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Using Generative Art to Convey Past and Future Climate Transitions
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Warming increases the differences among spring phenology models under future climate change.

Yunhua Mo1, Xiran Li2, Yahui Guo2

  • 1College of Water Sciences, Beijing Normal University, Beijing, China.

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|November 8, 2023
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Summary
This summary is machine-generated.

Predicting plant phenology under climate change is crucial. This study evaluated thirteen models, finding the M1 model (photoperiod and temperature) performed best, with varied predictions across scenarios.

Keywords:
PEP725SSP scenariosclimate changefuture predictionspring phenological model

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Area of Science:

  • Plant science
  • Ecology
  • Climate change research

Background:

  • Phenological models predict plant responses to environmental factors.
  • Understanding climate change impacts on phenology is vital.
  • Model performance across different climate scenarios remains understudied.

Purpose of the Study:

  • To assess the predictive performance of thirteen spring phenology models under two contrasting climate change scenarios (SSP126 and SSP585).
  • To compare the influence of dormancy phases and driving factors on model accuracy.
  • To project spring phenology changes in Germany from 2021 to 2100.

Main Methods:

  • Parameterization of thirteen spring phenology models (six one-phase, seven two-phase) using observational and meteorological data.
  • Prediction of spring phenology (start of growing season - SOS) using climate data from SSP126 and SSP585 scenarios.
  • Comparison of model performance, including dormancy impacts and driving factor influence.

Main Results:

  • All models outperformed the NULL model, with average SOS prediction correlation > 0.72.
  • The M1 model, driven by photoperiod and forcing temperature, showed the best performance across species.
  • Under SSP126, SOS initially advanced then delayed; under SSP585, SOS advanced ~0.14 days/year.
  • Model prediction variability (standard deviation) increased significantly, especially for two-phase and temperature-only models later in the century.

Conclusions:

  • The M1 model offers robust predictions for spring phenology under climate change.
  • Future phenological models should incorporate endodormancy release mechanisms for improved accuracy.
  • Dormancy phase and driving factors significantly influence model performance under varying climate scenarios.