A comparison of trends in extreme precipitation and surface winds in

Hawaii and Taiwan

Pao-Shin ChuDepartment of Atmospheric Sciences

School of Ocean, Earth Science and TechnologyUniversity of Hawaii-Manoa

Presented at the Department of Atmospheric sciences, National Central University August 4, 2015

Extreme weather events such as summer heat waves, cold spells in winter, strong gusty winds, typhoons, 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 of the WMO 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 daily precipitation data in Hawaii and Taiwan to investigate the possible changes of extremes.

• Their relationships with the El Nino-Southern Oscillation (ENSO) are examined.

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

Five climate change indices related to precipitation extremes, 1950s to 2007

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).

Data and climate change indices

COOP stations Numbers

The 1950s-2007 The 1960s-2007 The 1970s-2007 The 1980s-2007

37 41 50 65

Nonparametric Mann-Kendall test and Sen’s method

• Mann-Kendall test assumes that the time series dataset obeys the model:

• For data pair xj and xk, where j>k, the sign is calculated:

• The statistics S is calculated:

• If n10, the normal approximation statistics Y, which is based on S will be calculated.

• Positive S or Y means positive trend, negative S or Y means negative trend.

( )i i ix f t

1

1 1

sgn( )n n

j kk j k

S x x

1 if 0

sgn( ) 0 if 0

-1 if 0

j k

j k j k

j k

x x

x x x x

x x

1 if 0

( )

0 if 0

1 if 0

( )

SS

VAR S

Y S

SS

VAR S

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 can be represented by:

where Q is the slope to be estimated and B is a constant. • The slopes of all data pairs are calculated using

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.

( )f t Qt B ( )i i ix f t

j ki

x xQ

j k

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 applied to investigate the field significance. That is, it is also necessary to address the collective significance of a finite set of individual significance tests for the entire field.

• Assuming spatial independence, a binomial probability distribution is used to evaluate the overall significance of the trends.

Long-term Temporal Features

field significant at the 5% level

50s: the 1950s to 200760s: the 1960s to 2007Etc.

Intensity Frequency

Magnitude Magnitude

Drought

Percentage of stations with positive or negativetrend significant at the 10% level in different periods.

• Downward trends in SDII, R25, and R5d for Kauai and Oahu

• Upward trends in SDII in Hawaii

trends from the 1950s to 2007

Long-term Spatial Features

rainfall intensity

heavy rainfallfrequency

triangles

Long-term Spatial Featurestrends from the 1950s to 2007

• For CDD, overall upward trends. Most islands tend to show longer, consecutive periods of no precipitation days since 1950s.

• Kona side (vog influence?)

Magnitude

Drought

Relationship with ENSO

• 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 events. 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.

0%

20%

40%

60%

80%

100%

SDII (+) R25 (+) R5d (+) R95p (+) CDD (-)

Trends in climate change indices in Taiwan

• A study with Prof. P.L. Lin and his former graduate student, Chen Den-Jing

• 21 stations, typhoon season (July to October), daily precipitation data, 1950-2010

R50

• Rainfall in Taiwan during the typhoon season is contributed TCs, mesoscale convective systems, or local thunderstorms associated with diurnal cycle of summer heating patterns embedded in the prevailing southwesterly monsoon (Chen and Chen, 2003; Chen et al., 2007). Chen et al., (2004) found that the contribution of typhoon rainfall overwhelms that from convective systems over eastern and northern Taiwan in summer. For southwestern Taiwan which is on the windward side of southwesterly flows, typhoon rainfall is comparable to that of convective rainfall.

• Chen and Chen (2010) partitioned summer rainfall in Taiwan into 2 components: TC rainfall and seasonal monsoon rainfall. They defined TC rainfall days as when a TC center is located near Taiwan within 2.5 degree latitude and longitude (117.5-125.5E, 19.5-27.5N). The reminder of the rainfall systems that are not associated with TCs are termed as seasonal monsoon rainfall. Using their definition, we classify a wet day as either caused by TC or monsoon system. Because most typhoons that have impacts on Taiwan are short-lived, we will focus only on SDII and R50.

Summary (Taiwan)• Upward trends are noted for precipitation-related

climate change indices (e.g., rainfall intensity, extreme rainfall magnitude) and also for the drought duration index since 1950 for Taiwan. This is indication of more distinct dry-wet conditions during the typhoon season.

• Changes among different precipitation indices are more uniform and consistent for the plain stations in Taiwan. For high elevation stations in the CMR, the pattern in R50 is different from that of SDII and R5d. R50 may not be an adequate threshold value for defining heavy rainfall events in mountain stations where rainfall is high.

• For southern Taiwan, the long-term increase in CDD and the concurrent increasing precipitation intensity and magnitude is troublesome for the region where agriculture is a major economic sector.

• Also investigated the relative role of TC and non-TC related precipitation. An increase in precipitation intensity induced by TCs and monsoons is noted. Heavy rainfall days caused by TCs have also increased but changes caused by southwesterly monsoons are rather flat since 1950.

• Hypothesis for increase in TC rainfall intensity and magnitude affecting Taiwan? The weakening of steering flows and the slow down of translation speeds of typhoons (Chu et al., 2012)? Increasing TC track frequency in the vicinity of Taiwan (Tu/Chou/Chu, 2009) and (Hsu/Chu/Murakami, Zhao, 2014)?

Late season

• 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., D.-J. Chen, and P.-L. Lin, 2014: Trends in precipitation extremes during the typhoon season in Taiwan over the last 60 years. Atm. Sci. Lett., 15, 37-43.

• Chu, P.-S, Kim J-H, Chen YR. 2012. Have steering flows over the western North Pacific and the South China Sea changed over the last 50 years? Geophysical Research Letters, 39: L10704.

• Chiang, Y., P.-S. Chu, and P.-L. Lin, 2015: Characteristics of surface winds in Taiwan: Annual cycle and long-term trends.

To be presented at the AOGS, Singapore, 2015.

• Tu, J-Y, C. Chou, and P.-S. Chu, 2009: Abrupt shifts of typhoon activity in the vicinity of Taiwan and its association with the western North Pacific-East Asia climate change. Journal of Climate, 22, 3617– 3628. • Hsu, P.-C., P.-S. Chu, H. Murakami, and X. Zhao, 2014: An abrupt decrease in the late-season typhoon activity. J. Climate, 27, 4296-4312.