Wood Romances

At least as far back as Myhre et al 1998 [1], scientists have understood that it's possible to approximate the effective radiative forcing (ERF, the amount of change in the outgoing energy flux near the tropopause) by a simple logarithmic equation: ΔERF ≈ α*ln(C/C0) I say "approximate" because the actual calculations for the relationship between ΔF and CO2 from line by line radiative transfer models are a bit more complex than this. The above equation is simply the result of curve fitting that matches those calculations over the range of CO2 concentrations that we're mostly concerned with. The value for α scales the radiative forcing change for the log change in CO2 concentrations. Myhre's value for α was 5.35, and this was used in the IPCC's TAR and AR4 reports. More recent IPCC reports, though, have improved the ΔF2xco2 estimates, and we can solve for the α values implied by these changes in ΔF2xco2 with α = ΔF2xco2/ln(2) ΔF2xco2 ≈ α*ln(2) AR3: 3.71 ± 0.4 W...

A paper was published this week that argues that low-sensitivity models do a poor job of reproducing CERES-derived EEI trends. In the words of the paper, the authors used CMIP6 models "to illustrate that low climate sensitivity models have an EEI trend behavior that is inconsistent with the satellite-derived EEI trend." Even though models with an ECS near 3°C do a good job of reproducing current warming, CMIP6 models often differ in the in EEI trends. For instance, The CERES data show a stronger trend in EEI than the multi-model CMIP6 mean and higher EEI in 2023 than any of the CMIP6 models. However, for individual CMIP6 models and ensembles, EEI is comparable to or higher at other periods than the CERES value in 2023. The difference in trends can be seen by comparing the red CERES line to the black CMIP6 model mean. Even though the interannual variability in the CMIP6 models is consistent CERES observations, the observed trend in EEI, especially since about 2010, is higher t...