Top PDF Indian Ocean moisture flux variations during summer monsoon and its relation with Indian rainfall

Indian Ocean moisture flux variations during summer monsoon and its relation with Indian rainfall

Indian Ocean moisture flux variations during summer monsoon and its relation with Indian rainfall

[Keywords: El Nino; Indian Ocean; Indian summer monsoon rainfall; Moisture Flux] Introduction Indian monsoon is one of the main components of the climate system, both in regards to its strong interaction and variability 1-3 as well as its enormous socioeconomic impacts 4-6 . The link between atmospheric hydrology in the Pacific Ocean and the monsoonal moisture budget is more complex than originally portrayed in early studies of monsoon–El Niño Southern Oscillation (ENSO) coupling 7 . The meridional and zonal heating gradients that drive the large-scale atmospheric and oceanic monsoon circulations and hydrologic processes exist as a key component of the monsoon’s intraseasonal, seasonal, and interannual variability 7-10 . Seasonal hydrologic variations include the sustained enhancement of rainfall, winds, and surface evaporation in India. Neighbouring subsident environments such as the Arabian Sea (AS) and southern Indian Ocean (IO) enhance MF due to surface evaporation and divergent winds during summer. The variations compose the globe’s most intense seasonal inter-hemispheric mass and moisture exchange and links the Southeast Asia, AS, and southern IO regions 11-13 . Transport of moisture into a region where it can be trained into
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Insights from a joint analysis of Indian and Chinese monsoon rainfall data

Insights from a joint analysis of Indian and Chinese monsoon rainfall data

Furthermore, the analysis reveals the teleconnec- tions between monsoon rainfall and monsoon indices (IMI/WNPMI), SSTa and other large scale climate indices. For example, our results suggest two moisture sources for Indian monsoon and Chinese monsoon, i.e., the Indian Ocean and West Pacific Ocean, each of which dominates monsoon rainfall in different areas indicated by the joint PCs. The results also show that generally the SSTa in the previous winter season can be linked to the following sum- mer monsoon rainfall, and specifically with cold SSTa over the Indonesia region during winter season more monsoon rainfall would be distributed over India and Southern China in summer season. This provides predictability for Indian and Chinese monsoon rainfall through joint PC1 and PC2, which will be presented in more detail in a following paper (Zhou et al., 2011).
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The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

We note that in MetUM-GOML2.0 coupling acts to de- grade the basic state but generally to improve ISV and prop- agation. Other studies have shown changes in ISV, especially the MJO, in conjunction with changes in mean state, but a poorer mean state is not always associated with improved ISV as here. In atmosphere-only configurations of the Me- tUM, increasing the rate of mixing entrainment and detrain- ment for deep- and mid-level convection affects the tropical mean state precipitation (Bush et al., 2015; using GA3), in particular producing more rainfall over the north-west tropi- cal Pacific Ocean and less over the equatorial Indian Ocean and Maritime Continent. These increases in entrainment and detrainment rates also improve MJO forecast skill (Klinga- man and Woolnough, 2014a; using GA2). Furthermore, In- ness et al. (2003) improved the strength of MJO propagation through the Maritime Continent by reducing biases in SST and wind in the coupled Hadley Centre MetUM (HadCM3) through flux adjustments.
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The 2015 Indian summer monsoon onset - phenomena, forecasting and research flight planning

The 2015 Indian summer monsoon onset - phenomena, forecasting and research flight planning

1. Introduction The Indian summer monsoon is one of the most dramatic seasonal variations in climate, characterised by the reversal of prevailing winds between winter and summer that brings moisture from across the Indian Ocean during the monsoon. The monsoon lasts from June to September and supplies India with around 80% of its annual rainfall. With more than a billion people to feed, the monsoon is vital to Indian society. The most important event in the meteorological calendar for India is the monsoon onset. This first occurs around 1 June, in Kerala on the southwest coast (figure 1), and then progresses north and northwestwards, arriving in Delhi before July and reaching the Pakistan border around 15 th July. At each location, the arrival of the monsoon rains brings cooler conditions to replace the pre-monsoon heat, and is vital to farmers in regions where irrigation is not available, as well as to the coal and steel industries. Delayed onset can cause drought and hardship; violent rains around the time of onset can cause flooding and landslides, and every year thousands are killed by lightning, most of whom are farmers working in fields.
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Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models

Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models

Some studies suggest a weakening of the monsoon circula- tion in a number of CMIP-3 models under global warming (Tanaka et al., 2005; Ueda et al., 2006). The 850 hPa summer wind climatology from observational data shows the low- level monsoon circulation that carries moisture from over ocean to the Indian land region (Fig. 11). Figure 12 depicts the composite difference in the wind anomaly between the end of the 21st century under RCP-8.5 and the end of the 20th century from the CMIP-5 models. The majority of the models show an increase in wind speed (shaded) in the north of India and a decrease in wind speed in southern peninsular India as well as the north equatorial Indian Ocean by the end of the 21st century. Anomalies in wind direction (vectors) are opposite to the direction of the mean wind over the southern peninsular India, and along the direction of the mean wind over central and northern India, in most of the models. This could indicate a northward shift in the monsoon circulation in the future. The ensemble mean over all 19 models under con- sideration also shows the same pattern (Fig. 13). This pattern resembles that of the wind anomaly from the CMIP-3 mod- els (cf. Fig. 2a in Ueda et al., 2006). The monsoon circulation strengthens over northern India, but it weakens over the south of India. Figure 14 shows the meridional pattern of the zonal wind averaged over the longitudes 50–110 ◦ E for all 19 mod- els under consideration. The majority of the models show a slight northward shift in monsoon circulation of the order of about 2 ◦ by the end of the 21st century under RCP-8.5. Kitoh et al. (1997) suggest a similar northward shift in the mon- soon circulation under global warming. Such a latitudinal shift of the circulation would be important to consider when
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Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

land-ocean temperature contrast (Lee et al., 2009) or land– atmosphere feedbacks (Niyogi et al., 2010; Tuinenburg et al., 2011). There have been several other studies addressing the effects of LULCC over the Indian region (Lohar and Pal, 1995; Douglas et al., 2006, 2009; Niyogi et al., 2007; Saeed et al., 2009; Dutta et al., 2009; Nayak and Mandal, 2012). Apart from them, Lei et al. (2008), Kishtawal et al. (2009), and Ali et al. (2014) explored the impact of growing ur- banization in India and large-scale climate variability in the changes in extreme rainfall events. Interesting time slice ex- periments made with a global model have shown that an in- crease in crop and pasture land lead to a decrease in seasonal rainfall over India during the pre-industrial period (years 1700–1850) when the impact of anthropogenic activity or natural climate variations were minimal (Takata et al., 2009). Krishnan et al. (2015) made several experiments with a high- resolution global atmospheric model and concluded that a multitude of factors such as aerosols, land-use change, Indian Ocean warming, as well as GHGs, have together contributed to the observed weakening of the south-Asian monsoon and changes in frequency distribution of daily rainfall events dur- ing the later half of the 20th century. However, the impact of LULCC as a lone forcing component on the Indian sum- mer monsoon has not been quantified. It is also plausible that feedbacks due to variations in remote SSTs and snow cover may have modulated the local impacts due to LULCC. In this study, we hypothesize and demonstrate that LULCC has partly contributed to the observed decrease in moderate rain- fall events over CI during the monsoon season from 1951 to 2005, apart from the increasing trend in daily mean and max- imum temperatures. We have conducted experiments with a high-resolution regional climate model (RCM) RegCM4.0 and much improved and up-to-date land cover data over the Indian region to prove our hypothesis. No added external forcing in terms of aerosols or GHG concentrations is used in our experiments. Furthermore, additional experiments by removing the positive trend in Indian Ocean SSTs have also been made to isolate the impact of LULCC.
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The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

We note that in MetUM-GOML2.0 coupling acts to de- grade the basic state but generally to improve ISV and prop- agation. Other studies have shown changes in ISV, especially the MJO, in conjunction with changes in mean state, but a poorer mean state is not always associated with improved ISV as here. In atmosphere-only configurations of the Me- tUM, increasing the rate of mixing entrainment and detrain- ment for deep- and mid-level convection affects the tropical mean state precipitation (Bush et al., 2015; using GA3), in particular producing more rainfall over the north-west tropi- cal Pacific Ocean and less over the equatorial Indian Ocean and Maritime Continent. These increases in entrainment and detrainment rates also improve MJO forecast skill (Klinga- man and Woolnough, 2014a; using GA2). Furthermore, In- ness et al. (2003) improved the strength of MJO propagation through the Maritime Continent by reducing biases in SST and wind in the coupled Hadley Centre MetUM (HadCM3) through flux adjustments.
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Decadal to multi-decadal scale variability of Indian summer monsoon rainfall in the coupled ocean-atmosphere-chemistry climate model SOCOL-MPIOM

Decadal to multi-decadal scale variability of Indian summer monsoon rainfall in the coupled ocean-atmosphere-chemistry climate model SOCOL-MPIOM

for the month of June when both mechanisms are simulated together. Kodera ( 2004 ) found that more monsoon precipitation occurs over India during solar maximum periods due to the stratospheric variations by solar forcing. He proposed that the solar influence on monsoon is not due to direct heat- ing of the troposphere through radiative changes; instead it comes through the stratosphere by modulation of the upwelling in the equatorial troposphere. He found a very little correlation of solar radio flux at 10.7 cm (F10.7 index) and JA (July–August) northward near-surface (10 m) wind velocity with Indian Ocean SSTs which indicates that most of the variations in near-surface winds originate from atmospheric variations Kodera ( 2004 ). Furthermore, he studied the spatial structure of JA zonal means of zonal wind (U), temperature (T), and vertical velocity (ω) by correlating them with near-surface winds and F10.7 index from the surface up to 10  hPa. He found strong positive correlations of near-surface winds and F10.7 index with T in the equatorial region and northern hemisphere sub- tropics from the upper troposphere to the stratosphere. He observed that near-surface winds and F10.7 index have significant positive correlations with U in stratospheric subtropics. The spatial relationship of ω with near-surface winds and F10.7 index showed a strong downwelling at the equator and upwelling above the Indian region. Kodera also found that the Brewer Dobson circulation (BDC) is weak (strong) during high (low) solar activity. From a previous study of Hood and Soukharev ( 2003 ) he concluded that weak BDC increases the temperature of the tropopause region, consequently reducing the convective activity in the equatorial troposphere. It is evident from the above-men- tioned studies that solar forcing indirectly causes precipi- tation anomalies in the Indian and Pacific Ocean. Hence, coupled ocean-atmospheric effects as well as stratospheric dynamics should be considered for studying the influence of the sun on Indian monsoon which can be done by using a coupled atmosphere-ocean-chemistry climate model.
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Investigating the impact of land use land cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

Investigating the impact of land use land cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

land-ocean temperature contrast (Lee et al., 2009) or land– atmosphere feedbacks (Niyogi et al., 2010; Tuinenburg et al., 2011). There have been several other studies addressing the effects of LULCC over the Indian region (Lohar and Pal, 1995; Douglas et al., 2006, 2009; Niyogi et al., 2007; Saeed et al., 2009; Dutta et al., 2009; Nayak and Mandal, 2012). Apart from them, Lei et al. (2008), Kishtawal et al. (2009), and Ali et al. (2014) explored the impact of growing ur- banization in India and large-scale climate variability in the changes in extreme rainfall events. Interesting time slice ex- periments made with a global model have shown that an in- crease in crop and pasture land lead to a decrease in seasonal rainfall over India during the pre-industrial period (years 1700–1850) when the impact of anthropogenic activity or natural climate variations were minimal (Takata et al., 2009). Krishnan et al. (2015) made several experiments with a high- resolution global atmospheric model and concluded that a multitude of factors such as aerosols, land-use change, Indian Ocean warming, as well as GHGs, have together contributed to the observed weakening of the south-Asian monsoon and changes in frequency distribution of daily rainfall events dur- ing the later half of the 20th century. However, the impact of LULCC as a lone forcing component on the Indian sum- mer monsoon has not been quantified. It is also plausible that feedbacks due to variations in remote SSTs and snow cover may have modulated the local impacts due to LULCC. In this study, we hypothesize and demonstrate that LULCC has partly contributed to the observed decrease in moderate rain- fall events over CI during the monsoon season from 1951 to 2005, apart from the increasing trend in daily mean and max- imum temperatures. We have conducted experiments with a high-resolution regional climate model (RCM) RegCM4.0 and much improved and up-to-date land cover data over the Indian region to prove our hypothesis. No added external forcing in terms of aerosols or GHG concentrations is used in our experiments. Furthermore, additional experiments by removing the positive trend in Indian Ocean SSTs have also been made to isolate the impact of LULCC.
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Relation between outgoing longwave radiation and findlater jet over Arabian Sea during summer monsoon and influence on Indian monsoon rainfall

Relation between outgoing longwave radiation and findlater jet over Arabian Sea during summer monsoon and influence on Indian monsoon rainfall

Department of Environmental Sciences, Acharya Nagarjuna University, Guntur, AP, India *[E-mail: mvsm.au@gmail.com] Received 4 September 2018; revised 6 December 2018 This work analyses the relationship between outgoing Longwave radiation (OLR) and Findlater jet (FLJ) intensities at 850 hPa pressure level and also their relation with Indian summer monsoon rainfall (ISMR; June-September) for a period of 1997-2010 over Arabian Sea and India. FLJ is a low-level jet (LLJ) which can be observed during southwest monsoon months. This LLJ generally supports the large-scale moisture and momentum transport from ocean to atmosphere, which results in rainfall over India. FLJ and OLR are associated during the monsoon months. However FLJ (positively) and OLR (negatively) are related with ISMR. Monthly and seasonal correlation coefficients among FLJ, OLR and ISMR presented and the deviations during El Nino/La Nina are discussed. Based on this analysis we recommend that the variations in FLJ should include interannual variability in atmospheric dynamics.
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Performance of cumulus parameterization schemes in the simulation of Indian Summer Monsoon using RegCM4

Performance of cumulus parameterization schemes in the simulation of Indian Summer Monsoon using RegCM4

The Indian Summer Monsoon (ISM) is driven by organized large-scale convection; hence, its simulation is expected to depend on an appropriate representation of cumulus convection in the model. In the present study, the performance of different cumulus parameterization schemes is examined towards simulations of the ISM. The Regional Climate Model (RegCM4) is coupled with the Community Land Model (CLM 3.5) at 30 km resolution for the period May 1-September 30 for seasonal simulation of the ISM in three consecutive years, 2007, 2008, and 2009. Five numerical experiments with five convection schemes (Kuo, Grell, MIT, GO_ML [Grell over ocean and MIT over land], GL_MO [Grell over land and MIT over ocean]) are conducted for each of these three years. Some important features of the ISM simulated by the model, viz. low level westerly jet, upper level easterly jet, heat low, Tibetan high, etc., are analyzed and compared with that of the National Center for Environmental Prediction (NCEP) reanalysis. We found that the heat low over northwest India and Paki- stan in all the three years is better simulated by the model with the MIT convection scheme compared to other convection schemes, whereas spatial distribution and accuracy of surface temperature is better simulated using GL_MO rather than MIT. The low level westerly jet is well captured by the model with MIT with slightly weaker strength compared to the National Center for Environmental Prediction (NCEP) reanalysis. The location and strength of the tropical easterly jet is well predicted in each simulation with some uncertainty in strength, and are better simulated with MIT. The comparison of the model simulated rainfall with 0.5º × 0.5º datasets from the Climate Research Unit (CRU TS3.22) indicates that seasonal and monthly average rainfall are well simulated with MIT and GO_ML; however, the same over central and western India is significantly underestimated by the model with all the convection schemes. Comparatively, higher sensible heat flux and lower latent heat flux are noticed in the model simulation with all schemes. This change of fluxes affects surface temperature and rainfall simulation significantly. The statistical analysis indicates that surface temperature and rainfall are well reproduced by the model with GL_MO and GO_ML, but circulation is better simulated with MIT only. It is observed that although the bias in the model with MIT is slightly higher than that of the two mixed schemes, the spatial distribution and other synoptic features of surface temperature and rainfall during ISM are well simulated. Thus, considering overall performances, the RegCM4 with MIT the cumulus convection scheme provides better simulation of seasonal and monthly features of the monsoon.
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Teleconnection between Australian winter temperature and Indian summer monsoon rainfall

Teleconnection between Australian winter temperature and Indian summer monsoon rainfall

Australian winters are relatively mild, and extreme lows in Australia winter temperature are commonly associated with subseasonal cold spells or “cold-air outbreaks”, which are due to mid-latitude baroclinic wave activity. The proposed mechanism may also be viewed as an example of how south- ern hemispheric mid-latitude weather can influence north- ern hemispheric monsoon rainfall. Figure 7a–c show higher evaporation and drier air over the ocean just west of Aus- tralia during cold air outbreak years, which is consistent with the hypothesis that colder air travelling westwards from Aus- tralia results in drier air over the ocean just west of Australia and enhances evaporation. In addition, the SST west of Aus- tralia is anomalously cold during the outbreak years, exclud- ing the other possibility of warmer SST enhancing evapora- tion. Unfortunately, there is a lack of direct horizontal water vapour flux observations to show enhanced transport from Australia to India in cold-air outbreak years, and reanaly- sis datasets are too much subjected to uncertainties in model parametrisation for tropical convective rainfall to show reli- ably or significantly the flux anomalies for composites of a few years. Finally, gauge rainfall is anomalously high in the outbreak years (Fig. 7d).
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An Indian sphere of influence in the Indian Ocean?

An Indian sphere of influence in the Indian Ocean?

The appropriate counter to China’s encirclement of India is to build our own relations, particularly in our neighbourhood, on the basis of our national interests and magnanimity towards smaller neighbours. 73 As it expands its influence in the Indian Ocean region India also has had to accept the continuing role of the United States in the region. The United States, particularly with its base at Diego Garcia and its naval facilities in Singapore and the Gulf, seems likely to remain the predominant naval power in the Indian Ocean region for many years to come. However there are indications that the United States is willing to cede—and indeed encourage—a major regional naval role for India, particularly in the northeast Indian Ocean. For its part, India’s willingness to cooperate with the United States in achieving its ambitions is not as paradoxical as it may seem. As the former US Secretary of State, Dean Acheson, once conceded, the United States in developing its sphere of influence in the Western hemisphere in the nineteenth century relied on Britain, the then superpower, to enforce the Monroe Doctrine until the United States was sufficiently strong to do so itself. 74 Similarly, India may have good reason to cooperate with the United States while it builds its national power. However, with the exception of the United States, India will likely wish to cooperate with extra-regional navies in the Indian Ocean only as long as they recognize India’s leading regional role. 75 The apparent willingness of Japan to recognize India’s role
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MODELING INDIAN MONSOON (RAINFALL) VOLATILITY AS AN INDEX BASED RISK TRANSFER PRODUCT

MODELING INDIAN MONSOON (RAINFALL) VOLATILITY AS AN INDEX BASED RISK TRANSFER PRODUCT

Rainfall index based insurance has emerged as a promising alternative to traditional crop insurance. Now attempts are directed towards exploring the scope and applicability of rainfall indexation to sub serve the capital market to meet the requirements of a wide-range of players whose financial prospects are closely inter connected to rainfall outcome. This study focused on rainfall–indexation and suggested a unique ticker symbol MOX for each of the sample meteorological subdivisions of India. An estimating function based on time-series simple regression is attempted to enable determination of expected MOX value at the end of the monsoon season. Essential statistical properties of MOX series are captured to indicate the vast scope for creating a new class of financial instrument for hedging and portfolio management purposes. Widespread availability of reliable data for long periods make it attractive to private insurers and international reinsurers and should help developing countries explore international markets for risk sharing.
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Variations in Swells along Eastern Arabian Sea during the Summer Monsoon

Variations in Swells along Eastern Arabian Sea during the Summer Monsoon

Information on swells is required for navigation and op- eration of marine facilities. The data collected during the severe weather conditions form the design parameters for marine facilities. The presence of local wind sea and swell components propagating from distant storms can be identified by studying wave energy spectra. When seas and swells are present the wave energy spectrum will be multi-peaked with peaks corresponding to seas and swells. Along the Indian coast, about 60% of the wave spectra observed was multi-peaked and they were mainly single peaked when the significant wave height (H m0 ) was more
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Trends and Interannual Variability of Winds and Turbulent Heat Flux in the Indian Ocean Sector of Southern Ocean during 2000 2009

Trends and Interannual Variability of Winds and Turbulent Heat Flux in the Indian Ocean Sector of Southern Ocean during 2000 2009

The Southern Ocean (SO) hosts the strongest winds in the world oceans, which facilitates heat, moisture, and momentum exchanges between the ocean and atmosphere and forms the largest and important domains of the global climate. The air-sea heat fluxes play a paramount role in the redistribution of heat between the tropics and poles, thereby influencing the global climate through the meridional [1] and regional climate phenomena [2]. Momentum flux into the SO is a crucial for maintaining the eastward flowing Antarctic Circumpolar Current (ACC), Ekman pumping, upwelling along Antarctic coast, upper-ocean mixing, and large-scale ocean circula- tion. Determining the exchanges of momentum, sensible and latent heat flux (turbulent heat flux, hereafter Q) between the SO and atmosphere is vital to understand its role in the global climate system. The water tempera- ture in the current remains 2˚C - 3˚C warmer in some parts and 2˚C - 3˚C colder in other parts than the average. This zonal wavenumber-2 anomaly pattern moves eastwards as Antarctic Circumpolar Wave (ACW) and takes 8 - 9 years to circumvent the SO [3]. The ACW exerts a considerable influence on the weather patterns in southern Australia, South America and South Africa; the ACW anomalies are also associated with cyclonic activity [4]. The ACC facilitates a global transport of mass, heat and momentum, and climate signals from one ocean basin to another. The modulation of air-sea fluxes by the influence of the ACC is thus important to understand the va- riability of the global climate system, through the global overturning circulation [5]. Positive (negative) sea sur- face temperature (SST) anomalies are generated by anomalous latent heat flux, propelled by southward (north- ward) meridional wind stress anomalies resulting from geostrophic balance [6].
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The Indian Summer Monsoon from a Speleothem 18 O Perspective a Review

The Indian Summer Monsoon from a Speleothem 18 O Perspective a Review

90. Consortium, P. 2k; Ahmed, M.; Anchukaitis, K. J.; Asrat, A.; Borgaonkar, H. P.; Braida, M.; Buckley, B. M.; Büntgen, U.; Chase, B. M.; Christie, D. A.; Cook, E. R.; Curran, M. A. J.; Diaz, H. F.; Esper, J.; Fan, Z.-X.; Gaire, N. P.; Ge, Q.; Gergis, J.; González-Rouco, J. F.; Goosse, H.; Grab, S. W.; Graham, N.; Graham, R.; Grosjean, M.; Hanhijärvi, S. T.; Kaufman, D. S.; Kiefer, T.; Kimura, K.; Korhola, A. A.; Krusic, P. J.; Lara, A.; Lézine, A.-M.; Ljungqvist, F. C.; Lorrey, A. M.; Luterbacher, J.; Masson-Delmotte, V.; McCarroll, D.; McConnell, J. R.; McKay, N. P.; Morales, M. S.; Moy, A. D.; Mulvaney, R.; Mundo, I. A.; Nakatsuka, T.; Nash, D. J.; Neukom, R.; Nicholson, S. E.; Oerter, H.; Palmer, J. G.; Phipps, S. J.; Prieto, M. R.; Rivera, A.; Sano, M.; Severi, M.; Shanahan, T. M.; Shao, X.; Shi, F.; Sigl, M.; Smerdon, J. E.; Solomina, O. N.; Steig, E. J.; Stenni, B.; Thamban, M.; Trouet, V.; Turney, C. S. M.; Umer, M.; Ommen, T. van; Verschuren, D.; Viau, A. E.; Villalba, R.; Vinther, B. M.; Gunten, L. von; Wagner, S.; Wahl, E. R.; Wanner, H.; Werner, J. P.; White, J. W. C.; Yasue, K.; Zorita, E. Continental-scale temperature variability during the past two millennia. Nature Geoscience 2013, 6, 339–346, doi:10.1038/ngeo1797.
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Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach

Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach

rainfall averages over these region from the CPC-NCEP (0.25 ◦ × 0.25 ◦ ) observational gridded global rainfall dataset over the period 1979–2016 (Chen et al., 2008) and from the Pai et al. (2015) (0.25 ◦ × 0.25 ◦ ) observational gridded Indian rainfall dataset over the period 1979–2017 (Pai et al., 2015). In the remainder of this paper, we will mainly focus on the results obtained for the latter dataset, while those for the former are provided as parts of the Supple- ment. Using data taken from the ERA-Interim reanalysis (Dee et al., 2011) for the period 1979–2017, precursor re- gions are calculated from global weekly averaged gridded (1.5 ◦ × 1.5 ◦ ) fields including outgoing longwave radiation (OLR) at the top of the atmosphere, vertical velocity at 500 hPa (W500) and geopotential height at 200 hPa (Z200). The NAO weekly index is obtained by averaging daily data from NOAA (available at ftp://ftp.cpc.ncep.noaa.gov/ cwlinks/norm.daily.nao.index.b500101.current.ascii, last ac- cess: 13 January 2020). To identify MJO phases, we use the OLR MJO index (OMI) provided by NOAA (https:// www.esrl.noaa.gov/psd/mjo/mjoindex/, last access: 13 Jan- uary 2020). This metric features the first and second princi- pal components obtained by the empirical orthogonal func- tion (EOF) analysis of OLR in the tropical belt (between 30 ◦ N and 30 ◦ S) filtered to remove influences outside the MJO timescale (30–90 d). OMI PC2 corresponds to the first principal component of the real-time multivariate MJO index (RMM1), which is widely used in the literature (Wheeler and Hendon, 2004; Pai et al., 2011; Kiladis et al., 2014). More- over, the OMI has also been proven useful in describing the BSISO behaviour, which is relevant for the ISM break and active phases in summer (Wang et al., 2018). All time se- ries of MT rainfall, Z200 and all datasets analysed in this work are detrended and anomalies are calculated at weekly time steps. Thus, both the climatological and seasonal cy- cles are removed. Since the interannual variability may affect the analysis, we follow the approach proposed by Ding and Wang (2007) and filter the data by removing from each JJAS season its seasonal average.
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Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

The scores discussed above are calculated for each monthly forecast of rainfall over India from June to Sep- tember from NCEP CFS hindcast during the period from 1981 to 2005. The values for June to September forecasts are given in Tables 2-5 respectively for all the three cate- gories of the forecast (PAB, PBL and PNN). As seen from the values, the monthly probability forecast in terms of the three categories clearly able to provide useful guidance. In terms the accuracy of the forecast it is seen from Tables 2-5 that it is 60% or higher in all the three categories for all four months except for the forecasts of normal category of July (52%) and below normal cate- gory of August (56%). As discussed above the accuracy may be high but it does not penalize for misses and false alarms and more appropriate scores are “TS” and “HSS”. Although there is no definite cutoff value of TS or HSS above which the forecast can be considered to be good forecast the positive value and the threshold exceeding around 0.2 could be considered as a very good score. The “TS” and “HSS” for June and July is found to be greater than 0.2 for above and below normal categories but is less than 0.2 in case of normal categories with July fore- cast in the normal category giving negative HSS value. The negative value indicates that the chance forecast is better than the forecast. Also it is seen that during the first half of the season the TS and HSS scores are higher in the month of June compared to that of July indicating May ensembles give better forecast for June compared to June ensembles for July forecasts. Similarly for the sec- ond half of the season the “TS” and “HSS” scores give higher values for September forecasts compared to that of forecast for August indicating better skill in Septem- ber rainfall compared to August rainfall. With respect to the “POD” the forecast for August in the above normal category is the lowest with only 12% followed by 14% for the normal category of July and 20% in the normal category of June. The “POD” is found to be more than 70% for June forecast in the above and below normal categories and more than 50% during July and Sep- tember forecasts in the above and below normal catego- ries. The “FAR” is about 50% or less in case of June and July for above and below normal categories and much higher in case of below normal category of August and above normal category of September.
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Changes in Indian Summer Monsoon Using Neodymium (Nd) Isotopes in the Andaman Sea During the Last 24,000 years

Changes in Indian Summer Monsoon Using Neodymium (Nd) Isotopes in the Andaman Sea During the Last 24,000 years

Dramatic changes from a cold and dry last glacial to a warm and wet Holocene period intensified the Indian summer monsoon (ISM), resulting in vigorous hydrology and increased terrestrial erosion. Here we present seawater neodymium (Nd) data (expressed in ε Nd ) from Andaman Sea sediments to assess past changes in the ISM and the related impact of Irrawaddy– Salween and Sittoung (ISS) river discharge into the Andaman Sea in the northeastern Indian Ocean. Four major isotopic changes were identified: (1) a gradual increase in ε Nd toward a more radiogenic signature during the Last Glacial Maximum
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