Convection is often assumed to be controlled by the simultaneous environmental fields. But to what extent does it also remember its past behavior? This study proposes a new framework in which the memory of previous convective-scale behavior, “microstate memory,” is distinguished from macrostate memory, and conducts numerical experiments to reveal these memory types. A suite of idealized, cloud-resolving radiative–convective equilibrium simulations in a 200-km square domain is performed with the Weather Research and Forecasting (WRF) Model. Three deep convective cases are analyzed: unorganized, organized by low-level wind shear, and self-aggregated. The systematic responses to sudden horizontal homogenization of various fields, in various atmospheric layers, designed to eliminate their specific microstructure, are compared in terms of precipitation change and time of recovery to equilibrium. Results imply a substantial role for microstate memory. Across organization types, microstructure in water vapor and temperature has a larger and longer-lasting effect on convection than in winds or hydrometeors. Microstructure in the subcloud layer or the shallow cloud layer has more impact than in the free troposphere. The recovery time scale dramatically increases from unorganized (2–3 h) to organized cases (24 h or more). Longer-time-scale adjustments also occur and appear to involve both small-scale structures and domain-mean fields. These results indicate that most convective microstate memory is stored in low-level thermodynamic structures, potentially involving cold pools and hot thermals. This memory appears strongly enhanced by convective organization. Implications of these results for parameterizing convection are discussed.

Summary Background The obesity epidemic might affect patients with type 1 diabetes (T1DM), historically described as lean and insulin-sensitive subjects. Insulin resistance in T1DM might increase diabetic complications, especially cardiovascular disease. Therefore, the body mass index (BMI) in T1DM patients was analyzed in comparison to the general population. Furthermore, the impact of increased BMI on glycemic control and metabolic alterations was assessed. Methods Body mass index was compared overall and among four different age groups between adult T1DM ( n = 186), treated in the outpatient clinic between 2014 and 2016, and 15,771 individuals from the general population who took part at an Austrian health survey. Furthermore, parameters of glycemic control, lipid state, blood pressure and additional medication were compared between T1DM with a BMI under or above 27.5 kg/m 2 . Results Patients with T1DM had significantly higher BMI values than general population (25.9 ± 4.2 kg/m 2 vs. 25.3 ± 4.5 kg/m 2 ; p = 0.027), controlling for age group; however, prevalence of overweight (39.8% vs. 33.1%) and obesity (14% vs. 13.8%) was not significantly different. Within the 4 age groups only T1DM patients between 30 years and 49 years old had significantly higher BMI values compared to the general population (mean difference 1.9 kg/m 2 ; 95% confidence interval, CI: 0.96–2.83 kg/m 2 ). In T1DM, a BMI ≥27.5 kg/m 2 was associated with increased rates of hypertension, dyslipidemia, microalbuminuria, and increased insulin demand, whereas glycemic control was not affected. Conclusions In contrast to common descriptions T1DM patients have a higher BMI compared to the general population. Rates of overweight and obesity in T1DM equal those of the general population. Therefore, it is concluded that the obesity epidemic has reached T1DM patients and “double diabetes” might be an entity to consider.

The fluctuation–dissipation theorem (FDT) has been proposed as a method of calculating the mean response of the atmosphere to small external perturbations. This paper explores the application of the theory under time and space constraints that approximate realistic conditions. To date, most applications of the theory in the climate context used univariate, low-dimensional-state representations of the climate system and an arbitrarily long sample size. The authors explore high-dimensional multivariate FDT operators and the lower bounds of sample size needed to construct skillful operators. It is shown that the skill of the operator depends on the selection of variables and features representing the climate system and that these features change once memory (slab ocean) is added to the system. In addition, it is found that the FDT operator has skill in estimating the response to realistic sea surface temperature (SST) patterns, such as El Niño–Southern Oscillation (ENSO), despite the fact that these patterns were not part of the data used to produce the operator. The response of clouds is also studied; for variables that represent cloud properties, the decrease in skill in relation to decrease in sample size still maintains the key features of the response.

This study explores the role of the stratiform cloud scheme in the inter‐model spread of cloud feedback. Six diagnostic cloud schemes used in various CMIP (Coupled Model Intercomparison Experiment] climate models are implemented (at low and midlevels) into two testbed climate models, and the impacts on cloud feedback are investigated. Results suggest that the choice of stratiform cloud scheme may contribute up to roughly half of the intermodel spread of cloud radiative responses in stratocumulus (Sc) regions, and may determine or favor a given sign of the feedback there. Cloud schemes assuming a probability density function for total water content consistently predict a positive feedback in Sc regions in our experiments. A large negative feedback in Sc regions is obtained only with schemes that consider variables other than relative humidity (e.g., stability). The stratiform cloud scheme also significantly affects cloud feedback at the scale of the tropics and at global scale. Results are slightly less consistent for tropical means, likely indicating coupling with other boundary layer processes such as convective mixing.