The Annals of Applied Statistics

A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records

Lasse Holmström, Liisa Ilvonen, Heikki Seppä, and Siim Veski

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Abstract

Holocene (the last 12,000 years) temperature variation, including the transition out of the last Ice Age to a warmer climate, is reconstructed at multiple locations in southern Finland, Sweden and Estonia based on pollen fossil data from lake sediment cores. A novel Bayesian statistical approach is proposed that allows the reconstructed temperature histories to interact through shared environmental response parameters and spatial dependence. The prior distribution for past temperatures is partially based on numerical climate simulation. The features in the reconstructions are consistent with the quantitative climate reconstructions based on more commonly used reconstruction techniques. The results suggest that the novel spatio-temporal approach can provide quantitative reconstructions that are smoother, less uncertain and generally more realistic than the site-specific individual reconstructions.

Article information

Source
Ann. Appl. Stat., Volume 9, Number 3 (2015), 1194-1225.

Dates
Received: May 2014
Revised: May 2015
First available in Project Euclid: 2 November 2015

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1446488736

Digital Object Identifier
doi:10.1214/15-AOAS832

Mathematical Reviews number (MathSciNet)
MR3418720

Zentralblatt MATH identifier
06525983

Keywords
Bayesian modeling paleoclimate regression space–time modeling temperature proxy

Citation

Holmström, Lasse; Ilvonen, Liisa; Seppä, Heikki; Veski, Siim. A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records. Ann. Appl. Stat. 9 (2015), no. 3, 1194--1225. doi:10.1214/15-AOAS832. https://projecteuclid.org/euclid.aoas/1446488736


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References

  • Alley, R. B. and Agústsdóttir, A. M. (2005). The 8k event: Cause and consequences of a major Holocene abrupt climate change. Quat. Sci. Rev. 24 1123–1149.
  • Ammann, C. M., Joos, F., Schimel, D. S., Otto-Bliesner, B. L. and Tomas, R. A. (2007). Solar influence on climate during the past millennium: Results from transient simulations with the NCAR climate system model. Proc. Natl. Acad. Sci. USA 104 3713–3718.
  • Antonsson, K., Brooks, S. J., Seppä, H., Telford, R. J. and Birks, H. J. B. (2006). Quantitative palaeotemperature records iferred from fossil chironomid and pollen assemblages from Lake Gilltjärnen, northern central Sweden. J. Quat. Sci. 21 831–841.
  • Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall, London.
  • Birks, H. J. B., Heiri, O., Seppä, H. and Bjune, A. E. (2010). Strengths and weaknesses of quantitative climate reconstructions based on late-quaternary biological proxies. The Open Ecology Jounal 3 68–110.
  • Brynjarsdóttir, J. and Berliner, L. M. (2011). Bayesian hierarchical modeling for temperature reconstruction from geothermal data. Ann. Appl. Stat. 5 1328–1359.
  • Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York.
  • Dahl, E. (1998). The Phytogeography of Northern Europe: British Isles, Fennoscandia, and Adjacent Areas. Cambridge Univ. Press, Cambridge.
  • Erästö, P. and Holmström, L. (2005). Bayesian multiscale smoothing for making inferences about features in scatterplots. J. Comput. Graph. Statist. 14 569–589.
  • Erästö, P. and Holmström, L. (2006). Selection of prior distributions and multiscale analysis in Bayesian temperature reconstructions based on fossil assemblages. J. Paleolimnol. 36 69–80.
  • Erästö, P., Holmström, L., Korhola, A. and Weckström, J. (2012). Finding a consensus on credible features among several paleoclimate reconstructions. Ann. Appl. Stat. 6 1377–1405.
  • Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (2004). Bayesian Data Analysis, 2nd ed. Chapman & Hall/CRC, Boca Raton, FL.
  • Haslett, J. and Parnell, A. (2008). A simple monotone process with application to radiocarbon-dated depth chronologies. J. Roy. Statist. Soc. Ser. C 57 399–418.
  • Haslett, J., Whiley, M., Bhattacharya, S., Salter-Townshend, M., Wilson, S. P., Allen, J. R. M., Huntley, B. and Mitchell, F. J. G. (2006). Bayesian palaeoclimate reconstruction. J. Roy. Statist. Soc. Ser. A 169 395–438.
  • Holmström, L., Ilvonen, L., Seppä, H. and Veski, S. (2015a). Supplement A to “A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records.” DOI:10.1214/15-AOAS832SUPPA. An on line supplement.
  • Holmström, L., Ilvonen, L., Seppä, H. and Veski, S. (2015b). Supplement B to “A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records.” DOI:10.1214/15-AOAS832SUPPB. The data used in reconstructions.
  • Holmström, L., Ilvonen, L., Seppä, H. and Veski, S. (2015c). Supplement C to “A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records.” DOI:10.1214/15-AOAS832SUPPC. The Matlab code used in reconstructions.
  • Jansen, E. et al. (2007). Palaeoclimate. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller, eds.) 433–497. Cambridge Univ. Press, Cambridge.
  • Jones, P. D., Briffa, P. D., 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. (2009). High-resolution palaeoclimatology of the last millennium: A review of current status and future prospects. Holocene 19 3–49.
  • Journel, A. G. and Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, San Diego.
  • Juggins, S. and Birks, H. J. B. (2012). Quantitative environmental reconstructions from biological data. In Tracking Environmental Change Using Lake Sediments, Data Handling and Numerical Techniques 5 (H. J. B. Birks, A. F. Lotter, S. Juggins and J. P. Smol, eds.) 431–494. Springer, Dordrecht.
  • Kaipio, J. and Somersalo, E. (2005). Statistical and Computational Inverse Problems. Applied Mathematical Sciences 160. Springer, New York.
  • Korhola, A., Vasko, K., Toivonen, H. T. T. and Olander, H. (2002). Holocene temperature changes in northern Fennoscandia reconstructed from chironomids using Bayesian modelling. Qaternary Science Reviews 21 1841–1860.
  • Li, B., Nychka, D. W. and Ammann, C. M. (2010). The value of multi-proxy reconstruction of past climate. J. Amer. Statist. Assoc. 105 883–911.
  • Marcott, S. A., Shakun, J. D., Clark, P. U. and Mix, A. C. (2013). A reconstruction of regional and global temperature for the past 11,300 years. Science 339 1198–1201.
  • Masson-Delmotte, V. et al. (2013). Information from paleoclimate archives. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (T. F. Stocker, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia and P. M. Midgley, eds.) 383–464. Cambridge Univ. Press, Cambridge.
  • Moberg, A. and Bergström, H. (1997). Homogenization of Swedish temperature data. Part III: The long temperature records from Uppsala and Stockholm. Int. J. Climatol. 17 667–699.
  • NRC (2006). Surface Temperature Reconstructions for the Last 2000 Years. The National Academies Press, Washington.
  • Ohlwein, C. and Wahl, E. R. (2012). Review of probabilistic pollen-climate transfer methods. Quat. Sci. Rev. 31 17–29.
  • Paciorek, C. J. and McLachlan, J. S. (2009). Mapping ancient forests: Bayesian inference for spatio-temporal trends in forest composition using the fossil pollen proxy record. J. Amer. Statist. Assoc. 104 608–622.
  • Renssen, H., Seppä, H., Crosta, X., Goosse, H. and Roche, D. M. (2012). Global characterization of the Holocene thermal maximum. Quat. Sci. Rev. 48 7–19.
  • Renssen, H., Seppä, H., Heiri, O., Roche, D. M., Goosse, H. and Fichefet, T. (2009). The spatial and temporal complexity of the Holocene thermal maximum. Nat. Geosci. 2 411–414.
  • Robert, C. P. and Casella, G. (2004). Monte Carlo Statistical Methods, 2nd ed. Springer, New York.
  • Salonen, J. S., Ilvonen, L., Seppä, H., Holmström, L., Telford, R. J., Gaidamavičius, A., Stančikaitė, M. and Subetto, D. (2012). Comparing different calibration methods (WA/WA-PLS regression and Bayesian modelling) and different-sized calibration sets in pollen-based quantitative cllimate reconstructions. Holocene 22 413–424.
  • Sarmaja-Korjonen, K. and Seppä, H. (2007). Abrupt and consistent responses of aquatic and terrestrial ecosystems to the 8200 cal. yr BP cold event: A lacustrine record from Lake Arapisto, Finland. Holocene 17 455–464.
  • Seppä, H., Hammarlund, D. and Antonsson, K. (2005). Low-frequency and high-frequency changes in temperature and effective humidity during the Holocene in south-central Sweden: Implications for atmospheric and oceanic forcings of climate. Clim. Dyn. 25 285–297.
  • Seppä, H. and Poska, A. (2004). Holocene annual mean temperature changes in Estonia and their relationship to solar insolation and atmospheric circulation patterns. Quat. Res. 61 22–31.
  • Seppä, H., Bjune, A. E., Telford, R. J., Birks, H. J. B. and Veski, S. (2009). Last nine-thousand years of temperature variability in Northern Europe. Clim. Past 5 523–535.
  • Shakun, J. D. et al. (2012). Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature 484 49–54.
  • ter Braak, C. J. F. and Juggins, S. (1993). Weighted averaging partial least squares regression (WA-PLS): An improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269/270 485–502.
  • Tingley, M. P., Craigmile, P. F., Haran, M., Li, B., Mannshardt-Shamseldin, E. and Rajaratnam, B. (2012). Piecing together the past: Statistical insights into paleoclimatic reconstructions. Quat. Sci. Rev. 35 1–22.
  • Tingley, M. P. and Huybers, P. (2010a). A Bayesian algorithm for reconstructing climate anomalies in space and time. Part 1: Development and applications to paleoclimate reconstruction problems. J. Climate 23 2759–2781.
  • Tingley, M. P. and Huybers, P. (2010b). A Bayesian algorithm for reconstructing climate anomalies in space and time. Part 2: Comparison with the regularized expectation-maximization algorithm. Journal of Climate 23 2782–2800.
  • Toivonen, H. T. T., Mannila, H., Korhola, A. and Olander, H. (2001). Applying Bayesian statistics to organism-based environmental reconstruction. Ecol. Appl. 11 618–630.
  • Vasko, K., Toivonen, H. T. T. and Korhola, A. (2000). A Bayesian multinomial Gaussian response model for organism-based environmental reconstruction. J. Paleolimnol. 24 243–250.
  • Wiersma, A. P. and Renssen, H. (2006). Model data comparison for the 8.2 ka BP event: Confirmation of a forcing mechanism by catastrophic drainage of Laurentide Lakes. Quat. Sci. Rev. 25 63–88.
  • Woodward, F. I. (1987). Climate and Plant Distribution. Cambridge Univ. Press, Cambridge.

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