Climate Dynamics

, 33:1159 | Cite as

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

  • V. Moron
  • A. Lucero
  • F. Hilario
  • B. Lyon
  • A. W. Robertson
  • D. DeWitt
Article

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 <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 set-up 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 local-scale 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.

Notes

Acknowledgments

This research was supported by grants from the National Oceanic and Atmospheric Administration (NOAA), NA050AR4311004, the US Agency for International Development’s Office of Foreign Disaster Assistance, DFD-A-00-03-00005-00, and the US Department of Energy’s Climate Change Prediction Program, DE-FG02-02ER63413.The computing for this project was partially provided by a grant from the NCAR CSL program to the IRI. The NCEP CFS model runs were obtained from the U.S. Climate Prediction Center via the IRI Data Library. We wish to acknowledge the valuable contributions of Ms. Remedios Liwanag who help compile and quality control the daily rainfall data used in this research study. We also thank the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) for providing the daily rainfall records, and many thanks to the staff of the Climate Data Section of the Climatology and Agrometeorology Branch of PAGASA for their helpful efforts during the station selection. Lastly, we thank Simon Mason (IRI) for his help with CPT and Leo Ostwald (IRI) for his help in obtaining GCM runs.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • V. Moron
    • 1
    • 2
    • 3
    • 4
  • A. Lucero
    • 5
  • F. Hilario
    • 5
  • B. Lyon
    • 4
  • A. W. Robertson
    • 4
  • D. DeWitt
    • 4
  1. 1.UFR des Sciences Géographiques et de l’AménagementUniversité d’Aix-Marseille IAix en ProvenceFrance
  2. 2.CEREGE, UMR 6635 CNRSAix en ProvenceFrance
  3. 3.Institut Universitaire de FranceParisFrance
  4. 4.International Research Institute for Climate and SocietyColumbia UniversityNew YorkUSA
  5. 5.Philippine Atmospheric Geophysical and Astronomical Services AdministrationManilaPhilippines

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