Recommendations for diagnosing effective radiative forcing from climate models for CMIP6

By Piers M. Forster, T. Richardson, A. Maycock, Christopher J. Smith, B. Samset, G. Myhre, T. Andrews, Robert Pincus, Michael Schulz. 2016

249 citations recorded by the source corpus.

The usefulness of previous Coupled Model Intercomparison Project (CMIP) exercises has been hampered by a lack of radiative forcing information. This has made it difficult to understand reasons for differences between model responses. Effective radiative forcing (ERF) is easier to diagnose than traditional radiative forcing in global climate models (GCMs) and is more representative of the eventual temperature response. Here we examine the different methods of computing ERF in two GCMs. We find that ERF computed from a fixed sea surface temperature (SST) method (ERF_fSST) has much more certainty than regression based methods. Thirty year integrations are sufficient to reduce the 5–95% confidence interval in global ERF_fSST to 0.1 W m−2. For 2xCO2 ERF, 30 year integrations are needed to ensure that the signal is larger than the local confidence interval over more than 90% of the globe. Within the ERF_fSST method there are various options for prescribing SSTs and sea ice. We explore these and find that ERF is only weakly dependent on the methodological choices. Prescribing the monthly averaged seasonally varying model's preindustrial climatology is recommended for its smaller random error and easier implementation. As part of CMIP6, the Radiative Forcing Model Intercomparison Project (RFMIP) asks models to conduct 30 year ERF_fSST experiments using the model's own preindustrial climatology of SST and sea ice. The Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) will also mainly use this approach. We propose this as a standard method for diagnosing ERF and recommend that it be used across the climate modeling community to aid future comparisons.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ef8327e073bd6c38ded929a291b12d5f90d54e53

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Citations in the corpus

An Overview of the Atmospheric Component of the Energy Exascale Earth System Model (2019, methodology): “The differences in emissions and small differences in methodology compared to that used in Golaz et al. (2018) and the Forster et al. (2016) result showed that forcing estimates can depend significantly on the length of analysis interval indicated the sensitivity of the calculation.”
Improved Aerosol Processes and Effective Radiative Forcing in HadGEM3 and UKESM1 (2018, background): “In contrast to the traditional radiative forcing metric the ERF incorporates fast perturbation responses, such as changes in cloud cover, and is therefore a better representation of the historical global mean temperature change (Forster et al., 2016; Sherwood et al., 2015).”
The Radiative Forcing Model Intercomparison Project (RFMIP): Experimental Protocol for CMIP6 (in open review: 10.5194/gmd-2016-88) (2016, background): “Tests also found that the transient ERF fields suffer from year-to-year random noise, so 10-year averages of the three ensemble members are needed to quantify ERF to within 0.05 W m−2 (Forster et al., 2016).”
The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution (2019, methodology): “In this particular case, ERF2xCO2 is derived by linear regression (a methodology referred to as ERF_reg in the Forster et al., 2016, nomenclature).”
Implementation of the CMIP6 Forcing Data in the IPSL‐CM6A‐LR Model (2020, methodology): “First, fixed-SST (sea surface temperature) runs have been performed to diagnose the ERF (see Table 2, fourth column), following Forster et al. (2016).”
Causes of Higher Climate Sensitivity in CMIP6 Models (2020, unknown): “8 of 12 (Forster et al., 2016; Pincus et al., 2016).”
The Canadian Earth System Model version 5 (CanESM5.0.3) (2019, background): “However, the effective radiative forcing (Forster et al., 2016) in CanESM5 due to abrupt quadrupling of CO2 is very similar to that in CanESM2, suggesting that changes in feedbacks rather than forcings are the source of the higher ECS.”