Transcript Magnitude

Changes in precipitation extremes in a
warming climate
Pao-Shin Chu
Department of Meteorology
School of Ocean & Earth Science & Technology
University of Hawaii-Manoa
Honolulu, HI, U.S.A.
IPCC AR4 (2007)
Giambelluca et al., (2008)
Extreme events in temperature and precipitation such as
summer heat waves, cold spells in winter, heavy
rainfall/flooding, and drought are changing over time
The occurrence of extreme events is a
serious concern for society because of
their potential damage to humans,
property, public infrastructure,
agriculture, transportation, and others.
To better monitor and understand the
variations of extreme events, the Climate
Variability and Predictability (CLIVAR)
program has developed a suite of climate
change indices for a standard comparison.
• In this study, five of the relevant climate change
indices suggested by WMO/WCRP/CLIVAR are
applied to Hawaii daily precipitation data to
investigate the possible changes of extremes.
• Their relationships with the El Nino-Southern
Oscillation (ENSO) and the Pacific Decadal
Oscillation (PDO) are examined.
Data and climate change indices
Observational data: COOP data from NCDC
water year : July to June of the next year
winter season: November through April of the next year
In order to maintain data quality, some criteria are
applied to the data sets.
1. A month is considered as having complete data if there are 5 missing days.
2. A year is considered as complete if all months are complete according to (1).
3. A station series is considered as complete if it has 65% complete years
according to (2).
COOP stations Numbers
The 1950s-2007
The 1960s-2007
The 1970s-2007
The 1980s-2007
37
41
50
65
Data and climate change indices
Definition of the five indices
Perspective Index Definition
Unit
Intensity
SDII
Average precipitation intensity in wet days
mm/day
Frequency
R25
Annual total number of days with precipitation 25.4 mm
days
Magnitude
R5d
Annual maximum consecutive 5-day precipitation amount mm
Magnitude
R95p Fraction of annual total precipitation due to events
%
exceeding the 1961-90 95th percentile
Drought
CDD Annual maximum number of consecutive dry days
days
Methodology
 Mann-Kendall test and Sen’s method
 A two-sample Kolmogorov-Smirnov (K-S) test
 Fisher-Z transformation
 Nonparametric Mann-Whitney test (Tu, Chou, and
Chu, 2009, J. Climate, The abrupt shift of typhoon
activity in the vicinity of Taiwan …)
Nonparametric Mann-Kendall test and Sen’s method
• Mann-Kendall test assumes that the time series dataset obeys the model:
xi  f (ti )   i
• For data pair xj and xk, where j>k, the sign is calculated:
 1 if x j  xk  0

sgn( x j  xk )   0 if x j  xk  0

-1 if x j  xk  0
• The statistics S is calculated:
n 1
S 
n
 sgn( x
k 1 j  k 1
j
 xk )
• If n10, the normal approximation statistics Y, which is based on S will be
calculated.
 S 1
 VAR( S )

Y  0
 S 1

 VAR( S )
if S  0
if S  0
if S  0
• Positive S or Y means positive trend, negative S or Y means negative trend.
Nonparametric Mann-Kendall test and Sen’s method
• When using Sen’s method to estimate the slope of the trend, first assume that
f(t) in xi  f (ti )   i can be represented by:
f (t )  Qt  B
where Q is the slope to be estimated and B is a constant.
• The slopes of all data pairs are calculated using
Qi 
x j  xk
jk
where j>k. The median of all these slopes of data pairs is the Sen’s estimator
of slope.
• Mann-Kendall method tests whether the trend is increasing or
decreasing and estimates the significance of the trend.
• Sen’s method quantifies the slope of this trend.
• Missing values are allowed in these two methods, and the data need
not conform to any particular distribution. Besides, the Sen’s method is
not greatly affected by single data errors or outliers.
HRI: Hawaii Rainfall Index, 27 gages from 3 islands.
Data have been standardized.
• Using monthly precipitation data, Chu and Chen (J.
Climate, 2005) suggested there is a downward trend in
Hawaiian rainfall during the last century.
In addition to the significance test applied
to individual stations, field significance is
tested.
• In a given dataset, one would expect a certain
number of stations or grids to pass a significance
test at random. To ensure the significance at
individual stations is not due to random chance,
multiple testing is performed to investigate the
field significance (multiplicity problem).
• Assuming spatial independence, a binomial
probability distribution is used to evaluate the
overall significance of the trends.
Long-term Temporal Features
Intensity
Frequency
50s: the 1950s to 2007
60s: the 1960s to 2007
Etc.
Magnitude
Magnitude
Drought
field significant at
the 5% level
Long-term Spatial Features
trends from the 1950s to 2007
• Downward trends in SDII, R25,
and R5d for Kauai and Oahu
• Upward trends in Hawaii
Intensity
Frequency
Long-term Spatial Features
trends from the 1950s to 2007
• For CDD, overall upward trends.
Most islands tend to show
longer, consecutive periods of no
precipitation days since 1950s.
Magnitude
Drought
Features of time derivatives of trends from 30-year running series
 30-year running series are considered.
Time-dependent changes in extreme rainfall events are
examined for a 30-yr interval, but the series are moving
forward one year at a time.
• 1950-1979, 1951-1980, until 1978-2007 (29 series).
 Analyze the trends of every 30-year window.
 Calculate the time derivatives of the trends (this allows us
to determine whether the trends are stable or changing
with time).
SDII
30-year running series
Time series at Honolulu
International Airport
trends of 30-year running series
Trend of 1950-1979 is plotted in
the 1964, trend of 1951-1980 is
plotted in the 1965, etc.
Long-term downward trend
positive time derivative of trend
Long term trend is changing in time (gentle or
even changing sign).
30-year running series
CDD
Time series at Opihihale
trends of 30-year running series
Long-term upward trend
positive time derivative of trend
Long term trend is stable.
30-year running series
Intensity
Frequency
• Positive time derivatives and
long-term downward trends in
Kauai and Oahu.
• Negative time derivatives and
upward trends in Hawaii.
 Trends are changing.
30-year running series
Magnitude
Drought
• For CDD, positive time
derivatives and positive
trends.
 Trends are stable.
Relationship between climate change indices and ENSO/PDO
• Chu (1995) proposed a mechanism that associates
deficient precipitation in Hawaii during winter with El
Niño .
• Chu and Chen (2005) suggested deficient precipitation
in El Niño /+PDO phase, while abundant precipitation
during La Niña /-PDO phase in Hawaii using monthly
precipitation data.
The N-S vertical cross section of the meridional wind component and the
negative pressure vertical velocity between 150°W-165° at 11 standard
pressure levels for winter
Relationship with
ENSO
100%
80%
60%
40%
20%
0%
SDII (+)
R25 (+)
R5d (+)
R95p (+)
CDD (-)
• Positive correlations between four precipitation-related indices and
SOI, and negative correlations between CDD and SOI (the Fisher Z
transformation is applied to the original correlation coefficients).
• For La Niña event (large and positive SOI), Hawaii not only tends to
have more seasonal rainfall, but also receives more frequent heavy
rainfall. For El Niño years, there are fewer extreme events. For CDD,
shorter annual maximum consecutive dry days during La Niña events
while they are longer for El Niño years.
Return periods of rain storms
• Assessing the vulnerability of a region to
extreme rainfall and associated flood events is
an important step in disaster prevention
plans.
• The Generalized Extreme Value (GEV)
distribution and its L-moment estimation
ξ : location, α : scale, ĸ : shape
Oahu
• Previous analysis is based on a stationary GEV
model by assuming climate does not change
(stationary). Recently we have applied a nonstationary GEV method so that the parameters
are allowed to vary with time. Return levels
are changing with time.
Dynamical downscaling for future rainfall
variations for an island (C. Norton)
• IPCC AR4 GCMs
• WRF 3.2 model
Two-way nesting, very high-resolution (1 km)
resolution (super-computer)
Noah land surface
CAM radiation scheme (NCAR community Atmosphere
model)
Yonsei University boundary layer scheme
Tiedtke cumulus parameterization
Cloud microphysics (WSM6)
Domain
4 nested:
Parent: 50km
2nd: 25km
3rd: 5km
4th: 1km
28 Vertical layers
January rainfall for 2030 and 2040
Summary
• Trends of five climate change indices related to extreme
precipitation events in Hawaii are investigated using daily
observational records from the 1950s to 2007.
• A nonparametric rend analysis suggests long-term
downward trends for four precipitation-related indices, and
long-term upward trends for CDD.
• Time-dependent changes in extreme precipitation events
are examined for a 30-yr interval.
• A non-stationary GEV model is applied to examine trends in
return levels.
• The time derivatives of trends of the 30-yr running
series imply that there is a phase change for all 4
precipitation-related series. Since 1980s, there seems
to be an increase in precipitation intensity, frequency,
and magnitude of intense events in Oahu and Kauai.
For CDD, the long-term increasing trend is stable,
implying a stable lengthening of annual maximum
consecutive dry days.
• Positive relationships are found between the wetness
indices and SOI, and negative relations between CDD
and SOI. This suggests more precipitation extremes
during La Niña years and vice versa for El Niño years.
• Return levels are changing with time.
• Dynamical downscaling for future rainfall variations for
an island. Be applied to Taiwan?
• Chen, Y., P.-S. Chu, and T. Schroeder, 2011: Trends in
precipitation extremes and return levels in the Hawaiian
Islands under a changing climate. In preparation.
• Chu, P.-S., Y. Chen, and T. Schroeder, 2010: Changes in
precipitation extremes in the Hawaiian Islands in a warming
climate. J. Climate, 23, 4881-4900.
• Chu, P.-S., X. Zhao, Y. Ruan, and M. Grubbs, 2009: Extreme
rainfall events in the Hawaiian Islands.
J. Appl. Meteor. Climatol., 48, 502-516.
• Chu, P.-S. and H. Chen, 2005: Interannual and interdecadal
rainfall variations in the Hawaiian Islands. J. Climate, 18,
4796-4813.
Thank You!