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Currency Trading for Dummies PDF Free Download

ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines Embracing the E-commerce Revolution in Asia and the Pacific Asia is the world’s largest e-commerce marketplace and continues to grow rapidly. The Future is Asian by Parag Khanna is an interesting book on the rise of Asia and Asian systems - technocracy, illiberalism, meritocracy, economic dynamism and free trade. Khanna looks at Asia through a grand sweep of history and modern politics and economic pragmatism.

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Forex Trading for Dummies Free Ebook: How to Succeed in Forex Trading

Here's How You Can Succeed in Forex Currency Trading

The purpose of this book is to show you how to make money trading Currencies. Thousands of people, all over the world, are trading Forex and making tons of money. Why not you?

All you need to start trading Forex is a computer and an Internet connection. You can do it from the comfort of your home, in your spare time without leaving your day job. Please note that when trading Forex your capital is at risk.

And you don't need a large sum of money to start, you can trade initially with a minimal sum, or better off, you can start practicing with a demo account without the need to deposit any money.

Currency Forex allows even beginners the opportunity to succeed with financial trading. Actually people that have minimum financial track record can easily make money by learning how to trade currencies online.

This book features the in and outs of currency trading as well as strategies needed to achieve success in the trading.

Here are some of the topics you'll discover while reading the book:


* The single most critical factor to Forex trading success - ignore it at your own perils.
* Simple, easy to copy ideas that will enhance your chances of winning trades.
* What you need to succeed in currency trading.
* Advantages of trading Forex.

* Effective risk management strategies to help you minimize your risk and conserve your capital.
* Key factors to successful financial Forex trading.
* How to develop Forex trading strategies and entry and exit signals that work.
* A list of easy-to-follow tips to help you improve your trading successes.
* All this and much much more.

Table of Contents:
1. Introduction to Forex Trading
2. What is Forex Trading
3. How to Control Losses with 'Stop Loss'
4. How to Use Forex for Hedging
5. Advantages of Forex Over Other Investment Assets
6. The Basic Forex Trading Strategy
7. Forex Trading Risk Management
8. What You Need to Succeed in Forex
9. Technical Analysis As a Tool for Forex Trading Success
10. Developing a Forex Strategy and Entry and Exit Signals
11. A Few Trading Tips for Dessert

What is Forex Trading

Foreign exchange, popularly known as 'Forex' or 'FX', is the trade of a single currency for another at a decided trade price on the over-the-counter (OTC) marketplace. Forex is definitely the world's most traded market, having an average turnover of more than US$4 trillion each day.

Compare this to the New York Stock Exchange, that has a daily turnover of about US$70 billion and it is very obvious how the Forex market is definitely the largest financial market on the globe.

In essence, Forex currency trading is the act of simultaneously purchasing one foreign currency whilst selling another, mainly for the purpose of speculation. Foreign currency values increase (appreciate) and drop (depreciate) towards one another as a result of variety of factors such as economics and geopolitics. The normal objective of FX traders is to make money from these types of changes in the value of one foreign currency against another by actively speculating on which way foreign exchange rates are likely to turn in the future.

In contrast to the majority of financial markets, the OTC (over-the-counter) currency markets does not have any physical place or main exchange and trades 24-hours every day via a worldwide system of companies, financial institutions and individuals. Because of this, currency rates are continuously rising and falling in value towards one another, providing numerous trading choices.

One of the important elements regarding Forex's popularity is the fact that currency trading markets usually are available 24-hours a day from Sunday evening right through to Friday night. Buying and selling follows the clock, beginning on Monday morning in Wellington, New Zealand, moving on to Asian trade spearheaded from Tokyo and Singapore, ahead of going to London and concluding on Friday evening in New York.

The fact that prices are available to deal 24-hours daily makes certain that price gapping (whenever a price leaps from one level to another with no trading between) is less and makes sure that traders could take a position each time they desire, irrespective of time, even though in reality there are particular 'lull' occasions when volumes tend to be below their daily average which could widen market spreads.


Forex is a leveraged (or margined) item, which means that you are simply required to put in a small percentage of the full value of your position to set a foreign exchange trade. Because of this, the chance of profit, or loss, from your primary money outlay is considerably greater than in conventional trading.

Currencies are designated by three letter symbols. The standard symbols for some of the most
commonly traded currencies are:

EUR – Euros

USD – United States dollar

CAD – Canadian dollar

GBP – British pound
JPY – Japanese Yen

AUD – Australian dollar

CHF – Swiss franc
Forex transactions are quoted in pairs because you are buying one currency while selling another. The first currency is the base currency and the second currency is the quote currency.

The price, or rate, that is quoted is the amount of the second currency required to purchase one unit of the first currency. For example, if EUR/USD has an ask price of 1.2327, you can buy one Euro for 1.2327 US dollars.

There are so-called majors, for which around 75% of all market operations on Forex are held: the EUR/USD, GBP/USD, USD/CHF, and USD/JPY. As we see, the US dollar is represented in all currency pairs, thus, if a currency pair contains the US dollar, this pair is considered a major currency pair. Pairs which do not include the US dollar are called cross currency pairs, or cross rates. The following cross rates are the most actively traded:

EUR/CHF = euro-franc

EUR/GBP = euro-sterling

The future is asian pdf free download

EUR/JPY = euro-Yen

GBP/JPY = sterling-Yen

AUD/JPY = aussie-Yen

NZD/JPY = kiwi-Yen
To give you a taste of what is happening in the Forex arena here are some historical Forex events.

One of the most interesting movements in the Forex market involving the British pound took place in the September 16, 1992. That day is known as Black Wednesday with the British Pound posting its biggest fall. It was mostly seen in the GBP/DEM (British Pound vs. the Deutschemark) and the GBP/USD (British Pound vs. the US dollar) currency pairs.
The fall of the British pound against the US dollar in the period from November to December 1992 constituted 25% (from 2.01 to 1.51 GBP/USD).

The general reasons for this 'sterling crisis' are said to be the participation of Great Britain in the European currency system with fixed exchange rate corridors; recently passed parliamentary elections; a reduction in the British industrial output; the Bank of England efforts to hold the parity rate for the Deutschemark, as well as a dramatic outflow of investors. At the same time, due to a profitability slant, the German currency market became more attractive than the British one. All in all, the speculators were rushing to sell pounds for Deutschemarks and for US dollars. The consequences of this currency crisis were as follows: a sharp increase in the British interest rate from 10% to 15%, the British Government had to accept pound devaluation and to secede from the European Monetary System. As a result, the pound returned to a floating exchange rate.

Another intriguing currency pair is the US dollar vs. the Japanese Yen (USD/JPY). The US dollar and Japanese Yen is the third on the list of most traded currency pairs after the EUR/USD and GBP/USD. It is traded most actively during sessions in Asia. Movements of this pair are usually smooth; the USD/JPY pair quickly reacts to the risk peaking of financial markets. From the mid 80's the Yen ratings started rising actively versus the US Dollar. In the early 90's a prosperous economic development turned into a standstill in Japan, the unemployment increased; earnings and wages slid as well as the living standards of the Japanese population. And from the beginning of the year 1991, this caused bankruptcies of numerous financial organizations in Japan. As a consequence, the quotes on the Tokyo Stock Exchange collapsed, a Yen devaluation took place, thereafter, a new wave of bankruptcies among manufacturing companies began. In 1995 a historical low of the USD/JPY pair was recorded at -79.80.

The Future Is Asian Pdf Free Download Windows 10

The above started an Asian crisis in the years1997-1998 that led a Yen crash. It resulted in a tumble of the Yen-US dollar pair from 115 Yens for one US dollar to 150.
The global economic crisis touched almost all fields of human activities. Forex currency market was no exception. Though, Forex participants (central banks, commercial banks, investment banks, brokers and dealers, pension funds, insurance companies and transnational companies) were in a difficult position, the Forex market continues to function successfully, it is a stable and profitable as never before.

The financial crisis of 2007 has led to drastic changes in the world's currencies values. During the crisis, the Yen strengthened most of all against all other currencies. Neither the US dollar, nor the euro, but the Yen proved to be the most reliable currency instrument for traders. One of the reasons for such strengthening can be attributed to the fact that traders needed to find a sanctuary amid a monetary chaos.

Note: All trading involves risk. Only risk capital you're prepared to lose. Past performance does not guarantee future results. This post is for educational purposes and should not be considered as investment advice

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The Future Is Asian Pdf

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Small business grow. Written in a friendly, easy-to-understand manner, chock full of interesting, actual examples, The Small Business Bible contains everything you need to know to have a successful, fulfilling, enjoyable entrepreneurial journey. Covering the simple to the complex, The Small Business. Ebooks list page: 4693; 2017-11-12 PDF The Small Business Bible: Everything You Need to Know to Succeed in Your Small Business; 2013-02-02 The Small Business Bible: Everything You Need to Know to Succeed in Your Small Business; 2011-10-26 The Small Business Bible: Everything You Need to Know to Succeed in Your Small Business; 2010-04-30 The Small Business Bible: Everything You Need to Know. Praise for The Small Business Bible, Second Edition ‘‘The Small Business Bible, Second Edition is packed with extensive, relevant information for today’s entrepreneurs. Steve has provided a comprehensive look at every facet ofsmall business, including up-to-dateresearchontechnology,e-commerce,andothergrowthstrategies. The small business bible pdf free download. Steve is a frequent media guest and commentator, a sought-after small business spokesperson, and a serial entrepreneur. Visit him at Language English Format PDF Pages 592 ISBN 257 File size 5.3 MB Publisher John Wiley & Sons Published 2012 Edition 3 Downloadable 24 months Currency EUR ID 2453408 Copy protection Adobe DRM.

The Future Is Asian Pdf Free Download Pc

11.1 INTRODUCTION Occupying a vast domain including South Asia, the Indian subcontinent, South China Sea (SCS), East Asia, and the western North Pacific (WNP), the Asian monsoon is one of the most important components of the global climate system (Chang, 2004; Wang et al., 2006; Chang et al., 2011). Summer climate variability in Asia is closely related to the variations of the Asian summer monsoon (ASM) system, which is driven mainly by the differential heating of the Indian Ocean and the adjacent Asian landmass, including the snow cover over the Eurasian continent (Hahn and Shukla, 1976; Douville and Royer, 1996). Through the Walker circulation, the El Nin˜o Southern Oscillation (ENSO) also has a considerable impact on the variability of the ASM (Webster and Yang, 1992; Ju and Slingo, 1995; Arpe et al., 1998; Annamalai et al., 2007; Wang et al., 2000). The possible influence of increasing levels of atmospheric trace gases on the Asian monsoon has been one of the focal points of some recent studies. In theory, the rate of surface temperature increase is stronger over land than over oceans in the process of global warming, and monsoon should strengthen accordingly in response to the increased land sea thermal contrast (Ding, 1994; Turner and Annamalai, 2012; Wang et al., 2012a,b; Liu et al., 2014). However, this hypothesis does not consider the interaction of changes in large-scale atmospheric circulations, and whether it is effective for a complicated monsoon (e.g., the ASM) system remains unclear. In the literature, opinion differs on the response of the ASM to increased atmospheric greenhouse gas concentrations based on numerical experiments of individual or multiple climate models. For example, observational analysis by Kumar and Dash (2001) has indicated the role of increased land surface temperature in enhancing the monsoon, but weakening the monsoon and ENSO relationship at interannual timescales over recent decades. Meehl and Washington (1993) and Hu et al. (2000) found an intensification of both the ASM and its variability. The evolution of the ASM has distinct regional features and can be further separated into the East Asian summer monsoon (EASM) and the Indian summer monsoon (ISM). The EASM is projected to strengthen (Li and Zhou, 2010; Sun and Ding, 2011; Chen et al., 2012), strengthen slightly only over SCS (Ueda et al., 2006), vary little
The Asian Summer Monsoon. DOI: © 2019 Elsevier Inc. All rights reserved.
(Kimoto, 2005), and remain normal in terms of intensity (Li et al., 2010); the ISM is projected to significantly strengthen during the Indian monsoon rainfall (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012), slightly increase (Lal et al., 2001), and weaken in the monsoon circulation (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). Given that there are differences in climate models, emissions scenarios, and analysis methods among these studies, and that most do not assess whether climate models can reliably reproduce the present Asian monsoon circulation, it is difficult to obtain a consistent view of the future ISM and EASM. In this chapter, we will use the outputs of reliable climate models to project future changes in the ISM and EASM, including the mean monsoon and its variability, from the perspective of multiple climate models and climate dynamics. Based on all available data of climate models participating in the 5th Assessment Report, Intergovernmental Panel on Climate Change (IPCC, 2013), this study assesses the changes in the EASM and ISM under the representative concentration pathways (RCPs) mid low range scenario (RCP4.5). This chapter is organized as follows. Section 11.2 provides a description of observation data and coupled model intercomparison project phase 5 (CMIP5) simulations used in this chapter. Section 11.3 discusses the models’ ability to simulate the present climatology of the ASM circulation and precipitation, and select reliable models for further analysis. Section 11.4 investigates the possible future changes of the ISM and EASM under the RCP4.5 scenario. Summary and discussions are given in Section 11.5.
11.2 DATA AND MODELS Many climate models have been used to simulate present and future climates in the CMIP5. According to the availability of output data from the CMIP5 historical simulation, and the CMIP5 RCP4.5 simulation (Moss et al., 2010; Thomson et al., 2011), the results of 31 climate models archived in the CMIP5 are applied in this chapter. Basic information about these climate models and experiments is provided in Table 11.1. More details are available at cmip5/. In addition, data used to assess the ability of the models include monthly horizontal winds at 850 hPa level (u850 and v850) and sea level pressure (SLP) from the National Centers for Environmental Prediction National Center for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al., 1996). Precipitation data are from the global precipitation climatology projection (GPCP), with a resolution of 2.5000e 3 2.5000e (Adler et al., 2003). These reanalysis and analysis data are treated as observations. A multimodel ensemble mean with the same weights across the reliable models of concern is used to obtain common results of climate models in this chapter. All model and observation data were aggregated to a horizontal resolution of 2.5000e 3 2.5000e . This chapter is primarily focused on the results from the multimodel ensemble, therefore, issues surrounding model uncertainty will not be discussed. Considering that climate change projection in the CMIP5 begins from 2006, and that the NCEP/NCAR reanalysis data are more reliable after 1979 owing to the availability of satellite data, in this chapter the period 1979 2005 was chosen as the reference period.
Table 11.1 Basic Information on the 31 CMIP5 Models No.
Model Name
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1.875 3 1.25 1.875 3 1.25 2.8 3 2.8 2.8 3 2.8 2.8 3 2.8 1.25 3 0.94 2.5 3 1.9 3.75 3 7.5 0.75 3 0.75 1.875 3 1.875 1.4 3 1.4 2.8 3 3 2.8 3 1.67 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 1.875 3 1.25 2.0 3 1.5 3.75 3 1.875 2.5 3 1.25 3.75 3 1.875 2.8 3 2.8 2.8 3 2.8 1.4 3 1.4 1.875 3 1.875 1.875 3 1.875 1.1 3 1.1 2.5 3 1.875 2.5 3 1.875
CMIP5, Coupled model intercomparison project phase 5.
11.3 EVALUATION OF MODELS’ SIMULATION The climatology of 850 hPa wind and rainfall in observations and CMIP5 models is shown in Fig. 11.1. In observations (Fig. 11.1A), the strong southwesterly winds flow from the Indian Ocean to the Bay of Bengal (BOB), all the way to the northern SCS, and then turn to eastern China, the
FIGURE 11.1 The climatological distribution of JJA mean rainfall (shaded; units: mm/day) and 850 hPa wind (vectors; units: m/s) in observations and each CMIP5 model.
Korean peninsula, and Japan. The southwesterly winds affect the climate in East Asia with the southerly winds originating from the northwestern flank of the western Pacific subtropical high (WPSH; the anticyclone circulation over the western Pacific). The monsoon rainfall band is dominated by the WPSH. Corresponding to the strong westerly winds and the WPSH, there are four rainfall centers in GPCP: northern India, the BOB Indochina peninsula, SCS, WNP, and the monsoon rainfall band which is called Meiyu in China, Baiu in Japan, or Changma in Korea (along 30000e N). Meiyu is a climatic phenomenon closely related to the East Asian summer monsoon. It is
called Meiyu in China, Baiu in Japan and Changma in Korea (the introduction of Meiyu has been given in Chapter 3). Many CMIP5 models show similar biases in reproducing the position of the WPSH and the associated magnitude of monsoon rainfall band, as with previous models (Zhou and Li, 2002; Chen et al., 2010). It features a northward shift of the WPSH resulting in an underestimation of rainfall along the monsoon rainfall band from southern China to Japan, and an overestimation of rainfall over the SCS and WNP. Moreover, the rainfall over north India and BOB is also underestimated in most CMIP5 models mainly due to the biases in reproducing the intensity of the cross-equatorial flow over the western Indian Ocean and the associated westerly winds along 10000e 15000e N. The rainfall maximum over two monsoon regions of northern India BOB and from central China to Japan are missed in almost all the models, suggesting that simulation of the ASM rainfall remains a challenge for current climate models (Li and Zhou, 2010; Chen et al., 2000; Liu et al., 2012; Liang et al., 2019). The spatial correlation coefficient (SCC) of climatological JJA mean 850 hPa wind versus rainfall in CMIP5 models is calculated in Fig. 11.2. It is shown that the SCCs of wind are higher than those of rainfall in all the CMIP5 models and are, therefore, indicative of a better reproduction of wind than rainfall. The 31-model ensemble mean has better SCCs of wind and rainfall than other individual models.
FIGURE 11.2 Scatterplot of the spatial correlation coefficients (SCCs) of climatological JJA mean 850 hPa wind versus rainfall in CMIP5 models. The SCC is relative to NCEP/NCAR and GPCP over the ASM region (15000e S 50000e N, 30000e 180000e E).
The ASM is primarily driven by convective, radiative, and sensible heat sources/sinks, among which convective latent heating is the most important. During boreal summer, the most intense convection is observed over two regions, namely the BOB India Arabian Sea and the SCS Philippine Sea (Wang et al., 2001). The heat sources over the BOB and the Philippine Sea affect the ISM and EASM, respectively, because well-defined wind anomalies occur over and to the west of the convective heat sources as a Rossby wave response to the latent heat released in the two convection regions (Matsuno, 1966; Gill, 1980). As the lower-level circulation is dynamically consistent with convective heating in summer and the correlation between interannual variations of the south Asian and WNP convection is statistically insignificant (Wang and Fan, 1999), we focus on 850 hPa winds over the ISM and EASM regions, which represents the changes of monsoon circulation. The spatial distribution of zonal wind at 850 hPa level (u850) within two regions of (5000e 15000e N, 40000e 80000e E) and (20000e 30000e N, 70000e 90000e E) are used to evaluate the simulation of the ISM from CMIP5 models according to previous studies (Wang et al., 2001). Considering that the southerly winds prevail over eastern China and the western side of the WPSH in summer, and there is a clear relationship between the meridional wind and land sea thermal contrasts, the 850 hPa meridional wind (v850) averaged in the region 20000e 40000e N, 110000e 125000e N is used to evaluate the capacities of simulating the EASM in models (Wang, 2000, 2002; Sun and Ding, 2011; Jiang and Tian, 2013). To objectively measure the capacity of the models to simulate the ISM and EASM, the correlation coefficient between simulated and observed climatology of u850 and v850 in regional averages for the period 1979 2005 are calculated model-by-model, as well as the standard deviation and centered root-mean-square error (RMSE) of each simulation with respect to observation based on the ISM and EASM regions. As shown in the Taylor diagram (Taylor, 2001), all 31 climate models with data available (Table 11.1) could reliably reproduce the spatial distribution of u850 over the ISM region (Fig. 11.3A). Correlation coefficients range from 0.17 (GISS-E2-H) to 0.97 (CanESM2), and the values of GISS-E2-H and GISS-E2-R are not statistically significant at the 0.01 t-test significant level. Normalized centered RMSEs range from 0.25 (CanESM2) to 1.04 (GISS-E2-H), with the values of all the models being within 0.25 1.00 except for GISS-E2-H. There is a large spread in the ability of 31 climate models to represent the v850 over the EASM region (Fig. 11.3B). Correlation coefficients range from 0.03 (CMCC-CESM) to 0.87 (MIROC5), and the values of CMCC-CESM, FGOALS-s2, and MRI-CGCM3 are not statistically significant at the 0.01 t-test significance level. Normalized centered RMSEs range from 0.62 (MPI-ESM-MR) to 1.8 (MIROC-ESM), with the values of 13 models being more than 1.00 and those of the other models being within 0.62 1.00. Overall, the ability of most models to simulate the ISM’s circulation is found to be reliable and relatively concentrated, and the ability to simulate the EASM circulation is found to be relatively weak and dispersed. Accordingly, it is necessary to select more reliable models for projecting the future changes of the ISM and EASM. Two preconditions are set to identify reliable models for both the ISM and EASM. First, SCCs had to be positive and statistically significant at the 0.01 t-test significance level; and, second, normalized, centered RMSEs had to be less than 1.00. According to this procedure, and associated with the climatological rainfall and 850 hPa wind simulations over ASM region (Figs. 11.1 and 11.2), 13 CMIP5 models were applied to investigate the possible variation of the ISM and EASM in the future. The ensemble mean of the models is calculated based on the results of the 13 models: ACCESS1-0, BCCCSM1-1, CanESM2, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G,
FIGURE 11.3 Taylor diagram for displaying normalized pattern statistics of climatological u850 (A)within ISM regions of 5000e 15000e N, 40000e 80000e E and 20000e 30000e N, 70000e 90000e E, and (B) v850 within EASM region of 20000e 40000e N, 110000e 125000e E between the 31 CMIP5 models and observations in summer for the reference period 1979 2005. Each number represents a model (see Table 11.1); and observation is considered as the reference (REF). Standard deviation and centered root-mean-square errors are normalized by the reference standard deviation. The radial distance from the origin is the normalized standard deviation of a model; the correlation between a model and the reference is given by the azimuthal position of the mode; and the centered root-mean-square error between a model and the reference is their distance apart. In brief, the nearer the distance between a number and REF, the better the performance of the corresponding model.
GFDL-ESM2M, IPSL-CM5A-MR, MPI-ESM_LR, MPI-ESM_MR, and NorESM1-ME. Nevertheless, when comparing the ensemble mean results with previous studies, the different model ensembles may explain both minor and major differences. Therefore it is important to develop a set of reasonable metrics for model selection to allow comparison of climate projections between different studies and regions. The use of uniform metrics is an important issue for future climate change research.
11.4 PROJECTION OF THE INDIAN SUMMER MONSOON AND EAST ASIAN SUMMER MONSOON There are more than 20 indices of the ISM and EASM published in the literature (Wang et al., 2008). Considering that the correlation between interannual variations of the South and East Asian convection is statistically insignificant (Wang et al., 1993), two kinds of monsoon indices are selected to measure the variability of the ISM and EASM, respectively. Rainfall anomalies averaged over the core regions of the ISM and EASM are meaningful indices, yet the rainfall data in monsoon regions from models usually have a large model bias (Gao et al., 2011; Liang et al., 2009, 2019; Liu et al., 2015). To obtain more reliable results, corresponding circulation indices are desirable. To choose a circulation index dynamically consistent with convective heating, an ISM index is selected which is defined as the difference of u850 between a southern region (5000e 15000e N, 40000e 80000e E) and a northern region (20000e 30000e N, 70000e 90000e E) (indicated by the two solid-line boxes near India in Fig. 11.5). Such a defined index reflects both the intensity of the tropical westerly monsoon and the lower-tropospheric vorticity anomalies associated with the ISM trough (Wang et al., 2001). The ISM index not only represents well the rainfall anomalies averaged over an extended region including the BOB, India, and the eastern Arabian Sea, it is also highly correlated with the all-India summer rainfall with a correlation coefficient of 0.72 for the 50-year period from 1948 to 1997 (Parthasarathy et al., 1992). The selected index for the EASM is defined by the regionally averaged meridional wind at 850 hPa (v850) within the region of (20000e 40000e N, 110000e 125000e E, Fig. 11.5) (Wang, 2000, 2002; Sun and Ding, 2011; Jiang and Wang, 2005). Due to the EASM determined by the heat contrast between land and ocean, there is an obvious meridional wind circulation in East Asia. To reduce the influence of the differences in the ISM and EASM climatology among the models and their ensemble means, the normalized change of those intensity values relative to the climatology of the reference period 1979 2005 is chosen as the ISM or EASM indices for each model and their ensemble means.
11.4.1 FUTURE CHANGES OF THE INDIAN SUMMER MONSOON AND EAST ASIAN SUMMER MONSOON INDICES The future change of the ISM measured by the ISM index is shown in Fig. 11.4A. It is seen that the ISM intensity slightly decreases over the period 2006 99 relative to 1979 2005 based on the results of either individual models or their ensemble means. Table 11.2 shows the climate tendency rate and trend coefficient of each model and the 13-model ensemble mean of monsoon indices over the period 2006 99. It is seen that the ISM index decreased in 9 of 13 models and increased in
FIGURE 11.4 (A) The time series of the ISM index as measured by the difference of u850 between two regions (5000e 15000e N, 40000e 80000e E and 20000e 30000e N, 70000e 90000e E) from 13 CMIP5 models. (B) The EASM index as measured by the regionally averaged v850 within the region 20000e 40000e N, 110000e 125000e E. MME, MME_11-year, and MME_trend denote the 13-model ensemble mean, its 11-year smoothing averaged, and long-term trend, respectively.
models of BCC-CSM1-1, CanESM2, CNRM-CM5, and GFDL-ESM2M. However, most of these changes are small. Statistically significant decreased trends at the 0.05 t-test significance level can be identified in GFDL-CM3, GFDL-ESM2G, MPI-ESM-LR, MPI-ESM-MR, and the 13-model ensemble means. On the whole, there is a slightly decreasing change trend for the ISM under global warming based on the 13 reliable CMIP5 models. Such a slight weakening trend is consistent in direction with most previous studies (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). In addition, there are two distinct differences in the future with the comparison to the reference period. First, the averaged intensity of the ISM during 2006 99 is significantly weaker than that in the reference period of 1979 2005. Second, the standard deviation of the ISM index time series as derived from the 13-model ensemble mean decreased by 19.2% relative to the reference period, which implies a significant weakening of interannual variability of the ISM in the future. It disagrees with the increased interannual variability of the monsoon rainfall in the region of 0000e 20000e N, 40000e 110000e E under future global warming as revealed by a coupled model (Hu et al., 2000).
Table 11.2 The Climate Tendency Rate (CTR, unit: /a) and Trend Coefficients (TC) of Monsoon Indices in the Period 2006 99 by 13 Models and the 13-Model Ensemble Mean (TCs of 0.2 and 0.26 Reach the 0.05 and 0.01 Significance Levels, Respectively) ISM Models ACCESS1-0 BCC-CSM1-1 CanESM2 CMCC-CM CMCC-CMS CNRM-CM5 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M IPSL-CM5A-MR MPI-ESM-LR MPI-ESM-MR NorESM1-ME MME
CTR 0.002 0.005 0.002 0.000 0.005 0.004 0.011 0.007 0.000 0.004 0.007 0.006 0.003 0.006
TC 0.068 0.138 0.059 0.010 0.158 0.094 0.354 0.246 0.003 0.100 0.190 0.204 0.007 0.248
CTR 0.001 0.004 0.010 0.001 0.001 0.001 0.007 0.002 0.001 0.002 0.003 0.002 0.002 0.005
TC 0.020 0.101 0.406 0.021 0.027 0.029 0.218 0.053 0.021 0.058 0.091 0.086 0.063 0.208
Different from the ISM, Fig. 11.4B shows that there is a slightly increased trend for the intensity of the EASM as a whole, although the increased trend is small. The EASM index increases in 10 of the 13 models, and statistically significant increased trends can be identified in the 13-model ensemble mean and two individual models, namely CanESM2, and GFDL-CM3. There are still three models in which the trend of the EASM index is negative, which implies the future change in the EASM intensity is also model dependent, similar to the ISM. It is indicated to some extent that the previous conclusion on the EASM either strengthening (Ueda et al., 2006; Li and Zhou, 2010; Sun and Ding, 2011; Chen et al., 2012) or having little variation (Kimoto, 2005; Li et al., 2010) from individual or several models is uncertain. On the whole, the EASM intensity is slightly strengthened over the whole period of 2006 99 based on the 13-model ensemble mean result of either individual models or more models with statistically significant positive trends. In addition, it is noted that the standard deviation of the EASM index time series as derived from the 13-model ensemble mean decreases by 16.2% for 2006 99 relative to the reference period, although the long-term trend is positive. That means the interannual variability of the EASM intensity decreases, which disagrees with the increased interannual variability of the EASM rainfall under future global warming, as revealed by 13 CMIP3 models (Lu and Fu, 2010).
11.4.2 FUTURE CHANGES OF MONSOON CIRCULATION AND RAINFALL IN THE ASIAN SUMMER MONSOON REGION Fig. 11.5 illustrates the SLP and horizontal winds at 850 hPa in summer for the period of 1979 2005 from the 13-model ensemble mean (Fig. 11.5A), and the corresponding difference
FIGURE 11.5 (A) Climatology of horizontal wind at 850 hPa (m/s, vector) and SLP (Pa, shaded) in summer for the period 1979 2005; (B) the corresponding difference between the periods 2070 99 and 1979 2005 from the 13-model ensemble mean. Two red rectangles denote the ISMI of 5000e 15000e N, 40000e 80000e E and 20000e 30000e N, 70000e 90000e E; and the yellow rectangle is the EAMI (20000e 40000e N, 110000e 125000e E).
between the periods 2070 99 and 1979 2005 (Fig. 11.5B). It is shown that the models are generally able to reproduce the distribution of SLP and lower-level wind fields in Asia. Because the land is heated to a greater degree in the summer, it becomes significantly warmer than the oceans.
The Eurasian continent is controlled by thermal low-pressure systems whereas the oceans are controlled by high-pressure systems. The thermal contrasts due to differences in the warming of the land and oceans drives the monsoon circulation, forming strong southwesterly winds flowing from the Indian Ocean to the BOB, all the way to the northern SCS, and East Asia and the WNP. The southwesterly winds affect the climate in East Asia with southerly winds originating from the northwestern flank of the NWP anticyclone (Fig. 11.5A). During 2070 99, however, under future global warming, the ISM circulation weakens in climate models, which displays that the crossequatorial southerly, the ISM trough, and associated anomalous westerlies between 5000e and 15000e N, and easterlies over northern India are weakened (Fig. 11.5B). Accordingly, an anomalous anticyclonic circulation occurs over central India. It is also noted that the meridional land sea thermal contrast and thermal effects of the elevated Tibetan Plateau reinforce the interhemispheric thermal contrast resulting from differential solar radiation over Indian monsoon region (Fig. 11.5B). It appears inconsistent with the projected increases in surface land sea thermal contrasts (Turner and Annamalai, 2012; Wang et al., 2012a,b). The monsoon rainfall over the ISM region also significantly increases in the period 2070 99 relative to the reference period (Fig. 11.6), which is consistent with previous studies (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012). Sun et al. (2010) indicated that the ISM intensity positively correlates with the land sea thermal contrast in the lower and upper troposphere before year 2000; thereafter, a reduced upper-tropospheric thermal contrast leads to a weakened ISM circulation, despite an increasing lower-tropospheric thermal contrast. The slight strengthening of the EASM under future global warming is further confirmed by atmospheric circulation changes in the lower troposphere, as large-scale southerly wind anomalies (less than 1 m/s) at 850 hPa prevail over eastern and northeast China in the 13-model ensemble
FIGURE 11.6 The difference of rainfall between the periods 2070 99 and 1979 2005 from the 13-model ensemble mean (unit: mm/day).
mean for 2070 99 relative to the reference period (Fig. 11.4B). This is because, under future global warming, the temperature rises across East Asia and adjacent areas, but with a different magnitude between land and ocean (Sun and Ding, 2011). On the one hand, the rate of warming is faster over East Asia than over the same latitudes of the WNP and hence leads to an increased zonal thermal contrast (Jiang and Wang, 2005). Accordingly, the strengthening of heat low over the East Asian continent is larger in magnitude than the weakening of the WNP subtropical high, leading to an increased zonal SLP gradient and, in turn, southerly wind anomalies over East Asia. On the other hand, the rate of warming is faster over eastern China than over the SCS. This leads to increased meridional land sea thermal and sea level pressure gradients and, hence, southerly wind anomalies over southern China. Overall, these changes in zonal and meridional land sea thermal contrasts across East Asia and surrounding oceans are responsible for the slight strengthening of the EASM. The monsoon rainfall bands of the Meiyu/Baiu/Changma and WNP over the EASM region also increase significantly under global warming (Fig. 11.6), which agrees with the strengthening change of the EASM’s circulation. In previous work performed using the CMIP3 and CMIP5 datasets under global warming, the ISM was projected to significantly strengthen during the Indian monsoon rainfall (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012), slightly increase (Lal et al., 2001), and weaken in the monsoon circulation (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). On the other hand, the EASM was projected to strengthen (Li and Zhou, 2010; Chen et al., 2012), strengthen only over South China (Ueda et al., 2006; Sun and Ding, 2010), vary little (Kimoto, 2005), and remain normal (Li et al., 2010). Such discrepancies are not strange when viewed from this work. That is because that there are differences in climate models, emissions scenarios, analysis methods, and monsoon indices among the studies, and that most of them do not assess whether climate models can reliably reproduce the present Asian monsoon circulation. In addition, the ISM and EASM changes are determined largely by climate models used in the analysis. In other words, those projections are more or less model-dependent, owing to the limited number of climate models.
11.5 SUMMARY AND DISCUSSION Over recent years, many climate models have been used to project future climate change worldwide. Under the most representative RCP4.5 scenario, this chapter assessed the future changes of the ISM and EASM using the data of 13 reliable CMIP5 models. These models were selected from the available 31 climate models used in the IPCC Assessment Report 5 in terms of their ability to simulate the climatology of the ASM in the reference period of 1979 2005. Under future global warming, it was found that there is a slightly decreased trend for the intensity of the ISM’s circulation, with a significant weakening of interannual variability. The spatial distribution displays that the cross-equatorial southerly, the ISM trough and associated anomalous westerly between 5000e and 15000e N, easterlies over northern India are weakened, and an anomalous anticyclone occurs over central India. But the monsoon rainfall over the ISM region will significantly increase relative to the reference period. On the other hand, the EASM’s circulation and rainfall are strengthened, while the interannual variability of the EASM’s circulation is slightly weakened relative to 1979 2005.
The spatial distribution data exhibit that large-scale southerly wind anomalies at 850 hPa prevail over eastern and northeast China relative to the reference period. The changes both in zonal and meridional land sea thermal contrasts across East Asia and surrounding oceans are responsible for the slight strengthening of the EASM. Why do the ISM and EASM exhibit differing variations in the future under global warming? The regionality of the Asian monsoon variability is, in part, due to the spatial distribution of the thermal contrast between the ocean and continent. Over India, the meridional land sea thermal contrast and thermal effects of the elevated Tibetan Plateau reinforce the interhemispheric thermal contrast resulting from differential solar radiation. As such, the Indian monsoon is extremely energetic. Over East Asia and its adjacent marginal seas, however, an eastern western land sea thermal contrast dominates, which tends to induce a zonal pressure difference between the Asian continental low and the WPSH. In conjunction with the influence of the northern southern differential solar forcing, the WPSH, along with the EASM trough to its southwest and the subtropical front to its northwest, controls the monsoon circulation over East Asia. From their geographic setting relative to the continent ocean distribution, one would expect that the variability of the ISM and EASM might be dissimilar. The ISM and EASM are driven, in part, by two major convective heat sources. In summer, the two centers are anchored primarily in the BOB and the Philippine Sea, respectively. The changes in intensity and location of the two convection regions have fundamental impacts on the variability of the two monsoon subsystems. Note that the convection in East Asia is more directly affected by the Pacific SST anomaly than the convection over the BOB (Wang et al., 2000). In addition, the ways by which ENSO affects the two convective heat sources are also different (Lau and Nath, 2000; Wang et al., 2000). Therefore, the variations of the ISM and EASM could be very different. It was also noted that the future ASM changes are somewhat model and index dependent. This explains why there are discrepancies on this issue in previous studies, and implies that more emphasis should be given to the ensemble mean of multiple reliable climate models and the accompanying mechanisms. In addition, the horizontal resolution of state-of-the-art climate models is generally higher than before, but their ability to represent the East Asian climate is still inadequate and, hence, their projection is more or less uncertain. Given that the performance of high-resolution regional climate models to simulate the present climate over China is better overall than that of global climate models, and that future changes in monsoon precipitation over China are different between regional and global models (Gao et al., 2012), more attention should be given to dynamic downscaling studies at the regional scale. Finally, we would like to emphasize that there are uncertainties in future emissions or concentration scenarios of greenhouse gases and aerosols, and in radiative forcing scenarios. There are still incomplete aspects in the global climate and Earth system models. The lack of observation data hampers our understanding of climate change on the decadal and longer timescales. Our knowledge of natural climate variability over a range of timescales is also limited. All these factors lead to a level of uncertainty in future climate change projection.
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