Influence of Tropical Cyclone Landfalls on Spatiotemporal Variations in Typhoon Season Rainfall over South China

This study examined the impact of tropical cyclone (TC) landfalls on the spatiotemporal variations in the rainfall over South China for the period 1957–2005. The target region was selected to show the noteworthy contribution of TC landfalls to the total rainfall during the typhoon season (July–October). Two prevailing spatial variations in the rainfall were obtained from an EOF analysis. The first EOF mode displays singlesign variability over South China with an explained variance of 23.4%. The associated time series of this mode fluctuates on a decadal timescale and was found to be correlated with TC genesis in the South China Sea. The second EOF mode shows a seesaw pattern between Hainan Island/Guangdong Province and the remaining regions with an explained variance of 11.4%. This seesaw pattern results from an anti-correlation in seasonal TC landfalls between the two regions, which was found in previous studies. This is related to the strengthening (weakening) of the upper tropospheric jets and the corresponding development of a massive anticyclonic (cyclonic) circulation over East Asia. The EOF analysis was also conducted using just the data for rainfall caused by landfalling TCs. This revealed that the first EOF mode using just the TC-induced rainfall is nearly identical to the second mode from the total rainfall. The obvious seesaw pattern of the first mode when employing just the TC-induced rainfall in the EOF analysis implies that this pattern has larger temporal variability than the single-signed pattern (i.e., the first EOF mode using the total rainfall) in terms of TC landfalls. This study suggests that TC landfalls over South China and the accompanying rainfall significantly modulate the spatial variation of the typhoon season rainfall there.

Analysis of recent climate change over the Arctic using ERA-Interim reanalysis data

This study investigates recent climate change over the Arctic and its link to the mid-latitudes using the ERA-Interim global atmospheric reanalysis data from the European Center for Medium-Range Weather Forecast （ECMWF）. Since 1979, sub- stantial surface warming, associated with the increase in anthropogenic greenhouse gases, has occurred over the Arctic. The great- est warming in winter has taken place offshore in the Kara-Barents Sea, and is associated with the increase in turbulent heat fluxes from the marginal ice zone. In contrast to the marked warming over the Arctic Ocean in winter, substantial cooling appears over Siberia and eastern Asia, linked to the reduction of Arctic sea ice during the freezing season （September-March）. However, in summer, very little change is observed in surface air temperature over the Arctic because increased radiative heat melts the sea ice and the amount of turbulent heat gain from the ocean is relatively small. The heat stored in the upper ocean mixed layer in summer with the opening of the Arctic Ocean is released back to the atmosphere as turbulent heat fluxes during the autumn and through to the following spring. This warming of the Arctic and the reduced sea ice amplifies surface cooling over Siberia and eastern Asia in winter.

Technical Note on a Track-pattern-based Model for Predicting Seasonal Tropical Cyclone Activity over the Western North Pacific

Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.