Precipitation Variability and Trend in Hawaii

Pao-Shin ChuDepartment of Meteorology

School of Ocean and Earth Science and TechnologyUniversity of Hawaii

chu@hawaii.edu

Presented at the WRRC, October 3, 2013

Outline1. Precipitation and El Niño, La Niña, PDO2. Long-term (total) precipitation trend3. Trends in precipitation-related climate

change indices (WMO/WCRP/CLIVAR)4. Spatial distributions of precipitation

extremes5. Future projection in precipitation

(statistical and dynamical downscaling)

Contributors

• Ying Chen, Wendy Chen, Chase Norton, Xin Zhao, Tom Schroeder, Andre Marquez, Chris O’Conner

1. Precipitation, El Niño, La Niña,and PDO

Rain gauges for HRI (Hawaii

Rainfall Index); monthly rainfall

data from 9 stations on

each of three islands (Hawaii, Oahu, Kauai)

are used

El Niño and La Niña

PDO phases

+PDO -PDO

Hawaii Rainfall Index (HRI)

Winter (Nov-Apr) rainfall difference (inches) of the (El Niño/+PDO) minus (La Niña/-PDO) composite.

Dots for rainfall stations (272).

The nonparametric Mann-Whitney test for winter rainfall difference of the (El Niño/+PDO) minus (La

Niña/-PDO)

All stations are Oahu and kauai show confidence at the 90% level

Winter composite of sea surface temperature (SST) (shading) and surface wind (vectors). The unit for SST is

°C and for wind vectors is m/s.

Winter 200-hPa wind vectors. Isotach interval is 10 m/s. Area with wind speed greater than 40 m/s is

shaded.

Winter composite of east-west vertical circulation. Longitude-height section of zonal wind and negative pressure vertical velocity (U, -ω) is

averaged over 15-25°N. Shading is for statistical significance. The Hawaiian Islands are bordered approximately by two vertical lines.

hPa

2. Long-term (total) rainfall trend

Annual HRI time series from 1905 to 2013

Perspective Index Definition Unit

Intensity SDII Average precipitation intensity in wet days mm/day

Frequenc

y

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

3. Trends in climate change indices

Definition of the five climate change indices (WMO/CLIVAR)

The first four indices are related to the wetness conditions; CDD defines the duration of excessive dryness.

●Used a nonparametric Mann-Kendall method with the Sen’s test (MKS) to investigate trends in precipitation extremes (e.g., SDII, R5d, CDD).

●In contrast to the common linear regression which is subject to certain assumptions which may be invalid for extreme precipitation data sets, the advantages of the MKS approach is that the underlying data need NOT to conform to any probability distribution and missing data are allowed. Also the MKS is robust against outliers and skewed distributions (a robust trend detection method).

• Downward trends in SDII and R25 for Kauai and Oahu

• Upward trends in SDII for Big Island (rainfall became more intense since 1950)

trends from the 1950s to 2007triangles

Intensity

Frequency

Intensity

Long-term Spatial Features

Long-term Spatial Featurestrends from the 1950s to 2007

For CDD, positive trends prevail. Most islands tend to show longer, consecutive

periods of no precipitation days since 1950s (worse on Kona, Big Island and east Maui).

Magnitude

DroughtTrend patterns for R5d (5-d rainfall amounts) are

similar to that in SDII

Summary for Part 3• Trends of four climate change indicators are

examined over the last 60 years. Results reveal a regional pattern. Oahu and Kauai are dominated by long-term downward trends for 3 precipitation related indices, while increasing trends (SDII and R5d) are noted over the Big Island. Long-term upward trends of drought conditions (CDD) are observed on all the major islands. Kona and east Maui experienced significant drying conditions. More distinct dry-wet conditions over Big Island.

4. Spatial variability of precipitation extremes

Three different ways of defining heavy rainfall events on the mainland U.S. from climatological perspectives (e.g., Groisman et al., BAMS, 2001)

• The mean annual number of days on which 24-h accumulation exceeds a given daily rainfall amount (e.g., 50.8 mm for heavy rainfall and 101.6 mm for very heavy)

• The value associated with a specific daily rainfall percentile (e.g., 90th percentile for heavy rainfall and 99th percentile for very heavy rainfall)

• The annual maximum daily rainfall associated with a specific return period (e.g., 2-yr for heavy rainfall and 20-yr for very heavy rainfall)

• The return period, also known as recurrence interval, is interpreted to be the average time between occurrence of events of that magnitude or greater. It is commonly used for engineering design and risk analysis. For example, a 100-yr flood has a 1% chance of being exceeded in any one year.

• Estimated return periods of heavy rainfall in Hawaii using annual maximum daily rainfall and a generalized extreme value(GEV) distribution.

• For a GEV, a cumulative distribution function given by

(1)where μ, σ and ξ are the location, scale, and shape parameter, respectively.

• Estimates of the extreme quantiles, known as the return level zp, correspond to the return period (τ= 1/p), where p is the probability of occurrence

1/

( ) exp 1 , 1 0z zG zξ

µ µξ ξσ σ

− − − = − + + >

{ }1 log(1 ) , 0pz p ξσµ ξξ

− = − − − − ≠ (2)

Heavy rainfall events are common in Hawaii

• The interaction of synoptic systems with local topography often results in heavy rainfall events in Hawaii that cause damage to properties, agriculture, transportation, and public facilities (e.g., the Halloween flood of 2004 at the UH-Manoa, the 2006 flood events on Kahala Mall and Kauai, the December 2008 flood on Oahu and Kauai, the December 2010 floods).

Manoa flood

Kauai’s Ka Loko dam failure, March 2006 killed 7 people

• Pollutants carried away by stream flows during heavy rainfall events are one of the major threats to near-shores marine ecosystems, especially coastal coral reefs.

2004 flood in Manoa

Fig. 3 of Chu et al., 2009

Leopold, 1949; Garrett, 1980; Chen and Nash, 1994 (diurnal rainfall variation to an interaction among orographic uplifting, thermal forcing, and island blocking of the trade winds)

GEV model

Summary for Part 4• Heavy rainfall events in Hawaii are common and

tend to occur at lower elevations of the windward slope of the mountains. Local maxima of 20-25 days a year are observed on the Big Island, east Maui and Kauai.

• For 20-yr return period (very heavy event), daily maximum rainfall is more than 200 mm (~8 inches) for almost the entire Oahu.

• Results can be used for engineering design (urban drainage), flood insurance rate maps, and environmental regulations.

5. Projection of future climate change

• Projections of future climate changes are based on general circulation models (GCM) or earth system models (ESM)– the latest is the CMIP5

• GCM or ESM have a coarse horizontal resolution(100~200 km) while many Hawaiian islands are small (~50 km) with complex terrain

• Microclimates in Oahu cannot be adequately represented by coupled models and is necessary to downscale the model outputs through statistical or dynamical approaches to obtain local knowledge of future climatechange. This will provide water agencies with the information (e.g., rainfall, evaporation) needed to plan and prepare for these future changes.

Statistical downscaling• Finding an empirical relationship between large-scale

atmospheric variables (predictors, e.g., wind, humidity) and a small-scale variable (predictand, i.e., rainfall) using statistical methods

• Historical rainfall data from stations on Oahu, NCEP reanalysis II data, and GCM simulations to project future changes in precipitation extremes (exceed the 90th percentile of daily rainfall distribution) for Oahu.

• Only stations with at least 30 yrs of daily precipitation data are used. A 3 and 5 rule is then applied for selecting stations: if 3 consecutive days are missing then the month is removed or 5 non-consecutive days are missing then the month is removed. This filtering process results in 7 stations.

Oahu

Predictor selection

• Based on the Pearson correlation and Spearman rank correlation (robust and resistant) analysis between observations and NCEP data during 1979-2008 suggest 4 predictors: RH at 850 hPa, U at 850 hPa, V at 1,000 hPa, and SLP.

Why neural networks?• Traditionally, linear statistical methods are used to

explore the coupled variability between large-scale circulation and local rainfall. For rainfall extremes, their changes do not respond linearly to atmospheric forcings. For this reason, a nonlinear neural network method is used. It also offers advantages, including extracting maximum relationship between predictors and the desired response.

Makiki, Oahu floodMarch 2006

• Neural networks are trained through error-correction learning with the most common being the backpropagation, which uses the methodology of gradient-descent learning. The method initiates by assigning weights to the input vectors (predictors) and applying nonlinear stepwise transformations. The output is compared to the desired response (predictand) and an error between the output and desired is calculated. The errors are back signaled through the topology and the weights are adjusted according to the error. This process is repeated until a termination criterion is met.

Approach 1: Baseline test (1979-2008)• How well does a model reproduce the historical climate?

• Models which best match the average observed rainfall are identified; use absolute difference as a measure

• Assume GCM that most accurately represents rainfall is also the most adequate model that can simulate rainfall related large-scale circulation features well

Approach 2: Future Projections (2011-2040)

• 24 Models with multiple scenarios

• How does the model compare with all other models forfuture projections?

• Models with outlier projections (excessive anomalies) arerejected. GCMs that lie outside of 1 SD of the 24 model mean are discarded. Those that lie inside 1 SD are regarded as close to the consensus (i.e., convergence) andare thus ranked according to absolute difference fromthe mean.

Results:Test 1 Baseline Test 2 Projection

NCARCCSM3 CGCM3T47---Run-3.SR-A1B

MRI-CGCM2.3.2 CSIROMk3.5.SR-A1B

IPSLCM4 CGCM3T47---Run-4.SR-A2

ECHAM5 CSIROMk3.0.SR-A2

ECHO-G GISS-ER.SR-A2

GFDLCM2.1 GISS-ER.SR-B1

MIROC3.2-medres NCARPCM.SR-A1B

GISSAOM ECHAM5.SR-A2

INGV-SXG CGCM3T47---Run-4.SR-B1

MIROC3.2-hires GISS-ER.SR-A1B

GISSE-R MIROC3.2-medres.SR-A2

GFDLCM2.0 CNRMCM3.SR-A2

NCARPCM INMCM3.0.SR-B1

CGCM3T47 ECHO-G.SR-B1

CGCM3T63 CGCM3T47---Run-3.SR-B1

CSIROMk3.5 MIROC3.2-medres.SR-B1

CSIROMk3.0 GFDLCM2.0.SR-B1

Is there a ‘best’ model for both tests?

ECHAM5 A2 (T1+T2=12)

GISSE-R A2 (T1+T2=16)

GISSE-R B1 (T1+T2=17)

MIROC 3.2 A2 (T1+T2=18)

CSIROMk3.5 A1b (T1 +T2=18)

These 2 tests are then combined to find an overall high ranked model among all 24 models

Extreme rainfall frequency (Oahu) for observed (1979-2008), current climate (1979-2008) from model, and future climate (2011-2040) from

model. The corresponding 95% confidence interval of the storm frequency based on the bootstrap resampling method is given in

parenthesis.

Campbell Honolulu International Airport

Honolulu (Observatory) Paiko Punchbowl

1979-2008 Extreme Frequency

41 47 51 62 58

Confidence Interval (1979-2008)

[29,54] [36,63] [38,67] [48,79] [45,74]

1979-2008 Model Extreme

Frequency

32 43 39 51 47

Confidence Interval Model (1979-

2008)

[28,50] [32,56] [34,62] [40,71] [38,67]

2011-2040 Model Extreme

Frequency

50 59 53 68 64

Confidence Interval Model (2011-

2040)

[41,65] [53,79] [48,76] [63,93] [57,86]

Rainfall intensity (mm/day)

Campbell Honolulu International Airport

Honolulu (Observatory) Paiko Punchbowl

1979-2008 Average Extreme Intensity (mm/day)

90.4 78.5 74.4 81.4 88.8

Confidence Interval (1979-2008)

[80.8,105.7] [72.4,88.7] [67.6,84.7] [75.3,89.3] [82.5,99.0]

1979-2008 Average Extreme Model Intensity

(mm/day) 82.3 71.1 66.7 73.2 77.9

Confidence Interval Model (1979-2008)

[75.7,98.2] [68.1,83.6] [65.8,81.2] [68.4,81.8] [73.5,91.1]

2011-2040 Average Extreme Model Intensity

(mm/day)

75.1 66.2 61.7 66.9 71.1

Confidence Interval Model (2011-2040)

[67.2,87.4] [60.2,76.1] [54.9,69.4] [58.3,74.1] [66.8,83.7]

What is dynamical downscaling?• Dynamical downscaling is achieved using a high

resolution regional climate model (RCM) that is initialized with output from GCMs or ESMs. The Weather Research and Forecasting (WRF) model developed by NCAR is the standard RCM

• Advantages – physically based, high resolution (~1.1 km) numerical simulation to allow assessment of future change to any location (e.g., where the groundwater recharge is highest), RCM outputs drive even small-scale hydrologic models within watersheds

• Caveats – heavy computation demand because of the complexity of model physics, different data formats, long-time integration, high resolution, and nesting

• WRF 3.5 model (RCM)

Longwave and shortwave radiation schemesYonsei University boundary layer schemeTiedtke cumulus parameterizationCloud microphysics (WSM6)Noah land surface modelAlso need to modify land surface data sets(albedo, green vegetation, soil types, landcover) in the official WRF release

WRF (regional climate model)

• 4 domains configuration

• - Parent: 30 km (parent domain not shown)

• - d01: 10 km• - d02: 3.33 km• - d03: 1.11 km

March 2006 flood

1.1 km

Rainfall in shading (mm), elevation in contours (100 m), averaged surface winds in arrows (m/s)

Summary of Part 5• Results from statistical downscaling suggest that in the

next 30 years (up to 2040), the frequency of heavy rainfall events will increase but their mean intensity will decrease on leeward Oahu

• WRF is able to simulate rather realistic rainfall distribution and amounts for March 2006 but with underestimation; need to verify simulations more rigorously

• Simulated surface winds for March 2006 are southeasterlies to the east of Kauai, bringing moisture to the island

• Work in progress – simulation of future rainfall (e.g., next 50-yr)

Relevant publications• Chu, P.-S., 1995: Hawaii rainfall anomalies and El Niño.

J. Climate, 8, 1697-1703.

• Chu, P.-S., and H. Chen, 2005: Interannual andinterdecadal rainfall variations in the Hawaiian Islands.J. Climate, 18, 4796-4813.

• Chu, P.-S., X. Zhao, Y. Ruan, and M. Grubbs, 2009:Extreme rainfall events in the Hawaiian Islands. J. Applied Meteorology & Climatoogy, 48, 502-516.

• Chu, P.-S., Y. R. Chen, and T. Schroeder, 2010: Changes inprecipitation extremes in the Hawaiian Islands in a warmingclimate. J. Climate, 23, 4881-4900.

• Norton, C.W., P.-S. Chu, and T. Schroeder, 2011: Projectingchanges in future heavy rainfall events for Oahu, Hawaii: Astatistical downscaling approach. J. Geophysical Research, 116,D17110.