• Tropical Storm (TS)
  • Fundemantal mechanism of Tropical Storm.
  • There are six parameters of large-scale tropics for tropical storm genesis.
    The thermodynamic parameters are to support deep convection;
    1. Sufficient ocean thermal energy
    2. Enhanced mid-troposphere (700 hPa) relative humidity
    3.Conditional instability
    And the daily possibilities of genesis are below;
    1. Enhanced lower troposphere relative vorticity
    2. Weak vertical shear of the horizontal winds at the genesis region
    Additionally, TS is formed at the region where is at least 5o latitude away from the equator.
    These are necessary but not sufficient conditions. All of these conditions must be satisfied at the same time before tropical storm genesis. But, even though all of them are met, genesis of tropical storm may not be formed. All the conditions for tropical storm genesis may be considered as the possibility to support deep convection at a maximum low-level absolute vorticity.
  • TS spins counterclockwise in the Northern Hemisphere (NH) and has the opposite in the Southern Hemisphere (SH), with relating to variations in the spiral rainband. In NH, the cyclonic wind of TS makes the inward flow spiral upward in the spiral area of deep convection like the central eyewall or the spiral rainbands. And it makes outflow aloft just below the tropopause.
  • TS spins counterclockwise in the Northern Hemisphere (NH) and has the opposite in the Southern Hemisphere (SH), with relating to variations in the spiral rainband. In NH, the cyclonic wind of TS makes the inward flow spiral upward in the spiral area of deep convection like the central eyewall or the spiral rainbands. And it makes outflow aloft just below the tropopause.
  • Interseasonal Variability due to the El Nino Southern Oscillation (ENSO)
  • Basin-scale annual variation of TS can derive from forcings spatially and temporally. ENSO is one of mult-year influences on the TS variation.
    By the large-scale atmosphereic and oceanic changes, a warm sea surface temperature (SST) event (El Nino) and a cold SST event (La Nina) occur. These events affect genesis, track, and intensity of TS. The below figures show the differences of genesis and track between the warm and cold SST phase over the Western North Pacific.
  • Decadal Variability
  • In multi-century changes of tropical climate for decades, TS variability on long-time scaes has recently been analyzed. Variability of ENSO on decadal times has been studied from surface pressure and SST until now. This varability seems to be ENSO-like interdecadal variability over the Wertern North Pacific. It is obvious for the decadal variability to play a role to TS activities but not yet clear the mechanism of this phenomenon.

    In Atlanic, decadal variations of hurricane have been related to long-term chages of SST and vertical shear, linked to the Atlantic Multidecadal Oscillation (AMO). AMO is associated with variations of decadal timescales in the Ocean Conveyor Belt. With SST changes in the Ocean Conveyou Belt, hurricane activity is changed. Hurricane activity increases (decreases) in warm (cool) SST because of fast (slow) transport of waters by ocean currents.
  • References
  • Allan, R. J., 2000: ENSO and climatic variability in the past 150 years. El Nino and the Southern Oscillation: Multiscale Variability and Its Impacts on Natural Ecosystems and Society, H. F. Diaz and V. Markgraf, Eds., Cambridge University Press, 3-55.

    Bell, G. D., M. Chelliah, 2006: Leading tropical modes associated with interannual and multidecadal fluctuations in North Atlantic hurricane activity. J. Climate, 19, 590-612.
    Allan, R. J., 2000: ENSO and climatic variability in the past 150 years. El Nino and the Southern Oscillation: Multiscale Variability and Its Impacts on Natural Ecosystems and Society, H. F. Diaz and V. Markgraf, Eds., Cambridge University Press, 3-55.

    Chan, J. C. L., J. Shi, and C. Lam, 1998: Seasonal forecasting of tropical cyclone activity over the western North Pacific and the South China Sea. Wea. Forecasting, 13, 997-1004.

    Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669-700.

    Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part I: El Nino and the 30mb Quasi-Biennial Oscillation influences Mon. Wea. Rev., 112, 1649-1668.

    Updated at June 24. 2013
    Contact : Sung-Yoon Kim (cyanicwind@unist.ac.kr)
  • Arctic Oscillation (AO)
  • What is Arctic Oscillation!?
  • - Definition of AO
  • Leading Empirical Orthogonal Function (EOF) of Sea Level Pressure (SLP) anomaly in north of 20°N [Thompson and Wallace, 1998] and its index is defined as leading PC from the EOF pattern.
  • - Definition of AO
  • Phases of AO, Credit: NASA
  • Positive phase: Lower pressure over Arctic, higher pressure at mid-latitude (45°N). Polar vortex intense, the strong winds enclose around the North Pole, locking cold air in place, Storm tracks further north, wetter in Alaska, Scotland, Scandinavia, drier in Mediterranean and western US, colder in Greenland and Newfoundland
    Negative phase: Higher pressure over polar regions, lower pressure at mid-latitude (45°N). Weak polar vortex, allow intrusions of cold air to southward into North America, Europe, and Asia. It causes cold winter in these regions.
  • - Associated to extreme winter climate
  • Winter climate is associated AO phases because AO is predominant climate variability in Northern Hemisphere winter. For example, record cold snaps and heavy snowfall events across the United States, Europe and East Asia garnered much public attention during the winters of 2009/10 (the most strong negative AO for recent decades).
  • - Influential variables to improve predictability
  • Recent shrinking sea ice also can associated with large scale atmospheric responses such as negative phase of Arctic Oscillation, which is the most dominant atmospheric variability in winter, with colder climate over the mid-latitude in Northern Hemisphere [Cohen et al., 2012]
  • Snow cover
  • What we study regarding AO?
  • - Seasonal prediction
  • - Influential variables to improve predictability
  • In this research, AO prediction performances from two operational seasonal ensemble forecasting systems, UK Met Office Global Seasonal forecasting system version 4 (GloSea4) [Arribas et al., 2011] and The National Centers for Environmental Prediction (NCEP) coupled forecast system model version 2 (CFSv2) [Saha et al., 2012], are evaluated. AO predictability is marginally sensitive to initialization and time frequency of model. We examine the relationship to AO predictability from various initializations of the forecast systems.
  • AO Index Time Series
  • References
  • Arribas A., et al. (2011), The GloSea4 ensemble prediction system for seasonal forecasting, Mon. Wea. Rev., 139, 1891?1910.
    Cohen J., et al. (2012), Arctic warming, increasing snow cover and widespread boreal winter cooling, Environ. Res. Lett., 7 014007.
    Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Pan, H.-L., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., van den Dool, H., Zhang, Q., Wang, W., and Chen, M. (2012), The NCEP Climate Forecast System Version 2, J. Clim., submitted.
    Thompson, D. W. J., and J. M. Wallace (1998), The Arctic Oscillation signature in the wintertime geopotential height and temperature fields, Geophys. Res. Lett., 25, 1297? 1300.
    Updated at June 24. 2013
    Contact : Daehyun Kang (dhkang@unist.ac.kr)
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