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Pediatric drug development lags adult development by about 8 years (Mulugeta et al. in Pediatr Clin 64(6):1185–1196, 2017). In such context, many incentives, regulations, and innovative techniques have been proposed to address the disparity for pediatric patients. One such strategy is extrapolation of efficacy from a reference population. Extrapolation is currently justified by providing evidence in support of the effective use of drugs in children when the course of the disease and the expected treatment response would be sufficiently similar in the pediatric and reference population. This paper’s position is that, despite uncertainties, pediatric drug development programs should initially assume some degree of extrapolation. The degree to which extrapolation can be used lies along a continuum representing the uncertainties to be addressed through generation of new pediatric evidence. In addressing these uncertainties, the extrapolation strategy should reflect the level of tolerable uncertainty concerning the decision to expose a child to the risks of a new drug. This judgment about the level of tolerable uncertainty should vary with the context (e.g., disease severity, existing therapeutic options) and can be embedded into pediatric drug development archetypes to ascertain the extent of studies needed and whether simultaneous development for adults and adolescents be considered.

This study examines boundary layer turbulence derived from high temporal resolution meteorological measurements from 40 research flights over the western North Atlantic Ocean during the 2020 deployments of ACTIVATE. Frequency distributions of various turbulent quantities reveal stronger turbulence during the winter deployment than in summer and for cloud‐topped than in cloud‐free boundary layers during the summer deployment. Maximum turbulence kinetic energy (TKE) is most often within cloud from observations in winter and summer, whereas it is mostly below cloud in both seasons by a global model turbulence parameterization. Bivariate frequency distributions are consistent with the bivariate Gaussian probability distribution functions assumed for the closure of higher‐order turbulence/shallow convection parameterizations used by some global models. Turbulence simulated by the Community Atmosphere Model version 6 and the Energy Exascale Earth System Model Atmosphere Model version 2 using such parameterizations is not as strong as observed, with more TKE going into vertical wind perturbations rather than into zonal wind perturbations as observed, suggesting that the treatment of turbulence in Earth system models still needs to be further improved. Maximum turbulence kinetic energy is most often within cloud in observations but mostly below cloud by a global model parameterizationObservations are similar to bivariate normal probability distribution functions assumed to close higher‐order turbulence parameterizationsBoundary layer turbulence simulated by global models is weaker than observed Maximum turbulence kinetic energy is most often within cloud in observations but mostly below cloud by a global model parameterization Observations are similar to bivariate normal probability distribution functions assumed to close higher‐order turbulence parameterizations Boundary layer turbulence simulated by global models is weaker than observed

Lower‐tropospheric stability (LTS) and estimated inversion strength (EIS) have a widely accepted relationship with low cloud amount and are key observational foundations for understanding and modeling low‐level stratiform clouds. Using the updated surface‐based and satellite cloud data, we find that low cloud amount is not as strongly correlated with LTS, and not as sensitive to LTS, as established in the past. EIS does not provide a stronger correlation with low cloud amount than LTS over all eight regions (including the midlatitudes). Further analyzing the relationships between LTS and EIS with different types of low clouds, we find that there is a strong correlation of LTS and EIS with stratocumulus only. This explains the weaker correlation of low cloud fraction (including cumulus, stratocumulus, and stratus) to both LTS and EIS. These results also suggest the need to re‐evaluate these relationships in Earth system models. Low‐level clouds play an important role in the global radiation budget. Low cloud amount has two established linear relationships with lower‐tropospheric stability (LTS) and estimated inversion strength (ETS). These two relationships are re‐evaluated here using three separate observational cloud and two reanalysis datasets for three distinct but overlapping time periods. The analysis shows that the strength of the LTS and ETS relationships with low cloud amount varies, depending on the cloud data used. They have a strong relationship with stratocumulus cloud amount only. The lower‐tropospheric stability (LTS) and low cloud amount relationship is not consistent across different cloud datasetsEstimated inversion strength (EIS) is not a better estimator of low cloud amount in the midlatitudes compared to LTSLTS and EIS have the strongest relationships with stratocumulus amount among all types of low‐level clouds The lower‐tropospheric stability (LTS) and low cloud amount relationship is not consistent across different cloud datasets Estimated inversion strength (EIS) is not a better estimator of low cloud amount in the midlatitudes compared to LTS LTS and EIS have the strongest relationships with stratocumulus amount among all types of low‐level clouds