A New Method of Comparing Forcing Agents in Climate Models
We describe a new method of comparing different climate forcing agents (e.g., CO2, CH4, and solar irradiance) that avoids many of the ambiguities introduced by temperature-related climate feedbacks. This is achieved by introducing an explicit feedback loop external to the climate model that adjusts one forcing agent to balance another while keeping global mean surface temperature constant. Compared to current approaches, this method has two main advantages: (i) the need to define radiative forcing is bypassed and (ii) by maintaining roughly constant global mean temperature, the effects of state dependence on internal feedback strengths are minimized. We demonstrate this approach for several different forcing agents and derive the relationships between these forcing agents in two climate models; comparisons between forcing agents are highly linear in concordance with predicted functional forms. Transitivity of the relationships between the forcing agents appears to hold within a wide range of forcing. The relationships between the forcing agents obtained from this method are consistent across both models but differ from relationships that would be obtained from calculations of radiative forcing, highlighting the importance of controlling for surface temperature feedback effects when separating radiative forcing and climate response.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1229957
- Report Number(s):
- PNNL-SA-105400; 600305000
- Journal Information:
- Journal of Climate, Vol. 28, Issue 20; ISSN 0894-8755
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
Similar Records
Final scientific report for DOE award title: Improving the Representation of Ice Sedimentation Rates in Global Climate Models
Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations