NUS Presentation Title 2001InvestigatingtheRelationshipbetweenExtremeRainfallIntensityandTemperatureinSingaporeandMidwesternUS
Supervisor: Pat Yeh
Wei Yi
A0098821W
May 11, 2015, Final Year Project Presentation
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NUS Presentation Title 2001
Content
3 Data Analysis
4 Conclusions and Recommendation
2 Work Flow
1 Project Background
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Project Background
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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.
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Work Flow
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Knowledge on Clausius-Clapeyron
Relationship
Collection of data
Data analysis on rainfall
intensity and temperature relationship
1 2
Discussion and
Conclusion
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• 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
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Figure 1. Diagram showing major surface and atmosphericcomponents of the hydrologic cycle (Source: NationalAtmospheric and Oceanic Administration).
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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)
NUS Presentation Title 2001
v es : saturation vapor pressure (kPa);
v T : temperature in oC
Clausius–Clapeyron relation (2)
(Jones et al.,2010)7
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Clausius–Clapeyron relation (3)
, T2 – T1 =1oC;
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Assume T1 is -10oC to 30oC,
Clausius–Clapeyron relation (4)
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
NUS Presentation Title 2001
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)
Clausius–Clapeyron relation (5)
(Jones et al.,2010)10
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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
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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.
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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
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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
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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
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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.
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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 cli- mate zones—Hong Kong and the Netherlands, Hydrol. Earth Syst.Sci. Discuss., 8, 4701–4719,doi:10.5194/hessd-8-4701-2011.
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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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