Spatio-temporal variability and predictability of summer monsoon onset over the Philippines

Abstract

The spatio-temporal variability of boreal summer monsoon onset over the Philippines is studied through the analysis of daily rainfall data across a network of 76 gauges for the period 1977 to 2004 and the pentad Merged Analysis of Precipitation from the US Climate Prediction Center from 1979 to 2006. The onset date is defined using a local agronomic definition, namely the first wet day of a 5-day period receiving at least 40 mm without any 15-day dry spell receiving less than 5 mm in the 30 days following the start of that period. The onset is found to occur rather abruptly across the western Philippines around mid-May on average and is associated with the setup of a "classical" monsoonal circulation with low-level easterlies subsequently veering to southerly, and then southwesterly. The onset manifests itself merely as a seasonal increase of rainfall over the eastern Philippines, where rainfall occurs throughout most of the year. Interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, rather similar over the western and eastern Philippines, with a moderate to high amount of local-scale (i.e. station scale) noise. In consequence, the large-scale signal can be easily retrieved from any sample of at least 5-6 stations across the network although the localscale coherence and fingerprint of the large-scale signal of the onset date are found to be stronger over the central Philippines, roughly from Southern Luzon to Northern Mindanao. The seasonal predictability of local onset is analyzed through a cross-validated canonical correlation analysis using tropical Pacific and Indian Ocean sea surface temperature in March and the 850 hPa May wind field from dynamical forecast models as predictors. The regional-scale onset, defined as the average of standardized local-scale anomalies in onset date, shows good predictive skill (r ≈ 0.8). Moreover, most of the stations show weak to moderate skill (median skill = 0.28-0.43 depending on the scheme) with spatial averaging across stations typically increasing skill to > 0.6.

Exploring the effects of high temperature on mortality in four cities in the Philippines using various heat wave definitions in different mortality subgroups

“…S.J., the Chief of the Manila Observatory’s Meteorological Division. In the succeeding years, the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) adopted and modified the classification, which is now known as the Modified Coronas classification [26]. There are four climate types, namely Type I, II, III, and IV.…”

“…Type III, on the other hand, resembles Type I, but with maximum rainfall periods from May to October. Lastly, Type IV has a more or less even distribution of rainfall year-round [26,28]. …”

Statistical bias correction for climate change impact on the basin scale precipitation in Sri Lanka, Philippines, Japan and Tunisia

“…First of all, we examined model ability to capture major mesoscale and regional-scale climate features based on seasonal evolution in a synoptic scale domain, which influence basin-scale inter-seasonal precipitation. For examples, a pronounced Asia Pacific southwest monsoon system from May to November over Luzon Island in the Philippines (Murakami and Matsumoto, 1994;Moron et al, 2009), strong seasonal frontal systems and typhoons stirring around the Yoshino River from June 10 to November (Tachikawa et al, 2009), effects of the east Asian and south Asian monsoons and Tibetan Plateau high on the Kalu Ganga River, impacts of Mediterranean climate that include parts of Europe, and Mediterranean Sea and eastern Atlantic Ocean influences on the Medjerda River basin. Detailed information of local and regional domains, catchment areas, climate zones and number of stations is given in Table 1.…”

Seasonal march patterns of the summer rainy season in the Philippines and their long-term variability since the late twentieth century

“…In addition, it is well known that the seasonality of rainfall in the Philippines is largely affected by the El Niño-Southern Oscillation (ENSO) (Lyon et al 2006;Lyon and Camargo 2009;Moron et al 2009). For example, Ropelewski and Halpert (1987) showed that the El Niño brings about drier conditions around the Philippines between June and November.…”

“…Moron et al (2009) used rainfall data collected at 76 stations between 1977 and 2004 and data from the Climate Prediction Center Merged Analysis of Precipitation (CMAP) from 1979 to 2005 to study the spatial and temporal variability of the onset of the summer monsoon and to assess the seasonal predictability of local onset dates. They identified important climatic factors that are essential for the prediction of the onset: the sea surface temperature (SST) over the tropical Pacific and Indian Oceans in March and the wind field at the 850 hPa level in May.…”