Anchukaitis, K. J.: An efficient forward model of the climate controls on interannual variation in tree-ring width, Clim. Dynam., 36, 2419–2439, 2010.
Werner, J. P., Smerdon, J., and Luterbacher, J.: A pseudoproxy evaluation of bayesian hierarchical modelling and canonical correlation analysis for climate field reconstructions over Europe, J. Climate, 851–867, 2013.
Werner, J. P., Toreti, A., and Luterbacher, J.: Stochastic Models for Climate Reconstructions – How wrong is too wrong?, NOLTA Proc., IEICE, Tokyo, 2014.
Wikle, C. K., Milliff, R. F., Nychka, D., and Berliner, L. M.: Spatiotemporal hierarchical Bayesian modeling tropical ocean surface winds, J. Am. Stat. Assoc., 96, 382–397, 2001.
Wong, W. H. and Liang, F.: Dynamic weighting in Monte Carlo and optimization, P. Natl. Acad. Sci. USA, 94, 14220–14224, 1997.
Zhang, H.: Inconsistent estimation and asymptotically equal interpolations in model-based geostatistics, J. Am. Stat. Assoc., 99, 250–261, 2004.
Short summary
We present a Bayesian approach to simultaneously constrain the age models associated with time-uncertain proxies and inferring past climate in space and time. For the sake of exposition, the discussion focuses on annually resolved climate archives, such as varved lakes, corals, and tree rings, with dating by layer counting. Numerical experiments show that updating the probabilities associated with an ensemble of possible age models reduces uncertainty in the inferred climate.
We present a Bayesian approach to simultaneously constrain the age models associated with...