Extreme climate events such as droughts, cold snaps, and hurricanes can be powerful agents of natural selection, producing acute selective pressures very different from the everyday pressures acting on organisms. However, it remains unknown whether these infrequent but severe disruptions are quickly erased by quotidian selective forces, or whether they have the potential to durably shape biodiversity patterns across regions and clades. Here, we show that hurricanes have enduring evolutionary impacts on the morphology of anoles, a diverse Neotropical lizard clade. We first demonstrate a transgenerational effect of extreme selection on toepad area for two populations struck by hurricanes in 2017. Given this short-term effect of hurricanes, we then asked whether populations and species that more frequently experienced hurricanes have larger toepads. Using 70 y of historical hurricane data, we demonstrate that, indeed, toepad area positively correlates with hurricane activity for both 12 island populations of Anolis sagrei and 188 Anolis species throughout the Neotropics. Extreme climate events are intensifying due to climate change and may represent overlooked drivers of biogeographic and large-scale biodiversity patterns.
This article investigates combining a WRF-ADCIRC ensemble with track clustering to evaluate how uncertainties in tropical cyclone–induced storm tide (surge + tide) predictions vary in space and time and to explore whether this method can help elucidate inundation hazard scenarios. The method is demonstrated for simulations of Hurricane Irma (2017) initialized at 1200 UTC 5 September, approximately 5 days before Irma’s Florida landfalls, and 1200 UTC 8 September. Mixture models are used to partition the WRF ensemble tracks from 5 and 8 September into six and five clusters, respectively. Inundation is evaluated in two affected regions: southwest (south and west Florida) and northeast (northeast Florida through South Carolina). For the 5 September simulations, inundation in the southwest region varies significantly across the ensemble, indicating low forecast confidence. However, clustering highlights the areas of inundation risk in south and west Florida associated with different storm tracks. In the northeast region, every cluster has high inundation probabilities along a similar coastal stretch, indicating high confidence at a ~5-day lead time that this area will experience inundation. For the 8 September simulations, track and inundation in both regions vary less across the ensemble, but clustering remains useful for distinguishing among flooding scenarios. These results demonstrate the potential of dynamical TC–surge ensembles to illuminate important aspects of storm surge risk, including highlighting regions of high forecast confidence where preparations can reliably be initiated early. The analysis also shows how clustering can augment probabilistic hazard forecasts by elucidating inundation scenarios and variability across a surge ensemble.
Track and cyclone phase space (CPS) forecasts of Hurricane Sandy from four global ensemble prediction systems are clustered using regression mixture models. Bayesian information criterion, cluster assignment strength, and mean-squared forecast error are used to select optimal model specifications. Fourth-order (third order) polynomials for 168-h forecasts (60-h forecast segments) and 5 (6) clusters for track (CPS) forecasts are selected. Mean cluster paths from eight initialization times show that track and CPS clustering meaningfully partition potential tracks and structural evolutions, distilling a large number of ensemble members into several representative and distinct solutions. Rand index and adjusted Rand index calculations demonstrate a relationship between track and CPS cluster membership for both 168-h forecasts and 60-h forecast segments, indicating that certain tracks are preferentially associated with certain structural evolutions. These relationships are explained in greater detail using forecasts initialized at 0000 UTC 25 October. Storm-centered cluster composite maps of 500-hPa geopotential height and 850-hPa equivalent potential temperature for the 120-h forecast valid at 0000 UTC 30 October (initialized at 0000 UTC 25 October) indicate that both track and CPS clustering successfully capture variations in the Sandy–trough interaction and the strength of the lower-troposphere warm core of Sandy at the time of observed landfall. Together, these results illustrate the relationship between the track and structural evolution of Sandy and suggest the potential of multiensemble mixture-model path clustering for tropical cyclone forecasting.
A modified formula for calculating tropical cyclone (TC) potential intensity (PI) from a balance between energy production and frictional dissipation in the TC surface layer is developed. This modified formula accounts for energy production and frictional dissipation at multiple radii (and therefore at multiple wind speeds) along the TC inflow trajectory. The PI maximum wind speed values VMAX are calculated using this expanded formula for four canonical radial profiles of wind speed. These results are compared to PI VMAX values calculated using the standard assumption that all energy production and frictional dissipation relevant to maximum intensity occurs at the radius of maximum winds (RMW). The new PI formulation developed here results in PI VMAX values substantially higher than the standard PI VMAX for all four of the radial wind speed profiles examined; the difference is explained by the increase in the outer radial limit of energy production. This increase holds true even if outflow temperature increases with increasing radius, although the VMAX increases with increasing outer radius are somewhat more modest in this case. The extended PI formula yields VMAX values 3–17 m s−1 higher than the standard PI VMAX value when calculated with outer energy production–dissipation limits of 2.0–2.5 RMW, although it yields potentially unrealistic values when calculated with larger outer limits (e.g., 6 RMW). These results are presented as a potential explanation for why individual TCs can exceed their standard PI VMAX values in terms of the storm thermodynamics.
In this article, three tropical cyclones and their 120-h, 50-member ECMWF Integrated Forecasting System (IFS) ensemble track forecasts at 10 initialization times are considered. The IFS forecast tracks are clustered with a regression mixture model, and two traditional diagnostics (the Bayesian information criterion and a measure of strength of cluster assignment) are used to determine the optimal polynomial order and number of clusters to use in the model. In addition, cross-validation versions of the two diagnostics are formulated and computed to further aid in model selection. Both traditional and cross-validation diagnostics suggest that third-order polynomials and five clusters are effective options—although the evidence is less conclusive for the number of clusters than for the polynomial order, and the cross-validation diagnostics favor a smaller number of clusters than the traditional ones. Path clustering of IFS tropical cyclone track forecasts with this third-order polynomial, five-cluster regression mixture model produces interpretable partitions by direction and speed of motion for each of the storms and initialization times considered. Thus, this approach effectively synthesizes the forecast spreads within the IFS into a small number of representative trajectories. Based on how forecasts distribute across clusters, this approach also provides information on the likelihood of each such representative trajectory. If used operationally, this information has the potential to aid forecasters in parsing and quantifying the uncertainty in tropical cyclone track forecasts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.