Preprints
https://doi.org/10.5194/bg-2017-405
https://doi.org/10.5194/bg-2017-405
12 Oct 2017
 | 12 Oct 2017
Status: this preprint was under review for the journal BG. A revision for further review has not been submitted.

Causes of uncertainty in observed and projected heterotrophic respiration from Earth System Models

Cary Lynch, Corinne Hartin, Min Chen, and Ben Bond-Lamberty

Abstract. Heterotrophic respiration (RH) is a large component of the terrestrial carbon cycle, but one poorly simulated by Earth system models (ESMs), which diverge significantly in their historical and future RH projections. There is little understanding, however, of the causes of this variability and its consequences for future model development and scenario evaluation, and examining the relationships between RH and key climate variables may help to understand where and why models are divergent. We quantified the statistical relationships between RH and other terrestrial/climate variables across a suite of 25 ESMs from the Coupled Model Intercomparison Project phase 5 (CMIP5) for the 20th and 21st centuries, comparing the models both to each other and to an observation-driven global RH dataset. Compared to observations, ESMs consistency overestimate both the magnitude and climate sensitivity of global RH. The relationship between RH and surface air temperature (TAS) is strong, especially at high latitudes, and largely consistent across models. The observed RH and precipitation (PR) relationship is strong and positive (r ≥ 0.5, P < 0.005), but few models consistently show this sensitivity of RH to PR. The RH-TAS relationship explored here, and more pattern scaling methods more generally, can be used to efficiently explore uncertainty and projected changes in RH under a wide range of future emission scenarios, and understand how models' structural and parametric choices produce divergent results. Because uncertainty in RH has large effects on ESM projections of future climate, this may help direct attention to relationships in the carbon cycle that contribute to this uncertainty.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Cary Lynch, Corinne Hartin, Min Chen, and Ben Bond-Lamberty
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Cary Lynch, Corinne Hartin, Min Chen, and Ben Bond-Lamberty
Cary Lynch, Corinne Hartin, Min Chen, and Ben Bond-Lamberty

Viewed

Total article views: 1,212 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
797 353 62 1,212 174 59 70
  • HTML: 797
  • PDF: 353
  • XML: 62
  • Total: 1,212
  • Supplement: 174
  • BibTeX: 59
  • EndNote: 70
Views and downloads (calculated since 12 Oct 2017)
Cumulative views and downloads (calculated since 12 Oct 2017)

Viewed (geographical distribution)

Total article views: 1,177 (including HTML, PDF, and XML) Thereof 1,176 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Dec 2024
Download
Short summary
Heterotrophic respiration (RH) is a large part of the carbon cycle, but it is poorly simulated by climate models. We examine the relationships between RH and key climate variables to understand this uncertainty in observations and from climate models. Compared to observations, models overestimate both the RH trend and climatological relationships. In the future, the relationship between RH and temperature is strong and can be used to explore a wide range of future scenarios.
Altmetrics