skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 10:00 PM ET on Thursday, February 12 until 1:00 AM ET on Friday, February 13 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity
Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.  more » « less
Award ID(s):
2231237
PAR ID:
10492379
Author(s) / Creator(s):
; ;
Publisher / Repository:
AGU
Date Published:
Journal Name:
Journal of Advances in Modeling Earth Systems
Volume:
15
Issue:
10
ISSN:
1942-2466
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, npj Climate and Atmospheric Science)
    Changes in the tropical cyclone (TC) seasonal cycle can have profound impacts on compound hazards associated with TCs, such as consecutive summer rainfall and TC-heatwave compound events. However, only a few studies have explored future changes in TC seasonality, and they reach discrepant conclusions. In this study, we perform a high-resolution coupled climate simulation to study the future TC seasonal cycle and investigate the mechanisms of possible changes. The model simulation shows that, under the shared socio-economic pathway 5 8.5 scenario, the mean genesis date will shift significantly to later in the season in Northeastern Pacific (ENP) and North Atlantic (NA) but shift to later or earlier depending on the subregions in Northwestern Pacific (WNP). These shifts in TC seasonal cycles are induced by seasonally asymmetric changes in TC-favorable environmental conditions, which arise from seasonally asymmetric changes in large-scale circulation patterns, including the monsoon troughs, jet stream, and tropical zonal circulation. 
    more » « less
  2. ; ; (, Journal of Climate)
    El Niño–Southern Oscillation (ENSO) influences seasonal Atlantic tropical cyclone (TC) activity by impacting environmental conditions important for TC genesis. However, the influence of future climate change on the teleconnection between ENSO and Atlantic TCs is uncertain, as climate change is expected to impact both ENSO and the mean climate state. We used the Weather Research and Forecasting Model on a tropical channel domain to simulate 5-member ensembles of Atlantic TC seasons in historical and future climates under different ENSO conditions. Experiments were forced with idealized sea surface temperature configurations based on the Community Earth System Model (CESM) Large Ensemble representing: a monthly varying climatology, eastern Pacific El Niño, central Pacific El Niño, and La Niña. The historical simulations produced fewer Atlantic TCs during eastern Pacific El Niño compared to central Pacific El Niño, consistent with observations and other modeling studies. For each ENSO state, the future simulations produced a similar teleconnection with Atlantic TCs as in the historical simulations. Specifically, La Niña continues to enhance Atlantic TC activity, and El Niño continues to suppress Atlantic TCs, with greater suppression during eastern Pacific El Niño compared to central Pacific El Niño. In addition, we found a decrease in the Atlantic TC frequency in the future relative to historical regardless of ENSO state, which was associated with a future increase in northern tropical Atlantic vertical wind shear and a future decrease in the zonal tropical Pacific sea surface temperature (SST) gradient, corresponding to a more El Niño–like mean climate state. Our results indicate that ENSO will remain useful for seasonal Atlantic TC prediction in the future. 
    more » « less
  3. ; ; ; ; ; (, Geophysical Research Letters)
    Abstract Proxy‐based reconstructions of long‐term Atlantic tropical cyclone (TC) variability reveal low‐frequency oscillations in regional TC landfalls over the Common Era. However, the limited spatial coverage and increased uncertainty of the proxy records complicates assessments of this feature. Here we present a new multi‐ensemble set of synthetic TCs downscaled from the Last Millennium Reanalysis project, which is based on sea surface temperatures that more accurately reflect past conditions. Throughout ensemble members, there are coherent multi‐centennial shifts in landfalls with persistent intervals of increased (decreased) occurrence along the eastern US concurrent with inverse activity in the southwest Caribbean and Gulf of Mexico, associated with basin‐scale redistributions of storm tracks. The emergent TC‐dipole from modeled climate provides context and support for its presence within proxy‐reconstructions. Furthermore, dipole recurrence across ensembles demonstrates that it arises from sea surface temperature‐informed climate processes. However, timing differences between ensembles indicate that transient atmospheric variability influences dipole position. 
    more » « less
  4. ; ; (, Journal of Climate)
    Abstract Seasonal predictions of tropical cyclone (TC) landfalls are challenging because seasonal landfall count not only depends on the number and spatial distribution of TC genesis, but also whether those TCs are steered toward land or not. Past studies have separately examined genesis and landfall as a function of large-scale ocean and atmospheric environmental conditions. Here, we introduce a practical statistical framework for estimating the seasonal count of TC landfalls as the product of a Poisson model for seasonal TC genesis and a logistic model for landfall probability. We compute spatial variations in TC landfall and genesis by decomposing TC activity in the western North Pacific (WNP) basin into 10° × 10° bins, then identify coherent regions where El Niño–Southern Oscillation (ENSO) and the western extent of the Pacific subtropical high (WPSH) have significant influences on seasonal landfall count. Our framework shows that ENSO and the WPSH are weakly related to basinwide landfalls but strongly related to regional genesis and landfall probability. ENSO modulates the zonal distribution of TC genesis, consistent with past work, whereas the WPSH modulates the meridional distribution of landfall probability due to variations in steering flow associated with the Pacific subtropical high. These spatial patterns result in four coherent subregions of the WNP basin that define seasonal landfall variations: landfall count increases in the southwestern WNP during a positive WPSH and La Niña, the south-central WNP during a positive WPSH and El Niño, the eastern WNP during a negative WPSH and El Niño, and the northern WNP during a negative WPSH and La Niña. 
    more » « less
  5. ; ; ; (, Journal of Advances in Modeling Earth Systems)
    Abstract An open‐source, physics‐based tropical cyclone (TC) downscaling model is developed, in order to generate a large climatology of TCs. The model is composed of three primary components: (a) a random seeding process that determines genesis, (b) an intensity‐dependent beta‐advection model that determines the track, and (c) a non‐linear differential equation set that determines the intensification rate. The model is entirely forced by the large‐scale environment. Downscaling ERA5 reanalysis data shows that the model is generally able to reproduce observed TC climatology, such as the global seasonal cycle, genesis locations, track density, and lifetime maximum intensity distributions. Inter‐annual variability in TC count and power‐dissipation is also well captured, on both basin‐wide and global scales. Regional TC hazard estimated by this model is also analyzed using return period maps and curves. In particular, the model is able to reasonably capture the observed return period curves of landfall intensity in various sub‐basins around the globe. The incorporation of an intensity‐dependent steering flow is shown to lead to regionally dependent changes in power dissipation and return periods. Advantages and disadvantages of this model, compared to other downscaling models, are also discussed. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Text Encoded Conversion
  • XML
  • Save / Share this Record
  • Send to Email