and «no data or computer code appears to be archived in relation to the paper» and «the sensitivity of Shindell's TCR estimate to
the aerosol forcing bias adjustment is such that the true uncertainty of Shindell's TCR range must be huge — so large as to make his estimate worthless» and the seemingly arbitrary to cherry picked climate models used in Shindell's analysis.
Not exact matches
First, the quality of the data is important: whether it is the LGM temperature estimates, recent
aerosol forcing trends, or mid-tropospheric humidity — underestimates in the uncertainty of these data will definitely
bias the CS estimate.
First, the quality of the data is important: whether it is the LGM temperature estimates, recent
aerosol forcing trends, or mid-tropospheric humidity — underestimates in the uncertainty of these data will definitely
bias the CS estimate.
«Here, it is sufficient to note that many of the 20CEN / A1B simulations neglect negative
forcings arising from stratospheric ozone depletion, volcanic dust, and indirect
aerosol effects on clouds... It is likely that omission of these negative
forcings contributes to the positive
bias in the model average TLT trends in Figure 6F.
This point was also made by Schmidt et al. (2014), which additionally showed that incorporating the most recent estimates of
aerosol, solar, and greenhouse gas
forcings, as well as the El Niño Southern Oscillation (ENSO) and temperature measurement
biases, the discrepancy between average GCM global surface warming projections and observations is significantly reduced.
Aldrin's use of a uniform prior for ECS will have
biased up his mean and 95 % / 97.5 % bound estimates for sensitivity, but probably doesn't make much difference to his
aerosol forcing estimates.
That results in the data maximum likelihoods for direct and indirect
aerosol forcing being in the upper tails of the priors,
biasing the
aerosol forcing estimation to more negative values (and hence
biasing ECS estimation to a higher value).