Changes in tropical cyclones under stabilized 1.5 and 2.0 °C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols

Wehner, Michael; Reed, Kevin A.; Loring, Burlen; Stone, Dáithí A.; Krishnan, Harinarayan

doi:10.5194/esd-9-187-2018

Cited by 64 publications

(61 citation statements)

References 24 publications

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“…Figure 7 shows how tropical cyclone precipitation intensifies and increases in a warmer world. In line with previous studies (Wehner et al, 2018), we see that the PDF of TC precipitation shifts to higher rainrates under global warming. In Table 2 we show the percentiles and rainrates for extreme precipitation from TCs (annual and global).…”

Section: Global Tropical Cyclone Precipitationsupporting

confidence: 93%

“…of Mexico) in both climate scenarios. In line with what is expected for Atlantic hurricanes under global warming (Wehner et al, 2018;Knutson et al, 2019), we find that, on average, the number of TC days per year decreases from 40.1 (All-Hist)…”

Section: Tropical Cyclone Precipitation In Gulf Of Mexicosupporting

confidence: 90%

“…Some experts disagreed on edge cases, such as incipient events or those that were dissipating. However, we note that the disagreements between human labels was less severe than differences noticed in dedicated heuristic-based event detection intercomparison projects such as ARTMIP, TCMIP and IMILAST, which exhibit significant disparities in the presence or absence of labeled extreme events and their boundaries (Shields et al, 2018a;Wehner et al, 2018;Ullrich and Zarzycki, 2017;Neu et al, 2013;Walsh et al, 2010).…”

Section: Diversity Of Expert Labelsmentioning

confidence: 70%

“…Wehner et al (2018);Mitchell et al (2017);Wehner et al (2014)). The All-hist scenario runs from 1995-2015 and includes all natural and anthropogenic forcings.…”

mentioning

confidence: 99%

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ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

Kashinath¹,

Mudigonda²,

Kim³

et al. 2020

Preprint

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Abstract. Identifying, detecting and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection and segmentation have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of Deep Learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning; when labeled datasets are readily available. Reliable, labeled training data for extreme weather and climate events is scarce. We create ClimateNet – an open, community-sourced human expert-labeled curated dataset – that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification, i.e. segmentation, of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs or the background at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, hi-fidelity and highly precise analytics of climate data using a curated expert-labelled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns, and applied to observational and reanalysis data products via transfer learning.

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Section: Global Tropical Cyclone Precipitationsupporting

confidence: 93%

Section: Tropical Cyclone Precipitation In Gulf Of Mexicosupporting

confidence: 90%

Section: Diversity Of Expert Labelsmentioning

confidence: 70%

“…Wehner et al (2018);Mitchell et al (2017);Wehner et al (2014)). The All-hist scenario runs from 1995-2015 and includes all natural and anthropogenic forcings.…”

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confidence: 99%

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ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

Kashinath¹,

Mudigonda²,

Kim³

et al. 2020

Preprint

Self Cite

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“…Globally, anthropogenic aerosols in the atmosphere to date have been shown to cancel increases in potential intensity of TCs due to increases in greenhouse gasses (Sobel et al, 2016), suggesting that aerosol impacts on TC activity extend beyond TC-dust interactions. Aerosol impacts on TCs have also been suggested by high-resolution GCM work of Wehner et al (2018), which noted that the impact of aerosol forcing changes in future climate projections complicates future projections of TC activity.…”

Section: Introductionmentioning

confidence: 98%

Exploring the Impact of Dust on North Atlantic Hurricanes in a High‐Resolution Climate Model

Reed

Bacmeister

Huff

et al. 2019

Geophysical Research Letters

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The relationship between African dust and the climatology of tropical cyclones (TCs) in the North Atlantic is explored using the Community Atmosphere Model at a global horizontal resolution of 28 km. A simulation in which the aerosol model is modified to significantly reduce the amount of airborne dust is compared to a standard simulation. The simulation with reduced dust increases TC frequency globally, with the largest increase occurring in the North Atlantic. The increase in TC activity in the North Atlantic is consistent with an environment that is more conducive for the genesis and intensification of storms. TCs are more frequent (27%) and on average significantly longer lived (13%) in the low dust configuration but only slightly stronger (3%). This results in a 57% increase in accumulated cyclone energy per hurricane season on average. This work has implications for projections of future climate and resulting changes in TC activity.

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Material legacies can degrade resilience: Structure‐retaining disturbances promote regime shifts on coral reefs

Kopecky

Stier

Schmitt

et al. 2023

Ecology

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Standing dead structures of habitat‐forming organisms (e.g., dead trees, coral skeletons, oyster shells) killed by a disturbance are material legacies that can affect ecosystem recovery processes. Many ecosystems are subject to different types of disturbance that either remove biogenic structures or leave them intact. Here we used a mathematical model to quantify how the resilience of coral reef ecosystems may be differentially affected following structure‐removing and structure‐retaining disturbance events, focusing in particular on the potential for regime shifts from coral to macroalgae. We found that dead coral skeletons could substantially diminish coral resilience if they provided macroalgae refuge from herbivory, a key feedback associated with the recovery of coral populations. Our model shows that the material legacy of dead skeletons broadens the range of herbivore biomass over which coral and macroalgae states are bistable. Hence, material legacies can alter resilience by modifying the underlying relationship between a system driver (herbivory) and a state variable (coral cover).

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Changes in tropical cyclones under stabilized 1.5 and 2.0 °C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols

Cited by 64 publications

References 24 publications

ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

Exploring the Impact of Dust on North Atlantic Hurricanes in a High‐Resolution Climate Model

Material legacies can degrade resilience: Structure‐retaining disturbances promote regime shifts on coral reefs

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Changes in tropical cyclones under stabilized 1.5 and 2.0 °C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols

Cited by 64 publications

References 24 publications

ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

Exploring the Impact of Dust on North Atlantic Hurricanes in a High‐Resolution Climate Model

Material legacies can degrade resilience: Structure‐retaining disturbances promote regime shifts on coral reefs

Contact Info

Product

Resources

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Changes in tropical cyclones under stabilized 1.5 and 2.0 °C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols