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
The designations employed and the presentation of material in this 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 Cultural Organization Regional Office for Eastern Africa P.O. Box 30592, Nairobi 00100 GPO Kenya
Layout, design and printing by BrandMania Kenya (www.brandmania.co.ke)
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: majamboj@cordioea.net
2Eduardo Mondlane University Faculty of Science, Principal Campus C. P. 257, Maputo, MOZAMBIQUE Email: dfnica@gmail.com; veronicadove@uem.mz
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: j.bemiasa@ihsm.mg, j.bemiasa@odinafrica.net
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: prempathak_4@hotmail.com
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:haloissufo@gmail.com; issufo.halo@uct.ac.za
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