RISKS OF PRE-MONSOON EXTREME RAINFALL
EVENTS OF BANGLADESH: IS ANTHROPOGENIC
CLIMATE CHANGE PLAYING A ROLE?
Ruksana H. Rimi, K arsten Haustein, Emily J. Barbour, and Myles R. Allen
Anthropogenic climate change doubled the likelihood of the 2017 pre-monsoon extreme 6-day rainfall event atnortheast Bangladesh. The magnitude of this contribution is sensitive to the climatological period in use.
INTRODUCTION. Northeastern Bangladesh(NEB; 90.5°–92.5°E, 24°–25.5°N) has unique lowland areas known as haors. The “Boro” paddy (rice)
crop is cultivated in these haors in dry winter season (December–February) and harvested during
April–May. Boro accounted for ~55% of national
rice production per annum during 2011–16 and NEB
contributed to ~15% of the total Boro rice production
over this period (FAO 2017). NEB receives highest
annual mean rainfall of >4,300 mm, in contrast
to western Bangladesh, which receives 1,400 mm(Shahid 2010). Topographic uplifting of the southwesterly flow by the Meghalaya Plateau and othersurrounding mountains triggers heavy convective
rainfall at NEB in the pre-monsoon season (Murataet al. 2008, 2011; Sanderson and Ahmed 1979; Shahid2010). Pre-monsoon rainfall during March–April
facilitates growth of Boro but events >150 mm oftotal 6-day rainfall can cause early flash floods anddamage crops. On 27 March 2017, extreme rainfall
over NEB triggered the earliest f lash f lood since2000 (Ahmed et al. 2017). Subsequently, ~850,000
households were affected and ~220,000 ha of nearly
harvestable Boro were damaged. Crop failure in 2017contributed to a record 30% rice price hike compared
AFFILIATIONS: R imi, Haustein, and A llen —Environmental

Change Institute, School of Geography and the Environment,
University of Oxford, Oxford, United Kingdom; Barbour—
Environmental Change Institute, School of Geography and theEnvironment, University of Oxford, Oxford, United Kingdom,and CSIRO Land and Water, Canberra, ACT 2601, Australia.
CORRESPONDING AUTHOR: Ruksana H. Rimi,ruksana.rimi@ouce.ox.ac.uk
DOI:10.1175/BAMS-D-18-0152.1
A supplement to this article is available online (10.1175/BAMS-D-18-0152.2)© 2018 American Meteorological Society
For information regarding reuse of this content and general copyrightinformation, consult the AMS Copyright Policy.

AMERICAN METEOROLOGICAL SOCIETYto 2016 (FAO 2017). Analyzing NOAA’s Climate
Prediction Center (CPC) observation data, we findthat during 16 March–15 April 2017, 6-day total
rainfall over NEB amounted to 225 mm (i.e., 33.33%
higher than the flash flood triggering threshold of150 mm). The highest positive rainfall anomaly (upto 100 mm day–1; relative to 1979–2017) during 16
March–15 April 2017 is found at NEB (see red box inFig. 1a). To determine how rare this event is, we apply
a generalized Pareto (GP) distribution fit to CPC data
exceeding 90th percentile values, and find a return
time of ~70 years (Fig. 1d).
Most of the attribution studies are done for midlatitude extreme events of the developed countries
while very few of them focus on the tropical region(Marthews et al. 2015; Otto et al. 2015; Otto 2017) ordeveloping countries, even though these countries
experience extreme events and have the least capacity
to adapt with the consequent impacts (IPCC 2013).
To understand the drivers behind the 2017 floods,
here we investigate the probabilities of extreme 6-dayrainfall during pre-monsoon season in Bangladesh
and analyze whether or not anthropogenic climatechange has changed the odds of such events occurring(Allen 1999). We utilize the very large weather@home
regional climate model (RCM) ensemble based on
HadRM3P. We also investigate the potential contribution of radiative cooling from anthropogenic aerosols
as their presence can counterbalance the greenhousegas (GHG) induced intensification of the hydrological cycle (Li et al. 2016). Niño-3.4 and Indian Ocean
dipole (IOD) indices are used to quantify the role oftwo key climate modes. Additionally, two global climate models (GCMs) of MIROC5 and ETH_CAM4
are used to test the robustness of our results.
DATA AND METHODS. We use daily rainfall
data from Asian Precipitation Highly Resolved Observational Data Integration toward Evaluation ofWater Resources (APHRODITE) covering 1979–2007
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Fig. 1. (a) Spatial distribution of CPC rainfall (mm day–1) over Bangladesh during 16 March–15 April 2017 relative toclimatology (1979–2017). The red box indicates the study area of NEB. (b) Niño-3.4 (November–January) andIOD (February–April) indices during 1979–2017. The horizontal dashed black line in (c) indicates the thresholdvalue for heaviest rainfall events. The vertical dashed black rectangles in (b) and (c) indicate the association
between the heaviest rainfall events and IOD (Niño-3.4). (d) Return level plot of GP distribution fitted to CPC
observed extreme 6-day rainfall events exceeding a 90th percentile threshold. (e)–(g) Annual cycles of 6-day
rainfall over NEB as in models and observations (HadRM3P, ETH_CAM4 and MIROC5, respectively), with CPC(1979–2017 and 2004–16), GPCC (1988–2017), and APHRODITE (1979–2007).

(Yatagai et al. 2012), CPC global 0.5° analysis of dailyrain gauge measurements covering 1979–2017 (Chenet al. 2008), and Global Rainfall Climatology Centre (GPCC) daily rainfall data covering 1988–2017(Schamm et al. 2015). The higher-resolution (50 km)
weather@home HadRM3P RCM is nested in the
global atmosphere-only HadAM3P model [an atmospheric general circulation model (AGCM)] and isdriven by prescribed SSTs and sea ice concentration(SIC) (Massey et al. 2015; Guillod et al. 2017) andradiative forcing. Following Schaller et al. (2016), we
use 75,000 (30 × 2500) simulations for “Actual March2017” under factual conditions with observed 2017
GHG concentrations, OSTIA SSTs, and SIC (Donlonet al. 2012) and 15,7500 (30 × 5250) simulations for“Natural March 2017” under conditions that might
have been in a counterfactual world without past
GHG emissions and other pollutants. We also use
2,160,000 (360 × 30 × 200) simulations for each of
the Actual, Natural, and GHG-only climatology overthe 30 years (1986–2015) of reference period. Here theGHG-only scenario has current levels of GHGs but
the anthropogenic aerosols are set to pre-industrial
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levels. In addition, we use smaller ensembles fromtwo GCMs, with 1500 (30 × 50) simulations from
MIROC5 and 15,000 (30 × 500) simulations from
ETH_CAM4 representing Actual and Natural Marchduring a 10-yr (2006–15) period [see Mitchell et al.
(2017) for more details]. All model and observation data are regridded using bilinear interpolation
method and then area-averaged rainfall statistics overNEB are analyzed. We use the Niño-3.4 index (Rayneret al. 2003) to determine the lagged correlation with
March rainfall over NEB, and with the IOD index(Huang et al. 2017).
We quantified the change in the occurrence
probability of rainfall event, the risk ratio (RR), as
RR = Pf/Pcf,, where the probability of the event in thefactual climate is denoted by Pf and the probabilityof the same event in a counterfactual climate without
anthropogenic climate change is denoted by Pcf (NAS2016). An exception to this is the GHG-only scenario,
where RR is calculated using Pcf = actual climate
instead of natural climate.
As far as the HadRM3P’s performance with regard
to APHRODITE, GPCC, and CPC is concerned, theFig. 2. Return periods for 6-day rainfall events in March over NEB showing (a) HadRM3P Actual 2017 (black)and Natural 2017 (dark green) vs Actual climatology 1986–2015 (gray) and Natural climatology 1986–2015(light green) ensembles. (b) Actual climatology 2006–15 (gray), Natural climatology 2006–15 (light green),and GHG-only climatology 2006–15 (yellow). (c) Actual climatology 2006–15 (gray) and Natural climatology
2006–15 (light green) but as in MIROC5 (squares) and ETH_CAM4 (triangles) model ensembles. Each dot,triangle, or square represents one simulation of the HadRM3P, MIROC5, or ETH_CAM4 model, respectively,
and the shaded polygon shows 95% confidence intervals. The horizontal dashed deep brown line in each panel
indicates the extreme event of March 2017.

model reproduces the annual rainfall cycle with satisfactory agreement. However, monsoon rainfall isunderestimated in HadRM3P by ~30% (Fig. 1e) withan early onset. We are using model data for Marchrather than mid-March to mid-April to remove thisbias. MIROC5 is biased dry (Fig. 1g) in March (~50%),
whereas ETH-CAM4 (Fig. 1f) is biased extremely
dry (~40% in March and ~25%–50% throughout themonsoon season).
RESULTS AND DISCUSSION. Figure 2 illustrates how the return periods of March extreme
6-day rainfall events have changed from naturalcounterfactual climate to actual climate scenarios.
The likelihood of extreme 6-day rainfall event under 2017 SST conditions in March has increased by~20% (Fig. 2a) in HadRM3P compared to natural
conditions. We find RR of ~2 (100% increase) forthe 1986–2015 climatology period (Fig. 2a), butthen unaltered risks for the shorter 2006–15 period(for events < 200 mm; Fig. 2b). This suggests that
there are interesting nonlinear dynamic feedbacks
at play, which depend on the state of the climate(i.e., ENSO or IOD variability). For March and thepreceding winter (DJF 2016/17), very weak La Niña
conditions were present (see Fig. ES2c in the online supplemental material). While not conclusive,
HadRM3P results indicate that La Niña made drier
AMERICAN METEOROLOGICAL SOCIETY

conditions more likely over NEB (Fig. ES1c). Suchfeedback arises from global teleconnections, which
can be detected in the upper-level zonal wind anomaly fields, in HadRM3P and reanalysis (not shown). Ingeneral, we find a moderate correlation (r = 0.4 to 0.6;
no lag) between Niño-3.4 and NEB rainfall in March(Fig. ES2g). Regarding the interplay between ENSO
and IOD itself, we argue that ENSO leads most IODchanges as notable particularly during strong El Niño
events (Figs. 1b,c) because correlation between Niño3.4 during November to January and IOD during
February and March (r = 0.467; 4 months’ lead time; p= 0.0081 at 95% confidence level) is significant. While
positive IOD events tend to weaken the El Niño impact on the rainfall (e.g., Behera and Ratnam 2018),
we obtain mixed results for the five heavy rainfall
events (vertical dashed rectangles through Figs. 1band 1c) exceeding the 150-mm threshold. Only twoout of five events were associated with positive IOD
and El Niño conditions. March 2017 is drier not only
from an ENSO but also from an IOD point of view.
Since March is in the beginning of the pre-monsoon
season, small changes can cause drastically different
outcomes. Matsumoto (1997) and Ashfaq et al. (2009)
identified an early onset of the rainy days at NEB on
8 April and 21 May, respectively. We speculate thatthe current warming has shifted the pre-monsoon
heavy rain’s onset to even an earlier start. We notice
DECEMBER 2018|S3a decreasing risk for the extreme rainfall event ofMarch 2017 (225 mm) from Natural to Actual climate but then an increasing risk from natural to
GHG-only climate conditions during 2006–15. This
result demonstrates a considerable contribution of
the anthropogenic aerosols for March 2006–15 (Fig.
2b); however, this is not the same for 1986–2015 (notshown). MIROC5 and ETH_CAM4 simulate muchsmaller changes in risk, with ETH_CAM4 evensuggesting a drying trend, but none of these GCMs
captured the observed extreme event of 2017 (Fig. 2c).
CONCLUSIONS. Based on the results fromHadRM3P model only (for 1986–2015), we conclude
that anthropogenic climate change doubled the likelihood of extreme pre-monsoon rainfall (~100% morelikely) over NEB. Interestingly, the attribution signal
in March is sensitive to the chosen climatology period.
Natural ENSO and IOD variability influence interannual changes in rainfall risks, yet both indices madethe 2017 rainfall event less likely. The anthropogenic
aerosol cooling effect is noticeably observed during therecent decade (2006–15). How the interplay between
the two competing forces of GHGs and the anthropogenic aerosols influences the risks of extreme rainfall
events should be explored more. For the first time, thisstudy presents attribution assessment for pre-monsoon
extreme rainfall event for Bangladesh. Understanding
the physical mechanisms behind such event and using
a multimodel approach to incorporate associated uncertainties can be useful for further studies.
ACKNOWLEDGMENTS. We thank our colleagues at the Oxford e-Research Centre for theirtechnical expertise, the Met Office Hadley Centre
PRECIS team for their technical and scientific support for the development and application of weather@home, and all the volunteers who have donatedtheir computing time to climateprediction.net andweather@home.

REFERENCES
Allen, M: 1999: Do-it-yourself climate prediction. Nature, 401, 642, https://doi.org/10.1038/44266.
Ahmed, R., K. R. Rahaman, A. Kok, and Q. K. Hassan,
2017: Remote sensing-based quantification of theimpact of flash flooding on the rice production: A
case study over northeastern Bangladesh. Sensors, 17,
E2347, https://doi.org/10.3390/s17102347.
Ashfaq, M., Y. Shi, W.-W. Tung, R. J. Trapp, X. Gao,J. S. Pal, and N. S. Diffenbaugh, 2009: Suppression ofSouth Asian summer monsoon precipitation in theS4|DECEMBER 2018

21st century. Geophys. Res. Lett., 36, L01704, https://doi.org/10.1029/2008GL036500.
Behera, S. K., and J. V. Ratnam, 2018: Quasi-asymmetricresponse of the Indian summer monsoon rainfall to
opposite phases of the IOD. Sci. Rep., 8, 123, https://doi.org/10.1038/s41598-017-18396-6.
Black, M. T., and Coauthors, 2016: The weather@home
regional climate modelling project for Australia
and New Zealand. Geosci. Model Dev., 9, 3161–3176,
https://doi.org/10.5194/gmd-9-3161-2016.
Chen, M., W. Shi, P. Xie, V. Silva, V. Kousky, R. Higgins,
and J. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily
precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.
Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones,E. Fiedler, and W. Wimmer, 2012: The Operational
Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017.
FAO, 2017: Bangladesh: Severe floods in 2017 affectedlarge numbers of people and caused damage to the agriculture sector. Food and Agriculture Organization of
the United Nations, 6 pp., www.fao.org/3/a-i7876e.pdf.
Guillod, B. P., and Coauthors, 2017: weather@home 2:
Validation of an improved global-regional climate
modelling system. Geosci. Model Dev., 10, 1849–1872,
https://doi.org/10.5194/gmd-10-1849-2017.
Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global
surface temperature change. Rev. Geophys., 48,RG4004, https://doi.org/10.1029/2010RG000345.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5):
Upgrades, validations, and intercomparisons. J.
Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
IPCC, 2013: Climate Change 2013: The Physical Science
Basis. T. F. Stocker et al., Eds., Cambridge University
Press, 1535 pp.
Li, Z., and Coauthors, 2016: Aerosol and monsoon
climate interactions over Asia. Rev. Geophys., 54,866–929, https://doi.org/10.1002/2015RG000500.
Marthews, T. R., F. E. L. Otto, D. Mitchell, S. J. Dadson,
and R. G. Jones, 2015: The 2014 drought in the hornof Africa: Attribution of meteorological drivers [in“Explaining Extreme Events of 2014 from a ClimatePerspective”]. Bull. Amer. Meteor. Soc., 96, S83–S88,
https://doi.org/10.1175/BAMS-D-15-00115.1.
Massey, N., and Coauthors, 2015: weather@home—Development and validation of a very large ensemble
modelling system for probabilistic event attribution.
Quart. J. Roy. Meteor. Soc., 141, 1528–1545, https://doi.org10.1002/qj.2455.

Matsumoto, J., 1997: Seasonal transition of summer rainyseason over Indochina and adjacent monsoon region.
Adv. Atmos. Sci., 14, 231–245, https://doi.org/10.1007/s00376-997-0022-0.
Mitchell, D., K. and Coauthors, 2017: Half a degree additional warming, prognosis and projected impacts(HAPPI): Background and experimental design. Geosci. Model Dev., 10, 571–583, https://doi.org/10.5194/gmd-10-571-2017.
Murata, F., T. Terao, T. Hayashi, H. Asada, andJ. Matsumoto, 2008: Relationship between atmospheric conditions at Dhaka, Bangladesh, and rainfall
at Cherrapunjee, India. Nat. Hazards, 44, 399–410,
https://doi.org/10.1007/s11069-007-9125-2.
—, and Coauthors, 2011: Daytime thermodynamicand air flow structures over northeast Bangladesh
during the pre-monsoon season: A case study on
25 April 2010. J. Meteor. Soc. Japan, 89A, 167–179,
https://doi.org/10.2151/jmsj.2011-A11.
NAS, 2016: Attribution of Extreme Weather Events in theContext of Climate Change. National Academies ofSciences, Engineering and Medicine, 186 pp.
Otto, F. E. L., 2017: Attribution of weather and climateevents. Annu. Rev. Environ. Resour., 42, 627–646, https://doi.org/10.1146/annurev-environ-102016-060847.
—, R. James, and M. Allen, 2015: The science of attributing extreme weather events and its potential
contribution to assessing loss and damage associatedwith climate change impacts. Environmental Change
Institute, 4 pp., https://unfccc.int/files/adaptation/workstreams/loss_and_damage/application/pdf/attributingextremeevents.pdf.

AMERICAN METEOROLOGICAL SOCIETY

Rayner, N. A., D. E. Parker, E. B. Horton, C. K.
Folland, L. V. Alexander, D. P. Rowell, E. C. Kent,and A. Kaplan, 2003: Global analyses of sea surface
temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002
JD002670.
Sanderson, M., and R. Ahmed, 1979: Pre-monsoon rainfall and its variability in Bangladesh: A trend surface
analysis. Hydrol. Sci. Bull., 24, 277–287, https://doi.
org/10.1080/02626667909491867.
Schaller, N., and Coauthors, 2016: Human influence on
climate in the 2014 southern England winter floods
and their impacts. Nat. Climate Change, 6, 627–634,
https://doi.org/10.1038/nclimate2927.
Schamm, K., M., Ziese, K. Raykova, A. Becker, P. Finger,
A. Meyer-Christoffer, and U. Schneider, 2015: GPCC
full data daily version 1.0 at 1.0°: Daily land-surface
precipitation from rain-gauges built on GTS-based
and historic data, accessed 10 Dec 2017, https://doi.org/10.5676/DWD_GPCC/FD_D_V1_100.
Shahid, S., 2010: Rainfall variability and the trends of
wet and dry periods in Bangladesh. Int. J. Climatol.,
30, 2299–2313, https://doi.org/10.1002/joc.2053.
Yatagai, A., K. Kenji, A. Osamu, H. Atsushi, Y. Natsuko,and K. Akio, 2012: APHRODITE: Constructing along-term daily gridded precipitation dataset for Asia
based on a dense network of rain gauges. Bull. Amer.
Meteor. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1.

DECEMBER 2018

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