Rainfall contribution of North Indian Ocean cyclonic

disturbances over India in a warming climate

Kasturi Singh and Jagabandhu Panda

Dept. of Earth and Atmospheric sciences

National Institute of technology Rourkela, Odisha

Email: kasturi.env@gmail.com ; jagabandhu@gmail.com

By

Japan Geoscience Union Meeting (JpGU) 2019, Makuhari Messe, CHIBA , JAPAN

The changing climate has an impact on

changes in cyclonic disturbance (CD) activity

worldwide. These systems are mostly known

for the associated strong wind and the

devastation they cause.

Bay of Bengal sub-basin suffers 1.02 and

3.68 number of systems during pre-monsoon

and post-monsoon season. Arabian Sea

experiences nearly 0.38 and 0.98 number of

systems during peak CD seasons.Figure 1: Mean annual sea surface temperature (SST) anomaly

(oC) during 1880-2015. To determine the current warming

period, the SST data from International comprehensive Ocean-

Atmosphere Data Set (ICOADS) since 1880 is analysed.

The resulting rainfall is beneficial for agriculture and irrigation purpose over Southeast Asia (Dare et al.,

2012; Rodgers et al., 2000); however, may also result in destruction if the precipitation contribution is very

high.

On the year 2016, cyclone Vardah (very severe cyclonic storm; VSCS) after making landfall over southern

India and dumped ~ 382 mm of heavy rainfall over Chennai within 24 hours of making landfall (All India

Weather Summary, 2016).

Since, India also depends on agriculture as a factor of growth in economy, drought and heavy rainfall

possesses large impacts on the society and agricultural activity. Current study is going to be help for

improved disaster management plans, proper planning to avoid flood and better infrastructure design.

Further, the impact of climatic oscillating phenomena such as El Niño-Southern Oscillation (ENSO),

Madden–Julian oscillation (MJO) and Indian Ocean Dipole (IOD) is necessary to be studied.

When Indian Ocean is in the positive (negative) phase of the IOD, it is unfavourable (favourable) for

CD genesis and reduces (increases) CD frequencies in the NIO.

Enhanced (suppressed) convection, and high (low) tropical cyclonic heat potential (TCHP) in the Bay of

Bengal provides favourable (unfavourable) conditions for the TC activity under La Niña (El Niño)

regimes.

• To compute the rainfall contribution by the cyclonic disturbances (CDs) over India, TC best track

data is used from cyclone e-Atlas provided by India meteorological department (IMD) available at

www.rmcchennaieatlas.tn.nic.in.

• Present study considers all systems of intensity above 17 knots as CDs.

• The rainfall product used to achieve the task is a high-resolution (0.25o ×0.25o) gridded rainfall

product covering Indian region provided by IMD (www.imdpune.gov.in).

• ONI (Oceanic Nino Index) data is obtained from National Oceanic and Atmospheric Administration

(NOAA) climate prediction center, available at https://origin.cpc.ncep.noaa.gov/.

• Dipole Mode Index (DMI) data required for the present study is obtained from NOAA

(www.esrl.noaa.gov).

• The data required to compute the MJO days is obtained from Bureau of Meteorology, Australia

(www.bom.gov.au/climate/mjo/).

Rainfall computation

• The rainfall for each CD includes the precipitation when CD’s center was located within 500 km of the

Indian continent and covered more than half of area of the nearby grid boxes.

• Further, the rainfall for grid boxes that lie within 5o radius from the centre of the storm that made

landfall over India is collected for each CD along the path of its travel after making landfall.

• The size of a CD over Indian seas lies within an average of 300-600 km and size of 5o (~555 km) will

give nearly accurate rainfall instead of an over estimated value.

• The choice of 5o radius was also used by Dare et al. (2012), Larson et al. (2005), Kim et al. (2006),

Lee et al. (2010), Cry (1967), Lonfat et al. (2004), Lau et al. (2008), Yokoyama and Takayabu (2008),

Jiang and Zipser (2010), and Nogueira and Keim (2010).

Figure 2: Time series of 3 month running mean of the ONI (°C) during 1980–2016 (red and blue colour

indicates warm and cold years, respectively)

• During pre-monsoon season,

the active El Nino years are 1982,

1983, 1987, 1992, 1998, 2015 &

2016 and La Nina years are

1985, 1989, 1999, 2000, 2008, &

2011.

• For post-monsoon season, the

active El Nino years are 1982,

1987, 1991, 1997, 2002, 2004,

2009, & 2015 and La Nina years

are 1988, 1995, 1998, 1999,

2000, 2007, 2010, 2011, & 2016.

ENSO years determination

• If the ONI value (3 month running mean of SST anomalies in the Niño 3.4 region (5oN-5oS, 120o-170oW)) for five

consecutive months is ≥0.5oC (≤ -0.5oC), then the event is known as El-Nino (La-Nina) (Girishkumar and

Ravichandran, 2012; Mahala et al., 2015).

Years Dates

1985 22-24 May

1990 5 May, 8-11 May

1991 1-2 Jun

1995 6 May, 9 May

2004 5-6 May

2010 7 Jun

2014 29-31 May

2016 17 May

Pre-monsoon Season

Years Dates

1980 13-14 May

1981 31 Oct, 1-2 Nov, 9 Nov

1982 18-19 Oct

1984 12-14 Nov, 30 Nov, 1-2 Dec

1987 1-3 Nov

1990 2-4 Nov

1991 12-14 Oct

1992 8 Oct, 16 Nov, 2 Dec

1993 8-9 Nov, 3 Dec

1996 3-7 Dec

1998 17-19 Oct

2002 10-12 Nov, 21-22 Dec

2009 9-11 Nov

2011 26-27 Nov

2012 30-31 Oct

2013 6-12 Dec

Post-monsoon Season

MJO activated days

The determination of MJO days is done following Chen

and Genio (2009). The negative (positive) index value

represents enhanced (supressed) convection over the

region. A strong MJO event is the one with a negative index

value of < -1.

Figure 3: Classification of IOD years based on DMI data and its standard deviation (σ).

• The positive IOD years found are 1982,

1994, 1997, 2006, 2011, 2012, 2015.

• The negative IOD years found from the

current analysis are 1980, 1981, 1984,

1992, 1996, 1998, 2016.

IOD years determination

• The classification of positive and negative IOD years are done following Mahala et al. (2015). IOD event evolves in

spring (May/June), peaks in fall (October–November) and terminates in early winter (December) (Saji et al. 1999;

Mahala et al. 2015).

• The mean of the DMI from June to November of every year is computed and assigned the value to represent the

DMI of that particular year. Then, the mean and standard deviation [SD (σ)] of DMI for the climatology period

(1980–2016) have been computed.

• The year is categorized as +ve IOD year if the mean DMI (June–November) is greater than or equal to mean+1σ

and as –ve IOD if the mean DMI (June–November) is less than or equal to mean-1σ.

Figure 4: The track of CDs formed during (a) pre-monsoon and (b) post-monsoon for the analysis period over NIO.

The dotted line represents depression phase (17-33 kts), thin line represents storms of intensity between 34-63 kts and

thick solid line represents the storm of intensity higher than 64 kts.

During the period (1947 onward) considered,

~98 CDs observed over NIO region during pre-

monsoon season. Out of which nearly 70

(71.42%) number of systems either crossed or

grazed the Indian coast.

During post-monsoon season, ~325 numbers

of systems formed over NIO and ~283

(87.07%) numbers of system crossed or grazed

the Indian coast.

(a)

(b)

Figure 5: The spatial distribution of dissipation of CDs during (a) pre-monsoon and (b) post-monsoon for the

analysis period over NIO region.

• Highest dissipations are observed near

West Bengal (22.98°N and 87.85°E),

Andhra Pradesh (15.91°N and 79.74°E)

and Tamil Nadu (11.12°N and 78.65°E),

Gujrat (22.25°N, 71.19°E) and

Maharashtra (19.75°N, 75.71°E) states of

India during pre-monsoon season .

• Along east coast, most of the systems

dissipated over Tamil Nadu and Andhra

Pradesh among Indian eastern coastal

states during post-monsoon season.

Figure 6: Total accumulated rainfall (mm) contributed by CDs formed over NIO during (a) pre-monsoon

and (b) post-monsoon season respectively over India.

(a) (b)

Figure 7: Average annual accumulated rainfall (mm/year) contributed by CDs formed over NIO during

(a) pre-monsoon and (b) post-monsoon season respectively over India.

Figure 8: Decadal variation of annual mean CD rainfall (a) during pre-monsoon and (b) post-monsoon over

India for the analysis period.

p=0.008, t=6.133, t-crit=2.44

p= 3.62916E-07, t=23.781, t-crit=2.446

Figure 9: Annual CD rainfall (red) anomaly and annual mean crossed/gazed CD count (blue) anomalies from the mean

annual cycle during (a) pre-monsoon and (b) post-monsoon season over India.

The anomaly values of CD

frequency for pre-monsoon season

is having positive values

(decreasing trend) for the period

considered, however, the anomaly

values are negative for post-

monsoon and trend is decreasing

sharply over NIO.

The trend for annual CD rainfall

(CDR) anomaly during pre-

monsoon season is decreasing and

maintaining a stable trend during

post-monsoon season.

Figure 10: Percentage rainfall contributed by CDs formed over NIO during (a) pre-monsoon and (b) post-monsoon

season respectively over India.

Figure 11: Spatial distribution of trends of rainfall contributed by CDs during (a) pre-monsoon and (b) post-

monsoon season.

(a) (b)

Figure 13: Total contribution of rainfall (mm) by NIO CDs during pre-monsoon (upper panel) and post-monsoon

season (lower panel) over eastern coastal states of India in warming climate (1947-2016) scenario.

26 south

Pargana,

east and

west

Medinipur

Balasore, Bhadrak,

Kendrapara, Jagatsinghpur

Godavari,

coastal areas of

Prakasam,

Nellore and

partway

Chittoor

Thiruvallur

and

Chennai

Gajapati and

Ganjam

Nellore,

partly over

Chittoor

Kanchipuram,

Villupuram, Cuddalore,

Nagapattinam and

Thiruvarur

26 south

Pargana,

east and

west

Medinipur

Figure 14: Annual variation of maximum rainfall (mm) contributed by CDs over eastern coastal states of India during pre-

monsoon (upper panel) and post-monsoon (lower panel) seasons.

Figure 15: Total contribution of rainfall (mm) by NIO CDs during pre-monsoon (upper panel) and post-monsoon season

(lower panel) over western coastal states of India in warming climate (1947-2016) scenario.

Bhavnagar Sindhdurg

Dakshina

Kannda and

Udupi

Districts lying

above 10oN

Gir Somnath,

Amreli,

Ahmedabad

Ratnagiri,

Sindhdurg, Satara

and Osmanbad

Kolar and adjoining

southeast districts

Districts lying

above 10oN

Figure 16: Annual variation of maximum rainfall (mm) contributed by CDs over western coastal states of India during

pre-monsoon (upper panel) and post-monsoon (lower panel) seasons.

GJ MH GA KA KL

(a) (b)

Figure 17: Total accumulated rainfall (mm) contributed by CDs formed over NIO during MJO periods for (a) pre-

monsoon and (b) post-monsoon season respectively over India computed using IMD data. Here, the rainfall is depicted

in natural logarithmic scale of actual value.

(b)

(a)

Figure 18: Annual variation of average rainfall (mm) contributed by CDs formed over NIO and total number of CD days

during MJO periods for (a) pre-monsoon and (b) post-monsoon season respectively over India.

For pre-monsoon

season, during

recent years, the

rainfall contribution

is less under the

impact of MJO.

For post-monsoon season,

till 1993, it is observed that

the CD days have

comparatively low value,

however the rainfall

contribution is high. And

thereafter the CDR value

decreased, though the CD

days are observed to be high.

(b)(a)

Figure 19: Total accumulated rainfall (mm) contributed by CDs formed over NIO during El Nino periods for (a) pre-

monsoon and (b) post-monsoon season respectively over India. Here, the rainfall is depicted in natural logarithmic scale

of actual value.

(a)

(b)

Figure 20: Annual variation of average rainfall (mm) contributed by CDs formed over NIO and total number of CD days

during El Nino periods for (a) pre-monsoon and (b) post-monsoon season respectively over India.

Annual average

rainfall contribution is

very less (maximum

up to 18mm) during

pre-monsoon season.

The annual CD days are

observed to be high

(maximum value up to 14) in

comparison to MJO,

however, the rainfall values

are not very high.

(b)(a)

Figure 21: Total accumulated rainfall (mm) contributed by CDs formed over NIO during La Nina periods for (a) pre-

monsoon and (b) post-monsoon season respectively over India. Here, the rainfall is depicted in natural logarithmic scale

of actual value.

(a)

(b)

Figure 22: Annual variation of average rainfall (mm) contributed by CDs formed over NIO and total number of CD

days during La Nina periods for (a) pre-monsoon and (b) post-monsoon season respectively over India.

During La-Nina period,

there are few years where

the value of CD days are

high, however the rainfall

is low for pre-monsoon

season.

The maximum annual

average is up to 75mm

(second highest rainfall

contribution). The annual

CD days are ~13 days,

nearly equal to as that of

El-Nino years and much

less than MJO periods.

(a) (b)

Figure 23: Total accumulated rainfall (mm) contributed by CDs formed over NIO for (a) positive and (b) negative IOD

events respectively over India. Here, the rainfall is depicted in logarithmic scale of actual value.

(a)

(b)

Figure 24: Annual variation of average rainfall (mm) contributed by CDs formed over NIO and total number of CD days

during (a) positive and (b) negative IOD events respectively over India.

During IOD positive period,

the CD days are decreasing

during the considered period,

whereas the rainfall is high

for IOD positive years.

During IOD negative years,

till 1998, it is observed that

India was getting frequent

CD rainfall because of IOD

negative events and then it

started decreasing.

The highest annual average

rainfall value is ~79mm and

annual CD days is 20days,

much higher than other

events over NIO.

WRF EXPERIMENTAL SETUP

Model Domain 1115x304 grid-points, 36km resolution

Model Time-step 120s

Output Data Frequency 6-Hourly (Raw/Post-Processed)

Interior Nudging Analysis Nudging (qv in the mid-troposphere, u/v/ө´ in

the stratosphere)

PHYSICS SCHEMES

Cloud Microphysics Goddard Microphysics Scheme

Cumulus/Convection Modified BMJ Scheme+ Precipitating Convective

Cloud Scheme

Radiation RRTMG Scheme [RADT = 10MIN]

Land Surface Unified Noah Land-Surface Model

Surface Layer MM5 Monin - Obukhov Scheme

Planetary Boundary Layer Yonsei University (YSU) PBL Scheme

Sea Surface Temperatures Time-interpolated SSTs from CFSR + SST Skin

Scheme

Acknowledgements for

WRF simulated data

support

Dr. Ricardo Fonseca,

KU, Abudhabi, UAE

(Earlier at NTU,

Singapore as a post-

doc)

Dr. Tieh Yong Koh,

Associate Professor at

SUSS, Singapore

(Earlier at NTU as

Assistant Professor)

Dr. Chee Kiat TEO,

NTU Singapore

(a) (b)

Figure 25: Total accumulated rainfall (mm) contributed by CDs formed over NIO during MJO periods for (a) pre-

monsoon and (b) post-monsoon season respectively over India computed using WRF output. Here, the rainfall is

depicted in logarithmic scale of actual value for the period of 1989-2014.

(a) (b)

Figure 26: Total accumulated rainfall (mm) contributed by CDs formed over NIO during El Nino periods computed using

WRF output for (a) pre-monsoon and (b) post-monsoon season respectively over India for the period of 1989-2014. Here,

the rainfall is depicted in natural logarithmic scale of actual value.

(a) (b)

Figure 27: Total accumulated rainfall (mm) contributed by CDs formed over NIO during La Nina periods computed

using WRF output for (a) pre-monsoon and (b) post-monsoon season respectively over India for the period of 1989-

2014. Here, the rainfall is depicted in natural logarithmic scale of actual value.

(a) (b)

Figure 28: Total accumulated rainfall (mm) contributed by CDs formed over NIO during (a) Positive IOD and (b)

Negative IOD years respectively computed using WRF output for over India for the period of 1989-2014. Here, the

rainfall is depicted in natural logarithmic scale of actual value.

Major Findings The CDs crossing or gazing Indian coast is decreasing during pre-monsoon season along with rainfall

contribution by CDs. whereas, crossing/ gazing CD frequency during post-monsoon is having decreasing

trend with stable rainfall contribution by CDs.

Accumulated and average rainfall shows that AP and KL has received highest accumulated rainfall

contribution from CDs among all east and west coastal states during both the seasons.

The percentage contribution is higher over AP, northeast TN, GJ (highest percentage of ~70), west

Rajasthan during pre-monsoon season. During post-monsoon season, considerable rainfall contribution

(maximum up to ~60%) is seen over GJ, southern RJ, AP, OD and WB.

For pre-monsoon, the CD days are low, however the annual average rainfall is high during MJO

periods. La-Nina periods contributed second highest annual average CDR during pre-monsoon season.

For post-monsoon season, Negative IOD and La-Nina contributed higher CDR and the maximum CD

days are also observed to be higher.

During El-Nino period, though the CD days are high, annual average rainfall is found to be low

during both the NIO TC seasons.

The WRF simulated CDR are observed to be very low than that observed by IMD, however for the post-

monsoon the spatial distribution of CDR is well predicted by Model.

ReferencesChen, Y., and Del Genio, A. D., 2009. Evaluation of tropical cloud regimes in observations and a general circulation model. Clim. Dynam. , 32(2-3), 355-369.

Dare, R.A., Davidson, N.E. and McBride, J.L., 2012. Tropical cyclone contribution to rainfall over Australia. Mon. Wea. Rev., 140(11), pp.3606-3619. Available online

at: http://dx.doi.org/10.1175/MWR-D-11-00340.1.

Fonseca, R. M., Zhang, T., & Yong, K. T. (2015). Improved simulation of precipitation in the tropics using a modified BMJ scheme in the WRF model. Geoscientific

Model Development, 8(9), 2915-2928.

Girishkumar, M. S. and Ravichandran, M., 2012. The influences of ENSO on tropical cyclone activity in the Bay of Bengal during October–December. J. Geophys.

Res. Oceans, 117(C2).

Jiang, H. and Zipser, E.J., 2010. Contribution of tropical cyclones to the global precipitation from eight seasons of TRMM data: Regional, seasonal, and interannual

variations. J. Climate, 23(6), pp.1526-1543.

Kim, J.H., Ho, C.H., Lee, M.H., Jeong, J.H. and Chen, D., 2006. Large increase in heavy rainfall associated with tropical cyclone landfalls in Korea after the late 1970s.

Geophys. Res. Lett., 33(18). doi: 10.1029/2006GL027430.

Koh, T. Y., & Fonseca, R. (2016). Subgrid‐scale cloud–radiation feedback for the Betts–Miller–Janjić convection scheme. Quarterly Journal of the Royal

Meteorological Society, 142(695), 989-1006.

Larson, J., Zhou, Y. and Higgins, R.W., 2005. Characteristics of landfalling tropical cyclones in the United States and Mexico: Climatology and interannual variability. .

J. Climate, 18(8), pp.1247-1262.

Lau, K.M., Zhou, Y.P. and Wu, H.T., 2008. Have tropical cyclones been feeding more extreme rainfall?. J. Geophys. Res.: Atmospheres, 113(D23), doi:

10.1029/2008JD009963.

Lee, M.H., Ho, C.H. and Kim, J.H., 2010. Influence of tropical cyclone landfalls on spatiotemporal variations in typhoon season rainfall over South China. Adv. Atmos.

Sci., 27(2), pp.443-454.

Lonfat, M., Marks Jr, F.D. and Chen, S.S., 2004. Precipitation distribution in tropical cyclones using the Tropical Rainfall Measuring Mission (TRMM) microwave

imager: A global perspective. Mon. Wea. Rev., 132(7), pp.1645-1660.

Mahala, B. K., Nayak, B. K., and Mohanty, P. K., 2015. Impacts of ENSO and IOD on tropical cyclone activity in the Bay of Bengal. Nat. Hazards, 75(2), 1105-1125.

Nogueira, R.C. and Keim, B.D., 2010. Annual volume and area variations in tropical cyclone rainfall over the eastern United States. J. of climate, 23(16), pp.4363-

4374.

Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T., 1999. A dipole mode in the tropical Indian Ocean. Nature, 401(6751), 360.

Yokoyama, C. and Takayabu, Y.N., 2008. A statistical study on rain characteristics of tropical cyclones using TRMM satellite data. Mon. Wea. Rev., 136(10), pp.3848-

3862.

Thank You

For further queries contact us at kasturi.env@gmail.com ; jagabandhu@gmail.com