Precipitation clusters are spatially contiguous precipitating regions. Large clusters in the tropics are rare, extreme events that include organized precipitating systems. Changes to the probability distributions of tropical precipitation clusters under global warming are examined using models from the coupled model intercomparison project Phase 6 (CMIP6). Every analyzed model projects significant increases in frequencies of both very large‐sized clusters and clusters with very large area‐integrated precipitation (cluster power). The occurrence probability for the highest historical cluster power values increases by a factor between 4 and 15 among models in the end‐of‐century SSP5‐8.5 scenario. These changes primarily occur over the precipitating tropics: the western Pacific, Indian subcontinent, central and east Pacific convergence zones, and parts of South America. This spatial pattern is largely explained by Clausius‐Clapeyron scaling of current climate cluster power values. Societal impacts of cluster power increases could be acute in coastal regions of the Indian subcontinent and western Pacific islands.
Precipitation clusters are contiguous raining regions characterized by a precipitation threshold, size, and the total rainfall contained within—termed the cluster power. Tropical observations suggest that the probability distributions of both cluster size and power contain a power-law range (with slope ~ −1.5) bounded by a large-event “cutoff.” Events with values beyond the cutoff signify large, powerful clusters and represent extreme events. A two-dimensional stochastic model is introduced to reproduce the observed cluster distributions, including the slope and the cutoff. The model is equipped with coupled moisture and weak temperature gradient (WTG) energy equations, empirically motivated precipitation parameterization, temporally persistent noise, and lateral mixing processes, all of which collectively shape the model cluster distributions. Moisture–radiative feedbacks aid clustering, but excessively strong feedbacks push the model into a self-aggregating regime. The power-law slope is stable in a realistic parameter range. The cutoff is sensitive to multiple model parameters including the stochastic forcing amplitude, the threshold moisture value that triggers precipitation, and the lateral mixing efficiency. Among the candidates for simple analogs of precipitation clustering, percolation models are ruled out as unsatisfactory, but the stochastic branching process proves useful in formulating a neighbor probability metric. This metric measures the average number of nearest neighbors that a precipitating entity can spawn per time interval and captures the cutoff parameter sensitivity for both cluster size and power. The results here suggest that the clustering tendency and the horizontal scale limiting large tropical precipitating systems arise from aggregate effects of multiple moist processes, which are encapsulated in the neighbor probability metric.
more » « less- NSF-PAR ID:
- 10113686
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of the Atmospheric Sciences
- Volume:
- 76
- Issue:
- 10
- ISSN:
- 0022-4928
- Page Range / eLocation ID:
- p. 3063-3087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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