Physical Constraints and Modeling Uncertainties on the Intensication of the Global Hydrologic Cycle
Climate change induces shifts in the statistical distribution of rain in the form of more intense rainfall events and longer dry spells. This acceleration of the hydrologic cycle can be characterized by tiered increases in mean and extreme rainfall in response to warming. This dissertation revisits how this behavior is driven by the energetic, thermodynamic and dynamic properties of the atmosphere: by summarizing these processes into a few physical constraints, it evaluates the general performance of climate models and underscores the need for better understanding the interactions between cloud processes and the atmospheric circulation.
Changes in the atmospheric energy balance robustly contrains the 1-2%/K increase in global mean precipitation, and we use it to demonstrate that parameterizations shortwave radiative transfer for water vapor are a large source of modeling uncertainty. We then use a formula which approximates heavy precipitation rates to investigate changes in extreme events in a global climate model, in a superparameterized climate model and in an idealized cloud-resolving model. Detailed comparison of convective dynamics in these three modeling frameworks led to the following conclusions: (1) increases in extreme rainfall closely follow the 6-7%/K thermodynamic increase in humidity dictated by the Clausius-Clapeyron formula, while changes in convective instability and in the large-scale circulation have a negligible impact, and (2) an additional acceleration of 1-2%/K could arise from the reinforcement of mesoscale circulations associated with convective organization. These circulations are likely a crucial ingredient that connects the large-scale atmospheric flow to local convective processes, and their omission from current convective parameterizations might be a source
of error when modeling changes in the hydrologic cycle.
Capturing shifts in the entire distribution of rain from first principles is not realistically achievable on global scales. However, a better understanding of the physics that constrain the spatiotemporal and statistical properties of rain in smaller idealized modeling setups is a promising avenue towards reducing uncertainties in global climate models.