When is a trend meaningful? Insights to carbon cycle variability from an initial-condition large ensemble
Abstract Internal climate variability (ICV) creates a range of climate trajectories, which are superimposed upon the forced response. A single climate model realization may not represent forced change alone and may diverge from other realizations, as well as observations, due to ICV. We use an initi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
|
| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-024-00878-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Internal climate variability (ICV) creates a range of climate trajectories, which are superimposed upon the forced response. A single climate model realization may not represent forced change alone and may diverge from other realizations, as well as observations, due to ICV. We use an initial-condition large ensemble of simulations with the Community Earth System Model (CESM2) to show that ICV produces a range of outcomes in the terrestrial carbon cycle. Trends in gross primary production (GPP) from 1991 to 2020 differ among ensemble members due to the different climate trajectories resulting from ICV. We quantify how ICV imparts on GPP trends and apply our methodology to the observational record. Observed changes in GPP at two long-running eddy covariance flux towers are consistent with ICV, challenging the understanding of forced changes in the carbon cycle at these locations. A probabilistic framework that accounts for ICV is needed to interpret carbon cycle trends. |
|---|---|
| ISSN: | 2397-3722 |