Towards Dependence of Tropical Cyclone Intensity on Sea Surface Temperature and Its Response in a Warming World

Abstract

Tropical Cyclone (TC) systems affect global ocean heat transport due to mixing of the upper ocean and impact climate dynamics. A higher Sea Surface Temperature (SST), other influencing factors remaining supportive, fuels TC genesis and intensification. The atmospheric thermodynamic profile, especially the sea-air temperature contrast (SAT), also contributes due to heat transfer and affects TC's maximum surface wind speed (V max ) explained by enthalpy exchange processes. Studies have shown that SST can approximately be used as a proxy for SAT. As a part of an ongoing effort in this work, we simplistically explored the connection between SST and V max from a climatological perspective. Subsequently, estimated V max is applied to compute Power Dissipation Index (an upper limit on TC's destructive potential). The model is developed using long-term observational SST reconstructions employed on three independent SST datasets and validated against an established model. This simple approach excluded physical parameters, such as mixing ratio and atmospheric profile, however, renders it generally suitable to compute potential intensity associated with TCs spatially and weakly temporally and performs well for stronger storms. A futuristic prediction by the HadCM3 climate model under doubled CO 2 indicates stronger storm surface wind speeds and rising SST, especially in the Northern Hemisphere.

Temperature Control by Its Forecasting Applying Score Fusion for Sustainable Development

“…There is a relationship between temperature and sea level rise. The same conclusion was reached by Arora and Dash [21] in 2016, the sea-air temperature contrast also contributes to fuels tropical cyclone systems increasing its destructive power.…”

The Indian Ocean Dipole: A Missing Link between El Niño Modokiand Tropical Cyclone Intensity in the North Indian Ocean

“…What can be clearly seen in Table 8 (and corresponding Figure 10) is that a peak in the El Niño Modoki mode (7,19) leads the dip in DMI signal (14,23). This dip in the DMI then leads the TCPI minima (20,34).…”

“…This results from internal variability and external forcing. Similar to the eastern Pacific counterparts, central Pacific Ocean temperature anomalies have shown to give rise to the extra-tropical atmospheric teleconnections [7].…”

“…Similarly, Table 9 shows that a dip in the EMI (12,26) and the corresponding peaks in the DMI (19,28) are followed by peaks in TCPI (25,54). The time interval between EMI and DMI (7,2) and that between DMI and TCPI (6,26) sum up to the total time between EMI and TCPI (7+6,2+26). This is the same as the time intervals between EMI dips and the corresponding TCPI peaks (13,28), last row of Table 9 which again justifies our hypothesis.…”

“…HiFLOR, High-Resolution Forecast Oriented Low Ocean Resolution, model for 70-year run experiments demonstrated an increase in the TC frequency, intensity, and TC intensity distribution percentage by21st century [4]. A study performed using UK-Met Office climate model showed an increase in the TC intensification and a shift in the intensification distribution towards higher latitudes [7]. Future projections depending upon theory and high-resolution dynamical models suggest a rise in TC intensity [4] and the shift towards stronger storms with an intensity rise of 2-11% by 2100 and a fall in the TC frequency by 6-34% [5].…”