This paper presents a summary of several seminal observational and numerical studies on climatology and possible change mechanisms of the outer-core wind structure of a tropical cyclone (TC). This has generally been referred to as size, a term also used in this review despite various definitions being given in the literature. In all the ocean basins where TCs exist, TC size has been found to vary with season, year, decade, latitude and longitude. Such variations are related to the synoptic flow patterns in which the TCs are embedded. Several factors have been identified as responsible for changes in TC size, including: environmental humidity, vortex structure, sea surface temperature and planetary vorticity. Each of these factors can modify the transport of lower tropospheric angular momentum into the TC and cause changes in its size. The paper ends with a discussion of outstanding issues in the study of the outer-core wind structure of a TC.
The present study compares contributions of different environmental factors to the tropical cyclone (TC) genesis over the western North Pacific (WNP) during 2015 and 2016. A local instantaneous view of conditions for the TC genesis is adopted in the present study, which is distinct from the previous view of large-scale temporal averaged conditions. The present study also distinguishes the contributions of three time scale variations (synoptic, intraseasonal, and interannual) of a number of factors. Common to 2015 and 2016, the positive contribution of lower-level vorticity and upward motion to the TC genesis is mainly from the intraseasonal and synoptic components; the contribution of the barotropic energy conversion to the development of synoptic disturbances is larger from climatological mean winds and intraseasonal wind variations than from interannual wind variations; all three time scale variations of mid-level specific humidity contribute positively to the TC genesis; the barotropic energy conversion from climatological mean winds is due to the terms in relation to the meridional shear and zonal convergence of zonal wind. In comparison, the positive contribution of lower-level vorticity and mid-level specific humidity is larger in 2015 than in 2016 on all the three time scales; the contribution of the barotropic energy conversion in relation to the meridional shear of interannual variations of zonal wind and the zonal convergence of intraseasonal variations of zonal wind are larger in 2015 than in 2016; the vertical wind shear on all the three time scales and the sea surface temperature on the interannual time scale have a larger positive contribution to the TC genesis in 2016 than in 2015.
Convective initiation (CI) nowcasting often has a low probability of detection (POD) and a high false-alarm ratio (FAR) at subtropical regions where the warm-rain processes often occur. Using the high spatial and temporal resolution and multispectral data from the Advanced Himawari Imager (AHI) on board Japanese new-generation geostationary satellite Himawari-8, a standalone CI nowcasting algorithm is developed in this study. The AHI-based CI algorithm utilizes the reflectance observations from channels 1 (0.47 μm) and 7 (3.9 μm); brightness temperature observations from infrared window channel 13 (10.4 μm), the dual-spectral differences between channels 10 (7.3 μm) and 13, 13, and 15 (12.4 μm), and a tri-spectral combination of channels 11, 15, and 13, as CI predictors without relying on any dynamic ancillary data (e.g., cloud type and atmospheric motion vector products). The proposed AHI-based algorithm is applied to CI cases over Fujian province in the Southeastern China. When validated by S-band radar observations, the CI algorithm produced a POD as high as 93.33 %, and an FAR as low as 33.33 % for a CI case day that occurred on August 1, 2015, over Northern Fujian. For over 216 CI events that occurred in a 3-month period from July to September 2015, the CI nowcasting lead time has a mean value of ∼ 64 min, with a longest lead time over 120 min. It is suggested that false-alarm nowcasts that occur in the presence of capping inversion require further investigation and algorithm enhancements.
This study used the JRA-55 reanalysis dataset for analyzing the structure and environment of extratropical cyclones (ECs) that spawned tornadoes (tornadic ECs: TECs) between 1961 and 2011 in Japan. Composite analysis findings indicated that the differences between the structure and environment of TECs, and those of ECs that did not spawn tornadoes (non-tornadic ECs: NTECs), vary with the seasons. In spring (March–May), TECs are associated with stronger upper-level potential vorticity and colder mid-level temperature than NTECs. The colder air at the mid level contributes to the increase in convective available potential energy (CAPE) of TECs. TECs in winter (December–February: DJF) and those northward of 40°N in autumn (September–November: SON) are accompanied with larger CAPE than are NTECs. The larger CAPE for TECs in DJF is caused by larger moisture and warmer temperature at low levels and that for TECs northward of 40°N in SON (NSON) is caused by the colder mid-level temperature associated with an upper-level trough. The distribution of the energy helicity index also shows significant differences between TECs and NTECs for DJF and NSON. On the contrary, the distribution of the 0-1 km storm-relative environmental helicity (SREH) showed no significant differences between TECs and NTECs in most seasons except DJF. A comparison of TECs between Japan and the United States (US) shows that SREH and CAPE are noticeably larger in the US. These differences possibly occur because TECs in the US (Japan) develop over land (ocean), which exerts more (less) surface friction and diurnal heating.
In this paper, the response of tropical cyclone (TC) activity to El Niño-Southern Oscillation (ENSO) and coherent sea surface temperature anomaly (SSTA) in the Indian Ocean (IO) is investigated, with a particular focus on the decaying phase of El Niño. The TC anomalies are obtained from the database for Policy Decision making for Future climate change (d4PDF). This dataset is based on 100-member ensemble simulations for the period of 1951-2010 using the state-of-the-art atmospheric general circulation model (AGCM) forced with the observed SST as well as the historical radiative forcing. The AGCM utilized in the d4PDF is the Meteorological Research Institute Atmospheric General Circulation Model (MRI-AGCM) with about 60 km horizontal resolution. Our analysis revealed a prolonged decrease in TC frequency (TCF) over the tropical Western Pacific during the post-El Niño years until the boreal fall. Dominance of anomalous anticyclone (AAC) over the Western Pacific induced by the delayed warming in the tropical IO is the main factor for the suppressed TC activity rather than the local SST change. In contrast, the TC number over the South China Sea tends to increase during the post-El Niño fall (September to November). The physical reason can be ascribed to the weakening of the AAC associated with the termination of IO warming. Thus, we demonstrate that the effect of the IO warming should be taken into account when the ENSO is considered as an environmental factor for predicting TC activity.
The climate variability in monsoon and arid regions attributable to dynamic vegetation is investigated using NCAR's Community Earth System Model with the Dynamic Global Vegetation Model. Two present climate simulations, one using dynamics and the other using fixed vegetation cover, are carried out. A comparative analysis of the two simulations reveals that the climate in monsoon and arid regions exhibits different responses to dynamic vegetation. On the hemispheric scale, precipitation mainly increases in the Northern Hemisphere and decreases in the Southern Hemisphere in response to dynamic vegetation, while the surface temperature exhibits a consistent decrease. On the regional scale, precipitation decreases caused by dynamic vegetation are the main trend in monsoon regions except for the Asian monsoon region, while precipitation responses to vegetation change are weak in arid regions relative to monsoon regions. The surface temperature increases significantly because of dynamic vegetation only in the boreal winter Asian monsoon region, while the rest of the monsoon and arid regions mainly exhibit reduced surface temperatures. Therefore, the climate variability in the Asian monsoon region is clearly different from the other regions. Further analysis shows that dynamic vegetation can modulate variations in the east–west sea-level pressure gradient and lower-level meridional winds in East Asia, and it can strengthen (weaken) the East Asian summer (winter) monsoon. Mechanistic analysis reveals that the difference in hemispheric and regional climate variations may be due to changes in the dynamic vegetation-induced moisture flux and net surface radiative forcing.
Continuous observations of cloud droplet size distributions (DSDs) in low-level stratiform clouds have been conducted at a height of 458 m from Tokyo Skytree (a 634-m-high broadcasting tower in Tokyo) using a cloud droplet spectrometer. In this report, the characteristics of cloud parameters related to the cloud DSD from June to December 2016 are presented. The mean cloud droplet number concentration (Nc), average diameters, and effective diameters of cloud droplets in non-drizzling clouds were 213 cm−3, 7.3 μm, and 9.5 μm, respectively, which are close to the reported values for continental stratiform clouds. The relationship between liquid water content (LWC; g m−3), Nc (cm−3), and radar reflectivity (Z; mm6 m−3) was estimated as LWC = 0.17 Nc0.50Z0.45, with a coefficient of determination (R2) of 0.93. The observed cloud DSDs were well fitted by a lognormal distribution, and the average median diameter of the fitted DSD was 6.6 μm.
This study investigated the absolute values of column-integrated water vapor (precipitable water; PW) in the climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5), in terms of the relationships between PW and precipitation characteristics. We identified that global mean PW values are systematically much lower in CMIP5 models than in observations. This dry bias is most profound over the tropical ocean. The dry bias is partly due to biases in sea surface temperatures in the CMIP5-coupled climate models. However, the dry bias is also present in Atmospheric Model Intercomparison Project (AMIP) experiments, which implies the existence of other factors. The relationship between PW and rainfall characteristics shows that rainfall occurs when water vapor levels are lower than in observations, particularly in models with a relatively strong dry bias. This suggests that the reproducibility of rainfall characteristics may be associated with the dry bias.