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

Latent Heat Flux from NCEP reanalysis climatologyWorld Ocean Atlas Sea Surface Salinity climatology (WOA9)

Spurious trends in globally averaged monthly mean rainfall

NCEP reanalysisERA-40 CMAP

Globally averaged monthly precipitation minus evaporation

NCEP reanalysisERA-40

Imprints of Hawaiian Islands on thehydrological cycle

NCEP reanalysis latent heat flux climatology

Imprints of Hawaiian Islands on thehydrological cycle

NCEP reanalysis latent heat flux climatologyWOA sea surface salinity

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 ofNatural variability or first sign anthropogenic forcing?

We applied statistical downscaling for the wet and dry season average rainfall:Only very weak changes

Hawaii Rainfall Index

Figures from Diaz et al. (2008)

Synoptic-Statistical Downscaling for rainfall stations in Hawaii(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

Above average rainfall Below average rainfall

High-low composite

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

Dry Season Wet Season

SD model: Explained Variance

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

dry season wet season

Changes in the frequency of heavy rain events?

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

SOI PNA index

MLR: Number of events

Daily rainfall data

Associated regression pattern(SOI) and PNAI

Mid 1970th climate shift

Observed changesin number of events

MLR estimated changesin number of events

Associated regression pattern(SOI) and PNAI

How will future climate changeproject onto SOI and PNA

1958-1976 and 1977-2005

Observed mid-1970th shift6 model ensemble

projected changes (SRESA1B)

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)