Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

Abstract The Arctic–Boreal Region (ABR) has a large impact on global vegetation–atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO 2 . The ABR is a significant source of uncer...

Full description

Bibliographic Details
Published in:Global Change Biology
Main Authors: Rogers, Alistair, Serbin, Shawn P., Way, Danielle A.
Other Authors: Natural Sciences and Engineering Research Council of Canada
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2021
Subjects:
Online Access:http://dx.doi.org/10.1111/gcb.15958
https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15958
https://onlinelibrary.wiley.com/doi/full-xml/10.1111/gcb.15958
https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/gcb.15958
Description
Summary:Abstract The Arctic–Boreal Region (ABR) has a large impact on global vegetation–atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO 2 . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots‐on‐the‐ground science.