Cyclone Simulation via Action Minimization
Abstract
A postulated impact of climate change is an increase in intensity of tropical cyclones (TCs). This hypothesized effect results from the fact that TCs are powered subsaturated boundary layer air picking up water vapor from the surface ocean as it flows inwards towards the eye. This water vapor serves as the energy input for TCs, which can be idealized as heat engines. The inflowing air has a nearly identical temperature as the surface ocean; therefore, warming of the surface leads to a warmer atmospheric boundary layer. By the Clausius-Clapeyron relationship, warmer boundary layer air can hold more water vapor and thus results in more energetic storms. Changes in TC intensity are difficult to predict due to the presence of fine structures (e.g. convective structures and rainbands) with length scales of less than 1 km, while general circulation models (GCMs) generally have horizontal resolutions of tens of kilometers. The models are therefore unable to capture these features, which are critical to accurately simulating cyclone structure and intensity. Further, strong TCs are rare events, meaning that long multi-decadal simulations are necessary to generate meaningful statistics about intense TC activity. This adds to the computational expense, making it yet more difficult to generate accurate statistics about long-term changes in TC intensity due to global warming via direct simulation. We take an alternative approach, applying action minimization techniques developed in molecular dynamics to the WRF weather/climate model. We construct artificial model trajectories that lead from quiescent (TC-free) states to TC states, then minimize the deviation of these trajectories from true model dynamics. We can thus create Monte Carlo model ensembles that are biased towards cyclogenesis, which reduces computational expense by limiting time spent in non-TC states. This allows for: 1) selective interrogation of model states with TCs; 2) finding the likeliest paths for transitions between TC-free and TC states; and 3) an increase in horizontal resolution due to computational savings achieved by reducing time spent simulating TC-free states. This increase in resolution, coupled with a decrease in simulation time, allows for prediction of the change in TC frequency and intensity distributions resulting from climate change.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.A43I0362P
- Keywords:
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- 3315 Data assimilation;
- ATMOSPHERIC PROCESSESDE: 3360 Remote sensing;
- ATMOSPHERIC PROCESSESDE: 3372 Tropical cyclones;
- ATMOSPHERIC PROCESSESDE: 4313 Extreme events;
- NATURAL HAZARDS