Climate and infectious diseases each present critical challenges on a warming planet, as does the influence of climate on disease. Both are governed by nonlinear feedbacks, which drive multi-annual cycles in disease outbreaks and weather patterns. Although climate and weather can influence infectious disease transmission and have spawned rich literatures, the interaction between the independent feedbacks of these two systems remains less explored. Here, we demonstrate the potential for long-lasting impacts of El Niño–Southern Oscillation (ENSO) events on disease dynamics using two approaches: interannual perturbations of a generic SIRS model to represent ENSO forcing, and detailed analysis of realistic specific humidity data on an SIRS model with endemic coronavirus (HCoV-HKU1) parameters. Our findings reveal the importance of considering nonlinear feedbacks in susceptible population dynamics for predicting and managing disease risks associated with ENSO-related weather variations.

A ~ 50 km resolution atmospheric general circulation model (GCM) is used to investigate the impact of radiative interactions on spatial organization of convection, the model’s mean state, and extreme precipitation events in the presence of realistic boundary conditions. Mechanism-denial experiments are performed in which synoptic-scale feedbacks between radiation and dynamics are suppressed by overwriting the model-generated atmospheric radiative cooling rates with its monthly-varying climatological values. When synoptic-scale radiative interactions are disabled, the annual mean circulation and precipitation remain almost unchanged, however tropical convection becomes less aggregated, with an increase in cloud fraction and relative humidity in the free troposphere but a decrease in both variables in the boundary layer. Changes in cloud fraction and relative humidity in the boundary layer exhibit more sensitivity to the presence of radiative interactions than variations in the degree of aggregation. The less aggregated state is associated with a decrease in the frequency of extreme precipitation events, coincident with a decrease in the dynamical contribution to the magnitude of extreme precipitation. At regional scales, the spatial contrast in radiative cooling between dry and moist regions diminishes when radiative interactions are suppressed, reducing the upgradient transport of energy, degree of aggregation and frequency of extreme precipitation events. However, the mean width of the tropical rain belt remains almost unaffected when radiative interactions are disabled. These results offer insights into how radiation-circulation coupling affects the spatial organization of convection, distributions of clouds and humidity, and weather extremes.

General Circulation Models (GCMs) exhibit substantial biases in their simulation of tropical climate. One particularly problematic bias exists in GCMs’ simulation of the tropical rainband known as the Intertropical Convergence Zone (ITCZ). Much of the precipitation on Earth falls within the ITCZ, which plays a key role in setting Earth’s temperature by affecting global energy transports, and partially dictates dynamics of the largest interannual mode of climate variability: the El Nino-Southern Oscillation (ENSO). Most GCMs fail to simulate the mean state of the ITCZ correctly, often exhibiting a “double ITCZ bias”, with rainbands both north and south rather than just north of the equator. These tropical mean state biases limit confidence in climate models’ simulation of projected future and paleoclimate states, and reduce the utility of these models for understanding present climate dynamics. Adjusting GCM parameterizations of cloud processes and atmospheric convection can reduce tropical biases, as can artificially correcting sea surface temperatures (SSTs) through modifications to air-sea fluxes (i.e. “flux adjustment”). Here we argue that a significant portion of these rainfall and circulation biases are rooted in orographic height being biased low due to assumptions made in fitting observed orography onto GCM grids. We demonstrate that making different, and physically defensible, assumptions that raise the orographic height significantly improves model simulation of climatological features such as the ITCZ and North American rainfall as well as the simulation of ENSO. These findings suggest a simple, physically-based, and computationally inexpensive method that can improve climate models and projections of future climate.

Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea-level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We first explore correlations between SLR and TC activity by inference from two large‑scale factors known to modulate TC activity: potential intensity (PI) and vertical wind shear. Under the high emissions SSP5-8.5, SLR is strongly correlated with PI change (positively) and vertical wind shear change (negatively) over much of the western North Atlantic and North West Pacific. To explore the impact of the joint changes on flood hazard, we then conduct climatologyhydrodynamic modeling with New York City (NYC) as an example. Coastal flood hazard at NYC correlates strongly with global mean surface air temperature (GSAT), due to joint increases in both sea level and TC storm surges, the later driven by stronger and more slowly moving TCs. If positive correlations between SLR and TC changes are ignored in estimating flood hazard, the average projected change to the historical 100 year storm tide event is under-estimated by 0.09 m (7%) and the range across CMIP6 models is underestimated by 0.17 m (11 %). Our results suggest that flood hazard assessments that neglect the joint influence of these factors and that do not reflect the full distribution of GSAT changes will not accurately represent future flood hazard.

A vertically resolved analysis of the budget equation for the spatial variance of moist static energy (MSE) is used to diagnose processes associated with the development of tropical cyclones (TCs) in a high-resolution general circulation model (GCM) under realistic boundary conditions. Previous studies have shown that radiation provides an important feedback which enhances TC development. Here we examine the vertical contributions to this feedback by performing a series of mechanism-denial experiments in which synoptic-scale radiative interactions are suppressed either in the boundary layer or in the free troposphere. Although the boundary layer makes up a much smaller proportion of the atmospheric column than the free troposphere, the two experiments result in similar magnitude of reduction in global TC frequency, indicating that radiative interactions in the boundary layer and those in the free troposphere are of comparable importance in modulating TC frequency. Using instantaneous 6-houly outputs, an explicit computation reveals spatial patterns of the advection term during different TC stages. Instead of damping the spatial variance of MSE as noted in previous idealized studies, the advection term is found to promote the development of TCs. We attribute this result primarily to the explicit calculation of the advection term, however the influence of SST gradients cannot be ruled out. While the vertical component of the advection term is prominent in the middle troposphere, the horizontal component dominates in the boundary layer. These results provide additional insight of how different physical processes contribute to TC development in GCMs under realistic boundary conditions.