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Wind climate along the southwest coast of India (Kerala coast) has been analysed using 41 years of Climate Forecast System Reanalysis (CFSR) winds to delineate long-term trends and variability. The study reveals significant decreasing trends in annual mean wind speeds, of the order of −2.0 to −2.5 cm·s−1·year−1, for the period 1979–2019. Southwest monsoon has contributed the highest weakening trends (−3.0 to −4.0 cm·s−1·year−1) due to significant decrease in the southwesterly/westerly wind speeds. The wind climate and trends during the northeast monsoon and premonsoon seasons have been characterized largely by the prevalence of sea breeze and shamal–makran wind systems. We find that the intensity of both sea breeze and shamal–makran wind systems reduces from north to south along the Kerala coast, which is in consistent with the earlier studies. Decadal oscillations in wind speeds, driven by the decadal variability in large-scale atmosphere–ocean circulations, are evident. Recent changes in the frequency of occurrence of Indian Ocean Dipole in the Indian Ocean have determined the overturning trends since 2010, and resulted in increasing trends in the current decade along the central and northern coasts of Kerala. Furthermore, interannual variability in wind speeds has been linked to the El Niño–Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD).

The relationship of the tropical Indo-Pacific upper ocean heat content (HC) and the South China Sea (SCS) summer monsoon (SCSSM) onset during 1980-2020 was investigated. It was found that when HC anomalies in the preceding winter-spring are positive (negative) in the eastern Indian-western Pacific Ocean or negative (positive) in the southwestern Indian Ocean and eastern Pacific Ocean, SCSSM onset is usually early (late). Empirical orthogonal function (EOF) analysis suggested that the primary EOF modes of HC anomaly in both tropical Pacific and Indian Oceans are associated with El Nino-Southern Oscillation (ENSO). It is through modulating the westerly over the north Indian Ocean-SCS and the western north Pacific subtropical high (WNPSH) via the Walker circulation over the Indo-Pacific basin, monsoon circulation in the Indian Ocean that the ENSO related Indo-Pacific HC variation connects with the SCSSM onset. Both the second EOF modes for the tropical Pacific and Indian Ocean also show significant correlations with the SCSSM onset. The former related vertical motion over the SCS-far northwestern Pacific can influence the WNPSH and convection, thus occurrence of the SCSSM. The latter represents the intrinsic Indian Ocean HC variability; the air temperature variation associated with it can modulate the atmospheric circulation in the Indian Ocean-SCS through thermal wind balance, and thus the SCSSM onset by acting on the westerly over the SCS and the WNPSH. A western Pacific index and an Indian Ocean dipole index were defined to represent the leading HC modes in the tropical Pacific and Indian Ocean, respectively. A binary linear regression model was constructed using these two indices to improve SCSSM onset prediction.

Author(s): Kug, Jong‐Seong; Vialard, Jerome; Ham, Yoo‐Geun; Yu, Jin‐Yi; Lengaigne, Matthieu | Editor(s): McPhaden, Michael J; Santoso, Agus; Cai, Wenju | Abstract: The El Nino‐Southern Oscillation (ENSO) is a coupled ocean‐atmosphere phenomenon of variability that is a leading source of seasonal climate prediction skill across the globe. The first ENSO prediction was made in the mid‐1970s, but it was another 10–15 years before operational centers, using simple, coupled climate models, began to make routine ENSO predictions. These early forecast models were succeeded in the 1990s by more sophisticated dynamical and statistical models, which created the basis for real‐time seasonal outlooks over the globe. These models, and more recent multimodel ensembles, also inform our understanding and estimates of the predictability and prediction skill of ENSO, which varies seasonally and from decade to decade. ENSO predictability largely stems from slowly evolving oceanic conditions, with short‐term atmospheric fluctuations often limiting predictability on seasonal timescales. Despite improved models and better initializations, prediction skill remains low for forecasts passing through the boreal spring, the so‐called spring prediction barrier. Furthermore, prediction skill and predictability have varied significantly over the past couple decades. Higher skill and predictability are evident during periods of larger amplitude ENSO events (e.g., Eastern Pacific El Nino), whereas lower skill/predictability is associated with lower amplitude events (e.g., Central Pacific El Nino). These natural variations in our ability to predict ENSO, together with challenges during 2014–2016, motivate the search for understanding of how anthropogenic warming will influence seasonal ENSO prediction.

Interannual/decadal variability in the sea surface temperature at the south subtropical Indian Ocean plays a crucial modulator in the regional climate. The south Indian Ocean dipole mode is one of ...

The intra-seasonal variation of precipitation over Iran is examined in terms of the combined effects of the Madden–Julian Oscillation (MJO), Indian Ocean Dipole (IOD) and El Nino–Southern Oscillation (ENSO), using daily observations during the wet season (October–May) from 1961 to 2015. We have examined how the probability of daily rainfall above the upper tercile varies across MJO phases during positive and negative IOD and ENSO states. The results indicate that the response of Iran’s wet-season rainfall to the MJO is affected more by large-scale atmospheric variations associated with the IOD than by ENSO. The negative (positive) IOD strengthens (suppresses) the MJO–rainfall relationship in the wet and dry MJO phases. The variation in the atmospheric variables (relative humidity and vertical velocity) indicates more (less) humidity and upwards (downwards) motion which increases (decreases) precipitation in wet (dry) MJO phases during the negative IOD. The rainfall relationship with the MJO during the negative IOD is statistically significant, while the relationship during the positive IOD is weak; and no significant relationship is found during either phase of ENSO.

The 2019 October-December rains over East Africa were one of the wettest seasons on record, with many locations receiving more than double the climatological rainfall, leading to floods and landslides across the region. Above average rainfall continued into January 2020. The persistently high rainfall also contributed to the locust plagues that affected much of East Africa in January 2020. Wet conditions in East Africa are typically associated with El Nino and/or positive Indian Ocean Dipole events. In October-December 2019 a warm anomaly was present in the western Indian Ocean while a cool anomaly was present in the eastern Indian Ocean (a positive Indian Ocean Dipole); conditions known to give above average rainfall over East Africa. The warm anomaly in the western Indian Ocean persisted into January 2020. Seasonal and monthly forecasts correctly predicted above average rainfall during the October-December season. January rainfall is found to be correlated with sea surface temperatures over the western Indian Ocean. Climate model projections suggest that strong positive Indian Ocean Dipole events and wet October-December seasons may become more frequent under future climate change, with associated increased risks of floods.

Previous studies have shown that the Indian Ocean Dipole (IOD) has a significant impact on land temperatures over southern Europe. However, whether IOD influences rainfall over this region and how ...

Northern Europe and the UK experienced an exceptionally warm and wet winter in 2019/20, driven by an anomalously positive North Atlantic Oscillation (NAO). This positive NAO was well forecast by several seasonal forecast systems, suggesting that this winter the NAO was highly predictable at seasonal lead times. A very strong positive Indian Ocean dipole (IOD) event was also observed at the start of winter. Here we use composite analysis and model experiments, to show that the IOD was a key driver of the observed positive NAO. Using model experiments that perturb the Indian Ocean initial conditions, two teleconnection pathways of the IOD to the north Atlantic emerge: a tropospheric teleconnection pathway via a Rossby wave train travelling from the Indian Ocean over the Pacific and Atlantic, and a stratospheric teleconnection pathway via the Aleutian region and the stratospheric polar vortex. These pathways are similar to those for the El Niño Southern Oscillation link to the north Atlantic which are already well documented. The anomalies in the north Atlantic jet stream location and strength, and the associated precipitation anomalies over the UK and northern Europe, as simulated by the model IOD experiments, show remarkable agreement with those forecast and observed.

The 2019 Atlantic hurricane season exhibited above‐average Accumulated Cyclone Energy—60% of which was produced by Hurricanes Dorian and Lorenzo. Most tropical cyclone (TC) activity was concentrated in a ~6‐week period from late August to early October. During the early part of the season, relatively TC‐unfavorable conditions persisted in the main development region (MDR). The MDR environment became largely favorable in September, followed by an abrupt shift back to less conducive conditions in October coincident with a strongly positive Indian Ocean Dipole (IOD). The IOD produced an El Niño‐like teleconnection pattern observed through 200‐hPa velocity potential anomalies. In the subtropical Atlantic, above‐average sea surface temperatures persisted for much of the season, which may have contributed to increased activity at higher latitudes. Given the neutral El Niño‐Southern Oscillation conditions during the 2019 hurricane season, our study highlights the need for further analysis of IOD impacts on Atlantic TC activity. K. Wood was supported by the Office of Research and Economic Development and the Department of Geosciences at Mississippi State University. P. Klotzbach acknowledges a grant from the G. Unger Vetlesen Foundation. C. Schreck was supported by NOAA through the Cooperative Institute for Satellite Earth System Studies under Cooperative Agreement NA19NES4320002. The authors thank Dr. Chris Landsea and an anonymous reviewer for their helpful feedback, especially during the COVID-19 pandemic.

Birds are sensitive to climate variability and change. This pilot study explores variations in bird communities in India under contrasting phases of the El NinoSouthern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Statistical analyses were performed using monthly bird data for the years 1990-2015 in Goa and Tamil Nadu. Monthly precipitation, temperature, the Oceanic Nino Index and the Dipole Mode Index were used as explanatory variables for species richness (SR) and counts of three marker species (Brahminy Kite, Indian Pond Heron and Lesser Whistling Duck). Due to highly autocorrelated series, small sample sizes and many test permutations there is greater likelihood of Type I errors when interpreting correlation results. However, pooling bird data by climate mode and state revealed that SR was generally greater under El Nino or negative IOD (locally wet conditions) than under La Nina or positive IOD (locally dry conditions). In Goa, the Brahminy Kite, Indian Pond Heron and Lesser Whistling Duck had significantly higher counts during El Nino than La Nina events. In Tamil Nadu, there were significantly more Kites under negative than positive IOD phases. Regional variations in sensitivity may reflect species-dependent factors such as food availability and security of nesting sites. Further research is needed to establish causal mechanisms between climate mode and SR as well as into the combined effects of ENSO and IOD on bird communities across South Asia.

In this study, the year-to-year variability of surface air temperature (SAT) over China in winter was investigated during 1961–2014. The results indicate that the year-to-year SAT variability can explain more than 30% of the SAT variance over most parts of China, with up to 60% variance over southern China, northeastern China and northwestern China. The leading pattern of year-to-year SAT exhibits homogeneous variability over most parts of China, except in small areas over the Tibetan Plateau and southwestern China. The circulation over the northern Pacific is a key factor of this homogeneous variability pattern. An anomalous anticyclonic circulation and weakening midlatitude westerly jet in the middle and high troposphere over the northern Pacific are associated with the homogeneous warmth over China. The second pattern shows a south–north dipole, with variability in northeastern China opposite to that south of 25°N in China and over the Tibetan Plateau. The south–north dipole pattern is part of a global year-to-year SAT anomaly pattern because it exhibits a significant relationship with the year-to-year SAT over large parts north of 50°N over the Eurasian landmass. The north-cold/south-warm pattern is accompanied by a significant weakening of the Arctic Oscillation. In comparison, the atmospheric circulation anomalies associated with the homogeneous variability pattern are much weaker than those with the south–north dipole pattern. The anomalous Indian Ocean dipole in the previous autumn and the snow cover around China in November are the two key causes of the homogeneous variability pattern. Many factors, such as the tropical central Pacific sea surface temperature (SST), stratospheric quasi-biennial oscillation (QBO), Okhotsk sea ice and western Siberia snow cover, can significantly influence the south–north dipole pattern. Compared to the tropical Pacific SST, the impact of the Indian Ocean SST on the winter SAT over China is much more important.

The modulation of Pacific Decadal Oscillation (PDO) on the relationship of El Nino with southern China rainfall during early boreal winter (November–December) is investigated in this study. When El Nino occurs in positive phase of PDO, significantly positive rainfall anomalies only appears over the southwestern part of southern China. When El Nino occurs in negative phase of PDO, pronounced positive rainfall anomalies are observed over almost the whole southern China. Further analyses revealed that the anomalous Philippine Sea anticyclone (PSAC) and local meridional circulation play an important role in modulating the relationship of El Nino with southern China rainfall by PDO. During the negative PDO phase, under the combined influence of tropical Pacific and Indian Ocean dipole sea surface temperature anomalies pattern, the anomalous PSAC over the western North Pacific and rising motion associated with local meridional circulation over southern China provide sufficient water vapor and dynamical conditions for the formation of rainfall. During positive PDO phase, however, PSAC and local meridional circulation are only influenced by tropical Pacific and they become weak, not favoring the formation of southern China rainfall.

There is some evidence of rapid changes in the global atmosphere and hydrological cycle caused by the influence of climate variability. In West Africa, such changes impacts directly on water resources leading to incessant extreme hydro-meteorological conditions. This study examines the association of three global climate teleconnections (El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Atlantic Multi-decadal Oscillation (AMO)) with changes in terrestrial water storage (TWS) derived from both Modern-Era Retrospective Analysis for Research and Applications (MERRA, 1980–2015) and Gravity Recovery and Climate Experiment (GRACE,2002-2014). In the Sahel region, positive phase of AMO coincided with above-normal rainfall (wet conditions) and the negative phase with drought conditions, and confirms the observed statistically significant association (𝑟=0.62) between AMO and the temporal evolutions of standardised precipitation index. This relationship corroborates the observed presence of AMO-driven TWS in much of the Sahel region (though considerably weak in some areas). While ENSO appears to be more associated with GRACE-derived TWS over the Volta basin (𝑟=–0.40), this study also shows a strong presence of AMO and ENSO induced TWS derived from MERRA reanalysis data in the coastal West African countries and most of the regions below latitude 10°N. The observed presence of ENSO and AMO driven TWS are noticeable in tropical areas with relatively high annual/bimodal rainfall and strong inter-annual variations in surface water. The AMO has a wider footprint and sphere of influence on the region s TWS and suggests the important role of North Atlantic Ocean. IOD related TWS also exists in West Africa and its influence on the region s hydrology maybe secondary and somewhat complementary. Nonetheless, presumptive evidence from the study indicates that ENSO and AMO are the two major climatic indices more likely to impact on West Africa s TWS.

An important step in developing climate forecasts is the identification of an appropriate set of predictors. Seasonal rainfall forecasts for South Australia (SA) currently have low predictive skill. It is hypothesised that this is because the large-scale climate processes influencing SA's rainfall variability have yet to be completely identified and therefore are not adequately represented in forecast models. Here, relationships between large-scale climate influences and rainfall in SA are assessed with a focus on methods that account for non-linearities inherent in the climate system. A threshold method and also a novel method for climate predictor selection, Partial Mutual Information, are used to establish the hierarchy of importance of the key influences. From a large suite of potential predictors, variability in the Subtropical Ridge intensity, blocking and the gradient of sea surface temperatures (SSTs) in the tropical central and eastern Indian Ocean are found to be key indicators of seasonal rainfall variability in SA. Interactions between processes are shown to increase the amount of variability accounted for and therefore need to be better considered when producing seasonal forecasts. Notably, the view of which climate process(es) is(are) important changes depending on the input variables used, suggesting that it is not wise to make a priori assumptions about what is or is not important. Furthermore, even a perfect forecast of El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), the focus of forecasting efforts in Australia, will not necessarily lead to an improvement to forecast skill in SA given that these processes are not key for the region. The results of this analysis can inform the development of existing and new statistical and dynamical seasonal forecasting systems for SA, and for other regions where the impacts of climate variability are significant but not necessarily dominated by ENSO and IOD.

Inter-annual variability of precipitation over eastern Africa (Somalia, Ethiopia, and Kenya) is impacted by the Indian Ocean dipole (IOD), which has its maximum amplitude during autumn. During northern spring, sea surface temperature (SST) in the tropical Indian ocean is nearly always sufficiently high to sustain convection, but it exhibits no clear pattern of variability. Thus, precipitation variability over Eastern Africa during spring is not connected to any Indian Ocean inter-annual mode sustained by SST anomalies. Yet, seasonal variability in atmospheric convection might sustain interannual modes during spring independent from SST. We construct an index for the IOD based on outgoing longwave radiation anomaly (OLRA) instead of SST anomaly. During northern spring, analysis of this index shows that inter-annual precipitation over Eastern Africa is correlated with inter-annual variability similar to the IOD, as measured by OLR. The largest part of that relationship originates from western Indian Ocean, with smaller contributions from the Eastern Indian Ocean. Results indicate that atmospheric convection over the tropical Indian Ocean couples the atmospheric circulation over the eastern and western Indian basin irrespective of SST anomalies. Results from constructed analog analysis show that positive SST anomaly during northern winter caused by persistence of subsidence and Cloud-Radiation-SST negative feedback over the south eastern Indian Ocean from the previous positive IOD is associated with formation of negative IOD later during the following autumn.

Correlations between wet/dry variations and El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) across the Pearl River basin were analysed at annual and seasonal time scales using the Pearson correlation and moving correlation coefficient. The basin was divided into homogeneous climate zones using the fuzzy C-means method. The influences of ENSO, NAO, IOD, and PDO on annual and seasonal wet/dry variations and impacts of warm and cold episodes of these indices on Standardized Precipitation Index-based dry and wet conditions were investigated. Furthermore, the mechanisms of the influences were tried to be analysed from the perspective of moisture transmission. The results indicated that: (1) IOD, NAO, and ENSO are the principle factors driving annual, wet, and dry seasonal precipitation variations, respectively. Moreover, opposite correlations can be identified between precipitation and IOD, NAO, and ENSO in the same/previous year; (2) Correlations between precipitation variations at different time scales and climate indices are strongly stable. Furthermore, climate indices tend to have more significant and stable impacts on precipitation variations during the dry season than on annual precipitation and precipitation variations during the wet season; (3) There exist different moisture convergence and divergence phenomena in the Pearl River basin under anomalous year of climate indices, so the annual, wet, and dry seasonal precipitation characteristic will be affected; (4) ENSO exhibits remarkable impacts on the variations of dry conditions; and meanwhile ENSO and IOD have significant impacts on the variations of wet conditions. In general, the cold phase of climate indices tends to trigger a higher risk of droughts, and the warm phase of climate indices tends to cause higher rates of extreme wet conditions. Taking different climate indices as predictors, this study may help forecast precipitation variations at different time scales.

The Indian Summer Monsoon rainfall exhibits pronounced intraseasonal variability in the Bay of Bengal (BoB). This study examines the intraseasonal rainfall variability with foci on the coupling with sea surface temperatures (SST) and its interannual modulation. The lagged composite analysis reveals that, in the northern BoB, SST warming leads the onset of intraseasonal rainfall by 5 days. Latent heat flux is reduced before the rain event but is greatly amplified during the rainfall maxima. Further analysis reveals that this intraseasonal rainfall-SST relationship through latent heating is strengthened in negative Indian Ocean Dipole (IOD) years when the bay-wide local SST is anomalously warm. Latent heat flux is further increased during the intraseasonal rainfall maxima leading to strengthened rainfall variability. The moisture budget analysis shows this is primarily due to stronger low-level moisture convergence in negative IOD years. The results provide important predictive information on the monsoon rainfall and its active/break cycles.

Some of the major drivers of extreme temperatures in Australia are related to large‐scale variations in the climate. While many of the relationships between climate variability and heatwaves have been described, the preceding climate conditions and the circulation patterns responsible for interannual heatwave variations are still poorly understood. We have used rotated principal component analysis (PCA) to describe the variance of frequency, duration, magnitude and timing of Australian summer heatwaves. This provides a simple means of investigating both the spatial patterns of circulation and the preceding climate mode conditions of summer heatwaves. We find that spatial patterns of heatwave variability associated with El Nino‐Southern Oscillation (ENSO) were focused in the north and northeast of Australia for heatwave occurrence. Patterns of heatwave amplitude in southeastern Australia were also weakly related to ENSO. The PCA did not find any direct, concurrent Indian Ocean Dipole (IOD) or Southern Annular Mode (SAM) signal. However, lead correlations indicate that winter IOD conditions can have a weak indirect effect on heatwave occurrence in northeast Australia and similarly, winter SAM conditions can affect heatwave timing in central eastern Australia. Regressions reveal that ENSO related heatwave duration and frequency in the northeast are related to the movement of the Walker circulation, while southeastern heatwave amplitude is associated with a north–south movement of the Tasman high pressure system. These results may lead to improvements in understanding the predictability and general preparedness for heatwaves in the summer season.

This study investigated the Intertropical Convergence Zone (ITCZ) extreme shifts in the Southern Maritime Continent (SMC) region in austral spring during the period 1979–2015. The results have shown that the ITCZ Southern Boundary (SB) presents great correlations with a meridional Sea Surface Temperature (SST) gradient and Indian Ocean Dipole (IOD) events. However, besides these local forcing, the extreme ITCZ shifts are also influenced by El Nino-Southern Oscillation (ENSO) events and an associated zonal SST gradient. The extreme shifts are due mainly to the combination of changes in Walker Circulation (WC) in the Pacific and Indian Ocean (IO) and local SST anomalies. Therefore, during ITCZ southward extreme shifts, the WC is stronger leading to the strengthening of the northwest equatorial winds in the IO, which pumps moist air and boosts the diabatic heating in the SMC due to the local positive SST anomalies present in this region. Otherwise, during northward extreme shifts, the WC is weaker, not allowing the performance of the northwest equatorial winds in the IO, which associated with the local negative SST anomalies, leads to drier conditions in the SMC region. Another interesting result found here is that a strong meridional or zonal index not necessarily implies a larger shift. The combination of very strong ENSO and IOD events can lead to greater ITCZ SB shifts. Besides, the ITCZ SB shifts can act as a bridge linking IOD and ENSO.

Regional spatio-temporal assessment of extreme precipitation is essential to develop better climate adaptation and mitigation strategies. This study evaluated trends in precipitation extremes from 1985 to 2014 in the Kelantan River Basin (KRB), Malaysia. Forty-one climate stations that had

We present the first analysis of the combined El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) contribution to the monthly zonal means of total ozone (TO3) in the Northern Hemisphere for the 1970–2015 period. The Solar Backscatter UV merged TO3 (version 8.6) data set is examined using the standard multiple regression model with new proposed regressors accounting for the impact of the strong ENSO and IOD effects on the zonal mean ozone. The new regressors are selected using the principal component analysis applied to the standard ENSO and IOD indices. The ENSO and IOD induce ±2% month-to-month TO3 oscillations in the equatorial-, mid-, and even in high latitudinal zones. Long-term effects of ∼1% 10 year−1 are found. In autumn, a strong positive phase IOD is associated with ∼0.5% positive and ∼1.5% negative anomalies in the 30°–35°N and 65°–70°N regions, respectively. The IOD contribution to the zonal TO3 means is more pronounced than that of ENSO.

This study develops a procedure for seasonal forecasting river discharge from headwaters above strategically important hydropower plants in Kyrgyzstan and Tajikistan. The El Nino Southern Oscillation, North Atlantic Oscillation, and Indian Ocean Dipole indices were used as inputs. Predictability was evaluated for average summer inflows conditional on the tercile of the preceding winter climate mode. We find that the winter Nino 3.4 index was significantly positively correlated with following summer inflows to Nurek, Andijan, and Toktogul reservoirs during the period 1941–1980. Kruskal–Wallis and Kolmogorov–Smirnov tests show significant differences in the distributions of summer inflows depending on previous winter Nino 3.4 for all three reservoirs. At Nurek, summer inflows were on average 19% greater following a winter El Nino. During 1941–2016, mean summer inflows to Nurek reservoir linked to previous November–December Nino 3.4 achieved a Heidke Hit Proportion of 51–59% (compared with 33% expected by chance). Acceptable predictions of summer inflow volume were made 44% of the time. Higher inflows are explained by a south‐ westerly moisture flux that brings above average precipitation to Central Asia during winter El Nino conditions. Our procedure requires limited data, technical or computing resources—all considerations in data sparse, low capacity regions. Given planned developments of other large, headwater impoundments in Central Asia, early outlooks of discharge could contribute to improved dam safety, economic performance, and transboundary water sharing around such projects.

Previous studies suggest that climate change impacts significantly on the hydro-climatic processes within the Ganges–Brahmaputra–Meghna (GBM) River Basin (RB). This study examines the observed climate characteristics and potential strengths and limitations of three global high-resolution reanalyses and satellite remote-sensing products over the GBM RB for period 1980–2013 by (1) estimating trends and interannual variations of precipitation and temperature, and (2) isolating precipitation variations likely associated with El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The surface temperature trends show widespread warming across the basin with a maximum increase of 0.6 °C decade−1 over western Nepal and southern Tibet from 1980 to 2013. Rainfall changes from 1980 to 2013 indicate pronounced decline over high rainfall regions of northeast India, Bhutan, Nepal, and Bangladesh, especially from 1998 to 2013. Basin-wide averaged trends show rainfall decline of up to 39 mm decade−1 in June–August in the Brahmaputra–Meghna RB from 1998 to 2013. Temperature variability based on Principal Component Analysis indicates that the first mode is associated with sea surface temperature (SST) warming in the Arabian Sea and the western tropical Pacific Ocean, while the second mode appears to be significantly correlated to SST anomalies in the western (eastern) tropical Indian (Pacific) Ocean. ENSO and IOD events are found to significantly influence rainfall variability contributing to about 10–20% (ENSO) and 8–10% (IOD) of the annual rainfall, mainly over Bhutan, Nepal, Bangladesh, and north-eastern India. Among the three reanalysis products: European Centre for Medium-Range Weather Forecasts (ECMWF) retrospective analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR), ERA-Interim (and MERRA) agrees well with the observed precipitation (temperature) data sets while, CFSR shows the least skills in representing the spatio-temporal variations of precipitation and temperature.

The climate shift in 1976–1977 sets a clear distinction in the teleconnection between Indian summer monsoon rainfall (ISMR) and sea surface temperatures (SST) in the tropical oceans, particularly in the tropical Indian Ocean (TIO). Before the climate shift (Pre-77), the observed correlation between ISMR and SST was positive in the eastern equatorial Indian Ocean (EIO) and negative correlation prevailed in the western equatorial Indian Ocean (WIO), whereas after the climate shift (Post-77), the correlation pattern has reversed (i.e. positive correlation in WIO and negative correlation in EIO). The major reason for this shift is the consistent warming of SSTs throughout the tropics and frequent occurrence of IOD events. Further, it is noticed that during the earlier decades, the Indian Ocean was responding to the atmospheric forcing, particularly along the axis of strong cross equatorial flow, whereas in the recent decades, Indian Ocean dynamics are playing a crucial role in determining the SST in major parts of the Indian Ocean particularly in the EIO. Seasonal re-forecasts from six state-of-the-art global coupled atmosphere–ocean climate models which participated in ENSEMBLES faithfully capture the pre-77 dipole structure of correlations. But the post-77 correlation pattern is not simulated by any of the six models considered in this study. Two possible reasons are pointed out for the models' inability to capture the reversal of teleconnection: (1) in the coupled models, unchanged mean state of the winds inhibits ISMR to be governed by coupled dynamics in the TIO; (2) the strong, local SST–rainfall relationship in models even in post-77 period limits the influence of the teleconnections on the ISMR.

Precipitation in the Tibetan Plateau (TP) reaches its peak in summer. The seasonal projection skill of a statistical downscaling model (SDM) for summer precipitation in the TP was compared with that of direct model output. The SDM, which is based on canonical correlation analysis (CCA), significantly increased the projection skill. The CCA reveals the flow patterns behind the seasonal projection skill of summer precipitation in the TP between 1961 and 2012 and quantifies its relative contributions. East Asia 500 hPa geopotential height (ZG500), tropical Indian Ocean sea surface temperature (SST) and east Asia 850 hPa meridional water vapour flux (MWVF850), obtained from the Max Planck Institute Earth System Model, low-resolution (MPI-ESM-LR) simulations for phase 5 of the Coupled Model Intercomparison Project under the representative concentration pathway (RCP) 2.6, RCP4.5 and RCP8.5 scenarios are considered as potential predictors. The SDMs are established in 1961–2005, validated in 2006–2012 and applied in 2013–2100. The ensemble canonical correlation (ECC) is also applied to improve projection skill. The following results are obtained: (1) The SDM projection skill for each predictor is higher than that of the MPI-ESM-LR climate model, and ECC performs even better. (2) Spatial correlation patterns of different predictors with influence on the TP are well recognized by CCA. The high relevance of ZG500 can be explained by the thermal adaptation theory, that of SST exhibits a canonical Indian Ocean Dipole mode, and MWVF850 shows a simple water vapour link. (3) The amount of summer precipitation in the TP will slightly decrease under RCP2.6 by −3.4 mm decade−1, whereas RCP4.5 and RCP8.5 reveal an increase by 2.4 and 18.4 mm decade−1, respectively.

The modulated annual cycle (MAC) of the Indo-Pacific warm pool (IPWP) heat centre location is investigated using the empirical mode decomposition-ensemble empirical mode decomposition method. The MAC and its interannual variability mainly affect the zonal and vertical locations of the heat centre; in fall and winter of El Nino years, the heat centre moves eastward from its normal location, with very little change in its meridional position. Four patterns of the heat centre trajectory in the IPWP are identified using the self-organizing map algorithm. Patterns 1 and 3 are both similar to a saddle, but pattern 1 is more winding. The shapes of patterns 2 and 4 are similar, but tilt in different directions: pattern 2 tilts southwest whereas pattern 4 tilts west. Of all the four patterns, the persistence of pattern 2 is generally relatively high. The trajectory of the heat centre develops in the sequence of patterns: 1-2-4-3-1. Pattern 1 (pattern 4) is related to the cold (warm) phase of the El Nino-Southern Oscillation (ENSO), while pattern 2 (pattern 3) corresponds to the negative (positive) phase of Indian Ocean dipole (IOD). The sequence of patterns in the trajectory of the heat centre in the IPWP is related to the IOD-ENSO interactions on a biennial timescale.

Spatiotemporal variations of extreme precipitation regimes are both caused by the climate change and the natural climate variability. The trends of extreme precipitation indices as an issue of climate change and the possible teleconnection with the ocean–atmospheric modes as an issue of natural climatic variability have been analysed in China. Ten extreme precipitation indices have been used in this study, and the station-based extreme precipitation indices were gridded into 1°latitude × 1°longitude using the angular distance weighting method. For extreme precipitation, an index is more likely influenced by the local topography if it represents the more extreme precipitation and then the regional response of this index to climate changes is more complex. Results of the trends of extreme precipitation indices indicated that three zones can be generally identified in China, namely: (1) a significant drying tendency can be confirmed along the strip stretching in the northeast and southwest direction; (2) northwest China is dominated by a wetting tendency and slight increase of precipitation intensity; and (3) southeast China exhibits a slight wetting tendency but significant intensifying precipitation. The wetting tendency in northwest China may be caused by increasing precipitation during November, December, January, and February. Besides, the teleconnections between precipitation extremes and El Nino–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) were also analyzed, and results showed that the regional responses of precipitation extremes to ENSO, IOD, NAO, and PDO varied for the climate indices at different stages, such as the precipitation extremes generally tend to be decreasing in the central part of China at the same year of positive ENSO while increasing in the east part of China a year after the positive ENSO. This study provides insights into regional response of weather extremes to global climate indices in China, and these are valuable for developing measures to mitigate the hazards due to weather extremes.

Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter-annual wind variability. The objective of this paper is to examine the long-term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Nino Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step-wise multiple linear regression models were developed to select appropriate predictors among the various climate indices.

This study examines the relationship between clouds and sea surface temperatures (SSTs) during Indian Summer Monsoon (ISM). Observation reveals dominance of high-level clouds in the monsoon region. NCEP Climate Forecast System version 2 (CFSv2) is able to replicate the observed high-level cloud fractions although it underestimates the magnitude. Cloud–SST relationship for observation depicts dominance of positive correlation over equatorial Indian Ocean region which is well recapitulated in CFSv2 simulations. To investigate the most dominating patterns of interannual variability of rainfall, SST and clouds, empirical orthogonal function (EOF) analysis is performed on seasonal JJAS (June–September) dataset. EOF analysis signifies that high-level clouds are highly correlated with rainfall for observations during ISM season. This study has also investigated relationship of clouds and El Nino Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) during ISM period. Interannual variations of clouds connote significant correlation with ENSO index (Nino 3/Nino 3.4). First principal component (PC1) of high-level clouds and ENSO index indicate significant negative correlation. EOF analysis based on observation also connotes that first mode (second) of EOF analysis is associated with ENSO (IOD). It is also confirmed by maximum covariance analysis (MCA). CFSv2 is also able to depict the significant negative correlation between PC1 of high-level clouds and ENSO index. Observation based second principal component (PC2) of high-level clouds and IOD index exhibits significant positive correlation. It gives indication that PC2 of observed high-level clouds can be associated with IOD. In contrast, PC2 of CFSv2 simulated high-level cloud and IOD index are not correlated. EOF analysis based on CFSv2 shows that the first mode of EOF analysis is associated with ENSO; however, second mode of EOF is not related with IOD. MCA analysis also supports these findings. It means that CFSv2 has good ability to represent ENSO as compared with IOD.