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Tree leaf out response to temperature: comparing field observations, remote sensing, and a warming experiment.
Caroline A Polgar1, Richard B Primack, Jeffrey S Dukes
1Boston University, Boston, MA, 02215, USA, carolinepolgar@gmail.com.
International Journal of Biometeorology
|September 3, 2013
Summary
Warmer spring temperatures advance tree leaf out timing. However, field observations show a greater sensitivity to temperature than experimental warming or satellite data, highlighting the need for methodological understanding in climate change research.
Area of Science:
- Ecology
- Climate Change Science
- Phenology
Background:
- Leaf out timing is a key indicator of ecosystem response to climate change.
- Temperate ecosystems experience critical seasonal transitions marked by leaf emergence.
- Understanding phenological shifts is vital for ecological modeling and climate change impact assessment.
Purpose of the Study:
- To compare the effect of spring temperature on tree leaf out timing across three distinct data sources.
- To quantify discrepancies in leaf out sensitivity to temperature among field observations, remote sensing, and experimental warming.
- To inform researchers about methodological differences impacting phenological data interpretation for climate models.
Main Methods:
- Analysis of field observations of leaf out timing.
- Processing of remotely sensed satellite data for leaf out detection.
- Evaluation of tree responses under controlled experimental warming conditions.
Main Results:
- All three methods indicated earlier leaf out with increasing spring temperatures.
- Field observations showed the highest temperature sensitivity (6.1 days/°C), followed by remote sensing (3.7 days/°C), and experimental warming (2.1 days/°C).
- Significant, unexplained differences in leaf out timing sensitivity were found among the data sources.
Conclusions:
- Methodological variations significantly influence the observed relationship between spring temperature and tree leaf out.
- Researchers must account for these discrepancies when using phenological data for ecological models.
- Further investigation into the mechanisms driving these differences is crucial for accurate climate change impact predictions.
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