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Journal Article

Was the extreme Northern Hemisphere greening in 2015 predictable?

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Bastos, A., Ciais, P., Park, T., Zscheischler, J., Yue, C., Barichivich, J., et al. (2017). Was the extreme Northern Hemisphere greening in 2015 predictable? Environmental Research Letters, 12(4): 044016. doi:10.1088/1748-9326/aa67b5.

Cite as: https://hdl.handle.net/21.11116/0000-0006-9CA8-D
The year 2015 was, at the time, the warmest since 1880, and many regions in the Northern Hemisphere (NH) registered record breaking annual temperatures. Simultaneously, a remarkable and widespread growing season greening was observed over most of the NH in the record from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). While the response of vegetation to climate change (i.e. the long term trend) is assumed to be predictable, it is still unclear whether it is also possible to predict the interannual variability in vegetation activity.
Here, we evaluate whether the unprecedented magnitude and extent of the greening observed in 2015 corresponds to an expected response to the 2015 climate anomaly, or to a change in the sensitivity of NH vegetation to climate. We decompose NDVI into the long-term and interannual variability components, and find that the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO) explain about half of NDVI interannual variability. This response is in addition to the long-term temperature and human-induced greening trend. We use a simple statistical approach to predict the NDVI anomaly in 2015, using the PDO and AMO states as predictors for interannual variability, and temperature and precipitation trends for the long-term component.
We show that the 2015 anomaly can be predicted as an expected vegetation response to temperature and water-availability associated with the very strong state of the PDO in 2015. The link found between climate variability patterns and vegetation activity should contribute to increase the predictability of carbon-cycle processes at interannual time-scales, which may be relevant, for instance, for optimizing land-management strategies.