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Fennoscandian tree-ring anatomy shows a warmer modern than medieval climate

Nature volume 620, pages 97–103 (2023)Cite this article

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Abstract

Earth system models and various climate proxy sources indicate global warming is unprecedented during at least the Common Era1. However, tree-ring proxies often estimate temperatures during the Medieval Climate Anomaly (950–1250 ce) that are similar to, or exceed, those recorded for the past century2,3, in contrast to simulation experiments at regional scales4. This not only calls into question the reliability of models and proxies but also contributes to uncertainty in future climate projections5. Here we show that the current climate of the Fennoscandian Peninsula is substantially warmer than that of the medieval period. This highlights the dominant role of anthropogenic forcing in climate warming even at the regional scale, thereby reconciling inconsistencies between reconstructions and model simulations. We used an annually resolved 1,170-year-long tree-ring record that relies exclusively on tracheid anatomical measurements from Pinus sylvestris trees, providing high-fidelity measurements of instrumental temperature variability during the warm season. We therefore call for the construction of more such millennia-long records to further improve our understanding and reduce uncertainties around historical and future climate change at inter-regional and eventually global scales.

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Fig. 1: From Fennoscandian forests and lakes to QWA-based tree-ring data.
Fig. 2: A-FEN has an excellent warm-season temperature reconstruction ability and reveals that the current climate is substantially warmer than that of the medieval period.
Fig. 3: The climate models and A-FEN show unprecedented occurrences of warm extremes in the past century and comparable dynamics for medieval and current climates.
Fig. 4: X-FEN shows the medieval climate as notably warmer than that estimated by A-FEN.

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Data availability

The A-FEN reconstruction is available at the National Centers for Environmental Information on the NOAA homepage (https://www.ncei.noaa.gov/access/paleo-search/?dataTypeId=3). The data used in the reconstruction are available at the NOAA International Tree Ring Data Bank. The data used to perform our analysis as well as our results are uploaded to Zenodo and are freely accessible using the following link: https://doi.org/10.5281/zenodo.7993298. Source data are provided with this paper.

Code availability

The code that supports the findings of this study is available alongside source data on the Zenodo repository and can be accessed using the following link: https://doi.org/10.5281/zenodo.7993298.

References

  1. Neukom, R. et al. Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era. Nat. Geosci. 12, 643–649 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Esper, J., Düthorn, E., Krusic, P. J., Timonen, M. & Büntgen, U. Northern European summer temperature variations over the Common Era from integrated tree‐ring density records. J. Quat. Sci. 29, 487–494 (2014).

    Article  Google Scholar 

  3. Luterbacher, J. et al. European summer temperatures since Roman times. Environ. Res. Lett. 11, 024001 (2016).

    Article  Google Scholar 

  4. Fernández-Donado, L. et al. Large-scale temperature response to external forcing in simulations and reconstructions of the last millennium. Clim. Past 9, 393–421 (2013).

    Article  Google Scholar 

  5. Masson-Delmotte, V. et al. (eds) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2021).

  6. Neukom, R., Steiger, N., Gómez-Navarro, J. J., Wang, J. & Werner, J. P. No evidence for globally coherent warm and cold periods over the preindustrial Common Era. Nature 571, 550–554 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Frank, D., Esper, J., Zorita, E. & Wilson, R. A noodle, hockey stick, and spaghetti plate: a perspective on high‐resolution paleoclimatology. Wiley Interdiscip. Rev. Clim. Change 1, 507–516 (2010).

    Article  Google Scholar 

  8. Anchukaitis, K. J. & Smerdon, J. E. Progress and uncertainties in global and hemispheric temperature reconstructions of the Common Era. Quat. Sci. Rev. 286, 107537 (2022).

    Article  Google Scholar 

  9. Wilson, R. et al. Last millennium northern hemisphere summer temperatures from tree rings: Part I: the long term context. Quat. Sci. Rev. 134, 1–18 (2016).

    Article  ADS  Google Scholar 

  10. Schneider, L. et al. Revising midlatitude summer temperatures back to A.D. 600 based on a wood density network. Geophys. Res. Lett. 42, 4556–4562 (2015).

    Article  ADS  Google Scholar 

  11. Zhao, B. et al. Prolonged drying trend coincident with the demise of Norse settlement in southern Greenland. Sci. Adv. 8, eabm4346 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bradley, R. S., Wanner, H. & Diaz, H. F. The Medieval Quiet Period. Holocene 26, 990–993 (2016).

    Article  ADS  Google Scholar 

  13. Knutti, R. & Hegerl, G. C. The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nat. Geosci. 1, 735–743 (2008).

    Article  ADS  CAS  Google Scholar 

  14. PAGES2kConsortium. A global multiproxy database for temperature reconstructions of the Common Era. Sci. Data 4, 170088 (2017).

    Article  Google Scholar 

  15. Cook, E. R., Briffa, K. R., Meko, D. M., Graybill, D. A. & Funkhouser, G. The ‘segment length curse’ in long tree-ring chronology development for palaeoclimatic studies. Holocene 5, 229–237 (1995).

    Article  ADS  Google Scholar 

  16. Esper, J. et al. Orbital forcing of tree-ring data. Nat. Clim. Change 2, 862–866 (2012).

    Article  ADS  Google Scholar 

  17. Franke, J., Frank, D., Raible, C. C., Esper, J. & Brönnimann, S. Spectral biases in tree-ring climate proxies. Nat. Clim. Change 3, 360–364 (2013).

    Article  ADS  Google Scholar 

  18. Esper, J., Schneider, L., Smerdon, J. E., Schöne, B. R. & Büntgen, U. Signals and memory in tree-ring width and density data. Dendrochronologia 35, 62–70 (2015).

    Article  Google Scholar 

  19. Zhang, H. et al. Modified climate with long term memory in tree ring proxies. Environ. Res. Lett. 10, 084020 (2015).

    Article  ADS  Google Scholar 

  20. Esper, J. et al. Ranking of tree-ring based temperature reconstructions of the past millennium. Quat. Sci. Rev. 145, 134–151 (2016).

    Article  ADS  Google Scholar 

  21. McCarroll, D., Young, G. H. & Loader, N. J. Measuring the skill of variance-scaled climate reconstructions and a test for the capture of extremes. Holocene 25, 618–626 (2015).

    Article  ADS  Google Scholar 

  22. Büntgen, U. Scrutinizing tree-ring parameters for Holocene climate reconstructions. Wiley Interdiscip. Rev. Clim. Change 13, e778 (2022).

    Article  Google Scholar 

  23. Battipaglia, G. et al. Five centuries of Central European temperature extremes reconstructed from tree-ring density and documentary evidence. Glob. Planet. Change 72, 182–191 (2010).

    Article  ADS  Google Scholar 

  24. Von Storch, H. et al. Reconstructing past climate from noisy data. Science 306, 679–682 (2004).

    Article  ADS  Google Scholar 

  25. Belmecheri, S., Babst, F., Wahl, E. R., Stahle, D. W. & Trouet, V. Multi-century evaluation of Sierra Nevada snowpack. Nat. Clim. Change 6, 2–3 (2016).

    Article  ADS  Google Scholar 

  26. Lücke, L. J., Hegerl, G. C., Schurer, A. P. & Wilson, R. Effects of memory biases on variability of temperature reconstructions. J. Clim. 32, 8713–8731 (2019).

    Article  ADS  Google Scholar 

  27. von Arx, G., Crivellaro, A., Prendin, A. L., Čufar, K. & Carrer, M. Quantitative wood anatomy—practical guidelines. Front. Plant Sci. 7, 781 (2016).

    Google Scholar 

  28. Prendin, A. L. et al. New research perspectives from a novel approach to quantify tracheid wall thickness. Tree Physiol. 37, 976–983 (2017).

    Article  PubMed  Google Scholar 

  29. Björklund, J. et al. Scientific merits and analytical challenges of tree‐ring densitometry. Rev. Geophys. 57, 1224–1264 (2019).

    Article  ADS  Google Scholar 

  30. Björklund, J., Seftigen, K., Fonti, P., Nievergelt, D. & von Arx, G. Dendroclimatic potential of dendroanatomy in temperature-sensitive Pinus sylvestris. Dendrochronologia 60, 125673 (2020).

    Article  Google Scholar 

  31. Lopez-Saez, J. et al. Tree-ring anatomy of Pinus cembra trees opens new avenues for climate reconstructions in the European Alps. Sci. Total Envir. 855, 158605 (2023).

  32. Seftigen, K. et al. Prospects for dendroanatomy in paleoclimatology – a case study on Picea engelmannii from the Canadian Rockies. Clim. Past 18, 1151–1168 (2022).

    Article  Google Scholar 

  33. Allen, K. J., Nichols, S. C., Evans, R. & Baker, P. J. Characteristics of a multi-species conifer network of wood properties chronologies from Southern Australia. Dendrochronologia 76, 125997 (2022).

    Article  Google Scholar 

  34. Melvin, T. M., Grudd, H. & Briffa, K. R. Potential bias in ‘updating’ tree-ring chronologies using regional curve standardisation: re-processing 1500 years of Torneträsk density and ring-width data. Holocene 23, 364–373 (2013).

    Article  ADS  Google Scholar 

  35. Linderholm, H. W. & Gunnarson, B. E. Were medieval warm-season temperatures in Jämtland, central Scandinavian Mountains, lower than previously estimated? Dendrochronologia 57, 125607 (2019).

    Article  Google Scholar 

  36. Grudd, H. Torneträsk tree-ring width and density AD 500–2004: a test of climatic sensitivity and a new 1500-year reconstruction of north Fennoscandian summers. Clim. Dyn. 31, 843–857 (2008).

    Article  Google Scholar 

  37. Matskovsky, V. & Helama, S. Testing long-term summer temperature reconstruction based on maximum density chronologies obtained by reanalysis of tree-ring data sets from northernmost Sweden and Finland. Clim. Past 10, 1473–1487 (2014).

    Article  Google Scholar 

  38. Büntgen, U. et al. Prominent role of volcanism in Common Era climate variability and human history. Dendrochronologia 64, 125757 (2020).

    Article  Google Scholar 

  39. Guillet, S. et al. Climate response to the Samalas volcanic eruption in 1257 revealed by proxy records. Nat. Geosci. 10, 123–128 (2017).

    Article  ADS  CAS  Google Scholar 

  40. Wu, T. et al. An overview of BCC climate system model development and application for climate change studies. J. Meteorol. Res. 28, 34–56 (2014).

    Google Scholar 

  41. Landrum, L. et al. Last millennium climate and its variability in CCSM4. J. Clim. 26, 1085–1111 (2013).

    Article  ADS  Google Scholar 

  42. Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).

    Article  Google Scholar 

  43. Yukimoto, S. et al. A new global climate model of the Meteorological Research Institute: MRI-CGCM3 – model description and basic performance–. J. Meteorol. Soc. Japan. Ser. II 90A, 23–64 (2012).

    Article  Google Scholar 

  44. Bao, Q. et al. The flexible global ocean-atmosphere-land system model, spectral version 2: FGOALS-s2. Adv. Atmos. Sci. 30, 561–576 (2013).

    Article  Google Scholar 

  45. Phipps, S. et al. The CSIRO Mk3L climate system model version 1.0 – part 1: description and evaluation. Geosci. Model Dev. 4, 483–509 (2011).

    Article  ADS  Google Scholar 

  46. Miller, R. L. et al. CMIP5 historical simulations (1850–2012) with GISS ModelE2. J. Adv. Model. Earth Syst. 6, 441–478 (2014).

    Article  ADS  Google Scholar 

  47. Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI‐ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).

    Article  ADS  Google Scholar 

  48. Marsh, D. R. et al. Climate change from 1850 to 2005 simulated in CESM1(WACCM). J. Clim. 26, 7372–7391 (2013).

    Article  ADS  Google Scholar 

  49. Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).

    Article  ADS  Google Scholar 

  50. Johns, T. C. et al. Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Clim. Dyn. 20, 583–612 (2003).

    Article  Google Scholar 

  51. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  ADS  Google Scholar 

  52. Braconnot, P. et al. Evaluation of climate models using palaeoclimatic data. Nat. Clim. Change 2, 417–424 (2012).

    Article  ADS  Google Scholar 

  53. Wigley, T. M. L., Briffa, K. R. & Jones, P. D. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Appl. Meteorol. Climatol. 23, 201–213 (1984).

    Article  ADS  Google Scholar 

  54. Helama, S., Melvin, T. M. & Briffa, K. R. Regional curve standardization: state of the art. Holocene 27, 172–177 (2017).

    Article  ADS  Google Scholar 

  55. Andersson, G. Om talltorkan i öfra Sverige våren 1903 (Statens skogsförsöksanstalt, 1905).

  56. Pallardy, S. G. Physiology of Woody Plants 3rd edn (Academic, 2008).

  57. Vaganov, E. A., Hughes, M. K. & Shashkin, A. V. Growth Dynamics of Conifer Tree Rings: Images of Past and Future Eenvironments Vol. 183 (Springer Science & Business Media, 2006).

  58. Abbott, P. M. et al. Cryptotephra from the Icelandic Veiðivötn 1477 ce eruption in a Greenland ice core: confirming the dating of volcanic events in the 1450s ce and assessing the eruption’s climatic impact. Clim. Past 17, 565–585 (2021).

    Article  Google Scholar 

  59. Stoffel, M. et al. Estimates of volcanic-induced cooling in the Northern Hemisphere over the past 1,500 years. Nat. Geosci. 8, 784–788 (2015).

    Article  ADS  CAS  Google Scholar 

  60. Anchukaitis, K. J. et al. Last millennium Northern Hemisphere summer temperatures from tree rings: part II, spatially resolved reconstructions. Quat. Sci. Rev. 163, 1–22 (2017).

    Article  ADS  Google Scholar 

  61. McCarroll, D. et al. A 1200-year multiproxy record of tree growth and summer temperature at the northern pine forest limit of Europe. Holocene 23, 471–484 (2013).

    Article  ADS  Google Scholar 

  62. Neukom, R. et al. Inter-hemispheric temperature variability over the past millennium. Nat. Clim. Change 4, 362–367 (2014).

    Article  ADS  Google Scholar 

  63. PAGES2k-PMIP3 group. Continental-scale temperature variability in PMIP3 simulations and PAGES 2k regional temperature reconstructions over the past millennium. Clim. Past 11, 1673–1699 (2015).

    Article  Google Scholar 

  64. Lorenz, E. N. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141 (1963).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  65. D’Arrigo, R., Wilson, R., Liepert, B. & Cherubini, P. On the ‘divergence problem’ in northern forests: a review of the tree-ring evidence and possible causes. Glob. Planet. Change 60, 289–305 (2008).

    Article  ADS  Google Scholar 

  66. Büntgen, U. et al. The influence of decision-making in tree ring-based climate reconstructions. Nat. Commun. 12, 3411 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  67. Pawlowicz, R. M_Map: a mapping package for MATLAB, MATLAB package v.1.4m. https://www.eoas.ubc.ca/~rich/map.html (2020).

  68. Morice, C. P. et al. An updated assessment of near‐surface temperature change from 1850: The HadCRUT5 data set. J. Geophys. Res. Atmos. 126, e2019JD032361 (2021).

    Article  ADS  Google Scholar 

  69. Schweingruber, F. H., Bartholin, T., Schār, E. & Briffa, K. R. Radiodensitometric‐dendroclimatological conifer chronologies from Lapland (Scandinavia) and the Alps (Switzerland). Boreas 17, 559–566 (1988).

    Article  Google Scholar 

  70. Briffa, K. R. et al. A 1,400-year tree-ring record of summer temperatures in Fennoscandia. Nature 346, 434–439 (1990).

    Article  ADS  Google Scholar 

  71. Briffa, K. R. et al. Fennoscandian summers from AD 500: temperature changes on short and long timescales. Clim. Dyn. 7, 111–119 (1992).

    Article  Google Scholar 

  72. Gärtner, H., Lucchinetti, S. & Schweingruber, F. A new sledge microtome to combine wood anatomy and tree-ring ecology. IAWA J. 36, 452–459 (2015).

    Article  Google Scholar 

  73. von Arx, G. & Carrer, M. ROXAS – a new tool to build centuries-long tracheid-lumen chronologies in conifers. Dendrochronologia 32, 290–293 (2014).

    Article  Google Scholar 

  74. Denne, M. P. Definition of latewood according to Mork (1928). IAWA J. 10, 59–62 (1989).

    Article  Google Scholar 

  75. Björklund, J. A., Gunnarson, B. E., Seftigen, K., Esper, J. & Linderholm, H. W. Blue intensity and density from northern Fennoscandian tree rings, exploring the potential to improve summer temperature reconstructions with earlywood information. Clim. Past 10, 877–885 (2014).

    Article  Google Scholar 

  76. Schmidt, G. et al. Climate forcing reconstructions for use in PMIP simulations of the Last Millennium (v1.1). Geosci. Model Dev. 5, 185–191 (2012).

    Article  ADS  Google Scholar 

  77. National Research Council. Surface Temperature Reconstructions for the Last 2,000 Years (National Academies Press, 2007).

  78. Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree Ring Bull. 41, 45–53 (1981).

    Google Scholar 

  79. Matalas, N. C. Statistical properties of tree ring data. Int. Assoc. Sci. Hydrol. Bull. 7, 39–47 (1962).

    Article  Google Scholar 

  80. Piermattei, A., Crivellaro, A., Carrer, M. & Urbinati, C. The “blue ring”: anatomy and formation hypothesis of a new tree-ring anomaly in conifers. Trees 29, 613–620 (2015).

    Article  CAS  Google Scholar 

  81. Percival, D. B. & Walden, A. T. Spectral Analysis for Physical Applications (Cambridge Univ. Press, 1993).

  82. Huybers, P. pmtmPH.m v.1.0.0.0. https://www.mathworks.com/matlabcentral/fileexchange/2927-pmtmph-m (MATLAB Central File Exchange, 2022).

  83. Haurwitz, M. W. & Brier, G. W. A critique of the superposed epoch analysis method: its application to solar–weather relations. Mon. Weather Rev. 109, 2074–2079 (1981).

    Article  ADS  Google Scholar 

  84. Brad Adams, J., Mann, M. E. & Ammann, C. M. Proxy evidence for an El Nino-like response to volcanic forcing. Nature 426, 274–278 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  85. Blarquez, O. & Carcaillet, C. Fire, fuel composition and resilience threshold in subalpine ecosystem. PLoS ONE 5, e12480 (2010).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  86. Gao, C., Robock, A. & Ammann, C. Volcanic forcing of climate over the past 1500 years: an improved ice core‐based index for climate models. J. Geophys. Res. Atmos. 113, D23111 (2008).

    Article  ADS  Google Scholar 

  87. Toohey, M. & Sigl, M. Volcanic stratospheric sulfur injections and aerosol optical depth from 500 bce to 1900 ce. Earth Syst. Sci. Data 9, 809–831 (2017).

    Article  ADS  Google Scholar 

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Acknowledgements

This work has been funded by a grant from the Swiss National Science Foundation awarded to G.v.A., supporting J.B., K.S., M.V.F. and S.K. (Project XELLCLIM no. 200021_182398). M.S., P.F. and J.B. received funding from the SNF Sinergia project CALDERA (no. 183571). K.S. received funding from Formas grant no. 2019-01482. J.E. received funding from the ERC Advanced grant MONOSTAR (AdG 882727). We thank E. Rocha for assistance with Torneträsk samples from the Dendrolab at Stockholm University; S. Helama for making us aware of the early twentieth-century study55 describing the pine-tree canopy damage; and M. Timmonen and U. Büntgen for their assistance in sampling the dead-wood material at the N-scan site.

Author information

Author notes

  1. These authors contributed equally: Marco Carrer, Georg von Arx

Authors and Affiliations

  1. Swiss Federal Institute for Forest Snow and Landscape Research WSL, Birmensdorf, Switzerland

    Jesper Björklund, Kristina Seftigen, Marina V. Fonti, Sven Kottlow, Patrick Fonti, Daniel Nievergelt & Georg von Arx

  2. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Jesper Björklund, Marina V. Fonti, Patrick Fonti, Daniel Nievergelt & Georg von Arx

  3. Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    Kristina Seftigen

  4. Climate Change Impacts and Risks in the Anthropocene (C-CIA), University of Geneva, Geneva, Switzerland

    Markus Stoffel

  5. Dendrolab.ch, Department of Earth Sciences, University of Geneva, Geneva, Switzerland

    Markus Stoffel

  6. Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland

    Markus Stoffel

  7. Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA

    David C. Frank

  8. Department of Geography, Johannes Gutenberg University, Mainz, Germany

    Jan Esper

  9. Global Change Research Institute of the Czech Academy of Sciences (CzechGlobe), Brno, Czech Republic

    Jan Esper

  10. Earth and Life Institute, Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium

    Hugues Goosse

  11. Swedish Polar Research Secretariat, Abisko Scientific Research Station, Abisko, Sweden

    Håkan Grudd

  12. Department of Physical Geography, Stockholm University, Stockholm, Sweden

    Björn E. Gunnarson

  13. Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Björn E. Gunnarson

  14. Department of Land, Environment, Agriculture and Forestry (TeSAF), University of Padua, Padua, Italy

    Elena Pellizzari & Marco Carrer

Authors
  1. Jesper Björklund
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  15. Georg von Arx
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Contributions

J.B., G.v.A., K.S. and M.C. conceptualized the study. H. Grudd, B.E.G., J.E., M.C., E.P. and D.N. conducted fieldwork and provided physical samples for the wood anatomical analyses. G.v.A., M.C., M.V.F., S.K. and E.P. coordinated, processed and measured the wood anatomical data. J.B. and K.S. performed the analyses with input from H. Goosse, G.v.A., P.F., D.C.F. and M.S. J.B. wrote the paper and all authors have reviewed and helped to revise the paper.

Corresponding author

Correspondence to Jesper Björklund.

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Extended data figures and tables

Extended Data Fig. 1 Evaluation of the ability of the A-FEN and X-FEN reconstructions to capture extremes in climate targets.

The instrumental temperature data was sorted from coldest to warmest and plotted together with the reconstruction values of the corresponding years. The grey boxes are bound by the 10% coldest and warmest years, and the 10th and 90th percentile of the zscore temperatures, respectively. If an extreme reconstruction value is found within the grey box, the extreme is defined as “captured”. The sum of the captured values divided by the potential sum of values, is calculated and presented as a percentage of extreme value capturing (EVC). In McCarroll, et al.21, a significance testing was implemented, and for 160–170 years of climate data, p < 0.001 is achieved if more than 40% of values are captured. a) A-FEN’s ability to capture JJA temperature extremes. b) X-FEN’s ability to capture JJA temperature extremes. c) and d) show same analysis as a) and b) but using the target MJJA. Both datasets thus display significant amounts of extremes captured, but the A-FEN captures significantly more than the X-FEN for the MJJA target season. The X-FEN captures a higher percentage of cold extremes if the MJJA target season is used but the same percentage of warm extremes regardless of target season. The rationale for using MJJA as the target season for X-FEN is thus less clearcut than for the A-FEN. JJA is the target season used in the publications originally presenting the MXD data2,37 and is thus used in the main text for the other comparisons.

Source data

Extended Data Fig. 2 Monthly climate correlations across a range of frequency domains.

a) high-pass filtered data (cubic smoothing splines with 50 % frequency response cut-off at 40 years (HP40yrs)) correlated with identically treated temperature data. b) RCS detrended data correlated with untreated temperature data. c) low-pass filtered data (LP5yrs), and d) (LP10yrs), correlated with identically treated temperature data, respectively. The monthly temperature data were retrieved from HadCRUT568 (5° gridded monthly dataset, Lat. 65–70° N, Lon. 15–30° E). Correlation coefficients in white are significant at p < 0.01, and black coefficients are insignificant. When 10-year low-pass filtered data are used, the autocorrelation is so high that it is impossible to detect significance after adjusting for loss of degrees of freedom79, why it is meaningless to continue the analysis over even lower frequencies. The parameters or reconstructions reside on the y-axis, and each monthly temperature or monthly target season on the x-axis. First order autocorrelation, AR(1), of the JJA and MJJA temperatures are given on top of each panel as a reference, and the tree-ring parameter AR(1) can be found in the right margin of each panel. The period of analysis covers the full length-overlap between all datasets (1850–2019 for anatomical parameters and 1850–2010 for the X-FEN). The results are very similar if the 1850–2010 period is used for the QWA data. The delta radial cell wall thickness (DeltaCWTRAD) parameter was established as predictor for the A-FEN reconstruction due to overall performance in the analysis.

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Extended Data Fig. 3 Warm-season temperature reconstruction skill of A-FEN and X-FEN, as well as comparisons with existing large-scale reconstructions and regional climate simulations but using QWA data that has not been detrended using the RCS approach.

The non-QWA datatypes are identical to Fig. 2 of the Main manuscript and the vertical arrows have the exact positions and dimensions as in Fig. 2 for reference. a) A-FEN (produced in this study) calibrated using regional mean air MJJA temperatures68 (R2 ensemble range within brackets (a = 0.05)), and results for the X-FEN (from Wilson, et al.9) using corresponding JJA temperatures. The irregular winter/spring of 1902/1903, led to a massive dieback of yearly branch-shoots in the region55, highlighted by the yellow area. In these years with extremely narrow rings, the X-ray technique struggles to measure high MXD values due to its comparatively lower effective measurement resolution29 (see Extended Data Fig. 4). b) Replication and pairwise inter-series correlation (\(\bar{R}\)) of A-FEN in blue and the X-FEN in red. c) Centennial-scale variations (see Methods) compared between A-FEN, X-FEN, climate model simulations, and NH and global temperature reconstructions. The five large-scale reconstructions1,9,10,38,39, as well as the eleven regionally extracted climate-model simulations40,41,42,43,44,45,46,47,48,49,50 are represented by probabilistic percentile ranges. The vertical arrows highlight the overall discrepancies of the X-FEN compared to the other data.

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Extended Data Fig. 4 Illustration of the issue with comparatively low measurement resolution for X-ray MXD.

a) X-ray image with analysis track path indicated within the solid white rectangle, and examples of the effect of different effective measurement resolutions. b) The photosensors in a) build up measurement profiles, where the blue sensor builds the blue profile corresponding to a 20-micron effective measurement resolution, and the orange sensor builds up the orange profile corresponding to a 60 micron effective measurement resolution, approximating the effective measurement resolution of the X-ray methodology29. Note how the time series of MXD reflect inverse variations if developed using high-resolution or low-resolution equipment, i.e., the middle ring exhibits the lowest or highest value depending on resolution. The explanation for this is that very narrow latewood widths are associated with comparatively lower MXD values even though the “true” MXD value may be high. c) Relationships between TRW and A-FEN and d) LWW and A-FEN. e) Relationships between TRW and anatomical MXD (MXDCWT) and f) LWW and MXDCWT. g) Relationships between TRW and X-FEN and h) LWW and X-FEN. All datasets display correlations and using datapoints covering 850–2005 CE. Note how the X-FEN always is stronger correlated with TRW and LWW than the MXDCWT. A higher correlation is expected if TRW or LWW is affecting the measurement. Spearman rank correlation coefficients were used due to the possibly non-linear relationships between width and density. Rraw and rdiff. refers to untreated and first differenced data prior to correlations, respectively.

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Extended Data Fig. 5 Moving window correlation coefficients revealing that X-ray MXD exhibits a stronger relationship with ring width and latewood width than anatomical MXD.

Ring width (TRW) versus anatomical MXD (MXDCWT) and X-FEN, as well as latewood width (LWW) versus MXDCWT and X-FEN. Spearman rank correlations were used on RCS-detrended chronologies with a 100-year base-lengths and 10-year overlaps. For the anatomical MXDCWT data, 100 sub-sampled chronologies with 15 trees/year were used to create ensemble ranges represented in blue shades. Deviations from these blue shaded areas represent significant differences (p < 0.05) from the TRW and LWW correlations with MXDCWT, respectively. The X-FEN correlations often reside outside the blue areas, and at higher correlations with TRW and LWW respectively, indicating occasionally stronger dependence of X-FEN on these parameters.

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Extended Data Fig. 6 Comparison of A-FENs complete set of ensemble members with X-FEN.

RCS-detrended A-FEN (data from this study) versus the X-FEN (data from Wilson, et al.9), smoothed using cubic smoothing splines with 50% frequency response cut-off at 100 years. Note that no A-FEN ensemble member exhibit the protracted warmth during the MCA and the relatively low temperatures during the CWP, as does the X-FEN.

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Extended Data Fig. 7 Comparison of A-FEN versus a network of millennium-long Fennoscandian MXD datasets showing the wide range of medieval estimates and comparably modest modern warming.

The MXD based temperature reconstructions from Fennoscandia are retrieved from Wilson, et al.9, Schneider, et al.10 and McCarroll, et al.61 represented by a probabilistic percentile range.

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Extended Data Fig. 8 Spectral properties and first order autocorrelation of the reconstructions and models compared in this study.

a) Spectral properties of the A-FEN ensemble and X-FEN on the backdrop of the model ensemble range, as well as the range of a 1000 timeseries, of equal length to the A-FEN, of colored noise with a beta coefficient of 0.5. (Beta coefficient for White noise = 0, Pink noise = 1). b) Running autocorrelations AR(1) calculated for 100-year window lengths, shifted by 10-year lags.

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Extended Data Fig. 9 Superposed epoch analysis and comparisons over individual eruption years for the A-FEN ensemble, X-FEN and the model ensemble.

SEA’s using Gao, et al.86 event lists of the 10 a) and 30 b) of the largest (based on sulfate aerosol injection) northern Hemisphere events. The model simulations were all extracted from the corresponding grid cells Lat 65–70° N, Lon 15–30° E. We used only Gao et al as basis for the event lists because most models in our ensemble were forced with Gao et al, but note that this list may not be optimal for some models and the tree-ring data. We employed a model ensemble mean in the SEA, to explore the degree of volcanic cooling the models express. c-h) Proxy vs model response to some specific major volcanic events dated according to Toohey and Sigl87. The responses to U.E. 1453 CE, Huaynaputina and Eldgjá are pronounced in the proxy data but not in the models. The responses to Samalas and Tambora are pronounced in the models but not in the proxy data. The response to Parker is present in both models and proxy.

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Extended Data Table 1 Reconstruction statistics for Fennoscandian tree-ring anatomy A-FEN
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Björklund, J., Seftigen, K., Stoffel, M. et al. Fennoscandian tree-ring anatomy shows a warmer modern than medieval climate. Nature 620, 97–103 (2023). https://doi.org/10.1038/s41586-023-06176-4

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