Near-Time Sea Surface Temperature and Tropical Cyclone Intensity in the Eastern North Pacific Basin

Jien, Jerry Y.; Gough, William A.; Butler, Ken; Cheng, Vincent Y. S.; Arhonditsis, George B.

doi:10.1007/978-3-319-47594-3_3

Cited by 2 publications

(2 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 8b gives a misleading idea about the strength of correlation between the sea surface temperature and the central pressure: indeed, fitting the distribution on the full sample (instead of just the maximum pressure drop for each temperature value) shows a much weaker influence of sea surface temperature alone (see Fig. 8a), even on a weekly basis (see Jien et al, 2017). However, this instrumental variable is essential for preventing CATHERINA from producing unrealistically low central pressure.…”

Section: Mpdmentioning

confidence: 98%

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data

Guenedal

Drobinski²,

Tankov³

2022

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Tropical cyclones are responsible for a large share of global damage resulting from natural disasters, and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA), presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with the data from CMIP models offering a reliable solution to resolve tropical cyclones in climate projections. We illustrate this by producing damage projections in representative concentration pathways (RCPs) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses. This model was primary developed to provide the economic and financial community with reliable signals allowing for a better quantification of physical risks in the long term, to estimate, for example, the impact on sovereign debt.

show abstract

Section: Mpdmentioning

confidence: 98%

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data

Guenedal

Drobinski²,

Tankov³

2022

Geosci. Model Dev.

View full text Add to dashboard Cite

show abstract

“…However, in line with Emanuel (1988) and De-Maria and Kaplan (1994) the sea surface temperature can be used to fix a limit for the pressure drop. More precisely, to prevent the pressure drop from diverging in the projection, we cap it by the maximum observed pressure drop for the corresponding sea surface temperature: Figure 11 gives a somewhat misleading idea about the strength of correlation between the sea surface temperature and the central pressure: indeed, fitting the distribution on the full sample (instead of just the maximum pressure drop for each temperature value) shows a much weaker influence of sea surface temperature alone, even on a weekly basis (see Jien et al (2017) and Figure A3 in the Appendix). However, this instrumental variable is essential to prevent CATHERINA from producing unrealistically low central pressure.…”

Section: Maximum Pressure Drop (Mpd)mentioning

confidence: 99%

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with CMIP climate data

Guenedal

Drobinski

Tankov

2021

Preprint

View full text Add to dashboard Cite

Abstract. Tropical cyclones are responsible for a large share of global damage resulting from natural disasters and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA) presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with CMIP models’ data offering a simple solution to resolve tropical cyclones in climate projections. We illustrate it by producing damage projections in Representative Concentration Pathways (RCP) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses.

show abstract

Near-Time Sea Surface Temperature and Tropical Cyclone Intensity in the Eastern North Pacific Basin

Cited by 2 publications

References 56 publications

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with CMIP climate data

Contact Info

Product

Resources

About