Project Report. Cloud-SST interaction in Indian Summer Monsoon: Observations Vs CFSv2 Simulations. Ajay Kulkarni

Transcription

1 Centre for Modeling and Simulation Savitribai Phule Pune University Master of Technology (M.Tech.) Programme in Modeling and Simulation Project Report Cloud-SST interaction in Indian Summer Monsoon: Observations Vs CFSv2 Simulations Ajay Kulkarni CMS1316 Academic Year

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3 Centre for Modeling and Simulation Savitribai Phule Pune University Certificate This is certify that this report, titled Cloud-SST interaction in Indian Summer Monsoon: Observations Vs CFSv2 Simulations, authored by Ajay Kulkarni (CMS1316), describes the project work carried out by the author under our supervision during the period from January 2015 to June This work represents the project component of the Master of Technology (M.Tech.) Programme in Modeling and Simulation at the Center for Modeling and Simulation, Savitribai Phule Pune University. Dr. H.S. Chaudhari, Scientist-D Indian Institute of Tropical Meteorology (IITM), Pune India Dr. Samir Pokhrel, Scientist-D Indian Institute of Tropical Meteorology (IITM), Pune India Dr. B.S. Pujari, Faculty Centre for Modeling and Simulation Savitribai Phule Pune University Pune India Dr. Anjali Kshirsagar, Director Centre for Modeling and Simulation Savitribai Phule Pune University Pune India

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5 Centre for Modeling and Simulation Savitribai Phule Pune University Author s Declaration This document, titled Cloud-SST interaction in Indian Summer Monsoon: Observations Vs CFSv2 Simulations, authored by me, is an authentic report of the project work carried out by me as part of the Master of Technology (M.Tech.) Programme in Modeling and Simulation at the Center for Modeling and Simulation, Savitribai Phule Pune University. In writing this report, I have taken reasonable and adequate care to ensure that material borrowed from sources such as books, research papers, internet, etc., is acknowledged as per accepted academic norms and practices in this regard. I have read and understood the University s policy on plagiarism ( University_ pdf). Ajay Kulkarni CMS1316

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7 Abstract This study examines the relationship between clouds and SST during Indian Summer Monsoon (ISM). This study is based on observations as well as coupled model (CFSv2) simulations. Observation reveals the dominance of high level clouds in the monsoon region. CFSv2 is able to replicate the high level cloud fraction, however, it is underestimated as compared to observation. Cloud-SST relationship for observation shows that high-level clouds have positive correlation over equatorial Indian Ocean and negative correlation with Bay of Bengal. CFSv2 is able to recapitulate the observed correlations. To investigate the most dominating patterns of interannual variability for rainfall, SST and clouds, Principal Component Analyses (PCA) is performed on seasonal JJAS dataset. PCA shows that for observations high-level clouds are highly correlated with rainfall during ISM season. This study has also investigated the relationship of clouds and ENSO (El Niño Southern Oscillations) or IOD (Indian Ocean Dipole) during ISM period. PC1 (Principal component 1) of High level clouds and Nino3.4 index indicate the significant negative correlation. It pinpoints that PC1 of high level clouds might be associated with ENSO. CFSv2 is able to depict the significant negative correlation between PC1 of High level clouds and Nino3.4. Observation based PC2 (Principal component 2) of high level clouds and IOD index exhibits the significant positive correlation. It gives an indication that PC2 of high level clouds is associated with IOD. In contrast, PC2 of high level cloud and IOD index shows the insignificant negative correlation in CFSv2 simulations. It means that CFSv2 has a good ability to represent ENSO as compared to IOD. 7

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9 Acknowledgements The success and final outcome of this project required a lot of guidance and assistance from many people and I am extremely fortunate to have got this all along the completion of my project work. Whatever I have done is only due to such guidance and assistance and I would not forget to thank them. I respect and thank Dr. H. S. Chaudhari, Scientist-D and Dr. Samir Pokhrel, Scientist-D for giving me an opportunity to do the project work at Indian Institute of Tropical Meteorology (IITM), Pune and providing me all support and guidance which leads me to complete the project on time. I am extremely grateful to them for providing guidance and such a nice support though they had busy schedules. I am grateful to Prof. B.N.Goswami, Former Director, IITM, Pune and Dr. H.P. Borgaonkar, Scientist-E for giving me necessary facilities and permissions to pursue this work. I am also thankful to Dr. A. Suryachandra Rao, Scientist-F, Climate and Global Modeling Division, IITM for allowing me to use Lab and computer facilities. I would not forget to remember Dr. Anupam Hazra, Dr. Subodh K. Saha, Dr. M. Phani, Mr. S. Mahapatra and Mr. Kiran Salunke of IITM for their unlisted encouragement and more over for their timely support and guidance till the completion of my project. I am thankful to the Director, Centre for Modeling and Simulation, Dr. Anjali Kshirsagar and I owe my profound gratitude to my internal project guide Dr. B. S. Pujari, who took keen interest in my project work and guided me all along, till the completion of my project work by providing all the necessary information. I would like to thank all fellow colleagues of M.Tech, Modeling and Simulation for encouraging me to carry out my project work. I am deeply indebted to my respected parents for their never ending support and esteem. Above all, I am thankful to Almighty God for Eternal Blessings and Benevolence. Ajay Kulkarni CMS, SPPU 9

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11 Contents Abstract 7 Acknowledgments 9 List of Figures 13 List of Tables 15 1 Introduction Indian Summer Mosnoon (ISM) Clouds Classification of clouds Cloud and its linkage with Indian summer monsoon Sea Surface Temprature (SST) El Niño-Southern Oscillation (ENSO) Indian Ocean Dipole (IOD) Coupled Climate Model Motivation Climate Forecast System version 2 (CFSv2) Model Model History CFSv2 Model Components of CFSv Atmospheric Model Ocean Model Datasets used and Methodology Adopted Dataset used Observed dataset used Model datasets Design of Experiment Techniques used in the study Principal Component Analyses (PCA) Results and Discussions JJAS (June-September) Mean Patterns Clouds Rainfall Sea Surface Temprature (SST)

12 12 CONTENTS 4.2 Cloud-SST Relationship Cloud-rainfall Relationship Dominant mode of variability for rainfall, SST and clouds (using PCA) Rainfall High level clouds Mid level clouds Low level clouds SST Relationship of clouds with ENSO and IOD Summary and Conclusion 51

13 List of Figures 1.1 Climatological rainfall over Indian land points for JJAS (June-September; unit : mm).(based on India Meteorological Dataset of ) Different types of clouds (Source - UCAR) SST anomalies during El Niño event (Source - State Climate Office of North Carolina) SST anomalies during La Niña event (Source - State Climate Office of North Carolina) Positive IOD event represented by SST anomalies (e.g. positive IOD event of 1997). The east and west poles of the IOD are marked with black boxes (source Positive and Negative dipole mode of IOD(Source Formulation of spectral AGCM (Source- A Climate Modelling Primer) Distribution of clouds fraction in JJAS Mean from MODIS cloud dataset andcfsv2 model simulation for High level clouds, Mid level clouds, Low level clouds and Total clouds. Shaded color indicates the percentage of cloud fraction Model bias of high, mid, low and total cloud fraction. Model bias is calculated by subtracting observation from CFSv2 model data Distribution of JJAS mean rainfall from GPCP observations and CFSv2 model simulation. Model bias is calculated by subtracting observation from CFSv2 model data. Shaded color indicates precipitation in millimeter (mm) JJAS Mean Sea Surface Temperature (SST) from (a) OISST observations and (b) CFSv2 model simulation. (c) JJAS model SST bias (CFSv2 minus observation). Shaded color indicates temperature in degree Celsius Simultaneous cloud-sst correlations during JJAS mean for observation and CFSv2 for the period Correlation at 95% significance level is shown by tiny grid June-September (JJAS) average correlation between clouds and SST for observation and CFSv2. Correlation at 95% significance level EOF and PC of rainfall for observation and CFSv2 model EOF and PC of high level clouds for observation and CFSv2 model EOF and PC of mid level clouds for observation and CFSv2 model EOF and PC of low level clouds for observation and CFSv2 model EOF and PC of SST for observation and CFSv2 model

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15 List of Tables 4.1 Time series correlation of PC1 and PC2 of SST with high-level, mid-level and low-level clouds for observation Time series correlation of PC 1 and PC 2 of SST with high-level, mid-level and low-level clouds for CFSv Correlation of high-level, mid-level and low-clouds with NINO3, NINO3.4 and IOD indices for observation Correlation of high-level, mid-level and low-clouds with NINO3, NINO3.4 and IOD indices for CFSv2 model

16 16 LIST OF TABLES

17 Chapter 1 Introduction Indian Summer Monsoon rainfall (ISMR) has a profound impact on socioeconomic growth. About 80% of the annual rainfall over India occurs during the summer monsoon period (June - September). Weak monsoons are associated with the droughts and agriculture losses. The strong monsoons are linked with the devastating floods, accompanying life and other property damage. Therefore, the Summer Monsoon is inarguably an important facet of life in India (e.g. [1], [2], [3]). The Indian summer monsoon prevails over the Indian region for the four months, from June through September (JJAS). Numerical models have played a vital role in monsoon prediction Monsoon is traditionally defined as a seasonal reversing of wind accompanied by corresponding changes in precipitation, but now it is used to describe seasonal changes in atmospheric circulation and precipitation associated with the asymmetric heating of land and ocean. Usually, the term monsoon is used to refer to the rainy phase of a seasonallychanging pattern. The Indian summer monsoon is a deep moist baroclinic system. The following are the components of Indian summer monsoon: (1) monsoon trough over central India, (2) low level cross-equatorial flow over west Indian Ocean, (3) upper-level Tibetan high, (4) upper tropospheric tropical easterly jet (TEJ) over peninsular India, (5) Mascarene high, etc. Numerical model simulation should reflect these components of monsoon. The scientific basis for the skillful dynamical seasonal forecasting of ISMR is the slowly varying lower-boundary forcing (e.g., sea surface temperature (SST), soil moisture, snow cover, etc.), which can give rise to predictability of statistical characteristics of large-scale atmospheric circulation ( [4]). Current perception regarding the two-tier climate prediction, which predicts future atmospheric conditions using an Atmospheric and Coupled General Circulation Model (AGCM) alone forced by preforecasted SSTs ( [5]), are based on the postulation that the atmospheric models alone should be able to emulate the climate variations when the models are forced by the observed or perfectly predicted SSTs. As a result, many studies have used AGCMs (Atmospheric General Circulation Model) for the simulation of Indian monsoon circulation features and monsoon interannual variability (e.g. [6], [7], [8], [9], [10] etc.). However, they noted that simulations were poor for the ISMR. Wang et al. (2005) [11] showed that AGCMs, forced with observed sea surface temperature (SST), are generally unable to simulate the ISMR. They demonstrated that an AGCM coupled with an ocean model simulates realistic SST-rainfall relationships. This suggests that the coupled ocean-atmosphere processes are crucial in the monsoon regions, where atmospheric feedback on SST (Sea Surface Temperature) is critical. Coupled models present a slightly better skill in reproducing the spatial distribution of the rainfall variability as compared to AGCM ( [10]). 17

18 18 CHAPTER 1. INTRODUCTION Recently, the development of coupled ocean-atmosphere dynamical model prediction systems has provided important advances in the seasonal prediction ( [11], [12], [13]). Current generation coupled models have incorporated better theoretical understanding of climate, improved the physical basis of modelling, and inclusion of the high quality observations.despite the potential for tropical climate predictability, and the advances made in the development of climate models, the seasonal dynamical forecast of Indian summer monsoon (ISM) remains a challenging problem (e.g. [6], [14], [15]). It is generally believed that the skill of monsoon prediction increases when the model features such as atmosphere-ocean-land coupling, resolution and model physics are improved. Recently Saha et al. (2014) [16] have reported the better simulation by NCEP coupled model- CFSv2 (Climate Forecast System). The Indian monsoon is a coupled atmosphereocean system. Several studies ( [11], [17]) have argued that models ability of successful simulation of the summer monsoon is entirely dependent on the realistic representation of SST-rainfall relationship. Models are generally unable to depict the actual SST-rainfall relationship ( [18], [19]). However, coupled models (e.g. CFSv2) have some success in depicting these relationships. SST-rain relationship is widely studied ( [10], [18]). However, cloud-sst and cloud-precipitation relationships are yet to be investigated. Clouds play a leading role in radiative energy and water cycle balances ( [20]). The atmospheric hydrological cycle, latent heating, and cooling associated with clouds modify the atmospheric circulation ( [21]). In this viewpoint, cloud-sst and cloud-precipitation relationships have the utmost importance. Clouds typically cover almost two-third of the global surface and are responsible for precipitation. The most important atmospheric ingredient to form cloud is water and over 70% of the surface of our planet is covered with the oceans, containing 97% of planets water. It means that oceans are the primary sources of water, which is responsible for the formation of the clouds. SST of tropical oceans is one of the most important parameters, which determines the amount of evaporation, convection and thereby precipitation over the tropical region and it has an indirect relation to cloud formation. Thus, it is imperative to know how this cloud-sst interaction takes place. In this study, we have examined how the clouds and SST are related to each other in India Summer Monsoon (ISM). There are different types of clouds (high, mid, low levels). In this perspective, cloud-precipitation and cloud-sst relationships are also explored. We have used different satellite observations of clouds, precipitation and SST. To validate these relationships and results we have used fully coupled model-cfsv2 simulations. To get more details and to establish the relation between clouds and SST, we have used Principal Component (PC) analysis and different suitable statistical tests. Since Indian monsoon system is an atmosphere-ocean coupled system. Air-sea interaction phenomenon such as ENSO (El Niño and Southern Oscillation) and Indian Ocean Dipole (IOD) are important and they exhibit larger global impact. Through PC analysis, we have also further investigated whether these modes have any relationship with clouds, precipitation and SST

19 1.1. INDIAN SUMMER MOSNOON (ISM) Indian Summer Mosnoon (ISM) The word monsoon is derived from the Arabic word for season, and the distinguishing attribute of the monsoonal regions of the world is considered to be the seasonal reversal in the direction of the wind. Thus, monsoon is defined as a seasonal reversing wind ( [22], [23]) accompanied by corresponding changes in precipitation. The primary cause of the monsoon was the differential heating between ocean and land and the monsoon was considered to be a gigantic land-sea breeze. There is an alternative hypothesis in which the monsoon is considered as a manifestation of the seasonal migration of the intertropical convergence zone (ITCZ), or the equatorial trough, in response to the seasonal variation of the latitude of maximum insolation. The Asian monsoons may be classified into a few sub-systems, such as the South Asian Monsoon which affects the Indian subcontinent and surrounding regions, and the East Asian Monsoon which affects south China, Korea, and parts of Japan. Figure 1.1: Climatological rainfall over Indian land points for JJAS (June-September; unit : mm).(based on India Meteorological Dataset of ) The southwest summer monsoons occur from June to September (ISMR precipitation; Fig. 1.1). The Thar Desert and adjoining areas of the northern and central Indian subcontinent heats up considerably during the hot summers, which causes a low pressure area over the northern and central Indian subcontinent. To fill this void, the moistureladen winds from the Indian Ocean rush into the subcontinent. These winds, rich in moisture, are drawn towards the Himalayas, creating winds blowing storm clouds towards the subcontinent. The Himalayas act like a high wall, blocking the winds from passing

20 20 CHAPTER 1. INTRODUCTION into central Asia, thus forcing them to rise. With the gain in altitude of the clouds, the temperature drops and precipitation occurs. The southwest monsoon is generally expected to begin around the start of June and fade down by the end of September. The moisture-laden winds on reaching the southernmost point of the Indian Peninsula, due to its topography, become divided into two parts: the Arabian Sea branch and the Bay of Bengal branch. The Arabian Sea branch of the Southwest Monsoon first hits the Western Ghats of the coastal state of Kerala, thus making the area the first state in India to receive rain from the Southwest Monsoon. This branch of the monsoon moves northwards along the Western Ghats with precipitation on coastal areas, west of the Western Ghats. The eastern areas of the Western Ghats do not receive much rain from this monsoon as the wind does not cross the Western Ghats. The Bay of Bengal branch of Southwest Monsoon flows over the Bay of Bengal heading towards North-East India and Bengal, picking up more moisture from the Bay of Bengal. ISM is influenced by El Niño Southern Oscillation (ENSO) ( [24], [25]) and Indian Ocean Dipole (IOD, [26]; [27]). In this perspective, it is important to investigate the clouds, SST and air-sea interaction phenomena such as ENSO and IOD. 1.2 Clouds A cloud can be defined as collection of water droplets or ice crystals that are packed together densely enough to make it visible. Clouds are formed when the invisible water vapor in the air condenses into visible water droplets or ice crystals. Warmer the air more water vapor it can hold, as the air rises it gets cold and reduces the temperature of the air, decreasing its ability to hold water vapor so that condensation occurs. Figure 1.2: Different types of clouds (Source - UCAR)

21 1.2. CLOUDS 21 There is water around us all the time in the form of tiny air gas particles. There are also tiny particles floating around in the air such as salt, dust etc. These are called as the aerosols, when they collide condensation occurs. Eventually, bigger water droplets form around the aerosol particles and these water droplets start sticking together with other droplets forming clouds Classification of clouds Clouds can be classified as based on the form and altitude as shown in figure 1.2. Classification of clouds is explained below based on altitude: High altitude clouds High altitude clouds are the clouds, which are present above 18,000 feet, as these clouds are present at higher altitude these clouds mainly contains ice crystals. Different types of high altitude clouds are Cirrocumulus and Cirrus. Mid altitude clouds Mid altitude clouds are present in between 6,000 to 20,000 feet. These clouds may contain water droplets or ice crystals or the combination of both which depends on temperature. Different types of mid altitude clouds are Altocumulus and Altostratus. Low altitude clouds Low altitude clouds are present below 6000 feet. These clouds contain water droplets. Low altitude clouds are very close to the earths surface. Different types of low altitude clouds are Stratocumulus, Stratus and Cumulus.These clouds are majorly responsible for rain specially Cumulus clouds. Vertical clouds Vertical clouds have vertical growth like Cumulonimbus cloud. The upper part of vertical clouds contains ice crystals because their tops are present at higher altitude, while the lower part contains water droplets. Vertical clouds are responsible for thunderstorms and showers Cloud and its linkage with Indian summer monsoon Clouds typically cover almost two-thirds of the global surface and they modify outgoing longwave radiation (OLR) and reflect more solar radiation than the underlying surface. The cloud fractions simulated by General Circulation Models (GCMs) vary greatly. Clouds also play a leading role in radiative energy and water cycle balances. Currently, satellite remote sensing is the only means of observing cloud and other climate variables on a global scale. Most of the cloud systems that contribute to the monsoon rain originate over the oceans and show progressively northward propagation of the zone of maximum cloudiness ( [28]). Thus, the proper representation of clouds in climate models is one of the most important issues.

22 22 CHAPTER 1. INTRODUCTION 1.3 Sea Surface Temprature (SST) The monsoon system is a coupled ocean-atmosphere system. SST being an integral part of the Ocean plays a significant role in influencing ISMR (Indian summer monsoon rainfall). A large number of previous studies have pointed out the link between Indian Ocean SST and ISMR variability (e.g., [10], [29]). The convection over tropical Indian Ocean increases with warm SST and the SST threshold of 27.5 C initiates the organized deep convection ( [30]). There are various techniques by which SST can be measured such as weather satellite, shipboard measurements as well as there are thousands of floats in the oceans measuring temperature. These floats are used to validate satellite instruments in addition to sampling throughout the water column. SST plays very important role in controlling climate and it affects the earths atmosphere. SST is used as boundary condition for the atmospheric model. In case of coupled models, SST comes from the ocean model. SST is one of the sign/results of the exchange of energy between the ocean and atmosphere. Meteorological phenomena such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) or cyclones are the direct consequences of specific temperature variations at the sea-surface. The studies in the past ( [24], [31], [32]) have revealed that the interannual variability of Indian Summer Monsoon Rainfall (ISMR) is linked with ENSO phenomenon. It is also identified that IOD has an influence on the interannual variability of ISMR ( [27]). Brief details about ENSO and IOD are given below El Niño-Southern Oscillation (ENSO) El Niño-Southern Oscillation (ENSO) is a global coupled ocean-atmosphere phenomenon. It is one of the most prominent sources of interannual variations in weather and climate around the world. The existence of a global scale see-saw in the surface pressure was first hinted at the last century but it was Walker and Bliss (1932 [33], 1937 [34]) who documented the characteristics and extent of this pressure oscillation and associated changes in temperatures and precipitation, and gave it the name- Southern Oscillation (SO). Thus, the SO is a global teleconnection pattern in the atmosphere and was termed southern to distinguish it from some other teleconnectionpatterns, which turn out to be more regional interest. The Southern Oscillation (SO) is the atmospheric component of ENSO. The SO is principally a sea-saw in atmospheric mass involving exchanges of air between eastern and western hemispheres centered in tropical and subtropical latitudes with centers of action located over Indonesia and the tropical South Pacific Ocean. The Southern Oscillation Index (SOI) measures the strength of the SO. The SOI is computed from fluctuations in the surface air pressure difference between Tahiti in the South Pacific Ocean and Darwin in northern Australia. El Niño episodes are associated with negative values of SOI, meaning the pressure difference between Tahiti and Darwin is relatively small. El Niño is the oceanic component of ENSO. During El Niño, an abnormal warming of surface Ocean waters occurs in the eastern tropical Pacific or the central Pacific. In normal conditions, the water on the surface of the ocean is warmer then at the bottom

23 1.3. SEA SURFACE TEMPRATURE (SST) 23 because the sun heats it. In the tropical Pacific, winds blow in the easterly direction. These winds tend to push the surface water towards the west. As the water moves west, it heats up even mode because it is exposed longer to the sun. Meanwhile in the eastern Pacific along the coast of South America an upwelling occurs. i.e. deeper water colder water from the bottom of ocean moves up towards the surface away from the shore. El Niño features warmer than normal SSTs across the central and eastern equatorial Pacific (Figure 1.3). El Niño happens when weakening trade winds (which sometimes even reverse direction) allow the warmer water from the western Pacific to flow towards the east. The flattens out the sea level, builds up warm surface water off the coast of South America, and increases the temperature of the water in the eastern Pacific. The opposite phase of El Niño is called as La Niña. The La Niña features are cooler than normal SSTs across the central and eastern equatorial Pacific (Figure 1.4). During La Niña years, the trade winds are unusually strong due to an enhanced pressure gradient between the western and western Pacific. As a result, upwelling is enhanced along the coast of South America, contributing to colder than normal surface waters over the eastern tropical Pacific and warmer than normal surface waters in the western tropical Pacific. Figure 1.3: SST anomalies during El Niño event (Source - State Climate Office of North Carolina) La Niña is responsible for weather extremes in various parts of the world that are typically opposite to those associated with El Niño. Globally, La Niña is characterized by wetter than normal conditions west of the equatorial central Pacific over northern Australia and Indonesia during the northern hemisphere winter, and over the Philippines during the northern hemisphere summer. Wetter than normal conditions are also observed over southeastern Africa and northern Brazil, during the Northern hemisphere winter

24 24 CHAPTER 1. INTRODUCTION Figure 1.4: SST anomalies during La Niña event (Source - State Climate Office of North Carolina) season. During the northern hemisphere, the Indian monsoon rainfall tends to be greater than normal, especially in northwest India. Dryer than normal conditions are observed along the west coast of tropical South America, and at subtropical latitudes of North America (Gulf Coast) and South America (southern Brazil to central Argentina) during their respective winter season. ENSO events have been associated with droughts in Indonesia, India, Australia, Northeast Brazil and many other regions of the globe. They have been also associated with the floods in southern Brazil, Peru and Ecuador Indian Ocean Dipole (IOD) The Indian Ocean Dipole (IOD) is a coupled ocean and atmosphere phenomenon in the equatorial Indian Ocean that affects climate of Australia and other countries that surround the Indian Ocean basin ( [26]). Anomalous cooling of SST in southeastern equatorial Indian Ocean and anomalous warming of SST in western equatorial Indian Ocean normally characterize it. The IOD is commonly measured by an index that is the difference between SST anomalies in the western equatorial Indian Ocean (50 E-70 E, 10 S-10 N) and eastern equatorial Indian Ocean (90 E-110 E and 10 S-0 S). This gradient is named as Dipole Mode Index (DMI). When DMI is positive then, the phenomenon is referred as the positive IOD and when it is negative, it is referred as negative IOD. A positive IOD period is characterized by a cooler than normal water in tropical eastern Indian Ocean and warmer than normal water in the tropical western Indian Ocean as shown in Figure 1.5. A positive IOD SST pattern has been shown to be associated with a decrease in rainfall over Indonesia and Australia and convection occur over East Africa and India.

25 1.3. SEA SURFACE TEMPRATURE (SST) 25 Conversely, a negative IOD period is characterized by warmer than normal water in the tropical eastern Indian Ocean and cooler than normal water in the tropical western Indian Ocean as shown in Figure 1.6 (left panel). A negative IOD SST pattern has been shown to be associated with an increase in rainfall over Indonesia and Australia. Figure 1.5: Positive IOD event represented by SST anomalies (e.g. positive IOD event of 1997). The east and west poles of the IOD are marked with black boxes (source- Figure 1.6: Positive and Negative dipole mode of IOD(Source - It is also thought that the IOD has a link with ENSO events through an extension of the Walker Circulation to the west and associated Indonesian through flow (the flow of warm tropical ocean water from the Pacific into the Indian Ocean). Hence, positive IOD events are often associated with El Niño and negative events with La Niña.

26 26 CHAPTER 1. INTRODUCTION 1.4 Coupled Climate Model Coupled climate models are sophisticated tools designed to simulate the Earth climate system and the complex interaction between its components. Weather forecasting models must handle the properties of the atmosphere in three dimensions and work with current analyses of ocean surface temperatures and at least some basic land surface processes. These models have come to be known as Atmospheric General Circulation Models (GCMs). In parallel, studies of the oceans can concentrate on three-dimensional properties of the oceans and known as Ocean GCMs. When it comes to simulating the general behavior of the climate system over lengthy periods, however, it is essential to use models that represent and where it is necessary to conserve the important properties of the atmosphere, land surface and the oceans in three dimensions. At the interfaces, the atmosphere is coupled with the land and oceans through the exchange of heat, moisture and momentum. These models of the climate system are usually known as Coupled GCMs (called as CGCMs). All the coupled models are constructed using a coupler. The coupler is connected to all the model components. It is nothing but a program that transfers flexes between the model components. Coupling the ocean processes to atmospheric GCMs is a major challenge. These coupled models are extremely complicated and it also takes a long time to run, even on supercomputers. The model, which we are using for this study, is a coupled model Climate Forecast System version 2 (CFSv2). Details about CFSv2 model are provided in chapter Motivation Indian Summer Monsoon (ISM) is fully coupled land-atmosphere-oceanic system. Thus, ISM requires interaction between atmosphere and ocean. SST is playing an important role as a boundary conditions. For the atmosphere, clouds are one of the important parameters since they play a leading role in precipitation. Thus, it is crucial to have understanding of the cloud-sst feedback. There are several studies available for investigating the impact of SST on ISM. However, there are limited studies which investigate the effect of SST on the cloud or vice versa during ISM. Therefore, in this study our main purpose is to establish the relationship between SST-clouds-rainfall during ISM. Furthermore, we have also examined the dominant modes of variability for rainfall, SST and clouds. Whether these modes are related to air-sea interaction phenomena such as ENSO or IOD? Additionally, this study has also used CFSv2 coupled climate model to investigate the same.

27 Chapter 2 Climate Forecast System version 2 (CFSv2) Model The Climate Forecast System version 2 (CFSv2) is a fully coupled model representing the interaction between the Earths oceans, land and atmosphere. It was developed by National Centers for Environmental Prediction (NCEP). CFSv2 became operational in March Model History The first Climate Forecast Model (CFS) called as CFSv1 was implemented into operations at National Centers for Environmental Prediction (NCEP) in August 2004 and was the first quasi-global, fully coupled atmosphere-ocean-land model used at NCEP for seasonal prediction. Earlier coupled models at NCEP had full ocean coupling restricted to only the tropical Pacific Ocean. CFSv1 was developed from four independently designed pieces of technology, namely the NCEP Department of Energy (DOE) Global Reanalysis 2 (R2; [35]) that provided the atmospheric and land surface initial conditions, a global ocean assimilation system (GODAS) operational at NCEP in 2003 that provided the ocean initial states. NCEPs Global Forecast System (GFS) operational in 2003 that was the atmospheric model run at a lower resolution of T62L64, and the Modular Ocean Model, version 3 (MOM3) from Geophysical Fluid Dynamics Laboratory (GFDL). [49] 2.2 CFSv2 Model CFSv2 was designed to improve consistency between the model states and the initial states produced by the data assimilation system. CFSv2 was developed by NCEP to complete the following aspects: To carry out the extensive testing of a new atmosphere-ocean-sea ice-land model configuration including decisions on resolution etc. To make a coupled atmosphere-ocean-sea ice-land reanalysis for with the new system [resulting in the Climate Forecasts System Reanalysis (CFSR)] for the purpose of creating initial conditions for CFSv2 retrospective forecast runs. 27

28 28 CHAPTER 2. CLIMATE FORECAST SYSTEM VERSION 2 (CFSV2) MODEL To make retrospective forecasts with the new system using initial states from CFSR from 1982 to 2010 and onward to calibrate operational subsequent real-time subseasonal and seasonal predictions and To do operationally implementation of CFSv2. The CFSv2 model runs on multiple processes with message-passing tools uses a parallel programming model called MPMD. The three components of CFSv2 are: the atmospheric model (GFS), the ocean model (MOM4) and the coupler, each of which has its own data flow, runs independently, but they exchange data. [49] 2.3 Components of CFSv2 The atmospheric component of CFSv2 is the NCEP atmospheric Global Forecast System (GFS) model with significant improvement. GFS is a global spectral model. GFS have spectral resolution of T126 and 64 hybrid vertical levels and the ocean component is Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS) Modular Ocean Model version 4p0d (MOM4; [36]). In addition, it has a four level NOAH land surface model ( [37]) and two-layer dynamic sea ice model ( [38], [39]) coupled together with the Atmospheric and Ocean components. Atmospheric and Ocean component are explained briefly below Atmospheric Model The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). It is a spectral model. It means that GFS describes the present and future states of the atmosphere using mathematical equations whose graphical solution looks like waves. Spectral AGCM (Atmospheric General Circulation Models) are formulated in different way, the surface is retained as a grid and the atmospheric fields are held and manipulated in the form of waves as shown in Figure 2.1. The advantages of the spectral models are that the computation of gradient is easier and computation times are consequently reduced. Many wave-like features of the atmosphere are simulated with a wave formulation so the method has been very popular over the years. Spectral models are, however not usually formulated in all directions using waves: a vertical grid is used for vertical transfers and radioactive transfer and surface processes are modeled in this grid space. The computational flow of a spectral AGCM is that, the data fields are transformed to grid space at every timestep via FFT and Gaussian quadrature and back to spectral via Legendre transforms and Fourier transforms. Some of the basic fundamental equations for the atmospheric model are given below. These equations, which are specified, are not used directly in the model. After converting these equations into an appropriate co-ordinate system it can be used in the model. Conservation of momentum (F=ma) u component: u t = (u u x + v u y + w u z ) 1 P fv F riction ρ x } {{ } Advection of momentum

29 2.3. COMPONENTS OF CFSV2 29 Figure 2.1: Formulation of spectral AGCM (Source- A Climate Modelling Primer) v component: omega (ω) component: v t = (u v x + v v y + w v P y z ) 1 ρ } {{ } Pressure gradient force +fu F riction w t = (u w x + v w y + w w z ) 1 P ρ z g +Rotational F riction } {{ } Hydrostatic approximation In the above equations it is assumed as u, v and w are the components of velocity along x, y and z-axis respectively, Here x-axis, y-axis and z-axis represents east, north and upward direction respectively. All the above equations are presented in the opposite

30 30 CHAPTER 2. CLIMATE FORECAST SYSTEM VERSION 2 (CFSV2) MODEL direction so the negative sign is given for all the equations. It is assumed that P is pressure, ρ is density and forces are per unit mass. Terms in the equations can be explained as follows Advection of momentum is an important property of fluids to transfer a substance. Advection of momentum is necessary for the formation of clouds and the precipitation of water from clouds. Pressure gradient force describes the direction and at what rate pressure changes the most rapidly around a particular location. It is responsible for air to move like wind. Hydrostatic approximation occurs when external forces such as gravity forces are balanced by pressure gradient force. Here fu, fv denotes rotational force of the earth. Conservation of mass ρ t = (ρv ) A simple example is the continuity equation, which expresses conservation of mass. Here t is time, ρ is the density of dry air, and V is the three-dimensional velocity vector. Prognostic variables are governed by prognostic equations. The above equation is a prognostic equation, in which ρ is a prognostic variable. Equation of state P= ρrt Here P is pressure, T is temperature, ρ is density and R is dry air gas constant. Dry air gas constant is a theoretical sample of air that has no water vapor and its value can be given as J kg 1 K Ocean Model The Modular Ocean Model (MOM) is a numerical representation of the oceans hydrostatic primitive equations. It is designed primarily as a tool for studying the ocean climate system. The model is developed and supported by researchers at NOAAs Geophysical Fluid Dynamics Laboratory (GFDL) with contributions also provided by researchers worldwide. MOM version 4 (MOM4p0; [36]), which is a part of CFSv2 coupled model. Some basic fundamental ocean equations are specified below. These equations are for a fluid element located at (x,y,z) on the surface of our rotating planet and moving at velocity (u,v,w) relative to the surface. Zonal momentum equation Meridonal momentum equation du dt = 1 p ρ x + fv + 1 ρ dv dt = 1 p ρ y fu + 1 ρ τ x z τ x z

31 2.3. COMPONENTS OF CFSV2 31 Vertical momentum equation p z = ρg It is considered that ocean is in hydrostatic balance. A fluid is said to be in hydrostatic balance when it is at rest, or when the flow velocity at each point is constant over time. This occurs when external forces such as gravity are balanced by pressure gradient force. Continuity equation Salinity equation u x + v y + w z = 0 S t + u S x + v S y + w S z = (E P )S(z = 0) Here u is zonal velocity, v is meridional velocity, w is vertical velocity, ρ is density, p is pressure, S is salinity, g is acceleration due to gravity, τ is wind stress and f is the Coriolis parameter. While P-E is the freshwater input to the ocean.

32 32 CHAPTER 2. CLIMATE FORECAST SYSTEM VERSION 2 (CFSV2) MODEL

33 Chapter 3 Datasets used and Methodology Adopted 3.1 Dataset used Observed dataset used This study has used nine years of observed data from Jan 2003-Dec The monthly mean data for cloud fraction has been taken from Moderate Resolution Imaging Spectrometer (MODIS). In this dataset, clouds are classified into three types high-level, midlevel and low-level based on their cloud top pressures. Definition of for high-level clouds, mid-level clouds and low-level clouds can be given as, the clouds having cloud top pressure less than 480 hpa (<480hP a) are considered as high level clouds, the clouds having cloud top pressure in the range of 480 hpa and 640hPa (480hP a Mid 640hP a) are called as mid-level clouds and the clouds having cloud top pressure greater than 640 hpa (>640hP a) are considered as low-level clouds. Precipitation monthly mean data from Global Precipitation Climatology Project (GPCP; [41]) is used in this study. Sea Surface Temperature (SST) monthly mean data is used from OISST (Optimum Interpolation Sea Surface Temperature; [42]) Model datasets The CFSv2 coupled freerun dataset of 20 years has been utilized in this study. The CFSv2 has been ported on IBM High Performance Computing (HPC) system at Indian Institute of Tropical Meteorology (IITM), Pune. Initial conditions for the atmosphere and the ocean are taken from NCEP Climate Forecast System Reanalysis (CFSR; [43]; [44]). In the present experiment, the CFSv2 model has been integrated for 30 years. In our analyses, we have utilized last 20 years of simulation after excluding the first 10 model years for the spin-up purpose. 3.2 Design of Experiment Our main purpose was to study ISM and to explore cloud-sst-precipitation interactions. For the same, we have done all the analyses by computing JJAS (June-September) mean. To establish the relation, we have presented monthly mean plots of clouds, precipitation and SST. We also have plotted model bias of all the parameters to understand the errors present in CFSv2 model. We also have used Pearsons correlation coefficient to 33

34 34 CHAPTER 3. DATASETS USED AND METHODOLOGY ADOPTED check the relationship between cloud and SST. Further, we have used Principal Component Analyses (PCA) to investigate the unknown patterns in the present dataset. Details about PCA are given in subsection Techniques used in the study Principal Component Analyses (PCA) Principal Component Analyses (PCA) is among the most widely and extensively used methods in atmospheric science. Climate variations are the result of exceedingly complex non-linear interactions between very many degrees of freedom or modes. Both the weather and climate are characterized by non-linearity and high dimensionality. Consequently, a challenging task is to find ways to reduce the dimensionality of the system to few nodes of possible. A further, yet challenging task is to link these modes to the physics of the system. This method is in essence an exploratory tool, which allows a time display and a space display of the space-time field that may be useful to the atmospheric scientist. PCAs are multipurpose and have been used for examples in dimensionality reduction and pattern extraction. PCA is also known as Empirical Orthogonal Function (EOF) analyses. It has been used in atmospheric science since the late 1940s by Obukhov (1960) [45], Fukuoka(1951) [46]. Principal components of data, which is given by PCA are orthogonal to each other. It means that every principal component will show the unique pattern and they have zero correlation in-between them. These principal components are computed from covariance matrix or scatter matrix of anomalous data. PCA uses eigenvalue and eigenvector of the covariance or scatter matrix for analyses. Eigenvectors and eigenvalues exist in pairs; every eigenvector has a corresponding eigenvalue. An eigenvector represents a direction while eigenvalue is a number, which represents the variance of the data in the direction of the eigenvector. Thus, the eigenvector with the highest eigenvalue is, therefore, the principal component i.e. it represents the maximum variance in the data. Also, the amount of eigenvectors/eigenvalues that exist equals the number of dimensions of the dataset has. Below six general steps for computing a principal component analyses are given: Take the whole dataset consisting of d-dimension samples ignoring the class labels. Compute the d-dimensional mean vector. Compute the scatter matrix or covariance matrix of the whole data set. Compute eigenvectors and corresponding eigenvalues. Sort the eigenvectors by decreasing eigenvalues and choose k eigenvectors with the largest eigenvalues to form d k dimensional matrix W (where column represents an eigenvector). Using this d k eigenvector matrix to transform the samples onto the new subspace. This can be summarized by the mathematical equation: Y=W T X (Where X is d 1-dimensional vector representing one sample, and Y is the transformed k 1 dimension sample in the new subspace.)

35 3.3. TECHNIQUES USED IN THE STUDY 35 So, we can summarize the PCA as it aims to find a new set of variables that capture most of the observed variance from the data through the linear combination of the original variables.

36 36 CHAPTER 3. DATASETS USED AND METHODOLOGY ADOPTED

37 Chapter 4 Results and Discussions The model which is able to replicate the observed seasonal climatological mean more closely also tends to have better seasonal prediction skill ( [48]). Therefore, it is a basic requirement that the model used for seasonal prediction of ISMR should simulate reasonable mean monsoon in terms of rainfall, wind circulations, surface temperature, and so on. Saha et al. (2014) [49] have pointed out that CFSv2 is able to simulate the mean spatial pattern of rainfall, SST and winds quite realistically. The teleconnection of El Niño Southern Oscillation (ENSO) and Indian summer monsoon rainfall (ISMR) in terms of Nino3 SST and monsoon rainfall correlation is more realistic in CFSv2 ( [50]). With this background, it will be interesting to explore the mean patterns of clouds, SST and rainfall. 4.1 JJAS (June-September) Mean Patterns Clouds This study has mainly focused on India region including adjacent oceanic regions (Domain: 30 E-150 E; 35 S-35 N). JJAS (June-September) average of total cloud fraction from observation (MODIS) and CFSv2 simulations are presented in Figure 4.1 (a-h). Observation reveals that monsoon region is dominated by high level clouds (Figure 4.1a). Observation depicts the low amount of mid and low level clouds (Figure 4.1 c,e). It might be due accuracy and limitation of satellite dataset. In contrast, CFSv2 shows overestimation of mid and low level clouds(figure 4.2 d,f). For the total cloud fraction, more differences can be seen over the Indian region but for Indian Ocean region differences in cloud fraction are minimal (Figure 4.1 g,h). There are several issues and limitations regarding the use of satellite observations to cloud fraction studies such as the definition of the clouds can be different for observation and CFSv2 model ( [51]). Hu et al. (2008) [52] have also pointed out that low-level cloud fraction of CFSv2 is not exactly same as seen in the observation (e.g. MODIS). To explore model biases, CFSv2 cloud fraction bias is presented in Figure 4.2. It is the difference between model (CFSv2) and observations. CFSv2 is able to replicate the high level cloud fraction, however, it is underestimated as compared to observation (Figure 4.1 a-b). 37

38 38 CHAPTER 4. RESULTS AND DISCUSSIONS Figure 4.1: Distribution of clouds fraction in JJAS Mean from MODIS cloud dataset andcfsv2 model simulation for High level clouds, Mid level clouds, Low level clouds and Total clouds. Shaded color indicates the percentage of cloud fraction. In Figure 4.2, positive values indicate positive bias while negative values indicate negative bias. Positive (negative) bias indicates overestimation (underestimation) cloud fraction of CFSv2. High-level cloud fraction exhibit negative bias over the Indian region (30%-40%) as well as over some part of the Indian Ocean region (Figure 4.2a). In case of mid-level cloud fraction, CFSv2 has large overestimation (as seen from Figure 4.1cd). As a result, model bias for mid-level cloud fraction is positive (Figure 4.2b). It has