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The influence of decision-making in tree ring-based climate reconstructions.

Ulf BüntenKathy AllenKevin J AnchukaitisDominique ArseneaultÉtienne BoucherAchim BräuningSnigdhansu ChatterjeePaolo CherubiniOlga V Churakova SidorovaChristophe CoronaFabio GennarettiJussi GrießingerSebastian GuilletJoel GuiotBjörn GunnarsonSamuli HelamaPhilipp HochreutherMalcolm K HughesPeter HuybersAlexander V KirdyanovPaul J KrusicJosef LudescherWolfgang J-H MeierVladimir S MyglanKurt NicolussiClive OppenheimerFrederick ReinigMatthew W SalzerKristina SeftigenAlexander R StineMarkus StoffelScott St GeorgeErnesto TejedorAleyda TrevinoValerie TrouetJianglin WangRob WilsonBao YangGuobao XuJan Esper
Published in: Nature communications (2021)
Tree-ring chronologies underpin the majority of annually-resolved reconstructions of Common Era climate. However, they are derived using different datasets and techniques, the ramifications of which have hitherto been little explored. Here, we report the results of a double-blind experiment that yielded 15 Northern Hemisphere summer temperature reconstructions from a common network of regional tree-ring width datasets. Taken together as an ensemble, the Common Era reconstruction mean correlates with instrumental temperatures from 1794-2016 CE at 0.79 (p < 0.001), reveals summer cooling in the years following large volcanic eruptions, and exhibits strong warming since the 1980s. Differing in their mean, variance, amplitude, sensitivity, and persistence, the ensemble members demonstrate the influence of subjectivity in the reconstruction process. We therefore recommend the routine use of ensemble reconstruction approaches to provide a more consensual picture of past climate variability.
Keyphrases
  • climate change
  • convolutional neural network
  • image quality
  • neural network
  • heat stress
  • rna seq
  • computed tomography
  • deep learning
  • magnetic resonance
  • energy transfer