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IPRC Symposium on Ocean Salinity and Global Water Cycle

IPRC Symposium on Ocean Salinity and Global Water Cycle. Recent Trends and Future Rainfall Changes in Hawaii. Honolulu, Hawaii, 2010-08-02 Presentation by Oliver Elison Timm Acknowledgements: Tom Giambelluca Mami Takahashi Henry Diaz. Latent Heat Flux from NCEP reanalysis climatology.

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IPRC Symposium on Ocean Salinity and Global Water Cycle

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  1. IPRC Symposium onOcean Salinity and Global Water Cycle Recent Trends and Future Rainfall Changes in Hawaii Honolulu, Hawaii, 2010-08-02 Presentation by Oliver Elison Timm Acknowledgements: Tom Giambelluca Mami Takahashi Henry Diaz

  2. Latent Heat Flux from NCEP reanalysis climatology World Ocean Atlas Sea Surface Salinity climatology (WOA9)

  3. Spurious trends in globally averaged monthly mean rainfall NCEP reanalysis ERA-40 CMAP mm/day

  4. Globally averaged monthly precipitation minus evaporation NCEP reanalysis ERA-40 mm/day

  5. Imprints of Hawaiian Islands on thehydrological cycle NCEP reanalysis latent heat flux climatology

  6. Imprints of Hawaiian Islands on thehydrological cycle NCEP reanalysis latent heat flux climatology WOA sea surface salinity

  7. Hawaii Rainfall Index Rain-gauge observation1920-2005: recent negative trend? Chu et al. (2005): PDO and ENSO have a significant influence on the rainfall amounts in Hawaii. Recent negative trend part of Natural variability or first sign anthropogenic forcing? We applied statistical downscaling for the wet and dry season average rainfall: Only very weak changes Figures from Diaz et al. (2008)

  8. Synoptic-Statistical Downscaling for rainfall stations in Hawaii Above average rainfall Below average rainfall (Timm and Diaz J. Clim., 2009) 134 stations Wet and dry season average rainfall. Selected 6 of the 23 IPCC AR4 models Use surface meridional winds as predictors High-low composite

  9. Statistical downscaling (SD) for rainfall stations in Hawaii (Timm and Diaz J. Clim., 2009) 134 stations and analyzed the Wet and dry season average rainfall. Selected 6 of the 23 IPCC AR4 models Use surface meridional winds as predictors SD model: Explained Variance Wet Season Dry Season

  10. Statistical downscaling for the wet and dry season average rainfall:Only very weak changes projected in the ensemble mean. dry season wet season

  11. Changes in the frequency of heavy rain events? Daily rainfall data Heavy rain events: Daily precipitation > 95% quantile in the daily rainfall distribution (wet season, 1958-1976 base period) count the number of events in each wet season Examine the relationship between ENSO, PNA and numbers of events Apply Multiple Linear Regression (MLR) Number of events ~ SOI & PNAI Analyzed 12 selected stations with daily rainfall data MLR: Number of events SOI PNA index

  12. Mid 1970th climate shift Associated regression pattern (SOI) and PNAI Observed changes in number of events MLR estimated changes in number of events

  13. How will future climate change project onto SOI and PNA Observed mid-1970th shift Associated regression pattern (SOI) and PNAI 6 model ensemble projected changes (SRESA1B) 1958-1976 and 1977-2005

  14. IPRC Symposium onOcean Salinity and Global Water Cycle • Summary • Observations show decreasing trend in mean precipitation and heavy rain events • Attribution to anthropogenic forcing not possible yet • ENSO and PNA explain about 20-40% of the variability in number of heavy rain events • Future changes in ENSO-PNA: SRESA1B scenarios show no robust shifts in mean, covariance=> no significant changes is the frequency of heavy rain events Honolulu, Hawaii, 2010-08-02 Presentation by Oliver Elison Timm Acknowledgements: Tom Giambelluca Mami Takahashi Henry Diaz BUT: we do not know yet how other factors will change the frequency of heavy rain events (i.e. the unexplained part of the the variance)

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