Proceedings of the First IOCAFRICA Ocean Forecasting Workshop for
the Western Indian Ocean Region; IOC. Workshop report; Vol.:267;
2015Cultural Organization
UNESCO
Proceedings of the First IOCAFRICA Ocean Forecasting Workshop for
the Western Indian Ocean Region Institute for Meteorological
Training and Research Nairobi, Kenya
11 – 15 August 2014
Workshop Report No 267
Proceedings of the First IOCAFRICA Ocean Forecasting Workshop for
the Western Indian Ocean region Institute for Meteorological
Training and Research Nairobi, Kenya 11 – 15 August 2014
Edited by
Shigalla Mahongo Tanzania Fisheries Research Institute, Dar es
Salaam, Tanzania
Mika Odido IOC Sub Commission for Africa and the Adjacent Island
States, Nairobi, Kenya
Stella Aura WMO Regional Institute for Meteorological Training and
Research, Nairobi, Kenya
UNESCO 2015
Disclaimer
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publication do not imply the expression of any opinion whatsoever
on the part of the Secretariats of UNESCO and IOC concerning the
legal status of any country or territory, its authorities, or
concerning the delimitations of the frontiers of any country or
territory. The authors are responsible for the choice of the facts
and opinions presented within their chapter sections, and all
images are the authors unless otherwise cited. The opinions
expressed therein are not necessarily those of IOC or UNESCO and do
not commit the Organization.
Acknowledgements
Appreciation is expressed to all those who assisted in the
preparation of these proceedings, with special thanks to all the
ocean experts who participated in the workshop for their
contribution. We would like to thank the Principal of the Institute
for Meteorological Training and Research, Ms Stella Aura, and the
Director of the Kenya Meteorological Services and their staff for
the excellent arrangements made for the workshop.
The workshop was funded through the kind contribution of the
Government of Flanders, Belgium through the Flanders UNESCO Science
Trust fund. Project No. 513RAF2018 on the “African Summer School on
the Application of Ocean Data and Modelling products”
Edited by Shigalla Mahongo, Mika Odido, and Stella Aura
For bibliographic purposes this document should be cited as
follows:
UNESCO-IOC. Proceedings of the First IOCAFRICA Ocean Forecasting
Workshop for the Western Indian Ocean Region, Nairobi, Kenya, 11-15
August 2014. Mahongo S., Odido M. and Aura S. (Eds). Nairobi,
UNESCO, 2015 (IOC Workshop Reports, 267)
Cover image extracted from: Pacific Marine Environmental
Laboratory, National Oceanic and Atmospheric Administration.
(2004). Maximum computed tsunami amplitudes around the globe [Map].
Retrieved from
http://www.pmel.noaa.gov/images/headlines/2004-ampmap.jpg on 11th
December, 2014.
Published by the United Nations Educational, Scientific and
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3. OCEAN STATE FORECAST FOR THE WESTERN INDIAN OCEAN
3.1 Review the previous El Niño and Indian Ocean Dipole (IOD)
events and how they have affected coral bleaching in the Western
Indian Ocean (WIO) region Majambo Jarumani and Veronica Dove
.....................................................................................
7
3.2 Review of the previous El Niño and Indian Ocean Dipole events
and how they have affected cyclone incidents and intensity in the
Western Indian Ocean region for September to December (SOND) season
John Bemiasa, Charles Magori, Arnaud Nicolas, Dass Bissessur, and
Premnarain Ramathan Pathak
...........................................................................................
16
3.3 Predicted development of El Niño and Indian Ocean Dipole events
and possible impact on the ocean state in the Western Indian Ocean
region Premnarain Ramnath Pathak and Mohamed Khamis Ngwali
............................................ 35
3.4 Modelling the mean-state of the oceanographic conditions in the
Western Indian Ocean during September to December, using the
Regional Ocean Modelling System Issufo Halo
...................................................................................................................................
49
3.5 Using wave rider buoy and ocean remote sensing to forecast the
Western Indian Ocean region’s Ocean state for September to December
season Arnaud Nicolas and Dass Bissessur
...........................................................................................
63
3.6 Statistical forecasting of the Western Indian Ocean for
September to December season Joseph Amollo and Philip Sagero
..............................................................................................
75
DISCUSSIONS: SYNTHESIS OF REPORTS
(ii) Potential impacts of September to December (SOND) forecasts
...........................................................
91
REPORTS PREPARED FOR THE 38TH CLIMATE OUTLOOK FORUM
The impacts of ocean state, El Niño and IOD forecasts in the
Western Indian Ocean region
....................................................................................................................................
95
The climatology and a forecast of the WIO region’s ocean state, and
predicted developments of IOD and El Niño events during September –
December 2014
....................................................................................................................
106
DISCUSSIONS: SUMMARY AND RECOMMENDATIONS
.............................. 108
ANNEX I: LIST OF PARTICIPANTS
.................................................................
110
ANNEX II: LIST OF ACRONYMS
.....................................................................
111
FOREWORD The First IOCAFRICA workshop on Ocean Forecasting for the
Western Indian Ocean (WIO) region was organized by the IOC
Sub-Commission for Africa and Adjacent Island States (IOCAFRICA) in
collaboration with the World Meteorological Organisation’s (WMO)
Regional Institute for Meteorological Training and Research (IMTR)
from 11-15 August 2014 at IMTR in Nairobi, Kenya. It was held in
response to the recommendations made at the 35th Regional Climate
Outlook Forum (RCOF35) which called on the ocean experts group to
organize a workshop before the next RCOF to review the previous
ocean state fore- casts, and prepare new forecasts for the period
covered by the next RCOF. The products prepared should be shared
with the climate group and disseminated to users after the
RCOF.
The Intergovernmental Oceanographic Commission of UNESCO and the
Western Indian Ocean Marine Science Association (WIOMSA) have from
2005-2013 supported the participation of ocean experts from the
Western Indian Ocean region in four sessions of the Regional
Climate Outlook Forum (RCOF) for the Greater Horn of Africa region
organized by the IGAD Climate Prediction and Application Centre
(ICPAC). The Regional Climate Outlook Forums (RCOFs) were conceived
with an overarching responsibility to produce and disseminate
regional assessments of the climate for the upcoming rainfall
season. Built into the RCOF process is a regional networking of the
climate service providers and representatives of sector-specific
users. The goal of IOC and WIOMSA has been to enhance collaboration
between climate experts and marine scientists in order to improve
climate forecasts, as well as mitigate the impacts of climate
variability and change in the coastal and marine zones. The ocean
experts group have participated in the following RCOFs:
RCOF-15 March 2005, Mombasa, Kenya - Focused on Application of
Climate Information in planning and management of the coastal zone,
and marine and inland aquatic resources for sustainable
development.
RCOF-32 August 2012, Zanzibar, Tanzania - Focused on enhancing the
use of information of the Indian Ocean systems for improved climate
prediction and early warning of climate extremes over the Greater
Horn of Africa (GHA).
RCOF-33 February 2013, Bujumbura, Burundi - Focused on Building
Climate Resilience for Disaster Risk Reduction, Climate Change and
Adaptation for Sustainable Development in the GHA. An Ocean Experts
group was established during RCOF-33 with the objective of
enhancing regional collaboration between the oceans and climate
scientific communities to facilitate the generation of more
accurate seasonal climate forecasts for the GHA region, as well as
providing ocean data products to other stakeholders.
RCOF-35 August 2013, Eldoret, Kenya - In preparation for the
RCOF-35, the ocean experts group, established at RCOF-33 held a
meeting on 13-19 August 2013 at the IGAD Climate Prediction and
Application Centre – ICPAC in Nairobi, Kenya to develop products
for RCOF-35 and interact with the climate group working on the
consensus climate forecasts for RCOF-35. The results of the ocean
predictions were presented to the RCOF35 session (21-23 August
2013, Eldoret, Kenya)
The ocean experts noted that the main weakness of the marine and
coastal sector session at the RCOF was that it comprised mainly
researchers and academics and did not include other potential users
of RCOF products from the sector. Efforts should be made to include
other categories such as artisanal fishers, coastal tourism,
aquaculture, coastal developers, ports authorities, oil refineries,
oil explorers, resource managers and disaster response groups in
future RCOFs.
They also pointed out that the RCOFs only provide information on
rainfall forecasts while coastal communities require much more
information. The ocean experts group should work with the climate
group
i
during the preparation of the forecasts so as to develop the
products required by the marine sector. Additional climate
services/products required include: rainfall, wind speed/direction,
wave/swell heights, currents, SSTs and chlorophyll information to
identify fishing zones/grounds, tides and phases of the moon
(spring and neap tides). The RCOF forecasts/products should be
communicated to a wider user community at the coast, who will then
be able to use them and provide feedback.
The following tasks were proposed for the ocean experts group, to
be implemented before their participation in the next RCOF:
• Define categories of users that the ocean predictions will be
directed to. • Define products that will be prepared (including Sea
Surface Temperatures, Salinity, Ocean currents,
Tides/sea levels, Isotherm variability, IOD, 30-m depth variations,
and thermocline) • Identify appropriate ocean models and data
sources, taking into account discussions at previous
RCOFs, and the performance of the available models.
The ocean experts group should organize a workshop before the RCOF
to review the previous ocean state forecasts, and prepare new
forecasts for the period covered by the next RCOF. The products
prepared should be shared with the climate group and disseminated
to users after the RCOF.
The First IOCAFRICA workshop on Ocean Forecasting for the Western
Indian Ocean region was organized by IOCAFRICA in collaboration
with the Institute for Meteorological Training and Research in
response to these recommendations.
The results of this workshop were presented at the 38th Regional
Climate Outlook Forum held from 25-26 August 2014 in Addis Ababa,
Ethiopia.
ii
1. BACKGROUND The oceans cover 70% of the Earth’s surface, contain
over 97% of the world’s water and are a major forcing mechanism of
the Earth’s climate. They possess a total mass which is about 300
times larger than that of the atmosphere and a thermal heat
capacity which is about 1000 times greater. Hence, climate
predictability relies on understanding the processes that occur
within the ocean (Marshall and Plumb, 2007). The ocean-atmosphere
interactions have a profound impact upon social and economic
activities of the general society. Accurate climate outlook
forecasts will enhance safety of life and property as well as
conservation of the natural environment.
The ocean plays a crucial role in seasonal, interannual and longer
time fluctuations in climate, mainly through ocean-atmosphere
coupling. The dominant coupled ocean-atmosphere interaction, the El
Niño-Southern Oscillation (ENSO) anomaly patterns in Pacific Ocean
Sea-Surface Temperatures (SSTs), has a predominant influence on the
inter-annual variability of the global climate, including East
Africa’s climate (Indeje et al., 2000). The Indian Ocean SSTs
through the ocean-atmsphere coupled mode of variability, the Indian
Ocean Dipole (IOD), also plays a crucial role in the inter-annual
variability of East Africa’s climate. The IOD can explain some
climatic extremes over the East African region, which could not be
explained by ENSO (Saji et al., 1999). The inter-annual variability
of East Africa’s climate is mainly associated with perturbations in
the global SSTs, especially over the equatorial Pacific and India
Ocean basins, and the Atlantic Ocean to some extent (Mutai et al.,
1998; Indeje et al., 2000; Saji et al., 1999; Goddard and Graham,
1999). The modulation on SST is largely due to oceanic processes,
mainly through vertical and horizontal advection and upwelling
(Behera et al., 1999; Murtugudde et al. 2000).
Recent studies show it is insufficient to rely on prediction of SST
in the Pacific as an indicator of ENSO uptake over the Indian
Ocean. Even if the strength of the Pacific ENSO is accurately
predicted, the resulting pattern of rainfall and storm events
around the Indian Ocean varies markedly. For example the 1982/83
and 1997/98 El Niño events produced very different impacts. The
former event induced devastating drought in southern Africa and
Australia, yet the more recent episode produced floods in East
Africa and drought across Indonesia: an east–west dipole pattern.
It can be expected that local climatic conditions around the Indian
Ocean will depend not only on remote forcing, but also on local
patterns of SST and the manner in which the atmosphere
responds.
Studies suggest that the major systems controlling East African
rainfall are primarily forced by the Indian Ocean processes. The
processes in the Pacific Ocean play a secondary role (Hastenrath
and Polzin, 2003). However, the details of the interaction between
the ocean and the atmosphere in the Greater Horn of Africa (GHA)
are not fully understood. Whereas the SSTs in the Indian Ocean
basin play a crucial role in the region’s climate, the oceanic
processes controlling the evolution of the Indian Ocean SSTs are
not well understood.
In the West Indian Ocean a number of countries can benefit from
ocean applications. Most of the economic activities in this region
of the Indian Ocean for example shipping and fishing activities
along the coasts of East Africa and the adjacent island states
require reliable ocean state forecasts which include oceanographic
components.
Reliable and timely seasonal forecasts of the ocean state is vital
to the shipping and offshore industries, ports and harbours, to
safeguard operations and trade, facilitate coastal design and
management, and permit optimal exploitation of fisheries resources.
Therefore, forecasts of the ocean state will assist in reducing the
severity of the impacts of extreme ocean events like extreme waves
and tropical cyclones. In some parts of the region, cyclones cause
heavy swells which cause significant rises in sea levels that
affect coastal infrastructures such as roads and settlements,
undermine beach stability, and cause vertical scouring (Ragoonaden
1997). Seasonal forecasts of the ocean state will assist in
reducing
1
the severity of the impacts of extreme ocean events like extreme
waves and tropical cyclones. In some parts of the region, cyclones
cause heavy swells which cause significant rises in sea levels that
affect coastal infrastructures such as roads and settlements,
undermine beach stability, and cause vertical scouring (Ragoonaden
1997). Seasonal forecasts of the ocean state will also enhance the
accuracy of information given to policy and decision makers which
will assist in planning and mitigation of adverse impacts of
oceanic events. The availability of good disaster management
information will provide guidance on effective ways in addressing
the vulnerability of sensitive socioeconomic sectors and
sustainable resilience of the coastal communities (Aura et al.,
2011). The workshop will also enhance the skills of the
participants in ocean data management, sea state forecasting and
modelling and hence build capacity for the WIO region.
REFERENCES
Aura S., Ngunjiri C., Maina J., Oloo P., Muthama N.J., 2011:
Development of a Decision Support Tool for Kenya’s Coastal
Management. J Meteorol. Rel. Sci., 4 pp 37-47
Behera SK, Krishnan R & Yamagata T, 1999. Unusual
ocean-atmosphere conditions in the tropical Indian Ocean during
1994. Geophysical Research Letters, 26: 3001–3004.
Goddard L & Graham NE, 1999: Importance of the Indian Ocean for
simulating rainfall anomalies over eastern and southern Africa.
Journal of Geophysical Research, 104: 19099–19116.
Hastenrath S & Polzin D, 2003. Circulation mechanisms of
climate anomalies in the equatorial Indian Ocean. Mete- orologische
Zeitschrift, 12(2): 81-93.
Indeje, M., Semazzi, FH & Ogallo LJ, 2000. ENSO signals in East
African rainfall seasons. International Journal of Climatology,
20(1): 19-46.
Marshall J & Plumb RA, 2007. Atmosphere, Ocean, and Climate
Dynamics: An Introductory Text. Elsevier Academic Press.
344p.
Murtugudde R, McCreary JP & Busalacchi AJ, 2000. Oceanic
processes associated with anomalous events in the Indian Ocean with
relevance to 1997–1998. Journal of Geophysical Research, 105:
295–3306.
Mutai CC, Ward MN & Colman AW, 1998. Towards the prediction to
East Africa short rains based on sea surface temperature–atmosphere
coupling. International Journal of Climatology, 18: 975–997.
Ragoonaden S, 1997. Impact of sea-level rise on Mauritius. Journal
of Coastal Research (Special Issue), 24: 206-223.
Saji, NH., Goswami BN, Vinayachandran PN & Yamagata T, 1999. A
dipole mode in the tropical Indian Ocean. Nature, 401:
360-363.
2
2. WORKSHOP DESCRIPTION The main goal of the workshop was to
facilitate the generation of ocean state forecasts for the Western
Indian Ocean region for the September, October, November and
December 2014 period. The ocean state forecast products to be
developed included wave, swell and wind parameters (significant
wave height and direction, wave periods, swell height and
direction, and wind speed and direction), ocean heat content,
surface salinity, SSH etc. The session also focused on the
predicted developments of El Niño and Indian Ocean Dipole (IOD)
events, and their possible impacts on the ocean state in the
region. The session assessed how the previous El Niño events have
affected coral bleaching and cyclone incidents and intensity in the
region.
Specific Objectives
The specific objectives were aligned with the main theme for the
38th RCOF, which was “Early Warning for Early Action in order to
Reduce Risks Associated with Climate Variability and Change for
Resilience in the Horn of Africa”. The specific objectives were
therefore set as follows:
i. Prepare ocean state forecasts for September, October, November,
and December (SOND), and how this will link with the regional
climate
ii. Hold joint discussions with climate scientists to develop a
consensus ocean state forecast for the season
iii. Assess ocean state forecast products generated and the likely
impacts for the upcoming SOND season. iv. Study the predicted
developments of El Niño and IOD events and their possible impacts
on the ocean
state in the region v. Review the previous El Niño and IOD events
and how they affected coral bleaching and cyclone
incidents and intensity vi. Disseminate ocean state forecast
products to other stakeholders after validation.
Expected Outcomes
The expected outcomes included: i. Consensus ocean state forecast
products developed and disseminated to marine and coastal
management stakeholders ii. Ocean state forecast products
disseminated to stakeholders iii. Impacts of the ocean state
forecasts identified iv. Impacts of predicted and historical El
Niño and IOD events on the ocean state.
Workshop Format Six topics which reflected the specific objectives
of the workshop were identified prior to the workshop and assigned
to the participants to prepare draft reports, as follows:
1. Review the previous El Niño and IOD events and how they affected
coral bleaching in the WIO region (M. Jarumani, V.F. Dove)
2. Review the previous El Niño and IOD events and how they affected
cyclone incidents and intensity in the WIO region for SOND season
(J. Bemiasa, C. Magori, A. Nicolas, D. Bissessur, P. Pathak)
3. Predicted developments of El Niño and IOD events and their
possible impacts on the ocean state in the WIO region for SOND
season (P. Pathak, M. Ngwali, J. Amollo, P. Sagero)
3
4. Modelling the mean-state of the oceanographic conditions in the
WIO during SOND (I. Halo) 5. Using wave rider buoy and ocean remote
sensing to forecast the WIO region’s ocean state for SOND
season (D. Bissessur, A. Nicolas) 6. Using statistical models to
forecast the WIO region’s Ocean state for SOND (J. Amollo, P.
Sagero)
The participants identified the tools and data required for each of
these topics, performed the necessary analyses and prepared the
draft report and presentations on these. The reports were discussed
and sugges- tions made for improvements.
Further work was undertaken during the workshop and the reports
updated.
Ocean Forecasting Workshop for Western Indian Ocean held on 11th -
15th August 2014 in Nairobi, Kenya
4
6
3.1 Review the previous El Niño and IOD events and how they have
affected coral bleaching in the WIO region Majambo Jarumani1 and
Veronica Dove2
Abstract
Since the early 1980s, episodes of coral bleaching and mortality,
due primarily to climate-induced ocean warming, have occurred
almost annually in most parts of the Western Indian Ocean (WIO)
region. Bleaching is episodic, with the most severe events
typically accompanying coupled ocean–atmosphere phenomena, such as
the El Niño-Southern Oscillation (ENSO), which result in sustained
regional elevations of ocean temperature. Using bleaching data from
Reef Base, we review how El Niño and IOD events have affected coral
bleaching and mortality in the WIO region. The extent of bleaching
in the Western Indian Ocean during 1998 was unprecedented both in
extent and severity with repeated minor bleaching events reported
from 2000 to 2005. Coral bleaching and mortality during the El Niño
event of 1998 was most severe in Kenya, northern Tanzania and parts
of northern Mozambique. Coral reef conservation strategies now
recognize climate change as a principal threat, and are engaged in
efforts to allocate conservation activity according to geographic,
taxonomic, and habitat-specific priorities to maximize coral reef
survival. Efforts have been made to forecast and monitor bleaching
by using remote sensed observations and coupled ocean–atmosphere
climate models. In addition to these efforts, attempts to minimize
and mitigate bleaching impacts on reefs are immediately
required.
KEY WORDS: Climate change, Indian Ocean Dipole, El Niño-Southern
Oscillation, Western Indian Ocean, coral bleaching
1. Introduction
Coral reefs are the most spectacular and diverse marine ecosystems
on the planet today Complex and productive, coral reefs boast
hundreds of thousands of species, many of which are currently
undescribed by science. They are renowned for their biological
diversity and high productivity (Hoegh-Guldberg 1999). Coral reefs
also protect coastlines from storm damage, erosion and flooding by
reducing wave action across a coastline. The protection offered by
coral reefs also enables the formation of associated ecosystems
e.g. seagrass beds and mangroves which allow the formation of
essential habitats, fisheries and livelihoods.
1Coastal Oceans Research and Development in the Indian Ocean
(CORDIO), East Africa #9 Kibaki Flats, Kenyatta Beach, Bamburi
Beach. P.O. Box 10135-80101Mombasa, KENYA Email:
[email protected]
2Eduardo Mondlane University Faculty of Science, Principal Campus
C. P. 257, Maputo, MOZAMBIQUE Email:
[email protected];
[email protected]
Despite their importance, the worldwide coral reefs are
experiencing threats associated to coastal development, marine
pollution, sedimentation, overfishing and diseases. Additionally,
climate change is becoming a major threat to coral reefs by driving
increase in seawater temperature, intensity and frequency of
extreme thermal events and increase in ocean acidification
(Hoegh-Guldberg 1999; McClanahan et al. 2002; Hughes et al. 2003;
Sheppard 2003; Hoegh-Guldberg, et al. 2007, Baker et al., 2008;
Eakin et al., 2008). Coral bleaching and mortality events are a
dramatic phenomenon that have been associated to climate-induced
ocean warming observed in the world’s tropical or subtropical seas
(Baker et. al 2008, Veron et al. 2009).
Bleaching events have been increasingly reported since early 1980
(Glynn, 1993; Hoegh-Guld- berg, 1999; Hughes et al., 2003;
Hoegh-Guldberg et al., 2007). The events are episodic and most of
them are observed accompanying the coupled ocean–atmosphere
phenomena, such as the El Niño - Southern Oscillation (Baker et
al., 2008), which drives the elevation of sea surface temperatures.
Bleaching occurs when the symbiotic algae living within coral
tissues are expelled or die, leaving the white skeleton visible
through the tissue. Corals appear able to recover from short term
bleaching events in about 6 to 8 weeks, however if stressful
conditions persist for a few weeks or if the stress event was
severe, then mortality will occur (Wilkinson et al. 1999).
Bleaching events in the WIO region have been reported, most
significantly in 1998 with Transition from high to low mortality
with increasing depth as observed at numerous sites (Wilkinson et
al. 1999, McClanahan and Obura 1997, Hoegh-Guldberg 1999).
Bleaching events were also observed in 1983, 2005, 2007 and 2010 in
localized areas (McClanahan et al. 2007a).
Sustained abnormally high SST’s associated with widespread coral
bleaching have been related to El Niño Southern Oscillation (ENSO)
events. These events sounded the alarm for the future of coral
reefs and particularly for many communities of people in the Indian
Ocean dependent on these reefs for their livelihoods. Several
studies have been conducted on the impact of climate on the coral
reefs in the WIO region though they were mainly to assess the
impact of the ENSO and the response of reefs to the 1998
temperature anomaly (McClanahan et al. 2007a; Baker et al. 2008;
Graham et al. 2008; Ateweberhan and McClanahan 2010). In this view
we will review the previous El Niño and IOD events and how they
have affected coral bleaching in the WIO region.
2. Data and Methodology
2.1. Study area
The Western Indian Ocean (WIO) region is bounded to the West by the
mainland states of eastern Africa, and comprises the island states
of the Indian Ocean (Fig. 1). Nations within the region include
Kenya, mainland Tanzania, Zanzibar and Mozambique, Comoros,
Madagascar, Mauritius, Reunion and Seychelles, Maldives, with South
Africa in the southwest and Somalia in the northwest. The marine
ecosystems of the region are dominated by extensive coral reefs,
mangrove forests and sea grass beds (Muthiga et al., 1998). These
ecosystems support a large proportion of the coastal population and
affect millions of lives in one of the poorest and most densely
populated regions in the World (Souter and Linden, 2000). Coral
reefs in the western boundary comprise the continuous fringing
reefs and patch reefs. In the WIO island states, reefs circumscribe
these islands and form the main ecosystems.
8
Figure 1: A map of the study area showing coral reef areas and the
coral bleaching study sites.
2.2. El Niño Southern Oscillation Niño 3.4 is one index for ENSO
and is used on SST’s anomalies in the equatorial Pacific. It is
calculated by taking the average SST’s anomalies over 50 N - 50 S,
1700 W – 1200 W. The SST’S anomalies are computed from HadISST’S
data set that came from Met Office Hadley Centre Observation
(Rayner et al., 1996). This index has been extensively used in many
climatic diagnostic studies.
2.3. Indian Ocean Dipole (IOD)
The IOD sometimes referred to as the Indian Ocean Dipole zonal mode
(IODZM) is a major climatic mode found in the tropical Indian
Ocean. Its strength is measured through the IOD index, which has
been commonly expressed in terms of several variables including sea
level pressure, outgoing long-wave radiation and SST’S. In this
study the SST’s index definition of Saji et al., (1999) is used.
This index is based on the difference in SST’Ss between the
tropical western Indian Ocean (500 E - 700 E, 100 S - 100 N) and
the tropical southeastern Indian Ocean (900 E - 1100 E, 100 S -
Equator).
2.4. Coral Bleaching Observation Data
A set of coral bleaching data is hosted on reef base website. This
global bleaching data is based on status reports mainly by
organizations involved in coral reef research e.g. Global Coral
Reef Monitoring Network (GCRMN), Costal Oceans and Development in
the Indian Ocean (CORDIO) and Coral Reef Conservation Programme
(CRCP) among others. In this dataset, information on the occurrence
and severity of coral bleaching is provided in indices of; -1
(unknown), 0 (no bleaching), 1 (low bleaching), 2 (moderate
bleaching) and 3 (high bleaching).
9
2.5. Methodology
Monthly mean data of Niño 3.4 SST’s anomalies from HadISST’S and
IOD SST’S anomalies is analyzed to discern patterns of El- Niño/
La-Niño and IOD events. El- Niño/ La-Niño events are determined
using Niño 3.4 SST’s data from NCAR’s Data Analysis Section. The
start and end months of the different ENSO phases are determined
using the Niño 3.4 index such that values must exceed ±0.4C, as
used by Trenberth (1997), for at least 6 consecutive months.
3. Results
3.1. ENSO and IOD
The dominant mode of climate variability in the world is related to
El Niño Southern Oscillation (ENSO). The Indian Ocean Dipole (IOD)
on the other hand is a coupled ocean-atmosphere phenomenon
considered to be independent of ENSO. Figure 2 shows a time series
of standardized SST’s anomalies for ENSO and IOD indices. Over the
30 year period (1980-2010), strong El Niño events occurred in
1982-83, 1987-88 and 1997-98 with moderate to weak events observed
in 1986-87, 1991-92, 1994-95, 2002-03 and 2009-10. Positive IOD
events occurred in 1982-1983, 1987, 1991, 1994, 1997, 2003 and
2010. Years where El Niño and positive IOD coincided include 1982,
1987, 1991 and 1997.
Figure 2: Time series of Niño 3.4 and IOD anomaly indices
normalized by their standard deviation for the period
1980-2010.
10
11
The NCEP NCAR reanalysis data and plotting tool
(www.esrl.noaa.gov/psd/) was used to assess the Indian ocean-ENSO
relationship. Figure 3 confirms that El Niño events resulted in
warming up of the Indian Ocean. This suggests that the ENSO signal
is initiated in the Pacific Ocean and is then propagated westwards
into the Indian Ocean through atmospheric teleconnections.
NCEP/NCAR Reanalysis
Figure 3: Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W.,
Deaven, D., Gandin, L., & Joseph, D. (1996) The NCEP/NCAR
40-year reanalysis project. Bulletin of the American Meteorological
Society, 77(3), 437 - 471
Jan to May: 1980 to 2010: Surface SST
Seasonal Correlation w/ Jan to May Nino3.4
NCAA/ESRL Physical Science Division
3.2. Bleaching incidences
The extent of bleaching in the Western Indian Ocean during 1998 is
unprecedented in both the extent and severity. Repeated minor
bleaching events were reported after the 1998 incident as shown in
Figure 4 for East Africa and South West Indian Ocean regions.
East Africa Bleaching Severity
SW Indian Ocean Bleaching Severity
Figure 4: Plots of bleaching severity for East Africa coast and the
Southwest Indian Ocean region. Bleaching index was used to
determine the severity, where -1 (unknown severity), 0 (no
bleaching) 1 (low severity), 2 (medium bleaching) and 3 (high
bleaching).
12
Coral cover during 1997/98 (prebleaching), 1999 and 2001/02 at
monitoring sites in East Africa
Table 1: The coral cover before the 1997/98 extensive bleaching and
after the bleaching event in East Africa. Coral bleaching and
mortality during the El Niño event of 1997-98 was most severe in
Kenya, northern Tanzania and parts of northern Mozambique. The most
severely damaged reefs suffered levels of coral mortality between
50-90%. Recovery since the extensive bleaching and mortality has
been patchy.
Figure 5: Plots of relationship between bleaching and modes of
climate variability indices. The circles represent years when coral
bleaching was recorded while the solid line represents the
regression line.
13
4. Discussion
In this study, the relationship of ENSO and IOD with coral
bleaching is reviewed in order to explain if there is any
connection. The dominant mode of climate variability in the world
is related to El Niño Southern Oscillation (ENSO) and the Indian
Ocean Dipole (IOD). Often, the formation of the IOD coincides with
the development of El Niño in the Pacific but there are years when
positive IOD did not coincide with El Niño Figure 2. The
correlation between the Indian Ocean SST’s and ENSO cycle is
illustrated in Figure 3 where the correlation was calculated during
bleaching months (January – May) as the sun moves from south to
north. Strong positive correlation of 0.6 is observed between the
Indian Ocean SST’s and the Niño 3.4 index. This suggests that the
ENSO signal initiated in the Pacific and propagated into the Indian
Ocean region through atmospheric teleconnections. Therefore with
development of El Niño in the central pacific, the more likely that
corals will be affected by increased SST’s in the region.
The number of coral reef bleaching reports, driven principally by
episodic increases in sea temperature, has increased dramatically
since the early 1980s (Glynn, 1993; Hoegh-Guldberg, 1999). The
frequency and scale of coral bleaching events during the past few
decades have been unprecedented, with hundreds of reef areas
exhibiting bleaching at some point, and, on occasion whole ocean
basins affected. Many of the WIO’s reefs suffered high coral
mortality during the 1997-98 ENSO, as it was the warmest period
ever with 1–2°C above normal SST’s recorded. Minor bleaching were
observed from 1999-2004 while mortality was minimal compared to
1998 bleaching event. Coral bleaching and mortality during the El
Niño event of 1997-98 was most severe in Kenya, northern Tanzania
and parts of northern Mozambique, and diminished to virtually
nothing in the south (Table 1 adapted from Obura 2002b). The most
severely damaged reefs suf- fered levels of coral mortality between
50-90%. Recovery since the extensive bleaching and mortality has
been patchy in all the countries with marine protected areas
showing higher recovery rates of coral cover.
Positive correlations between the widespread coral bleaching and El
Niño-IOD events are observed in Fig- ure 5. The large mode of
climate variability.
5. Conclusion
The most extensive coral bleaching ever reported has occurred
during the 1997-1998 period. Bleaching events and extensive
mortality result in poor coral cover and possibly fewer new coral
recruits. In the short term, this will impact adversely on the
economies of many WIO countries particularly those reliant on
tourism and fisheries income. With the recent bleaching event
linked to global climate change, the consequences would be serious
for many coral. Therefore there is a need to improve bleaching
forecast and monitoring programmes in the region.
14
References Ateweberhan, M., & McClanahan, T. R. (2010).
Relationship between historical sea-surface temperature variability
and climate change-induced coral mortality in the western Indian
Ocean. Marine Pollution Bulletin, 60(7), 964-970.
Baker, A. C., Glynn, P. W., & Riegl, B. (2008). Climate change
and coral reef bleaching: An ecological assessment of long-term
impacts, recovery trends and future outlook. Estuarine, Coastal and
Shelf Science, 80(4), 435-471.
Glynn, P. W. (1993). Coral reef bleaching: ecological perspectives.
Coral reefs, 12(1), 1-17.
Graham, N.A.J, T.R. McClanahan, M. A. MacNeil, S. K. Wilson, N.V.C.
Polunin, S. Jennings, P. Chabanet S. Clark, M. D. Spalding, Y.
Letourneur, L. Bigot, R Galzin, M. C. O¨hman, K.C. Garpe, A. J.
Edwards, C. R. C. Sheppard (2008). Climate warming, marine
protected areas and the ocean-scale integrity of coral reef
ecosystems. PLoS ONE 3:e3039.
Hoegh-Guldberg O., P.J. Mumby, A.J. Hooten, R. S. Steneck, P.
Greenfield, E. Gomez (2007). Coral reefs under rapid climate change
and ocean acidification. Science, 318:1737–1742.
Hoegh-Guldberg, O (1999). Climate change, coral bleaching and the
future of the world’s coral reefs. Marine and freshwater research,
50(8), 839-866.
Hughes, T. P., A. H. Baird, D. R. Bellwood, M. Card, S. R.
Connolly, C. Folke, and J. Roughgarden (2003). Cli- mate change,
human impacts, and the resilience of coral reefs. Science, 301:
5635, 929-933.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D.,
Gandin, L., & Joseph, D. (1996), The NCEP/ NCAR 40-year
reanalys project. Bulletin of the American Meteorological Society,
77(3), 437 – 471
McClanahan T R, M. Ateweberhan, C. A. Muhando,J. Maina, M.S.
Mohammed (2007a). Effects of climate and seawater temperature
variation on coral bleaching and mortality. Ecological Monographs,
77,503–525.
McClanahan, T.R, N.V.C. Polunin, and T. Done (2002). Ecological
states and the resilience of coral reefs. Conserv Ecol 6:18.
McClanahan, T.R. and D. Obura, (1997). Sediment effects on shallow
coral communities in Kenya. J. Exp. Mar. Biol. Ecol. 209: 103¬
122.
Muthiga, N., L. Bigot, and A. Nilsson (1998). East Africa: Coral
reef programs of eastern Africa and the Western Indian Ocean.
ITMEMS.
Rayner, N.A., E.B. Horton, D.E. Parker, C.K. Folland, and R.B.
Hackett (1996). Version 2.2 of the global sea-ice and sea surface
temperature data set, 1903-1994. Clim. Res. Tech. Note 74.
Sheppard, C.R.C (2003). Predicted recurrences of mass coral
mortalityin the Indian Ocean. Nature 425, 294e297.
Souter, D.W.; O. Lindén, (2000). The Health and Future of Coral
Reef Systems. Ocean & Coastal Management, 43(8-
9):657-688.
Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Met.
Soc., 78, 2771-277
Veron, J. E. N., L. M., Devantier, E. Turak, , A. L Green, S.
Kininmonth, M. Stafford-Smith,., and N. Peterson (2009).
Delineating the coral triangle. Galaxea, Journal of Coral Reef
Studies, 11 (2), 91-100.
Wilkinson, C., O. Linden, H. Cesar, G. Hodgson, J. Rubens, and A.E.
Strong (1999). Ecological and socioeconomic impacts of 1998 coral
mortality in the Indian Ocean: An ENSO impact and a warning of
future change? Amp 28, 188- 196.
Wilkinson, C.R., (1999). Global and Local Threats to Coral Reef
Functioning and Existence: Review and Predictions. Marine
Freshwater Resources 50, 867–878.
15
16
3.2 Review of the previous El Niño and IOD events and how they have
affected cyclone incidents and intensity in the WIO region for SOND
season John Bemiasa1, Charles Magori2, Arnaud Nicolas3, Dass
Bissessur3, and Premnarain Ramathan Pathak4
Abstract
This study reviews the previous events of El-Niño and the Indian
Ocean Dipole (IOD) and examines whether there exist link with
Tropical Cyclone (TC) systems as well as their intensity in the
Western In- dian Ocean (WIO) region for
September-October-November-December (SOND) season. The findings are
summarized as follows: i) In the WIO region, the TC season
generally starts in November and ends in May; ii) Cyclones are more
likely to occur during La Niña as compared to during El Niño
events; iii) Warming of the tropical Pacific Ocean over the past
several months has primed the climate system for an El Niño in 2014
while the IOD index has been below −0.4°C (the negative threshold)
since mid-June; iv) The Chance of an El Niño in 2014 has reduced
and given the recent easing in conditions and model outlooks,
indicates it is unlikely to be strong; v) Below normal rainfall and
less TC incidents are expected during the SOND season in the WIO
region.
Keywords: El Niño, Indian Ocean Dipole, Tropical cyclones, WIO,
SOND
1.0 Introduction
Tropical cyclones have significantly affected populations in the
Indian Ocean region over the past several decades. Future
vulnerability to tropical cyclones is likely to increase due to
factors including population growth, urbanization, increasing
coastal settlement, and climate change induced El Niño and Indian
Ocean Dipole (IOD) events. The objective of this report is to
review previous El Niño and IOD events and how they relate to
cyclone incidents and intensity in the Western Indian Ocean (WIO)
region.
1Institut Halieutique et de Sciences Marines (IHSM) P.O. Box 141
Route du Port, 601 Toliara, MADAGASCAR Email:
[email protected],
[email protected]
2Kenya Marine and Fisheries Research Institute (KMFRI) Mombasa,
Kenya
3Mauritius Oceanographic Institute (MOI) Quatres Bornes,
Mauritius
4Mauritius Meteorological Service Vacoas, Mauritius.
1.1 What is El Niño?
The term El Niño refers to the situation when sea surface
temperatures in the central to eastern Pacific Ocean are
significantly warmer than normal. This recurs every three to eight
years and is generally associated with a strong negative phase in
the Southern Oscillation pendulum.
El Niño is characterized by unusually warm ocean temperatures in
the Equatorial Pacific, as opposed to La Niña, which characterized
by unusually cold ocean temperatures in the Equatorial Pacific. El
Niño is an oscillation of the ocean-atmosphere system in the
tropical Pacific having important consequences for weather around
the globe.
Among these consequences are increased rainfalls across the
southern tier of the US and in Peru, which has caused destructive
flooding, and drought in the West Pacific, sometimes associated
with devastating brush fires in Australia. Observations of
conditions in the tropical Pacific are considered essential for the
prediction of short term (a few months to 1 year) climate
variations.
The Southern Oscillation Index (SOI) provides a simple measure of
the strength and phase of the Southern Oscillation and Walker
Circulation. The SOI is calculated from the monthly mean air
pressure difference between Tahiti and Darwin. A single month with
a strongly negative SOI does not of itself mean an El Niño is
taking place. Sustained negative values over a period of several
months are more usual when an El Niño is developing in the Pacific.
Equally, the SOI may occasionally rise close to zero for a month or
two during an El Niño event.
During El Niño episodes the SOI becomes persistently negative (say
below –7). Air pressure is higher over Australia and lower over the
central Pacific in line with the shift of the Walker
Circulation.
El Niño events usually emerge in the March to June period. It is at
this time of year that we can first expect to see falling SOI
values and a weakening of the Walker Circulation heralding the
onset of an event. An event usually reaches its peak late in the
year before decaying during the following year.
Southern Oscillation Index (SOI) 1994-2004
Figure1: An eleven-year period showing typical fluctuations in the
SOI. Positive SOI values are shown in blue, with negative in
orange. Sustained positive values are indicative of La Niña
conditions, with sustained negative values indicative of El Niño
conditions (Source: Australian Bureau of Meteorology).
17
18
1.2 About the Indian Ocean Dipole
The Indian Ocean Dipole (IOD) is a coupled ocean and atmosphere
phenomenon in the equatorial Indian Ocean that affects the climate
of Australia and other countries that surround the Indian Ocean
basin (Saji et al. 1999).
The IOD is commonly measured by an index that is the difference
between Sea Surface Temperature (SST) anomalies in the western
(50°E to 70°E and 10°S to 10°N) and eastern (90°E to 110°E and 10°S
to 0°S) equatorial Indian Ocean. The index is called the Dipole
Mode Index (DMI).
A positive IOD period is characterized by cooler than normal water
in the tropical eastern Indian Ocean and warmer than normal water
in the tropical western Indian Ocean (see map below for an example
of a typical positive IOD SST pattern). A positive IOD SST pattern
has been shown to be associated with a decrease in rainfall over
parts of central and southern Australia.
1.3 What is a Tropical Cyclone?
Tropical Cyclones are low pressure systems that form over warm
tropical waters and have gale force winds (sustained winds of 63
km/h or greater and gusts in excess of 90 km/h) near the centre.
Technically they are defined as a non-frontal low pressure system
of synoptic scale developing over warm waters having organized
convection and a maximum mean wind speed of 34 knots or greater
extending more than half-way around near the centre and persisting
for at least six hours.
The gale force winds can extend hundreds of kilometers from the
cyclone centre. If the sustained winds around the centre reach 118
km/h (gusts in excess 165 km/h), then the system is called a severe
tropical cyclone. These are referred to as hurricanes or typhoons
in other countries.
The circular eye or centre of a tropical cyclone is an area
characterized by light winds and often by clear skies. Eye
diameters are typically 40 km but can range from under 10 km to
over 100 km. The eye is sur- rounded by a dense ring of cloud about
16 km high known as the eye wall which marks the belt of strongest
winds and heaviest rainfall.
Tropical cyclones derive their energy from the warm tropical oceans
and do not form unless the sea-surface temperature is above 26.5°C,
although once formed, they can persist over lower sea-surface
temperatures. Tropical cyclones can persist for many days and may
follow quite erratic paths. They usually dissipate over land or
colder oceans.
No previous attempts have been carried out in WIO region to relate
the occurrence and intensity of tropical cyclones to ENSO and IOD
events. However, Chang et al. (2006) have conducted a study
covering the Southern Indian Ocean (SIO) region. Most studies have
been in Pacific and Atlantic Oceans.
The time span of data and information available for ENSO and IOD
index and cyclone incidents and in- tensity in the region during
the SOND season is short (11 years) to enable statistical analysis
to examine whether they are related.
2.0 Data and Methods
For this exercise, we did not carry out data analysis. To prepare
this report, we have relied on data and information obtained from
the websites of National Oceanic and Atmospheric Agency (NOAA) and
Australia Bureau of Meteorology (BOM). Part of the data used for 8
days prediction (SST, SSS, Sea surface currents) is from the
operational data assimilation from the near real time global HYCOM
(HYbrid Coordinate Ocean Model or HYCOM) and Navy Coupled Ocean
Data Assimilation (NCODA) based ocean prediction system output by
Global Ocean Data Assimilation Experiment (GODAE) platform
(Resolution: 1/12 degree).
3.0 Results and Discussion
3.1 Review of the past and current El Niño (SOI)
El Niño indicators ease: Despite the tropical Pacific Ocean being
primed for an El Niño during much of the first half of 2014, the
atmosphere above has largely failed to respond, and hence the ocean
and atmosphere have not reinforced each other. As a result, some
cooling has now taken place in the central and eastern tropical
Pacific Ocean, with most of the key El Niño regions returning to
neutral values. Figure 2 indicates the El Niño Southern Oscillation
(ENSO) Tracker status.
Figure 2: ENSO tracker (Source: Australian Bureau of
Meteorology)
While the chance of an El Niño in 2014 has clearly eased,
warmer-than-average waters persist in parts of the tropical
Pacific, and the (slight) majority of climate models suggest El
Niño remains likely for spring. Hence the establishment of El Niño
before year's end cannot be ruled out. If an El Niño were to occur,
it is increasingly unlikely to be a strong event.
Given the current observations and the climate model outlooks, the
Bureau’s ENSO Tracker has shifted to El Niño WATCH status. This
means the chance of El Niño developing in 2014 is approximately
50%, which remains significant at double the normal likelihood of
an event.
El Niño is often associated with wide scale below-average rainfall
over southern and eastern inland areas of Australia and
above-average daytime temperatures over southern Australia. Similar
impacts prior to the event becoming fully established regularly
occur.
The Indian Ocean Dipole (IOD) index has been below −0.4 °C (the
negative IOD threshold) since mid-June, but needs to remain
negative into August to be considered an event. Model outlooks
suggest this negative IOD is likely to be short lived, and return
to neutral by spring. A negative IOD pattern typically brings
wetter winter and spring conditions to inland and southern
Australia. The following Figure 3 shows the variation of SOI for
the period January 2012 to October 2014.
19
20
30 Day Moving SOI
Figure 3: Southern Oscillation Index between January 2012 to
October 2014 (Source: Australian Bureau of Meteorology).
The Southern Oscillation Index (SOI) has remained around −5 to −6
over the past two weeks. The latest approximate 30-day SOI value to
27 July is −5.2.
Weekly Sea Surface Temperatures: Warm SST anomalies remain in the
western and eastern tropical Pacific Ocean. Cooling has continued
over the past fortnight, with the temperature of surface waters in
the central Pacific now near-average (see SST anomaly map for the
week ending 27 July). Positive anomalies also remain in areas of
the Indian Ocean and the northern Pacific Basin, particularly along
the western US coastline. The warmer than average temperatures in
the eastern Indian Ocean and western Pacific are a typical for a
developing El Niño event, and the temperature gradient between
these areas and the central Pacific may be playing a role in
reducing atmospheric feedbacks.
The following Figure 4 summarizes the weekly SST over the Pacific
and Indian Ocean for the period of 21-27 July 2014.
SSTA 1.0X1.0 NMOC OCEAN ANOMALIES (C) 20140721 20140727
Index Previous Current Temperature change
(2 weeks) NINO3 +0.7 +0.6 0.1 °C cooler NINO3.4 +0.3 +0.0 0.3 °C
cooler NINO4 +0.4 +0.3 0.1 °C cooler
Baseline period 1961–1990
Figure 4: weekly SST over the Pacific and Indian Ocean for the
period of 21 -27 July 2014. (Source: Australian Bureau of
Meteorology).
Monthly sea surface temperatures: The equatorial Pacific continued
to warm in the east during June. The sea surface temperature (SST)
anomaly map for June shows warm anomalies along the entire equator,
with further warm anomalies to Australia’s northwest, around much
of the Maritime Continent and east of the Philippines, as well as
along the coastline of North America (Figure 5).
SSTA 1.0X1.0 NMOC OCEAN ANOMALIES (C) 20140721 20140630
Index Previous Current Temperature change (2 weeks)
NINO3 +0.7 +0.6 0.1 °C cooler NINO3.4 +0.3 +0.0 0.3 °C cooler NINO4
+0.4 +0.3 0.1 °C cooler
Baseline period 1961–1990
Figure 5: Sea surface temperature (SST) anomaly for the period of
01st - 30 June 2014. (Source: Australian Bureau of
Meteorology).
21
22
5-day sub-surface temperatures : The sub-surface temperature map
for the 5 days ending 27 July shows waters across the equatorial
Pacific are generally near average, to slightly below average.
However, it is worth noting that a substantial area of the central
to eastern Pacific has low data coverage (cross markings on image
indicate point observations). Other sources of sub-surface data
have been considered (Figure 6).
TAO/TRITON 5-Day Temperature (oC) End Date: July 27 2014 2oS to 2oN
Average
Figure 6 : 5-day sub-surface temperatures, end date: July 27, 2014
between 2°S to 2°N. (Source: Australian Bureau of
Meteorology)
Monthly sub-surface temperatures: The four-month sequence of
sub-surface temperature anomalies (to July) shows a significant
break down of warm anomalies in the top 100 m over the past month.
The July sub-surface plot doesn’t show a consistent warm signal,
with a mixture of weaker warm and cool anomalies across the
sub-surface (Figure 7).
Pacific Ocean Eq Anomaly = 0.5oC
Figure 7 : Monthly sub-surface temperatures anomaly over the
Pacific Ocean for the period April-July 2014. (Source: Australian
Bureau of Meteorology).
Trade winds: Weak westerly wind anomalies are present over part of
the western tropical Pacific, on and to the north of the equator
(Figure 8), while there are near-average across the remainder of
the tropical Pacific (see anomaly map for the 6 days ending 27
July). These westerly anomalies have been present over the past
fortnight and, if continued, could drive further warming of surface
waters in the central and eastern Pacific. Sustained westerly wind
anomalies would be a sign that the atmosphere could be falling into
alignment with the signs of a developing El Niño in the
ocean.
23
Ending on July 27 2014
Figure 8 : Trade wind anomalies over the tropical Pacific, ending
on July 2014. (Source: Australian Bureau of Meteorology).
The Madden–Julian Oscillation (MJO) is currently in phase 7
(western Pacific), a situation which favours westerly wind
anomalies over the tropical Pacific. During La Niña events, there
is a sustained strengthen- ing of the trade winds across much of
the tropical Pacific, while during El Niño events there is a
sustained weakening of the trade winds.
Cloudiness near the Date Line: Cloudiness near the Date Line has
continued to fluctuate around the long-term average during the past
two weeks (Figure 9). Cloudiness along the equator, near the Date
Line, is an important indicator of ENSO conditions, as it typically
increases (negative Outgoing Longwave Radiation -OLR anomalies)
near and to the east of the Date Line during El Niño and decreases
(positive OLR anomalies) during La Niña.
24
Figure 9: Cloudiness near the Date Line over the Pacific Ocean for
the period 2011-2014. (Source: Australian Bureau of
Meteorology).
TAO Project Office/PMEL/NOAA
3.2 Review of past and current IOD index
Values of the Indian Ocean Dipole (IOD) have remained in negative
territory since mid-June (Figure 10a). The latest weekly index
value to 27 July is −0.7 °C. Waters to the south of Indonesia are
warmer than average while sea surface temperatures in parts of the
Arabian Sea are cooler than average. If values of the IOD index
below −0.4 °C persist until early-to-mid August, 2014 will be
considered a negative IOD year.
Climate models surveyed in the model outlooks (BOM, NOAA, Indian
National Centre for Ocean Informa- tion Services-INCOIS) favor a
return to neutral IOD values over the coming months (Figure
10b).
Figure 10(a):Values of the IOD index for the period July 2009 to
August 2014
Figure 10(b): Predictive Ocean Atmosphere Model for Australia
(POAMA) monthly mean forecast for the period April 2014 to April
2015.
26
3.3 Review of past and present WIO cyclone systems
Tropical cyclones in the IO region are influenced by a number of
factors, and in particular variations in the El Niño – Southern
Oscillation. In general, more tropical cyclones cross the region
during La Niña years, and fewer during El Niño years (BOM).
It can be noticed that for the last 11 years, positive IOD events
in the Western Indian Ocean region only occurred in December 2006
and October, November and December 2012. In 2006 IOD event, a weak
El Niño was observed whereas in the 2012 IOD event, a weak La Niña
was observed.
In the 2006 IOD event (positive IOD), there was a weak El Niño and
the occurrence of the first 3 cyclones of the cyclonic season; in
the 2012 IOD event (positive IOD), there was a weak La Niña and the
occurrence of the first 3 cyclones of the cyclonic season as well
(Table 1).
Year SOND Month
ONI value
1.0 Positive IOD
1.0 Positive IOD
1.0 Positive IOD
-1.0 Positive IOD
-1.0 Positive IOD
-1.0 Positive IOD
>1.5
MTS - Moderate Tropical Storm; ITC - Intense Tropical Cyclone; STS-
Severe Tropical Storm; TC- Tropical Cyclone
Table1: Summary of Tropical Cyclones and IOD status in WIO region
during 2006 El Niño and 2012 La Niña events.
27
3.4 Does El Niño/La Niña and IOD affect cyclone incidents and
intensity?
According to Centre for Australian Weather and Climate Research
(CAWCR), statistical analysis of the records for the last 40 years
of tropical cyclones in the Indian Ocean indicates no obvious
systematic, ENSO related variations of seasonal tropical cyclone
frequency or location in the North and South Indian Oceans.
However, more careful studies of Indian Ocean cyclones are needed.
It is likely that meaningful seasonal influences are present and
may be elucidated in more detailed analyses.
(www.cawcr.gov.au/publications/BMRC_archive/tcguide/ch5/ch5_2.htm)
A statistically significant correlation exists between the October
SOI and tropical cyclone frequency around Australia during the
season which starts in November, so the measured SOI has a
predictive value.
Around the northwest of Australia, more cyclones occur in years
when there is a highly positive SOI (i.e. La Niña) in the months
prior to the cyclone season. Also a high SOI is associated with an
increased likelihood of TCs early in the season (Nov/Dec), whereas
late TCs (in April/May) tend to happen when the October SOI was
strongly negative (in which case La Niña conditions are often in
place by May). The reduced number of TCs in El Niño years includes
a higher percentage of intense cyclones (category 2 or higher). So
the number of severe TCs affecting West Australia is about the same
whatever the SOI. There are other factors affecting Indian Ocean TC
frequency.
Figure 11 shows the average annual number of tropical cyclones in
the Indian Ocean during El Niño (a) and La Niña (b) period of
1969/70 and 2005/06 seasons.
Figure 11(a): Average annual number of tropical cyclones in the
Indian Ocean during El Niño years. Analysis based on 2 x 2 degree
resolution gridded analysis using 36 years of data (Source:
Australian Bureau of Meteorology).
Figure 11(b): Average annual number of tropical cyclones in the
Indian Ocean during La Niña years. Analysis based on 2 x 2 degree
resolution gridded analysis using 36 years of data (Source:
Australian Bureau of Meteorology).
According to Figure 11, the average numbers of tropical cyclones in
the Indian Ocean during La Niña years were on average higher than
during El Niño years. This implies that if El Niño is predicted in
the region, then the numbers of cyclones are expected to be
less.
4.0 Concluding Remarks
Warming of the tropical Pacific Ocean over the past several months
has primed the climate system for an El Niño in 2014. However, in
the absence of the necessary atmospheric response, Pacific Ocean
temperatures have either stabilized, or some cooling has occurred.
Despite some further easing in the model outlooks, a majority of
international climate models still indicate El Niño is likely to
develop during spring 2014. While there are some differences in
ENSO outlooks, the near-average to drier-than-average signal across
eastern Australia is generally consistent between international
models.
The Indian Ocean Dipole (IOD) index has been below −0.4°C (the
negative IOD threshold) since mid-June 2014. Model outlooks suggest
the IOD is likely to return to neutral by spring. A negative IOD
typically brings wetter winter and spring conditions to inland and
southern Australia with corresponding cool and drier conditions in
the WIO region. It is likely that the effects of the Indian Ocean
and Pacific are competing to some degree, minimizing the likelihood
of broader rainfall signals.
Values of the Indian Ocean Dipole (IOD) have remained in negative
territory since mid-June. The latest weekly index value to 27 July
is −0.7 °C. Waters to the south of Indonesia are warmer than
average while sea surface temperatures in parts of the Arabian Sea
are cooler than average.
If values of the IOD index below −0.4 °C persist until early-to-mid
August, 2014 will be considered a negative IOD year. Climate models
surveyed in the model outlooks favor a return to neutral IOD values
over the coming months.
In the WIO region, the TC season generally starts in November and
ends in May. The average numbers of tropical cyclones in the Indian
Ocean during La Niña years were on average higher than during El
Niño
29
years implying that if El Niño is predicted in the WIO region, then
the numbers of cyclones are expected to be less during the SOND
season.
Statistical analysis (by Centre for Australian Weather and Climate
Research) of historical records of tropical cyclones in the Indian
Ocean does not indicate a direct link with between ENSO related
variations of seasonal tropical cyclone frequency or location in
the region. Therefore, detailed studies of Indian Ocean cyclones
and how they are relate with ENSO and IOD are needed.
5.0 References
Chang-Hoi, H., Joo-Hong, K., Jee-Hoon, J., Hyeong-Seog, K. and
Chen, D (2006). Variation of tropical cyclone activity in the South
Indian Ocean: El Niño–Southern Oscillation and Madden-Julian
Oscillation effects. Journal of Geophysical Research: Atmospheres
(1984–2012), Volume 111, Issue D22.
Saji, N. H., Goswami, B. N., Vinayachandran, P. N., Yamagata, T
(1999). A Dipole Mode in the Tropical Indian Ocean. Nature, VOL
401, 360-363p.
www.bom.gov.au/jsp/ncc/climate_averages/tropical-cyclones/index.jsp
www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml
www.ggweather.com/enso/oni.htm
www.s.u-tokyo.ac.jp/en/utrip/archive/2013/pdf/05MoLan.pdf
www.jamstec.go.jp/frsgc/research/d1/iod/e/iod/iod_observations.html
www.cawcr.gov.au/publications/BMRC_archive/tcguide/ch5/ch5_2.htm
APPENDIX 1
Previous (From 2002 to 2013) Cyclone, El Niño and IOD events in the
WIO for SOND season Year SOND
Month Cyclone Name
El Niño & La Niña Inten- sities (ONI)
ONI value
63 - 88 Moderate El Niño 1.3 no IOD < 1.5
2002 Nov Boura Severe Tropical Storm 89 -117 Moderate El Niño 1.3
no IOD < 1.5 2002 Nov Crystal Tropical Cyclone 118 -165 Moderate
El Niño 1.3 no IOD < 1.5 2002 Dec Delfina Severe Tropical Storm
89 - 117 Moderate El Niño 1.3 no IOD < 1.5 2003 Oct Abaimba
Moderate Tropical
Storm 63 - 88 Moderate El Niño 0.4 no IOD < 1.5
2003 Nov Beni Tropical Cyclone 118 -165 Moderate El Niño 0.4 no IOD
< 1.5 2003 Dec Cela Tropical Cyclone 118 -165 Moderate El Niño
0.4 no IOD < 1.5 2003 Dec Darius Severe Tropical Storm 89 - 117
Moderate El Niño 0.4 no IOD < 1.5 2004 Nov Arola Severe Tropical
Storm 89 - 117 Weak El Niño 0.7 no IOD < 1.5 2004 Nov Bento Very
Intense
Tropical Cyclone >212 Weak El Niño 0.7 no IOD < 1.5
2004 Dec Chambo Tropical Cyclone 118 -165 Weak El Niño 0.7 no IOD
< 1.5 2005 Nov Alvin Intense Tropical
Cyclone 166 - 212 Weak La Niña -0.5 no IOD < 1.5
2006 Dec Anita Moderate Tropical Storm
63 - 88 Weak El Niño 1.0 positive IOD
> 1.5
166 - 212 Weak El Niño 1.0 positive IOD
> 1.5
2006 Dec Clovis Severe Tropical Storm 89 - 117 Weak El Niño 1.0
positive IOD
> 1.5
2007 Nov Ariel Severe Tropical Storm 89 - 117 Moderate La Niña -1.2
no IOD < 1.5 2007 Nov Bongwe Severe Tropical Storm 89 - 117
Moderate La Niña -1.2 no IOD < 1.5 2007 Dec Celina Moderate
Tropical
Storm 63 - 88 Moderate La Niña -1.2 no IOD < 1.5
2007 Dec Dama Moderate Tropical Storm
63 - 88 Moderate La Niña -1.2 no IOD < 1.5
2008 Oct Asma Moderate Tropical Storm
63 - 88 Weak La Niña -0.5 no IOD < 1.5
2008 Nov Bernard Moderate Tropical Storm
63 - 88 Weak La Niña -0.5 no IOD < 1.5
2008 Dec Cinda Severe Tropical Storm 89 - 117 Weak La Niña -0.5 no
IOD < 1.5
31
ONI value
1.4 no IOD < 1.5
63 - 88 Moderate El Niño
1.4 no IOD < 1.5
166 - 212 Moderate El Niño
1.4 no IOD < 1.5
89 - 117 Moderate El Niño
1.4 no IOD < 1.5
-1.5 no IOD < 1.5
-1.0 no IOD < 1.5
-1.0 no IOD < 1.5
166 - 212 Weak La Niña
-1.0 N e g a t i v e IOD
> 1.5
89 - 117 Weak La Niña
-1.0 N e g a t i v e IOD
> 1.5
-1.0 N e g a t i v e IOD
> 1.5
166 - 212 Weak La Niña
-0.3 no IOD < 1.5
166 - 212 Weak La Niña
-0.3 no IOD < 1.5
35
3.3 Predicted development of El Niño and IOD events and possible
impact on the ocean state in the WIO region Premnarain Ramnath
Pathak1 and Mohamed Khamis Ngwali2
Abstract
Sea Surface Temperature (SST), an ocean parameter, has long been
used as predictor in seasonal rainfall forecasting by many climate
centers and regional meteorological office with very good success.
SARCOF for instance exploits the relation between rainfall
occurrence & SST distribution to elaborate summer rainfall
outlook over the southern African countries ENSO and IOD index also
based on SST have proved to be related to drought and flood events
as well as other weather related calamities. If ENSO and IOD have
proved to be useful for forecasting atmospheric state then their
impact on the ocean is a natural way forward and is worth a
detailed study. The outcome will tell if the two indexes are of any
value in ocean forecast. In the event that a correlation indeed
exists, a statistical method can be devised to forecast ocean
variable like current, salinity etc. based on them. This is the
objective of the present project and we will restrict our
investigations in the Western Indian Ocean.
Keywords: SST, IOD and El Niño events
Introduction
The SWIO is famous because of its western boundary current called
the Agulhas which apart from supporting rich and unique ecosystems
plays a major role in the global oceanic circulation. Monitoring
and forecast of future ocean states in this part of the Indian
Ocean is essential not only for proper management of its maritime
resources but also for preservation of the vulnerable biodiversity.
Many countries either have boundaries or are surrounded by this
vast salt water bodies or are also dependent on it for food and raw
materials. People from various economic sectors like fishing
industries, maritime transport and others are particularly
concerned with sea hazard. The ocean user will thus need medium
range ocean forecast for their planning. An acceptable forecast
method that we will try to develop to meet their need should be
simple, cheap,and efficient and not time consuming. It should
additionally be easily understood by the end user. A statistical
method similarly to that used traditionally by meteorologist for
seasonal rainfall forecasting meet all these criteria. El Niño and
IOD will be used as predictor to forecast parameters of interest
like waves, SST, current etc. and they will then be the predict
ant. The forecast output will be easily interpreted. It will be
categorical and the forecasted element will be defined as: - (a)
Normal (b) Above normal (c) Below normal, (d) Extreme and so
on.
1Mauritius Meteorological Services St. Paul Road B.4,
Vacoas-Phoenix, MAURITIUS Email:
[email protected]
2Tanzania Meteorological Agency Zanzibar, Tanzania
Data & Method
NOAA Extended Reconstructed Sea Surface Temperature V3b (ERSST)
gridded data was used to calculate the El Niño and IOD indexes.
Salinity and ocean currents were also used for the
comparison.
Figure 1: For Dipole Mode index.
For IOD each grid point within the area labeled WEST in the
diagram, the average SST for a fixed, long period of time is
computed. This average is then subtracted from the individual SST
values within the same period at that same grid point. A grid of
SST anomaly time series is thus obtained. These are then averaged
to obtain a single SST anomaly time series representing west area.
The same procedure is applied to region east. The Dipole Mode Index
(DMI) is finally calculated by subtracting the eastern SST anomaly
time series from the western one.
In this project three time periods were used:- • 1854-2014 •
1950-2014 • 1980-2014
For El Niño the procedure is quite simple. The average SST in a
rectangular box centered over the equatorial pacific (Longitude
160W-80W; Latitude 10S-10N). This definition of El Niño in terms of
SST instead of SOI was preferred for consistency because both DMI
and El Niño is computed using the same dataset.
Method
The ocean variables are correlated with El Niño and DMI using the
Open Grads software. The latter also displays the correlation as
colourful graphic leading to easy interpretations. Suppose a
positive DMI (Fig.1 above) is positively correlated to Ocean
current within a particular area of the WIO then if upcoming DMI is
forecast to be positive, then the Ocean current in this area is
expected to be above normal. The coefficient of correlation gives
the strength of the link.
37
Concerning the DMI and El Niño forecast their respective time
series will be applied to a high frequency filter to remove noise.
Next the filtered series will be decomposed into their persistence,
periodicity and trend components. From the latter three components
the series can be projected forward in time to obtain the future
values of the two indexes. It has been shown that El Niño occurs
with a periodicity of 3-7 years. In this project this exercise will
not be undertaken because it is very tedious and time
consuming.
Moreover the mentioned forecast is done by reputed centres like
JAMSTEC and NOAA and is freely available on the web. The format
they are made available also suits our purpose as they mention the
indexes to be positive, negative, weak etc. and the way they are
needed by our method.
El Niño Time series Figure 2(a): Importance of periodicity in El
Niño forecast
38
IOD Time series Figure 2(b): Importance of periodicity in IOD
forecast
39
Results
1. Correlation of SST with EL-NINO
Figure 3: EL-NINO correlated to SST for the period January 1854 to
January 2014 (monthly)
Figure 4:EL-NINO correlated to SST for the period January 1950 to
January 2014 (monthly)
40
Figure 5: SST in the Indian Ocean during a strong El Niño event
(April 1998)
2. Correlation of SST with DMI
Figure 6: DMI correlated to SST for the period January 1854 to
January 2014 (monthly)
41
Figure 7: DMI correlated to SST for the period January 1950 to
January 2014 (monthly)
3. Correlation of Salinity with El Niño
Figure 8: Salinity correlated with Elnino for the period January
1980 to January 2014 (monthly)
42
Figure 9: Salinity during strong El Niño event (April 1998)
Figure 10: Salinity during strong La Niña event (October
1988)
43
4. Correlation of Salinity with IOD
Figure 11: Salinity correlated with IOD for the period January 1980
to January 2014 (monthly)
5. Correlation of El Niño with Ocean Current
Figure 12: Correlation of El Niño with Ocean Current
44
Figures 13: Ocean current pattern during a strong El Niño event
(April 1998)
45
Figures 14: Ocean current pattern during a strong La Niña event
(Oct 1988)
46
Early-Aug CPC/IRI Consensus Probabilistic ENSO Forecast
Figure 15: Probability Enso Forecast (From IRI for Climate and
Society) for SOND have an increasing El Niño probability.
Figure 16: Predicted SST anomaly for SON Base period for estimation
of anomalies is 1983-2006.
47
Discussion
1. Correlation of SST with EL-NINO Results observed indicated that
there is no major change in the correlation pattern from the time
period (January 1950-January 2014) and the (January 1854-January
2014) dataset as seen in Figs.4 & 5. This proved that El Niño
impact on SST has remained the same for nearly two centuries. Thus
El Niño is a very stable index against which to predict atmospheric
and oceanic parameters. SST in the Western Indian Ocean is
positively correlated to El Niño. El Niño means overall above SST
normal in the WIO.
2. Correlation of SST with DMI From Figs.7 and 8 there is a clear
indication of positive IOD. That is positive SST anomaly in Western
side of Tropical Indian Ocean and negative SST anomaly in the
eastern side.For IOD the correlation coefficient and pattern from
the two datasets are a bit different meaning that IOD is more
stable over time compared to El Niño. Therefore it is sufficiently
consistent to be used as predictor.
3. Correlation of Salinity with El Niño In the northern parts of
the WIO region salinity decreases during strong El Niño whereas in
the southern parts salinity is positively correlated but the
relation is weak Fig. 9. Possible reason could be heavy rainfall or
down welling.
4. Correlation of Salinity with IOD From Fig. 12 we observe a
positive IOD but the correlation is dominantly negative. Positive
IOD implies decrease in salinity over most part of the Indian
Ocean. The biggest correlation lies in an area SE of
Madagascar.
5. Correlation of El Niño with Ocean Current Strong negative
correlation exists between El Niño and the northern part of the
Agulhas currents within the Mozambique Channel especially along the
African coasts Fig. 13. Note that the current bifurcation near the
northern tip of Madagascar into the Agulhas is more significant
during La Niña Fig. 15. Otherwise no marked difference in the
current pattern is observed during the two phases. According to
International Research Institute (IRI) for Climate and Society
monthly summary status of El Niño, La Niña, and the Southern
Oscillation, or ENSO, based on the NINO3.4 index (120-170W, 5S-5N)
during June through July the observed ENSO conditions remained near
the borderline of a weak El Niño condition in the ocean, but the
atmosphere so far has shown little involvement. Indian Ocean
forecast: A weak negative IOD is predicted for the tropical Indian
Ocean from summer to fall in 2014. However, its uncertainty remains
high because of the large spreads in the prediction plumes.
Conclusion and recommendations
We found that SST in the Western Indian Ocean is positively
correlated to El Niño. According to SST analysis for the period
January 1854 to January 2014 there is also clear indication of
positive IOD with positive SST anomaly in Western side of tropical
Indian Ocean and negative SST anomaly in the eastern side. IOD is
more stable over time compared to El Niño. Therefore it is
sufficiently consistent to be used as predictor. So we can expect
an above normal SST for the coming SOND season in the WIO.
Whereas according to JAMSTEC SST forecast anomaly for SON 2014
(base period for estimation of anomalies is 1983-2006) a weak
negative IOD is predicted for the Tropical Indian Ocean.
Most of the ENSO prediction models indicate more warming coming in
the months ahead, leading to sustained El Niño conditions by the
middle or late portion of northern summer.
48
Whereas according to JAMSTEC SST forecast anomaly for SON 2014
(base period for estimation of anomalies is 1983-2006) a weak
negative IOD is predicted for the Tropical Indian Ocean.
Most of the ENSO prediction models indicate more warming coming in
the months ahead, leading to sustained El Niño conditions by the
middle or late portion of northern summer.
We would like to note that: • Time was short to gather all the data
and perform the analysis for the different correlations. • Since
participants were from different countries a good interaction and
discussion was not possible
before the project, though we did communicate through emails. •
Participants should discuss among themselves the results before the
start of the reports. • More time should be allowed before
submitting the finalized report. • Finalized reports of each group
should be shared.
References
Behera S. & Yamagata T. 2001: Subtropical SST dipole in the
Southern Indian Ocean Journal of Geographical Re- search Letters,
Volume 28, NO2 pp 327-330
Diaz H. & Markgraf V. 2000: El Niño and the Southern
Oscillation: Multi-scale Variability and global and regional
Impacts (Cambridge University Press London).
Loschnigg, J., Meehl, G. A., Arblaster, J. M., Compo, G. P., and
Webster, P. J. 2003: The Asian monsoon, the tropospheric biennial
oscillation, and the Indian Ocean Dipole in the NCAR CSM. Journal
Climate, 16, 1617–1642.
Nicholls N, Chang P and Zebiak S 2003: El Niño and the Southern
Oscillation (Encyclopedia of Atmospheric Scienc- es, Volume 4, pp
713-724. Academic Press London)
Reason C, Allan R, Lindesay J and Ansell T 2000: ENSO and Climatic
Signals across the Indian Ocean basin in the global context: Part
1, Interannual Composite Patterns. International Journal of
Climatology 20, pp 1285 – 1327
Saji N.H, Goswami B.N, Vinayachandran P.N & Yamagata T 1999: A
dipole Mode in the tropical Indian Ocean (Nature Volume 401 pp
360-363)
Webster P.J, Loschnigg J. P., Moore A.M and Leben R.R, 1999:
Coupled Ocean-atmosphere dynamics in the Indian Ocean during
1997-98. Nature 104 pp 356-360
Yu L. and Rienecker M. 2000: Indian Ocean warming of 1997- 1998.
Journal of Geographical Research, Volume 105, C7 pp 16,923
-16,939
International Research Institute (IRI) for Climate and
Society
National Oceanic and Atmospheric Administration (NOAA)
NOAA Extended Reconstructed Sea Surface Temperature V3b (ERSST)
www.noaa.gov
Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
www.jamstec.go.jp
3.4 Modelling the mean-state of the oceanographic conditions in the
western Indian Ocean during September - December, using the
Regional Ocean Modelling System Issufo Halo1
Abstract A Regional Ocean Modelling System (ROMS) has been used to
simulate the state of the oceanographic conditions in the west
Indian Ocean. The model domain has a horizontal resolution of about
18.5 km, and has 45 sigma vertical layers. The model was forced by
monthly climatological dataset which allows to investigate the
equillibrium state in a controlled environment. Analysis based on
surface fields of monthly mean windstress, ocean currents,
temperature and sea surface elevation, for September, October,
November and December, were based on 4-years model outputs.
The results suggest that the model is able to capture reasonably
well the main oceanographic processes, such as upwelling in the
Somali coast, the cross equatorial cell north of Madagascar, the
East African Coastal Current, the southern gyre, the great whirl
and the Socotra eddy, oceanic responses associated with the
southwest and southeast Monsoons.
Keywords: climatology runs, forecast, ocean models, variability,
upwelling
1. Introduction
Understanding the dynamics of the coastal oceans is important for
managing coastal ecosytems, hence protecting lives and planning
sustainable development (Halo et al., 2013). The northwest Indian
Ocean is a region of strong ocean variability driven by both
oceanic and atmospheric processes that occurs at a large variety of
temporal and spatial scales. It was not that long-time ago when
significant changes on the atmospheric circulation have impacted
significantly the state of the ocean in the region, inducing modes
of climate variability such as El-Nino Southern Oscillations
(ENSO), Indian Ocean Dipoles (IOD), causing life loss and
starvation in the northwest Indian Ocean countries (Schott et al.,
2009).
In face of the present climate change environment, it becomes
crucial to acquire a more complete understanding of the ocean
dynamics in the region. Therefore the question follows: If a
laboratory of oceanography in a developing country needs to provide
marine forecast to service the needs of the country, manage water
pollution, and other environmental problems, what alternatives can
be found (March- esiello et al., 2008) This is precisely what Ocean
African Climate Experts, within the Intergovernmental Authority on
Development (IGAD) need to address, based on current understanding
of the oceanographic and atmospheric conditions of the
region.
Marchesiello et al., 2008 have shown that affordable regional
marine forecast systems can be implemented successfully at a
relatively low financial cost. The keys involve the use of a set of
state-of-the-art oceanic and atmospheric models, that can be
downscalled to refine the results from the global-scale models
(Debreu et al., 2012), in order to fit regional applications
(Marchesiello et al., 2001, Marchesiello et al., 2003),
1University of Cape Town Private Bag X3, Rondebosch 701 Cape Town,
SOUTH AFRICA Email:
[email protected];
[email protected]
integration of the regional models in both nowcast and forecast
modes (Marchesiello et al., 2008). The system relies on operational
global ocean circulation models for initial and lateral boundary
conditions and operational global atmospheric models for surface
forcing (using bulk formulation). Alternatively regional
atmospheric models can be used to provide small-scale surface
forcing. The data can be obtained via the Open-source Project for a
Network Data Access Protocol (OPeNDAP). As also stressed by
Marchesiello et al. (2008), the system greatly depend on internet
connections and data availability.
In an attempt to move towards an implementation of the ocean
forecast system, we investigate the ability of the Regional Ocean
Modelling System (ROMS) to reproduce mean oceanographic properties
that are known to have a large impact on whether and climate in the
northwest Indian Ocean. Only the months of September, October,
November and December are investigated for the purpose of this
work.
There are around the world several ocean models freely available.
Our current experience in this field has shown that the most used
one in the oceanic environment around Africa is the Regional Ocean
Modelling System (ROMS). ROMS is a new generation of ocean
circulation models designed especially for accurate simulation of
regional, basin-scale and coastal ocean processes, using higher
order numerics (Shchepetkin et al., 2005). It has a free-surface
formulation and terrain following sigma-coordinate. The model
solves the primitive equations of motion in a planetary rotating
frame, using the Boussinesq and Hydrostatic approximations, on a
staggered C-grid. Here we use ROMS forced by climatological fields
to simulate the state of the ocean in the West Indian Ocean. The
set-up of the conFig.uration is presented below.
2. Data and Methods
The conFig.uration was built using ROMSTOOLS (Penven et al.,
2008a). The domain spans from 30oE to 80oE, and 30oS to 24oN, with
a horizontal grid resolution of 1/6o (~ 18.5 km), and 45
sigma-vertical layers, yielding 329x337 lateral grid points (Figure
1). The vertical stretching of the sigma-layers were made by
setting the controlling parameter at sea-floor (øb=0), and at
sea-surface (øs=6), and thickness controlling layer (hc=10). The
domain was widely extended to allow a better representation of the
large-scale oceanographic properties in the region. The solution
from this model will be used at the lateral open boundaries of a
small domain over this grid (to be implemented), which will allow a
refinement of the solution using a 2-way nesting approach (Debreu
et al., 2012).
The outermost model (herein IGAD Model, Figure 1) was forced at
surface by climatology fields (wind-stress, fresh-water fluxes,
heat-flux and salt-flux) gridded at 1/2o derived from COADS05 (da
Silva 1994). To boost the air-sea interaction over COADS05 product,
the heat-flux was augmented using a 9-km pathfinder SST as a
restoring term for the boundary layer of about 60 days, hence
causing little damping of faster phenomena like mesoscale eddies
(except for the SST signature of long-lived eddies) (Colas et al.,
2012). Similarly for salt-flux with respect to the COADS05 sea
surface salinity. To augment COADS wind-stress, we used QuikScat
satellite Scatterometer Climatology of Ocean Winds (SCOW) fields
(1999 - 2009) (Risien and Chelton, 2008), gridded at 1/4o.
SCOW is known for its ability to capture small-scale features that
are dynamically important to both the ocean and the atmosphere
(e.g., SST gradient), but are not resolved in other observationally
based wind atlases or in NCEP-NCAR reanalysis fields (Risien and
Chelton, 2008).
The lateral open boundaries were derived from hydrographic dataset
from the World Ocean Atlas 2009 (WOAS09) (Conkright et al., 2002),
gridded at 1o. At the bottom, we used GEBCO1 sea-floor topography.
To minimize pressure gradient errors, the maximum value for
topography smoothing (Slope parameter) was set to r=0.25. The
minimum and maximum depth at the shore were hmin=50 m and hmax=500
m respectively.
51
30 oS
15 oS
0 o
15 oN
30 oN
Figure 1: Extension of the IGAD model domain showing the grid size
coverage. Each grid cell has about 18.5 km.
3. Preliminary results and discussion
In this section we investigate preliminary results of the first 4
years of the simulation of the wind stress intensity at the sea
surface, velocities field, sea surface temperature (SST), and sea
surface height (SSH). Winds stress is an important dynamics force
that transfer heat and momentum from the atmosphere to the ocean.
Figure 2 shows their distribution, monthly averaged for September
(a), October (b), November (c) and December (d). Maximum intensity
is observed during the month of September (Figure 2a) both at the
northern tip of Madagascar and at the coast of Somali. On the other
hand minimum values are observed in the equatorial band, southwest
coast of India, within the Mozambique Channel, and in the Red Sea.
For October period (Figure 2b), maximum wind stress peaks only at
the northern tip of Madagascar. Further decrease is observed in
November (Figure 2c) in the sout