NUS Presentation Title 2001 Investigating the Relationship ... · NUS Presentation Title 2001 13 Layout of weather stations in Illinois Region Station Name 3-letter ID Coordinate

NUS Presentation Title 2001InvestigatingtheRelationshipbetweenExtremeRainfallIntensityandTemperatureinSingaporeandMidwesternUS

Supervisor: Pat Yeh

Wei Yi

A0098821W

May 11, 2015, Final Year Project Presentation

1

NUS Presentation Title 2001

Content

3 Data Analysis

4 Conclusions and Recommendation

2 Work Flow

1 Project Background

2


Project Background

3

v Global warming has been a big issue over the years.

v Clausius-Clapeyron (C-C) relation dictates that theprecipitable water content in atmosphere will increase ata rate of 7% / ºC, which may lead to higher risk offlooding.

v Analyze the dependence of precipitation intensity onatmosphere temperature in Singapore as well as Illinoislocated in the Midwestern US following the C-C relation.

v Investigate the potential impacts of climate warming onfuture flood occurrence.


Work Flow

4

Knowledge on Clausius-Clapeyron

Relationship

Collection of data

Data analysis on rainfall

intensity and temperature relationship

1 2

Discussion and

Conclusion

43


• Q: amount of water vapor stored in the atmospheric column (L)

• E: amount of Evaporation (L/T)• P: amount of Precipitation (L/T)• MC: Moisture Convergence (L/T)

Atmospheric Water Balance

5

Figure 1. Diagram showing major surface and atmosphericcomponents of the hydrologic cycle (Source: NationalAtmospheric and Oceanic Administration).


6

v Saturation vapor pressure is the maximum pressure at

which the atmosphere can hold.

v Saturation vapor pressure increases with an increase in

air temperature.

v Increase in the moisture-holding capacity of atmosphere

is approximately 7% per degree Celsius rise given by the

C-C relation.

Clausius–Clapeyron relation (1)


v es : saturation vapor pressure (kPa);

v T : temperature in oC


(Jones et al.,2010)7



, T2 – T1 =1oC;

8


9

Assume T1 is -10oC to 30oC,


0.0600

0.0650

0.0700

0.0750

0.0800

-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Scaling α

Temperature T1 (ºC)

T1 (oC) Scaling α

-10 0.0786 -8 0.0779 -6 0.0772 -4 0.0765 -2 0.0759 0 0.0752 2 0.0746 4 0.0740 6 0.0733 8 0.0727 10 0.0721 12 0.0716 14 0.0710 16 0.0704 18 0.0698 20 0.0693 22 0.0688 24 0.0682 26 0.0677 28 0.0672 30 0.0667


P2 ＝ P1 × (1 + α ) ΔT

P1 : original precipitation intensity (L/T)

P2 : new precipitation intensity after increase of temperature (L/T)

α=0.068, Clausius-Clapeyron (C-C) scaling of 6.8% per ºC-1 at 25ºC

ΔT : difference in temperature(ºC)


(Jones et al.,2010)10


11

Literature Review

Type of data for analysis Observed Relationships Temperature Range Possible Reasons

Hourly rainfall and daily mean temperature

Super C-C relationship (scaling≈14% and above)

>10ºC(Berg and Haerter, 2011);>15ºC (Shaw et al., 2011);

<24ºC (G.Lenderink et al., 2011);>12ºC (Lenderink and van Meijgaard,

2008);summer/winter(Mishra et al.,2012)

Higher frequency of convective precipitation than large scale

precipitation. (P.Berg 2011; Haerter and Berg, 2009;Berg and Haerter,

2011;Shaw et al., 2011);Feedback from the dynamics of the convective cloud due to latent heat release; More latent heat release leads to stronger cloud updrafts feeding back again onto the rainfall formation.(Trenberth et al.,

2003; Lenderink and van Meijgaard, 2008; G.Lenderink et al., 2011;Mishra

et al.,2012)

Declined relationship/Sub C-C relationship

Summer(Trenberth and Shea, 2005);10ºC to 20ºC (Berg et al., 2009);

>25ºC (Hardwick Jones et al., 2010);<15ºC (Shaw et al., 2011);

>24ºC (G.Lenderink et al., 2011); >22ºC (Haerter et al., 2010)

Soil Drying out at higher temperature leads to decrease in relative humidity and moisture availability (Trenberth and Shea, 2005; Berg et al., 2009;

Hardwick Jones et al., 2010; Shaw et al., 2011);(G.Lenderink et al.,

2011;Haerter et al., 2010)

Daily rainfall and daily mean temperature

Declined relationship/Sub C-C relationship

>15ºC (Utsumi et al., 2011);>8-10ºC (Lenderink and van Meijgaard,

2008)

Daily rainfall becomes not representative for real rainfall dutation

with increase in temperature. (Utsumi et al., 2011; Lenderink and van Meijgaard,

2008)


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Red numbers represent stations with temperature data from 1985 to 2014.

Layout of weather stations in SingaporeStation Number Station Name

6 Paya Lebar Meteorological Station 7 Macritchie Reservoir

11 Ama Keng Telephone Exchange 23 Tengah Meteorological Station 24 Changi Meteorological Station 25 Seletar Meteorological Station

29 Serangoon Sewage Treatment Works

31 St.James Complex 33 Jurong Pier Road

35 Ulu Pandan Sewage Treatment Works

36 Woodleigh Filters 39 Jurong Industrial Waterworks

40 Singapore Mandai Orchids

43 Upper Air Observatory Kim Chuan Road

44 Nanyang Technological University 46 Singapore Island Country Club 47 C.R.R.S. Yio Chu Kang Road50 Ngee Ann Polytechnic 51 Kranji Turf Club55 Insitute of Mental Health 60 Sentosa Telecommunication Station 63 Pumping Station, International Road 64 Bukit Panjang Telecom 66 Kranji Reservoir 72 Prince Edward Road


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Layout of weather stations in Illinois

Region Station Name 3-letter ID Coordinate Year Range

North

Big Bend BBC 41.63°N,90.04°W 2004-2010

DeKalb DEK 41.84°N,88.85°W 2003-2010

Freeport FRE 42.28°N,89.67°W 2003-2010

Monmouth MON 40.93°N,90.72°W 2003-2010

St. Charles STC 41.90°N,88.36°W 2003-2010

Stelle STE 40.95°N,88.16°W 2003-2010

Mid

Bondville BVL 40.05°N,88.37°W 2003-2010

Champaign CMI 40.08°N,88.24°W 2003-2010

Peoria ICC 40.71°N,89.51°W 2003-2010

Springfield LLC 39.73°N,89.61°W 2003-2010

Perry ORR 39.81°N,90.82°W 2003-2010

Kilbourne SFM 40.16°N,90.09°W 2003-2010

South

Brownstown BRW 38.95°N,88.96°W 2003-2010

Dixon Springs DXS 37.44°N,88.67°W 2003-2010

Fairfield FAI 38.38°N,88.39°W 2003-2010

Belleville FRM 38.52°N,89.84°W 2003-2010

Olney OLN 38.74°N,88.10°W 2003-2010

Rend Lake RND 38.14°N,88.92°W 2003-2010

Carbondale SIU 37.70°N,89.24°W 2003-2010

NUS Presentation Title 2001Methodology

Data from NEA Singapore; Figure is drawn by using MATLAB.

Step:1. Data Collection2. Ten bins of precipitation-

temperature pairs with equal sample size

3. precipitation intensities are ranked to determine the 95th and 99th percentiles.

4. The median temperature of the events is determined in each bin.

(Jones et al., 2010)

95th percentile (dashed)α=0.06

99th percentile(Solid) α=0.06

Hourly

precipitation(mm)

C-C-like scaling of 6.8%.ºC-1

14

Big Bend, Illinois

P2＝ P1 × (1 + α ) ΔT


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Analysis of scaling variation by using of daily and hourly rainfall in Singapore(1985-2014)

v A declined relationship(negative scaling) can beobserved for analysis ofdaily rainfall intensity andtemperature relationship inSingapore (Utsumi et al.,2011 and Berg et al., 2009).

v Utsumi et al. [2011] believedthat the decrease in theextreme daily rainfall intensityat high temperature is causeby decreasing in rainfallduration.


16

Analysis of scaling variation by using of daily and hourly rainfall in Illinois(2003-2010)

v A sub C-C scaling was found for all the stations inIllinois if daily data was used for analysis.


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Analysis of scaling variation by using different percentiles of hourly rainfall data

v Figures show that the scaling increases with increase in percentile ofprecipitation intensity for all the meteorological stations located in bothSingapore and Illinois.

v The results indicate that fully saturation is less likely to be present at rainfallevents with lower precipitation intensity (Hardwick Jones et al., 2010;Lenderink and van Meijgaard, 2008).

α α

Singapore Illinois


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Analysis of scaling variation by using data obtained from different regions in Singapore

0% to 5%6% to 8%

9% to 13%

14% to 17%

18% and above


193% to 5%

5% to 7%

7% to 9%

v Most stations present a C-C like scalingin whole temperature range. However,super C-C and sub C-C relation alsoshow in some temperature range ofcertain stations.

v Analysis of scaling variation with seasonsrather than the whole year basis may beeasier to observe the scaling differencein different regions in Illinois.

Analysis of scaling variation by using data obtained from different regions in Illinois


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Analysis of scaling variation with months in Singapore

NE Monsoon-WetNE Monsoon-Wet

NE Monsoon-Dry

SW Monsoon

Declined relation


21

Analysis of cumulative scaling variation with months in Singapore


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Analysis of scaling variation with seasons in Illinois

v During spring and winter, the mid andsouthern part of Illinois present a C-C likescaling while sub C-C can be observed innorthern Illinois.

v Super C-C, sub C-C and declined C-Crelationship can be observed in somestations in summer and autumn.

v Both obvious super C-C relationship candeclined relationship can be observedduring summer in all three parts in Illinois.

North

α

Mid

South

α

α


23

Conclusionv High frequency of convective precipitation play an important role

in the super C-C relationship especially in Singapore (Trenberth etal., 2003; Lenderink and van Meijgaard, 2008; Berg and Haerter,2011; G.Lenderink et al., 2011; Shaw et al., 2011; Mishra et al.,2012).

v Lower supply of moisture due to soil drying out (Berg et al.,2009;Trenberth and Shea, 2005) might be a rational explanation fora sub C-C or declined relationship between extreme rainfallintensity and temperature for both Singapore and Illinois.

v Clausius-Clapeyron relationship appears to constrain the extremeprecipitation in most part of Illinois, northwest US currently, but itdoes not mean it will remain unchanged with changing climate.


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Recommendation

v Strong recommendation for obtaining daily mean temperaturein all 25 stations in Singapore to analyze the scaling change.

v A longer year of data recording in Illinois is recommended.Long term data indeed will show a more accurate result thatreflect the influence of global warming towards the extremerainfall intensity and temperature relationship.

v Further analysis regarding relationship between relativehumidity and temperature is recommended. (Hardwick Jones etal., 2010; Lenderink and van Meijgaard, 2008).


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NUS Presentation Title 2001 ReferencesBerg, P., J. O. Haerter, P. Thejll, C. Piani, S. Hagemann, and J. H. Christensen (2009), Seasonalcharacteristics of the relationship between daily precipitation intensity and surface temperature, J.Geophys. Res., 114, D18102,doi:10.1029/2009JD012008.

Berg, P., Haerter, J.O., Unexpected increase in precipitation intensity with temperature -A result of mixingof precipitation types?Atmos.Res. (2011),doi:10.1016/j.atmosres.2011.05.012

Groisman, P. Ya., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. N. Razuvauv, 2005:Trends in intense precipitation in the climate record. J. Climate, 18, 1326–1350.

Haerter, J. O., and P. Berg (2009), Unexpected rise in extreme precipitation caused by a shift in rain type?,Nat. Geosci., 2(6), 372–373,doi:10.1038/ngeo523.

Hardwick Jones, R., S. Westra, and A. Sharma (2010), Observed relationships between extreme sub-dailyprecipitation, surface temperature, and relative humidity, Geophys. Res. Lett., 37, L22805,doi:10.1029/2010GL045081.

Liu, S. C., C. Fu, C.-J. Shiu, J.-P. Chen, and F. Wu (2009), Temperature dependence of globalprecipitation extremes,Geophys.Res. Lett., 36, L17702,doi:10.1029/2009GL040218.

Lenderink, G., and E. van Meijgaard (2008), Increase in hourly precipitation extremes beyondexpectations from temperature changes,Nat. Geosci., 1, 511–514,doi:10.1038/ngeo262.

Lenderink, G., and E. van Meijgaard (2010), Linking increases in hourly precipitation extremes toatmospheric temperature and moisture changes, Environ. Res. Lett., 5, 025208, doi:10.1088/1748-9326/5/2/025208.

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Lenderink, G., H. Y. Mok, T. C. Lee, and G. J. van Oldenborgh (2011), Scaling and trends of hourlyprecipitation extremes in two different climate zones—Hong Kong and the Netherlands, Hydrol. Earth Syst.Sci. Discuss., 8, 4701–4719,doi:10.5194/hessd-8-4701-2011.

Meehl, G., et al. (2007), Global climate projections, in Climate Change 2007: The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on ClimateChange, edited by S. Solomon et al., pp. 747–845,Cambridge Univ.Press,Cambridge, U. K.

Mishra, V., J. M. Wallace, and D. P. Lettenmaier (2012), Relationship between hourly extreme precipitation andlocal air temperature in the United States, Geophys.Res. Lett., 39, L16403,doi:10.1029/2012GL052790.

O’Gorman, P. A., and T. Schneider (2009), The physical basis for increases in precipitation extremes insimulations of 21st-century climate change, Proc. Natl. Acad. Sci. U. S. A., 106(35), 14,773–14,777,doi:10.1073/pnas.0907610106.

Shaw, S. B., A. A. Royem, and S. J. Riha (2011), The relationship between extreme hourly precipitation andsurface temperature in different hydroclimatic regions of the United States, J. Hydrometeorol., 12(2), 319–325,doi:10.1175/2011JHM1364.1.

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Utsumi, N., S. Seto, S. Kanae, E. E. Maeda, and T. Oki (2011), Does higher surface temperature intensifyextreme precipitation?,Geophys.Res.Lett., 38, L16708,doi:10.1029/2011GL048426.

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References(Cont’s)

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NUS Presentation Title 2001 Investigating the Relationship ... · NUS Presentation Title 2001 13 Layout of weather stations in Illinois Region Station Name 3-letter ID Coordinate