Response to : Interactive comment on “ Modeling the contributions of global air temperature , synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

The static energy content of the atmosphere is increasing at the global scale, but exhibits important subglobal and sub-regional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called ‘warming-holes’ 10 (i.e. locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New non-linear statistical 15 models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic scale meteorology derived from T and specific humidity (Q) at 850 hPa and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θe over regions that have previously been identified as 20 exhibiting ‘warming holes’ confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using Artificial Neural Networks (ANN) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minand max-θe, where more complex models built using ANN with multiple 25 hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme max-θe. Over the entire domain the ANN with 3 hidden layers exhibits high accuracy in predicting max-θe > 347 K. The median hit rate for max-θe > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08. 1 Motivation and objectives 30 Extreme heat is associated with significant societal and environmental impacts, and a number of prior studies have elaborated on the drivers of extreme air temperatures (T) and made projections of extreme T and the associated Prof. Sara C. Pryor 10/30/2017 11:17 AM Deleted: A n Prof. Sara C. Pryor 10/30/2017 11:17 AM Deleted: is Prof. Sara C. Pryor 10/30/2017 11:27 AM Formatted: Font:Italic Prof. Sara C. Pryor 10/30/2017 11:27 AM Formatted: Font:Italic Prof. Sara C. Pryor 10/30/2017 11:29 AM Formatted: Font:Italic Prof. Sara C. Pryor 10/30/2017 11:26 AM Formatted: Font:Italic Prof. Sara C. Pryor 11/1/2017 9:11 AM Deleted: It is shown that soil moisture ( 35 Prof. Sara C. Pryor 10/30/2017 11:28 AM Deleted: )