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Abstract. Potential intensity (PI) is the maximum speed limit of a tropical cyclonefound by modeling the storm as a thermal heat engine. Because there aresignificant correlations between PI and actual storm wind speeds, PI is auseful diagnostic for evaluating or predicting tropical cyclone intensityclimatology and variability. Previous studies have calculated PI given a setof atmospheric and oceanographic conditions, but although a PI algorithm –originally developed by Kerry Emanuel – is in widespread use, it remainsunder-documented. The Tropical Cyclone Potential Intensity Calculations inPython (pyPI, v1.3) package develops the PI algorithm in Python and for thefirst time details the full background and algorithm (line by line) used tocompute tropical cyclone potential intensity constrained bythermodynamics. The pyPI package (1) provides a freely available, flexible,validated Python PI algorithm, (2) carefully documents the PI algorithm andits Python implementation, and (3) demonstrates and encourages the use of PItheory in tropical cyclone analyses. Validation shows pyPI output is nearlyidentical to the previous potential intensity computation but is animprovement on the algorithm's consistency and handling of missingdata. Example calculations with reanalyses data demonstrate pyPI's usefulnessin climatological and meteorological research. Planned future improvementswill improve on pyPI's assumptions, flexibility, and range of applications andtropical cyclone thermodynamic calculations. 

Unconventional and historic, the first-ever virtual PALeo constraints on SEA level rise (PALSEA; pastglobalchanges.org/palsea) Express workshop (pastglobalchanges.org/ calendar/2020/127-pages/2043) was held in September, fostering valuable scientific exchanges among new and established community members. Eight invited speakers, 23 poster presenters, and a record number of over 200 attendees focused on improving their understanding of ice-sheet and solid-Earth processes that drive paleo sea-level change. 

In 2012, Hurricane Sandy hit the East Coast of the United States, creating widespread coastal flooding and over $60 billion in reported economic damage. The potential influence of climate change on the storm itself has been debated, but sea level rise driven by anthropogenic climate change more clearly contributed to damages. To quantify this effect, here we simulate water levels and damage both as they occurred and as they would have occurred across a range of lower sea levels corresponding to different estimates of attributable sea level rise. We find that approximately $8.1B ($4.7B–$14.0B, 5th–95th percentiles) of Sandy’s damages are attributable to climate-mediated anthropogenic sea level rise, as is extension of the flood area to affect 71 (40–131) thousand additional people. The same general approach demonstrated here may be applied to impact assessments for other past and future coastal storms.

 

We examine the relationship between interannual variability of potential intensity (PI) and tropical cyclone (TC) actual intensity (AI) and the factors contributing to this variability across all global ocean basins. Using best‐track data and three reanalysis products from 1980–2016, we find that the Western North Pacific is the only basin that yields consistently significant correlations between AI and PI sampled along the TC tracks. In contrast to a previous study, the North Atlantic does not yield statistically significant correlations. This is because the correlation between AI and PI in the North Atlantic is sensitive to the length of the time period considered and the individual years within that time period. Both thermodynamic efficiency and air‐sea disequilibrium contribute to interannual variability in along‐track PI.

 

Previous studies have interpreted Last Interglacial (LIG;∼129–116 ka) sea‐level estimates in multiple different ways to calibrate projections of future Antarctic ice‐sheet (AIS) mass loss and associated sea‐level rise. This study systematically explores the extent to which LIG constraints could inform future Antarctic contributions to sea‐level rise. We develop a Gaussian process emulator of an ice‐sheet model to produce continuous probabilistic projections of Antarctic sea‐level contributions over the LIG and a future high‐emissions scenario. We use a Bayesian approach conditioning emulator projections on a set of LIG constraints to find associated likelihoods of model parameterizations. LIG estimates inform both the probability of past and future ice‐sheet instabilities and projections of future sea‐level rise through 2150. Although best‐available LIG estimates do not meaningfully constrain Antarctic mass loss projections or physical processes until 2060, they become increasingly informative over the next 130 years. Uncertainties of up to 50 cm remain in future projections even if LIG Antarctic mass loss is precisely known (±5 cm), indicating that there is a limit to how informative the LIG could be for ice‐sheet model future projections. The efficacy of LIG constraints on Antarctic mass loss also depends on assumptions about the Greenland ice sheet and LIG sea‐level chronology. However, improved field measurements and understanding of LIG sea levels still have potential to improve future sea‐level projections, highlighting the importance of continued observational efforts.