Editing Openai/6938382f-f9e4-8003-a158-6d812c1bce84 (section)

===== 2.3 Treatment of measurement vs structural/model error =====

Frank is right that:
* Calibration vs observations constrains model structural error, analogous to instrument calibration. Propagation_of_Error_and_the_Re…

But then he:
* Treats structural error as if it were a per-step additive uncertainty on the prognostic variable (tropospheric energy flux), and
* Propagates it forward as if each year added a new independent realization of that error.

If the LWCF error is truly systematic (his own lag-1 and pair-wise correlation analysis argues that strongly), then:
* It behaves like a bias in the mean flux or spatial pattern, not as a zero-mean random variable with fresh draws each year.
* A bias affects the level of simulated temperature (and probably feedback strength), but: - It does not blow up as √N in time. - Much of that bias cancels when we take anomalies relative to a baseline period, which is exactly what is done in projections and in observational datasets.

He actually undercuts himself: he stresses that the TCF error is deterministic and systematic (R≥0.95, common patterns across models; Table 1, Figure 4). Propagation_of_Error_and_the_Re…

 That is not the error structure for which Eq. 6 is appropriate.