Transcript Projections of Future Changes in Heavy Rainfall Frequency

GC51D-1024
GC51D-1024
2011 AGU Fall Meeting
2011 AGU Fall Meeting
HIGH AND LOW RAINFALL EVENTS IN HAWAI‘I IN RELATION TO LARGE-SCALE CLIMATE ANOMALIES IN THE PACIFIC
Mami Takahashi1, Oliver Elison Timm2, Thomas W. Giambelluca1, and Henry F. Diaz3
1Department
of Geography, University of Hawai‘i at Mānoa, HI, USA
2International Pacific Research Center, University of Hawai‘i at Mānoa, HI, USA
3NOAA/ESRL/CIRES, University of Colorado, Boulder, CO, USA
Email:
Takahashi: [email protected]; Elison Timm: [email protected]
Giambelluca: [email protected]; Diaz: [email protected]
Heavy Rain Events
Data & Methods
We selected 12 stations with daily reported
precipitation amounts between 1958–2005 (see
map). This period was found to be most suitable
based on data availability and homogeneity. The
selected stations have the lowest numbers of
missing observations (80–99% complete). We
concentrate on the wet season months
(October-April).
Introduction
Future climate change is expected to increase the frequency and intensity of
extreme weather events in the coming decades. But the confidence is
considerably low in projecting hydrological changes on a local scale directly
from global climate models (GCMs). Here we present results form our regional
statistical downscaling analysis of daily heavy rainfall events and low rainfall
months in the Hawaiian Islands.
The goal of our research:
• To understand how large-scale circulation anomalies have affected the rainfall
characteristics across the Hawaiian Islands during the 20th century.
• Apply this information in statistical downscaling to project model scenario
simulations from the IPCC reports.
The major islands of Hawaii are located in the NE
trade wind zone. For most regions, the bulk of the
annual rainfall occurs during the months of
November through April. Wettest areas are usually
found along the windward slopes of mountains
(maximum 11,000 mm/a).
Dry regions are characterized by sporadic rainfall,
with a few heavy rain events
during the year contributing
more than 50% to the annual totals.
• Develop products that provide quantitative estimates for changes in the
occurrence of heavy rain days and changes in the probability of very low rainfall
months.
Fig. 1
Trend from mid to late 20th century
Kaneha, Kauai
Composite anomalies for dry months at two stations: 700
hPa specific humidity (g/kg in colors) and 500 hPa geopotential (contours in m).
We work with geopotential height in 500 hPa, winds in 1000
and 700 hPa, humidity and moisture transport (700 hPa) and
temperature difference (1000 hPa minus 500 hPa). For each
station a set of anomaly maps is obtained. The anomaly
patterns are used to define a projection index that measures
for each month the similarity between the composite pattern
and the actual circulation. This method is applied to NCEP
reanalysis data and model scenarios likewise.
Fig. 2
Each station has its own projection index time series that is
translated into a probability measuring the likelihood for a dry
month. We calculated the ratio of the number of dry months
versus the total months within different projection index
ranges (bins) and fitted a piecewise linear function through
the data points.
1st PCA mode:
.
Negative loading
Positive loading
Figure 1:Change in probability for dry months
(precipitation < 10% quantile) 1977-2008
minus 1949-1976.
21st century future scenarios:
Fig. 3
Heavy rain days
SOI
We selected 131 stations with monthly precipitation amounts
between 1948–2007, using only wet season months
November through April. Months with rainfall below the 10%
quantile were identified and composite anomaly maps were
derived from the NCEP reanalysis data.
Lahaina, Maui
.
Figure (a): Number of heavy rain days per season for 1958-1976 (left circles) and
1977-2005 (right circles); (b) Regression based estimates using SOI and PNAI.
PNAI
Hawaii
Results: Dry months analysis
regression model
SOI +PNAI
observed
Data & Methods
Mean Annual Rainfall
Results: Heavy rain analysis
NCEP reanalysis 500 hPa and 1000 hPa geopotential height data (October-April season,
1958 - 2005) are used as large-scale circulation
anomalies in this study. We regressed the 1000
hPa 500 hPa geopotential height anomalies onto
the instrumental Southern Oscillation Index
(SOI) and Pacific North American index (PNAI),
respectively. These patterns (see below) are
used to project the climate change scenario
simulations onto these two dominant modes of
North Pacific climate variability.
Low Rainfall Months
Rainfall in Hawaii
SOI & PNAI
Summary of 6 model simulations 2046-2065 (red) and 2081-2100 (pink) A1B
and A2 together. Left: Smoothed histogram using estimated changes from all
stations and all simulations. Middle: 20-yr mean average SOI and PNAI
projections. Contours show the estimated number of heavy rain events for the
12-station average. Right: The mid-1970s climate shift is of similar magnitude
as the projected future changes in SOI and PNAI.
less
more dry months
Station sample
neg. PCA loading
pos. PCA loading
Lahaina, Maui
Figure 2: Principal component analysis (PCA)
loading pattern for the low rainfall probability
indices (131 stations). PCA was derived from
the MPI ECHAM5 simulation 1980-2000.
Fig. 4
Geospatial interpolation
(experimental stage)
Kaneha, Kauai
Piecewise linear transfer function between multivariate
circulation projection index and probability for dry months
conditions.
Acknowledgements
The work reported here was supported by the Pacific Island
Ecosystems Research Center (Biological Resources
Discipline, USGS), and the US Fish and Wildlife Service
through the Pacific Island Climate Change Cooperative (CA
#12200-94023), and the U.S. Army Corps of Engineers
Cooperative Agreement #W912HZ-11-2-0035. Oliver Elison
Timm acknowledges the support by the Japan Agency for
Marine-Earth Science and Technology (JAMSTEC) through
its sponsorship of the International Pacific Research Center.
References:
R
Figure 3: Change in winter drought probability. Shown is the ratio R between the probability
2046-2065 and 1981-2000 (dashed) and 2081-2100 and 1981-2000 (solid line). Red lines
show the smoothed histogram of R from samples using all stations with positive PCA loading
(see Figure 2) and all scenarios. Blue lines show the density estimates from stations with
negative PCA loading. Figure 4 shows a first map produced with spatial interpolation methods
to project future drought risk areas for the Hawaiian Islands.
•
Elison Timm, O., H. F. Diaz, T. W. Giambelluca, and M. Takahashi,
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•
Giambelluca TW, Chen Q, Frazier AG, Price JP, Chen Y-L, Chu P-S,
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http://rainfall.geography.hawaii.edu.
•
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