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Relevant interannual climate modes affecting the BoB
The IO has long been viewed as largely passive, with interannual variations arising from remote forcing of El Niño – Southern Oscillation (ENSO). This vision has changed at the turn of the XXIst century with the discovery of a specific mode of climate variability in the IO referred to as the Indian Ocean Dipole (IOD) mode [e.g. Saji et al., 1999; Webster et al., 1999]. The interannual variability in the northern IO is therefore mainly driven by these two modes: ENSO through its remote signature over the IO and the direct impact of the IOD.
ENSO is the strongest mode of interannual climatic variability on Earth [e.g. Wang and Picaut, 2004; McPhaden, 2004]. It originates in the tropical Pacific and consists of two opposite phases, the warming phase called ‘El Niño’ and the cooling phase called ‘La Niña’, recurring approximately every 2-7 years. Its positive phase (El Niño) is characterized by warm SST anomalies in the central and eastern tropical.
Pacific associated with enhanced deep atmospheric convection in the western and central Pacific. These SSTA usually appear in spring and amplify under the effect of the Bjerknes feedback [Bjerknes, 1969], a positive air–sea feedback loop in the tropical Pacific. ENSO usually lasts about one year (from late spring to late winter) and peaks toward the end of the year. ENSO is often described by the Niño3 or Nino3.4 indices, calculated as the averaged SST anomalies over the Niño3 (150°W-90°W, 5°N-5°S) or Nino3.4 region (170°W-120°W, 5°N-5°S).
Figure 1.13. (Top) Correlation of November- January Niño3 index with SST averaged over the eastern equatorial Pacific (160–120°W, 5°S–5°N; black), the tropical IO (40–100°E, 20°S–20°N; red), the southwest IO (50–70°E, 15–5°S; green), and the eastern equatorial IO (90–110°E, 10°S–equator; blue). (Bottom) Seasonality of the major interannual IO climate modes, IOD and ENSO. [Taken from Schott et al., 2009].
These ENSO-induced changes in deep atmospheric convection in the central Pacific have worldwide climatic impacts through atmospheric teleconnections [e.g. Trenberth et al., 1998]. Within the tropics, most of the ENSO remote impacts occur through shifts of the Walker circulation. For example, the eastward shift of the Walker circulation during an El Niño induces anomalous subsidence, increased surface solar heat flux and reduced surface wind over the IO. As a result, the entire IO basin warms during an El Niño [Figure 1.13; Klein et al., 1999; Ohba and Ueda, 2005; Xie et al., 2009]. This warming peaks in winter and spring, and can last until early summer, two seasons after the peak of ENSO, possibly maintained by local air–sea interactions over the IO [Xie et al., 2009; Du et al., 2009]. The effect of ENSO on the Indian summer monsoon has also been noted [e.g. Walker, 1924; Gershunov et al., 2001; Fasullo, 2004; Xavier et al., 2007].
Interannual variability of freshwater fluxes
The patterns of precipitation over the BoB and adjoining continents [e.g. Gadgil, 2003] as well as the riverine freshwater supply to the BoB [Papa et al., 2012] vary significantly from year to year. The standard deviation of interannual variability of the summer monsoon rainfall amounts to approximately 10% of the long-term mean summer rainfall [Gadgil, 2003]. Precipitation in two regions, the “Western Ghats” and “Ganges-Mahanadi Basin”, accounts between 80-90% of the interannual variability of Indian continental summer rainfall [Vecchi and Harrison, 2004]. The year-to-year variability of the summer monsoon rainfall (Figure 1.16) is sufficient to trigger drought and flood conditions, with major agricultural, economic and social impacts [e.g. Gadgil and Kumar, 2006]. Many factors influence the variations of the summer monsoon precipitation on interannual timescales, including ENSO [e.g. Pant and Parthasarathy, 1981; Rasmusson and Carpenter, 1982; Meehl, 1987; Webster and Yang, 1992], the IOD [e.g. Cherchi and Navarra, 2012] and the snow cover on the Tibetan plateau and in Eurasia [e.g. Blanford, 1884; Hahn and Shukla, 1976; Meehl, 1994; Shuen et al., 1998; Wu and Kirtman, 2003]. Of these factors, the strongest association has been found with ENSO, although the relationship appears to have weakened in recent decades [Kumar et al., 1999].
In-situ SSS observations and related climatologies
Historically, the BoB is a poorly sampled basin (except maybe during the first Indian Ocean International Expedition). From 2002 onwards, with the advent of Argo, the number of individual profiling floats has tremendously increased (Figure 1.18), improving the observational spatial coverage compared to the historical period. The in-situ observation dataset used in this thesis (Figure 1.19a) comprises all the available in-situ SSS measurements available over the BoB during the 2006-2014 periods and will be more extensively described in chapter 2. Its coverage remains however sparse along the coast and in the Andaman Sea (Figure 1.19). This is mainly because Argo profilers with a parking depth of 1000 m, cannot access the continent shelf, which is typically shallower than 200 m. Most of the data very close to the Indian coast consist of oceanographic cruises data.
Interannual SSS variations and related mechanisms
The scarcity of available SSS in-situ observations and the lack of reliable continental freshwater forcing prevented for long investigating the interannual SSS variations within the Bay. The recent improvement of the SSS observing system and the availability of interannually-varying river runoff estimates [Jian et al., 2009; Furuichi et al., 2009; Papa et al., 2010, 2012] recently allowed Chaitanya et al. [2014b] to provide a preliminary description of the year-to-year SSS variability over the recent period in the northern BoB. They revealed that the year-to-year variability of SSS is particularly strong in the northeastern part of the BoB during the past few years (Figure 1.22), with anomalous spells of about one pss amplitude. Based on a simple mixed layer salinity budget, they suggested that interannual variability of SSS is mainly driven by fresh water fluxes, with the variability of oceanic surface circulation associated with IOD events also potentially playing some role.
Aside in-situ measurements, remotely sensed surface salinity data from Aquarius and SMOS provide an interesting alternative to assess this year-to-year SSS variability. Using SMOS data, Durand et al. [2013] demonstrated that the positive 2011 IOD event (negative 2010 IOD event) was associated with fresh (salty) anomalies in the central IO south of the equator and salty (fresh) anomalies just north of the equator, and attributed these SSS variations to variations in horizontal advection process associated with IOD-related surface currents changes.
Intercomparison of SMOS and Aquarius
SMOS and Aquarius are the first two satellite missions carrying L-band radiometers onboard. Even though both radiometers are measuring the brightness temperature, they have differences in their architecture and operation principles. The SMOS satellites measures the microwave radiation emitted from the earth’s surface using an L-band (1.4 GHz) passive radiometer ‘MIRAS’. The SMOS radiometer captures ‘brightness temperature’ images by combining the signals measured with 69 receivers arranged in Y-shape. Hence SMOS needs an accurate image reconstruction. Aquarius carries 3 passive radiometers, and 1 active scatterometer, operating at 1.4 GHz & 1.2 GHz respectively. The 3 radiometers measure the brightness temperature directly and the scatterometer measures the surface roughness. Surface roughness modifies the brightness temperature of the sea surface. For Aquarius, the surface roughness data collected by its own scatterometer simultaneously with brightness temperature is used to derive the surface salinity data. But SMOS have to depend on information from other sources, which will not be simultaneous with its measurements. SMOS swath is larger than 1000km and the field of view has a hexagon-like shape and ‘measurement image’ is taken every 1.2 seconds. The SMOS achieves global coverage (revisit time) in every 3 days. For Aquarius, the total swath is 390km and revisit time is 7 days [Aretxabaleta et al., 2010; http://www.esa.int/;!http://podaac.jpl.nasa.gov/aquarius].
Surface fresh water fluxes (precipitation)
In Chapter 4 (describing seasonal salinity variability), precipitation are based on a blending of several satellite products including two of the most widely used data sets: Global Precipitation Climatology Project (GPCP) [Huffman et al., 1997] and CPC Merged Analysis of Precipitation (CMAP) [Xie and Arkin, 1997]. The CMAP produces pentad and monthly analyses of global precipitation in which observations from rain gauges are merged with precipitation estimates from several satellite-based algorithms. The analyses are on a 2.5° x 2.5° grid and extend back to 1979. The GPCP is a mature global precipitation product that uses multiple sources of observations, including surface information and satellites. GPCP product is available on monthly (2.5°×2.5°), pentad (2.5°×2.5°) and daily estimates (starting from 1996, 1°×1°). In Chapter 5 (describing interannual salinity variability), the model is forced with interannually varying precipitations from the GPCP (1990-1996 pentad estimates, 1996-2012 daily estimates).
Table of contents :
CHAPTER 1 – INTRODUCTION
1.1. GENERAL INTRODUCTION
1.1.1. Particularities of the Indian Ocean climate
1.1.2. A brief historical overview of Indian Ocean circulation knowledge
1.1.3. The Bay of Bengal haline structure and its climatic consequences
1.2. THE BAY OF BENGAL CLIMATE VARIABILITY
1.2.1. Geography of the Bay of Bengal
1.2.2. Seasonal timescales
1.2.2.1. Atmospheric variability
1.2.2.2. Oceanic Response
1.2.3. Interannual timescales
1.2.3.1. Relevant interannual climate modes
1.2.3.2. Interannual variability of freshwater fluxes
1.3. THE BAY OF BENGAL SALINITY
1.3.1. Salinity observations
1.3.1.1. In-situ SSS observations and related climatologies
1.3.1.2. Remote sensing initiatives
1.3.2. Seasonal SSS variations and related mechanism
1.3.3. Interannual SSS variations and related mechanisms
1.4. SCIENTIFIC QUESTIONS
1.5. ORGANIZATION OF THESIS
CHAPTER 2 – DATA AND METHODOLOGY
2.1. GENERAL INTRODUCTION
2.2. OBSERVATION DATA
2.2.1. In-situ SSS dataset
2.2.2. SSS Climatology
2.2.3. Satellite SSS
2.3.MODEL CONFIGURATION
2.3.1. Model setup
2.3.2.Forcing datasets
2.3.3. Reference and sensitivity experiments
2.3.4. Model mixed layer salinity budget
CHAPTER 3 – ASSESSMENT OF SMOS AND AQUARIUS SURFACE SALINITY RETRIEVAL IN THE BAY OF BENGAL
3.1. INTRODUCTION
3.2. DATASETS AND METHODS
3.2.1. SMOS level-3 data
3.2.2. Aquarius level 3 data
3.2.3. Validation datasets
3.2.4. Colocation method
3.3. GENERAL EVALUATION OF THE REMOTELY SENSED SSS PRODUCTS
3.4. SEASONAL EVALUATION OF REMOTELY SENSED PRODUCTS
3.5. INTERANNUAL EVALUATION OF REMOTELY-SENSED PRODUCTS
3.6. SUMMARY AND DISCUSSION
3.6.1. Summary
3.6.2. Discussion
3.6.3. Perspectives
CHAPTER 4 – A MODELLING STUDY OF THE PROCESSES GOVERNING THE SURFACE SALINITY SEASONAL CYCLE IN THE BAY OF BENGAL
4.1. INTRODUCTION
4.2. DATA AND METHODS
4.2.1. Model description and setup
4.2.2. The salt budget in the model
4.2.3. Validation datasets
4.3. VALIDATION
4.4. PROCESSES OF THE SSS SEASONAL CYCLE ALONG THE INDIAN COASTLINE
4.4.1. Overall picture
4.4.2. Northern Bay of Bengal
4.4.3. The Western BoB and the Southern Tip of India
4.5. SUMMARY AND DISCUSSION
4.5.1. Summary
4.5.2. Discussion
Appendix. Energy required for increasing surface salinity by vertical mixing
CHAPTER 5 – INTERANNUAL VARIABILITY OF BAY OF BENGAL SEA SURFACE SALINITY: A MODELLING APPROACH
5.1. INTRODUCTION
5.2. DATA AND METHODS
5.2.1. Model configuration and forcing
5.2.2. Reference and sensitivity experiments
5.2.3. SSS validation datasets
5.3. VALIDATION OF MODELLED INTERANNUAL SSS VARIATIONS
5.4. PROCESSES DRIVING SSS INTERANNUAL VARIABILITY
5.4.1. Main patterns of SSS variability
5.4.2. Relative importance of each forcing
5.5. SUMMARY AND DISCUSSION
5.5.1. Summary
5.5.2. Discussion
CHAPTER 6 – SUMMARY AND PERSPECTIVE
6.1. SUMMARY
6.2. PERSPECTIVES
REFERENCES
