Dry summers come and go

“We aren’t the first scientists to confirm this trend,” says Dr. Tobias Scharnweber, one of the authors of the article. “However, what is new in our reconstruction is that we were able to calculate these growth rates using our own data method that we had developed especially for this project. This enabled us to show that average summer rainfall amounts were much lower at the time of the Mediaeval Climate Optimum, i.e. approximately 1000 years ago, than previously presumed. Maybe ‘one-in-a-century’ summers, like the one we had in 2018, were not that rare back then.” click here

Tobias Scharnweber, Karl-Uwe Heußner, Marko Smiljanic, Ingo Heinrich, Marieke van der Maaten-Theunissen, Ernst van der Maaten, Thomas Struwe, Allan Buras & Martin Wilmking . Removing the no-analogue bias in modern accelerated tree growth leads to stronger medieval drought. Scientific Reports, volume 9, Article number: 2509 (2019).

In many parts of the world, especially in the temperate regions of Europe and North-America, accelerated tree growth rates have been observed over the last decades. This widespread phenomenon is presumably caused by a combination of factors like atmospheric fertilization or changes in forest structure and/or management. If not properly acknowledged in the calibration of tree-ring based climate reconstructions, considerable bias concerning amplitudes and trends of reconstructed climatic parameters might emerge or low frequency information is lost. Here we present a simple but effective, data-driven approach to remove the recent non-climatic growth increase in tree-ring data. Accounting for the no-analogue calibration problem, a new hydroclimatic reconstruction for northern-central Europe revealed considerably drier conditions during the medieval climate anomaly (MCA) compared with standard reconstruction methods and other existing reconstructions. This demonstrates the necessity to account for fertilization effects in modern tree-ring data from affected regions before calibrating reconstruction models, to avoid biased results.

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