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In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019–2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001–2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Nino Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires.

Seasonal and interannual variations of chlorophyll-a (chl-a) in the upper Gulf of Thailand (uGoT) were obtained using new regionally tuned algorithms applied to Moderate Resolution Imaging Spectroradiometer-Aqua. This long time-series (2003–2017) data were analyzed in the context of variations in environmental conditions associated with the Southeast Asian Monsoon. Chl-a distribution patterns were distinct for the non-monsoon (NOM), southwest-monsoon (SWM), and northeast-monsoon (NEM) seasons. During the SWM/NEM, high/low chl-a concentrations were associated with high/low precipitation and river discharge. During the NOM chl-a concentrations were generally low, because of low precipitation. In general, chl-a variability was tightly coupled to discharge from the Chao Phraya and Tha Chin rivers. Chl-a concentrations were generally higher in the north, but chl-a accumulation in the east/west of the uGoT could be linked to piling of freshwater to the east/west during the SWM/NEM caused by changes in wind direction and the reversal of currents. Interannual changes in chl-a were attributed to El Niño-Southern Oscillation (ENSO) rather than Indian Ocean Dipole (IOD) driven changes in precipitation, river discharge, and wind patterns. During the SWM, positive/negative chl-a anomalies coincided with high/low precipitation and river discharge during La Niña/El Niño. During the NEM, positive/negative chl-a anomaly coincided with high/low river discharge and strong/weak wind during La Niña/El Niño. Meanwhile, during NOM, positive chl-a anomaly could be attributed to anomalous high wind speed and precipitation during El Niño.

Understanding past changes in the characteristics of climate extremes forms an essential part of viable countermeasures to cope with climate-induced risks in a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands (Vietnam) by employing Sen’s innovative trend analysis method in conjunction with the well-defined extreme rainfall indices. The outcomes show less spatially coherent trends in the intensity, frequency, and duration of extreme rainfall events across the study area, and most analyzed indices exhibited non-monotonic trend forms. The overall trends in the intensity and frequency, represented by maximum 1-day, 5-day precipitation amount (Rx1day, Rx5day), very and extremely wet days (R95p and R99p), and the number of very and extremely heavy precipitation days (R20mm and R50mm), were characterized by significant increases at three or four sites. Concerning dry and wet extremes, observed increases in consecutive dry days (CDD) and decreases in consecutive wet days (CWD) were identified significantly simultaneously at three sites. There is high domination of significant increases in high-value subgroups of these indices. Several sites also exposed significantly increasing trend behaviors in these indices within all low-, medium-, and high-value subgroups, thereby implying the intensity and frequency have intensified over recent decades. This study also indicated potential linkages between annual peaks of several extreme rainfall indices, characterizing intensity, frequency, and wet duration, usually coincided with negative Indian Ocean Dipole and La Nina events, while dry extremes were usually found during El Nino events based on correlation analysis.

The Indian Ocean Dipole (IOD), a climatic anomaly, results in sustained sea surface temperature (SST) variations between tropical western and eastern Indian Ocean temperatures. In this study, we studied the variations to inculcate the teleconnections between IOD and Indian summer monsoon rainfall (ISMR) distribution across the country for the period 1960-2020 for all the three phases of ISMR. We analyzed rainfall, SST and low-level wind circulation anomalies for the above mentioned time horizon. Positive IOD events noticeably resulted in increase in summer monsoon rainfall distribution across the country respectively while its negative counterpart led to decrease in rainfall except for the commencement phase of ISMR. The variations in SST, wind circulation and moisture movement processes across the Indian Ocean characterize significant changes in rainfall during the positive and negative phases of IOD especially during the recent decades (1991-2020). The recent time horizon also witnesses enhanced low-level equatorial jets (LEJ) across the equatorial Indian Ocean and the Arabian Sea during the positive IOD events as compared to the prior decades (1960-1990). The effect of moisture convergence zone is also analyzed which results in above rainfall conditions across northeastern and central India. Conversely, negative IOD events were found to subdue any such moisture movement mechanisms. Furthermore, and additional investigation to analyze the effect of IOD on the retreating/withdrawal monsoon across northeast India has been done and it has been observed that a stronger positive IOD is detrimental to the seasonal rainfall (May- September) over North East India (-0.7 one month lag correlation). Furthermore, the DMI index of April-May presented a clear indication of monsoon activity over the area during the withdrawal or retreating phase of the summer monsoon, i.e., during September.

Surface O3 pollution has become one of the most severe air pollution problems in China, which makes it of practical importance to understand O3 variability. A south–north dipole pattern of summer-mean O3 concentration in the east of China (DP-O3), which was centered in North China (NC) and the Pearl River Delta (PRD), has been identified from the simulation of a global 3-D chemical transport model for the period 1980–2019. Large-scale anticyclonic (cyclonic) and cyclonic (anticyclonic) anomalies over NC and the PRD resulted in a sharp contrast of meteorological conditions between the above two regions. The enhanced (restrained) photochemistry in NC and restrained (enhanced) O3 production in the PRD contributed to the DP-O3. Decreased sea ice anomalies near Franz Josef Land and associated warm sea surface in May enhanced the Rossby wave source over northern Europe and West Siberia, which eventually induced an anomalous Eurasia-like pattern to influence the formation of the DP-O3. The thermodynamic signals of the southern Indian Ocean dipole were stored in the subsurface and influenced the spatial pattern of O3 pollution in the east of China mainly through the Hadley circulation. The physical mechanisms behind the modulation of the atmospheric circulations and related DP-O3 by these two climate anomalies at different latitudes were evidently verified by large-scale ensemble simulations of the Earth system model.

For many applications, it would be extremely useful to have insights into river flows at timescales of a few weeks to months ahead. However, seasonal predictions of this type are necessarily probabilistic which raises challenges both in generating forecasts and their interpretation. Despite this, an increasing number of studies have shown promising results and this is an active area for research. In this paper, we discuss insights gained from previous studies using a novel combined water balance and data-driven approach for two of Africa's largest lakes, Lake Victoria and Lake Malawi. Factors which increased predictability included the unusually long hydrological response times and statistically significant links to ocean-atmosphere processes such as the Indian Ocean Dipole. Other lessons learned included the benefits of data assimilation and the need for care in the choice of performance metrics.

Variations of chlorophyll-a (chl-a), sea surface temperature (SST) and net primary productivity (NPP) along with fish catch were studied along the coastal waters off Karnataka, west coast of India from January 2007 to December 2016. These variations are linked with the Indian Ocean Dipole and El Nino/Southern Oscillation—Nino3.4 SST index. NPP was estimated by using vertically generalized production model. Change in NPP values over the decade (2007–2016) was studied in context of global reports of decline in productivity over the period. It is very important to understand the pattern of NPP variability as well the interrelationship with SST over a long period to study its impact on distribution of the fishery resources. Trend showed that there is reduction in productivity after 2012 and this trend continued in subsequent years. During the study period, an increasing trend in SST is observed along the Karnataka coast. The present study shows that a strong El Nino event was seen in 2015, along with declined productivity in the coastal waters off Karnataka by ~ 24% and increase in SST by ~ 1% from the average of 10 years (2007–2016). Increased SST and low ocean productivity resulted in reduced fish catch with the decline of 6.7% in total fish catch along the Karnataka coast from 2014 to 2015. In 2016, 5.29 lakh tones of fish catch, was estimated resulting in 19.6%, increase compared to 2015. During southwest monsoon, sea surface height anomalies (SSHa) showed negative anomaly trend and increased Ekman pumping resulted in upwelling over the study area.

The Indian Ocean Dipole (IOD) is recognised as an important driver of interannual climate variability over different regions of the globe, including the regional monsoon systems. In particular, positive (negative) phases of IOD tend to be associated with above-normal (below-normal) monsoon rainfall over the Indian subcontinent. Realistic simulation of the IOD and Indian summer monsoon connection, however, remains a challenge in many of the state-of-the-art climate models. This study presents an analysis of IOD and its links to the Indian monsoon based on the historical simulations from the IITM Earth System Model (IITM-ESM) and other models that participated in the 6th phase of Coupled Model Intercomparison Project (CMIP6). Our findings indicate that the IITM-ESM provides not only a fairly realistic simulation of the ocean–atmosphere coupled interactions and the Bjerknes feedback processes associated with IOD events but also better captures the summer monsoon precipitation response over the Indian subcontinent during IOD events, as compared to several CMIP6 models. The physical mechanisms contributing to the improved simulation of IOD and its monsoon connection in the IITM-ESM are evaluated in this study.

In recent decades, wave power (WP) energy from the ocean is one of the cleanest renewable energy sources associated with oceanic warming. In Indo-Pacific Ocean, the WP is significantly influenced by natural climate variabilities, such as El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO). In this study, the impact of major climate variability modes on seasonal extreme WP is examined over the period 1979–2019 using ERA5 reanalysis data and the non-stationary generalized extreme value analysis is applied to estimate the climatic extremes. Independent ENSO influence after removing the IOD trends (ENSO|IOD) on WP are evident over the eastern and central Pacific during December–February (DJF) and March–May (MAM), respectively, which subsequently shifts towards the western Pacific in June–August (JJA) and September–November (SON). The ENSO|PDO impact on WP exhibits similar yet weaker intensity year round compared to ENSO. Extreme WP responses due to the IOD|ENSO include widespread decreases over the tropical and eastern Indian Ocean (IO), with localized increases only over the South China and Philippine (SCP) seas and Bay of Bengal (BOB) during JJA, and the Arabian Sea during SON. Lastly, for the PDO|ENSO, the significant increases in WP are mostly confined to the Pacific, and most prominent in the North Pacific. Composite analysis of different phase combinations of PDO (IOD) with El Niño (La Niña) reveals stronger (weaker) influences year-round. The response patterns in significant wave height (SWH), peak wave period (PWP), sea surface temperatures (SST), and sea level pressure (SLP) helps to explain the seasonal variations in WP.

The single variable-dependent drought cannot adequately define the onset and withdrawal characteristics of the droughts. A Multivariate Standardised Drought Index (MSDI) is developed in the present study, based on precipitation and soil moisture using bivariate copula function. Reconnaissance Trivariate Drought Index (RTDI) is also developed combining precipitation, soil moisture and evapotranspiration. MSDI and RTDI represent meteorological and agricultural droughts by linking the climate status in an effective way. The best fitted copulas obtained for bivariate and trivariate analyses are Frank and Student’s t copulas respectively. The two drought indices are developed and tested to study the onset and withdrawal characteristics of drought and their trends. Cross-wavelet analysis (CWA) is performed to identify the substantial effect of large-scale climate anomalies on the derived drought indices. The large-scale climate factors like sea surface temperature (SST), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD) and Indian summer monsoon rainfall (ISMR) are considered in this study. ENSO, IOD and ISMR showed significant influences on the drought variability. The 3-month MSDI is significantly influenced by ISMR while SST showed a significant teleconnection with RTDI-3. The SST showed a strong influence on both 6-month MSDI and 6-month RTDI. This study is robust and reliable for future drought assessment and will provide a great platform to develop warning criteria on onset and termination of droughts.

Precipitation extremes will generally intensify in response to a warming climate. This robust fingerprint of climate change is of particular concern, resulting in heavy rainfall and devastating floods. Often this intensification is explained as a consequence of the Clausius–Clapeyron law in a warmer world, under constant relative humidity. Here, based on an ensemble of CMIP5 global climate models and high-resolution regional climate simulations, we take the example of Southwest Asia, where extreme storms will intensify beyond the Clausius- Clapeyron scaling, and propose an additional novel mechanism for this region: the unique increase in atmospheric relative humidity over the Arabian Sea and associated deep northward penetration of moisture. This increase in humidity is dictated by changes in circulation over the Indian Ocean. Our proposed mechanism is consistent with the recent, most extreme storm ever observed in the region. Our findings advance a new understanding of natural climate variability in this region, with substantial implications for climate change adaptation of the region’s critical infrastructure.

This study investigates the role of driving atmospheric forces [winds, net heat flux, and evaporation–precipitation (E–P)] and the possible mechanisms on the mixed layer depth (MLD) spatiotemporal variability in the Bay of Bengal (BoB) using a finer-resolution (~ 9 km) Regional Ocean Modeling System (ROMS). The model simulation is configured for 2004–2012 with initial and boundary conditions from Mercator Ocean data, atmospheric forcing from ECMWF, and climatological river input. Comparison with in situ observations shows that the model well produces the mean seasonal characteristics of MLD variability. Spatial correlations have been carried out between the atmospheric forces and MLD to determine the relative influence of these forces on the MLD variability. During the southwest monsoon, strong southwesterly wind plays a crucial role in deepening the MLD up to 80 m in the southern BoB. In winter, the decrease in net heat flux and the increase in positive E–P in the upper ocean deepen the MLD in the northern bay to a depth of ~ 60 m. However, the high density stratification in the northern BoB due to the large inflow of freshwater from various rivers limits the seasonality of MLD, particularly in the northern BoB. The positive correlation between E–P and MLD suggests that the advection of the freshwater plume by the southward East India Coastal Current (EICC) forms a thin mixed layer in the western BoB. The model results well captured the contrasting Indian Ocean Dipole (IOD) years and reveal that the interannual variability of MLD seems to be connected to the IOD events in the BoB. The MLD is shallow during the positive phase of IOD (pIOD) in 2006, whereas the mean MLD is more profound (~ 50 m) during the negative period of IOD (nIOD) in 2010. In pIOD years, the anomalous upwelling coastal Kelvin waves (KWs) propagate, reflect Rossby waves, and trigger upwelling to form a shallow MLD throughout the BoB. The positive net heat flux at the air-sea interface also plays a dominant role in the MLD shoaling in pIOD year, as shortwave radiation increases and exceeds the cooling effect of latent heat flux during this period.

We assess the ability of the Bureau of Meteorology’s ACCESS-S1 dynamical forecast system to simulate and predict extreme fire weather over Australia during austral spring (SON) and summer (DJF) on subseasonal timescales. Specifically, we focus on the roles of the El Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), Madden–Julian Oscillation (MJO), and two modes of persistent high-pressure in the Australian region characterised as (i) split-flow blocking highs and (ii) subtropical ridge Tasman highs (STRH). The observed likelihood of extreme fire weather increases over most of Australia in association with El Nino, the positive IOD, negative SAM and low split-flow blocking, in both seasons. These increases are generally largest in SON over the southeast. Notable increases in the likelihood of extreme fire weather also occur north of 30° S during low STRH activity, and over the southeast during MJO phase 3. Using retrospective forecasts at lead times of 2–3 weeks for the period 1990–2012, we show that ACCESS-S1 simulates reasonably well the observed modulation of extreme weekly-mean fire weather by each climate driver, however the simulated changes in probabilities are often weaker than those observed. Each climate driver plays an important role in providing predictive skill for regions where ACCESS-S1 captures a high likelihood of experiencing extreme fire weather conditions. The results of this study highlight windows of forecast opportunity during active climate driver phases that can be useful to regional users in fire management, emergency services, health, national park management, and the agriculture and energy sectors.

The impact of the 2019 super positive Indian Ocean Dipole (PIOD) event, the strongest in the last four decades, which also co-occurred with an El Nino and a strong summer monsoon, on Indian Ocean sea surface salinity (SSS) is examined using the Soil Moisture Active Passive satellite measurements. Salt budget estimation suggests a predominant, nearly ocean-wide influence by surface freshwater flux and horizontal advective terms. Subsurface ocean influence on the salt budget occurs mainly in the southeastern tropical Indian Ocean (SETIO). The PIOD event suppressed the influence of the El Nino, thereby causing anomalous high precipitation in western India, and leading to an unusual freshening in the southeastern Arabian Sea (AS), which is subsequently advected towards the equatorial Indian Ocean (EIO). In the western Bay of Bengal (BoB), following the waning of monsoon-influenced precipitation in the fall, SSS becomes anomalously salty and traverses towards the AS against the flow of anomalous surface currents. During the peak of the summer monsoon in August–September and the peak of the PIOD event in September–November, SSS in the EIO exhibited tendency for freshening, mainly driven by westward advection of freshwater from the eastern BoB. Conversely, in the SETIO, there was tendency for salinification due to suppression of precipitation, enhanced upwelling of high subsurface salinity, and northward advection of salty water. During December to January of the following year, these salinity tendencies reversed, with salinification in the EIO and freshening in the SETIO.

The Sundarbans being one of the most significant tropical mangroves in the globe supports a substantial amount of biodiversity. Understanding how this tropical mangrove will respond to recent global environmental changes remains crucial for its future existence. Thus, this study aims at understanding the long-term changes in the vegetation of the Sundarbans by using satellite data. Normalized Difference Vegetation Index (NDVI) was utilized to discover the changes in flora in the Sundarbans mangrove forest (SMF) for the preceding 30 years (1989–2019) from Landsat imageries. Regardless of the prevalent apprehension about the impact of different climate variabilities, such as El Nino, Indian Ocean Dipole (IOD), and ENSO Precipitation Index (ESPI), SMF is currently in its recovery stage with the increased extent of dense and moderate vegetation. Severe degradation of this mangrove forest in 2009 predominantly coincided with extreme events (consecutive cyclones) at that period, whereas changes in the other two decades were quasi-natural and did not reveal any significant correlation to different climatic indices. The existence of improved management policies, restriction of random entries, and cutting down trees, concurring with a salinity increase, give opportunistic mangrove species, a chance to grow which contributed to the present rate of repossession of SMF.

Based on the NCEP/NCAR reanalysis data, the COBE sea surface temperature (SST) and the GPCC precipitation, the influences of El Nino-South Oscillation (ENSO) on the variability of winter rainfall anomalies in Southeast China is analyzed under the synergistic effect of Indian Ocean Dipole (IOD). Winter precipitation and atmospheric circulation of the years of IOD concurring with ENSO are compared with single IOD or ENSO, to reveal the mechanism of synergistic effects on the variability of winter rainfall anomalies in Southeast China. The results show that the correlation between IOD/ENSO and the winter precipitation in Southeast China has increased since 1980s. These correlations were significant in years of IOD and ENSO co-occurrence compared to years of IOD or ENSO only, which is mainly due to the lagged atmospheric thermal and dynamic responses to an IOD forcing in synergy with ENSO. The positive IOD (PIOD) events can trigger and modify the anticyclone to the east of India, which transports moisture from the tropical Indian Ocean to Southeast China. In addition, El Nino events can strengthen the abnormal anticyclone over Philippines in winter, which is conducive to maintain the water vapor channel from the tropical western Pacific to Southeast China. Information flow analysis shows that the causalities between IOD and ENSO were enhanced after 1980s, causing the significant increase in the frequency of winter abnormal precipitation in the years of IOD and ENSO concurrence. Furthermore, the higher frequency of PIOD with El Nino (compared to negative IOD with La Nina) attained a ratio of 2:1 after the 1980s, enhancing the Southeast China winter precipitation events associated with IOD and ENSO and the generation of interdecadal variability. This study is helpful to understand the mechanisms of winter precipitation changes in Southeast China, and to improve the forecast accuracy of winter extreme precipitation events.

Lightning characteristics in India are examined with satellite-based Lightning Imaging Sensor (LIS) and ground-based Indian Lightning Detection Network (ILDN). LIS observations indicated that synoptic weather systems are major contributors of lightning in Indian hotspots. Western disturbances (mid-tropospheric systems with extratropical origin) were the greatest contributor of lightning in the Himalayas (93%), tropical cyclonic storms and low-pressure systems (oceanic in origin) were key lightning contributors in parts of eastern India (43%), and lower tropospheric troughs were major contributors in other hotspots. For the first time, this study reported the occurrence of significantly high lightning activity before active monsoon spells in the Central India region (65–87° E, 18–27° N). Consequently, satellite-based lightning observations could be used to predict active monsoon spells. Harmonic analysis was used to study diurnal lightning-flash density. The maximum observed standardized diurnal amplitude of lightning activity was 0.35, and maximum explained diurnal variation was 15%. Also, for the first time, we compared the ILDN data and LIS observations, and found good agreement regarding lightning variability. LIS data showed an increase in annual lightning activity in tropical South-western India (SWI), and the results also suggested that during El Nino and negative Indian Ocean Dipole periods, SWI experiences above-average lightning activity.

The Murray-Darling Basin (MDB) is Australia’s major agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April–September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, cool season river heights are available year to year. The 27-year period April–September Hay and Wagga Wagga river heights exhibit decreases between 1965 and 1991 and 1992–2018 not matched by declining April-September catchment rainfall. However, permutation tests of means and variances of late autumn (April–May) dam catchment precipitation and net inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April–September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lower April–May catchment precipitation and increased mean temperatures. Machine learning attribute detection revealed the southern MDB drivers as the southern annular mode (SAM), inter-decadal Pacific oscillation (IPO), Indian Ocean dipole (IOD) and global sea-surface temperature (GlobalSST). Continued catchment drying and warming will drastically reduce future water availability.

This paper intends to explore rice yield fluctuations to large-scale atmospheric circulation indices (LACIs) in Bangladesh. The annual dataset of climate-derived yield index (CDYI), estimated using principal component analysis of Aus rice yield data of 23 districts, and five LACIs for the period 1980–2017 were used for this purpose. The key outcomes of the study were as follows: three sub-regions of Bangladesh, northern, northwestern, and northeastern, showed different kinds of CDYI anomalies. The CDYI time series in north and northeastern regions exhibited a substantial 6-year fluctuation, whereas a 2.75- to 3-year fluctuation predominated the northwestern region. Rice yield showed the highest sensitivity of LACIs in the northern region. Indian Ocean dipole (IOD) and East Central Tropical Pacific SST (Nino 3.4) in July and IOD index in March provide the best yield prediction signals for northern, northwestern, and northeastern regions. Wavelet coherence study demonstrated significant in-phase and out-phases coherences between vital climatic variables (KCVs) and CDYI anomalies at various time-frequencies in three sub-regions. The random forest (RF) model revealed the IOD as the crucial contributing factor of rice yield fluctuations in the country. The multifactorial model with different LACIs and year as predictors can predict rice yield, with the mean relative error (MRE) in the range of 4.82 to 5.78% only. The generated knowledge can be used to early assess rice yield and recommend policy directives to ensure food security.

Based on daily precipitation data from 109 stations in the Yangtze River Basin (YRB) over the past 36 years (1980`-2015), the Empirical Orthogonal Function (EOF) is employed to analyze changes in autumn precipitation. We used the monthly mean re-analysis datasets of atmospheric circulation and sea surface temperature (SST) to investigate the possible causes of the two leading modes, based on which the predictive equations were constructed and tested. The results of the EOF analysis show that the variance contribution of the first mode is 31.07%, and the spatial distribution shows a uniform variation over the whole region. The variance contribution of the second mode is 15.02%, and the spatial distribution displays a north-south dipole pattern in the YRB. The leading mode shows a dominant interannual variation, which is mainly due to the West Pacific subtropical high and anticyclones over the Philippine islands. The SST field corresponds to the positive phase of the eastern Pacific El Nino and the tropical Indian Ocean dipole. The second mode may be related to the Indian Ocean-East Asian teleconnection and early withdrawal of the summer monsoon. The SST field corresponds to a weaker central Pacific El Nino. Through a stepwise regression analysis, SST anomalies in some areas during summer show a good predictive effect on the autumn precipitation mode in the YRB region.

The predominance of swells or wind-seas at a location can be inferred from the spectral peak wave period (Tp). The present study examines the climatology of Tp and its variability in the Bay of Bengal (BoB) during the last 40 years based on reanalysis data. The Andaman and Nicobar Islands and Sri Lanka significantly alter the spatial distribution of Tp in the BoB. Spatial variation in Tp was less than 10% for 1° latitude, except for regions blocked by the landmass. Inter-annual changes in the annual average Tp were 8–18% at different locations. In most areas, climatological monthly Tp was largest in March, April, and October, when the winds were weak. The high Tp tongue extends to the BoB through the eastern side of the bay. The study also examines the relationship between three major natural climate variabilities; El Nino–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM) with Tp anomalies in the BoB. We observed that negative ENSO and SAM events enhance the long-period waves in the BoB, while a negative IOD event generates more short-period waves in the BoB.

The socio-economic impacts of floods calls for improved understanding of flood drivers. While the large-scale circulation anomalies associated with rainfall anomalies is a well-studied, the interaction of the phases of the Indian Ocean Dipole and the El Nino Southern Oscillation with rainfall anomalies over Tanzania is comparatively less understood. This paper fills this knowledge gap by studying the circulation anomalies associated with heavy rainfall events over northern Tanzania during the 2011 October-December season. A total number of 28 heavy rainfall events were observed, whereby 8, 11, and 9 events are recorded during October, November, and December, respectively. Bukoba is the only region that experienced more heavy rainfall events during each month of the study. Analysis of circulation anomalies reveals that heavy rainfall events are significantly related to moisture flux convergence that was transported through the northeasterly, southeasterly, and westerly pathways. The remarkable active upward motion that is conducive for heavy rainfall events also occurs over northern Tanzania. The moisture flux in October and November was under the influence of the cyclonic circulation over the south-central Indian Ocean and Indian Ocean Dipole. In December, westerly from the Congo basin forest play a significant role in enhancing the moisture flux. The results demonstrate the interplay between Indian Ocean Dipole and westerly in modulating moisture flux over northern Tanzania at a sub-seasonal time scale. The current study offers useful angles of contribution in understanding the mechanism controlling the country’s weather, therefore improving forecasting accuracy.

El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can reduce the amount of rainfall in Indonesia. The previous study found that ENSO and IOD derived from the OISST dataset have an association with hotspots in Indonesia, especially in southern Sumatra dan Kalimantan. But the correlation results are still too small, and the correlation strength between regions has not been analyzed. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. We use a singular value decomposition method to quantify this HCM. Besides OISST, ERA5 is an estimation data often used for weather forecast analysis. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. Based on variance explained and correlation strength, the hotspot in Indonesia is more sensitive to ENSO and IOD derived from ERA5 than OISST. Consequently, the ERA5 data more useful to statistical analysis that requiring a substantial correlation.

Tropical peatlands are threatened by climate change and land-use changes, but there remain substantial uncertainties about their present and future role in the global carbon cycle due to limited measurements. Here, we present measurements of carbon dioxide and methane emissions between mid-2017 and mid-2020 as well as nitrous oxide emissions between 2019 and 2020 at two contrasting sites at a coastal peatland in Sumatra, Indonesia. We find that greenhouse-gas emissions from intact peatland increased substantially due to an extreme drought caused by a positive Indian Ocean Dipole phase combined with El Nino. The emission in the degraded site was two times greater than that at the intact site. The smaller emission from the intact peatland suggests that protecting the remaining intact tropical peatlands from degradation offers important climate benefits, avoiding greenhouse-gas emissions of 24 ± 5 tCO2e ha−1 yr−1 (average ± standard deviation) at our study site in Indonesia. During a period of drought, an intact tropical peatland in Indonesia released half the amount of greenhouse gases as was released from a degraded site, according to a direct comparison of eddy covariance measurements at a pair of peatland sites in Sumatra.

This study analyzed trends and variability in climatic water availability (CWA) across the African continent using monthly precipitation and potential evapotranspiration (PET) over the period 1901–2015. Climatic water availability was characterized in terms of precipitation minus PET totals. Predictability of the variation in CWA was tested using climate indices. Large positive values of the CWA (or few drought incidents) were confined to areas (such as sub-region along the Gulf of Guinea, the western part of the equatorial region, and the Ethiopian Highlands) that receive large amounts of precipitation. Drought incidence in these areas was generally low and characterized by severity in the range 0–44% indicating moderate to extreme wetness. Areas which experienced increasing CWA or wetting trends were confined within the Tropics. These wetting trends were mostly insignificant (p > 0.05). Drying trends (or decreasing CWA) occurred mainly in areas outside the Tropics. These drying trends (especially in the CWA of the months from April to September) were mainly significant (p

The seasonal prediction skills in the CAMS-CSM (the acronym stands for the Chinese Academy of Meteorological Sciences Climate System Model) climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981–2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphere–ocean–land–sea ice fully coupled general circulation model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components. The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Pacific, in association with El Nino-Southern Oscillation (ENSO) events. The anomaly correlation coefficient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3- and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Pacific (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time.

The Indian Ocean Dipole (IOD) is one of the major climate modes in the tropical Indian Ocean (TIO), influencing the surrounding climate and society. Under anthropogenic warming, the IOD is modulated by the changes in the ocean–atmosphere coupling in the TIO. This study focuses on the IOD seasonal phase locking, which means that the IOD usually peaks in boreal autumn. Based on high-emission scenario future projections, we found that the IOD phase locking changes substantially under anthropogenic warming. The early onset positive IOD that peaks in boreal summer occurs more frequently in the future climate. Our further investigation reveals that this robust change in IOD phase locking is related to the mean state responses to global warming. The shallower thermocline in the eastern equatorial IO is more suitable for IOD developing, helping IOD strengthen in boreal summer. When the basin-wide IOD pattern has fully developed, by contrast, the weakened atmospheric feedback is not conducive to maintaining zonal sea surface temperature gradient, leading to IOD weakened in boreal autumn. Therefore, the IOD peaks earlier under global warming. The change in IOD phase locking shows a potential impact on the surrounding climate, such as Indian summer monsoon variability.

The spatiotemporal variability of southwest China (SWC) spring (March–April–May) rainfall (SWCSR) during 1961–2017 is investigated using rainfall data from 75 meteorological stations over SWC in this study. It is found that (1) SWC can be divided into four subregions using the rotated empirical orthogonal function (REOF) method: southern Yunnan province (SY), northwestern Yunnan province (NWY), eastern Guizhou province (EG), and northeastern Sichuan province (NES). (2) All subregions show significant 2–8-year periodicity on the interannual timescale. However, only SY rainfall shows significant interdecadal periodicity. (3) The spring La Nina has a significant impact on the simultaneous rainfall over SY. The sea surface temperature anomalies (SSTAs) in the central North Pacific during late winter can be an important oceanic signal for the prediction of spring rainfall variability over NWY. The southern Indian Ocean dipole (SIOD)–like SSTA pattern during late autumn (early winter) can be an important predictor of rainfall variability over EG (NES) during the following spring. Moreover, the impact of the late autumn SIOD-like pattern on the following spring rainfall over EG is stronger than the impact of the early winter SIOD-like pattern on the NES rainfall during the following spring.

The El Nino-Southern Oscillation (ENSO) has great impacts on the Indian Ocean sea surface temperature (SST). In fact, two major modes of the Indian Ocean SST namely the Indian Ocean Basin (IOB) and the Indian Ocean Dipole (IOD) modes, exerting strong influences on the Indian Ocean rim countries, are both influenced by the ENSO. Based on a combined linear regression method, this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent, developing, and decaying stages. After removing the ENSO impacts, the spring peak of the IOB disappears along with significant decrease in number of events, while the number of events is only slightly reduced and the autumn peak remains for the IOD. By isolating the ENSO impacts during each stage, this study reveals that the leading impacts of ENSO contribute to the IOD development, while the delayed impacts facilitate the IOD phase switch and prompt the IOB development. Besides, the decadal variations of ENSO impacts are various during each stage and over different regions. These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean, and the present method may be useful to study climate variabilities independent of ENSO.

The complex interaction between the Indian Ocean dipole (IOD) and El Niño–Southern Oscillation (ENSO) is further investigated in this study, with a focus on the impacts of the IOD on ENSO in the subsequent year [ENSO(+1)]. The interaction between the IOD and the concurrent ENSO [ENSO(0)] can be summarized as follows: ENSO(0) can trigger and enhance the IOD, while the IOD can enhance ENSO(0) and accelerate its demise. Regarding the impacts of IOD(0) on the subsequent ENSO(+1), it is revealed that the IOD can lead to anomalous SST cooling patterns over the equatorial Pacific after the winter following the IOD, indicating the formation of a La Niña–like pattern in the subsequent year. While the SST cooling tendency associated with a positive IOD is attributable primarily to net heat flux (thermodynamic processes) from autumn to the ensuing spring, after the ensuing spring the dominant contribution comes from oceanic processes (dynamic processes) instead. From autumn to the ensuing spring, the downward shortwave flux response contributes the most to SST cooling over the central and eastern Pacific, due to the cloud–radiation–SST feedback. From the ensuing winter to the ensuing summer, changes in latent heat flux (LHF) are important for SST cooling, indicating that the release of LHF from the ocean into the atmosphere increases due to strong evaporation and leads to SST cooling through the wind–evaporation–SST feedback. The wind stress response and thermocline shoaling verify that local Bjerknes feedback is crucial for the initiation of La Niña in the later stage.

The influence of mesoscale dynamics on variability of phytoplankton biomass in terms of chlorophyll-a (chl-a) concentration was studied in the coastal waters of the South Eastern Arabian Sea (SEAS) using long-term satellite data. Satellite-derived chl-a, sea level anomaly, sea surface temperature, and sea surface wind data for the period 1998–2016 were compiled from various sources and analysed to investigate the chl-a variability associated with coastal upwelling and mesoscale eddies. The Empirical Orthogonal Function and Morlet wavelet analyses were performed to estimate the quantitative variability and the result showed strong seasonal and interannual modulation in chl-a concentration and associated environmental variables. The Okubo–Weiss criterion was applied for the identification of mesoscale eddies. The results indicated the presence of cyclonic (cold core) eddies during the summer monsoon season (June–September). The wind-induced upwelling and the cyclonic eddies were most intense during the summer monsoon season, causing higher values of chl-a compared with other season. It is revealed that the variability of chl-a, which might be attributed to seasonal and interannual differences in the surface and sub-surface nutrients, is caused either by coastal upwelling or cyclonic eddies. In particular, the wind-induced upwelling strongly controls the spatial and temporal variability of chl-a compared with mesoscale eddies along the SEAS. The regression model we adopted points out the dominant role played by the wind and its forcing bring about variability in chl-a. The occurrence of extreme climatic events such as El Nino, La Nina and Indian Ocean Dipole (IOD) was noticed during the study period and particularly taken into account to understand the interannual fluctuations in chl-a and associated environmental variables. The relative variability in chl-a concentration was prominent during strong El Nino, La Nina, and IOD. We have attempted to determine the relationship between chl-a with coastal upwelling and mesoscale eddies, the overall importance of such physical forcings, and their influence on bio-production in the SEAS.

The purpose of this study was to know the results of the relation between short wave radiation (SWR) and sea temperature. This study used data of SWR and sea temperature from RAMA buoy which part of the data was obtained by the INA-PRIMA 2019. Besides, the SWR and Sea Temperature model data from ERA-5 and Copernicus were required to see these spatial and temporal variations. Diurnal analysis to determine the sea temperature responds to SWR parameters. While monthly analysis to see the variations of SWR and the sea temperature during Indian Ocean Dipole (IOD). The results show that there is a different response at sea temperature for each layer to the SWR parameter in diurnal. SWR can affect sea temperatures until 20 meters of depth. There is a time lag between 2 and 3 hours when the sun heats the sea until the sea surface temperature increases. The 20 meters of depth has a lag time until 4 hours. As for 40 to 80 meters of depth, the sea temperature was not longer responded by SWR, and the temperature is changed by the strength of these mixing.Warm pools are generally located in East Indian Ocean and the high SWR were very strong in West Indian Ocean along an anual.

This study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer [December–February (DJF)] rainfall over northeastern Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150°E–165°W and 10°S–10°N) and SSSI (50°–95°E and 10°S–10°N)] covaries with Australian rainfall, particularly in the northeast region. Composite analysis that is based on high or low SSS events in the SSSP and SSSI regions is performed to understand the physical links between the SSS and the atmospheric moisture originating from the regions of anomalously high or low, respectively, SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole with anomalously wet conditions over Australia and conversely show the signature of co-occurring El Niño and positive Indian Ocean dipole with anomalously dry conditions there. During the high SSS events of the SSSP and SSSI regions, the convergence of incoming moisture flux results in anomalously wet conditions over Australia with a positive soil moisture anomaly. Conversely, during the low SSS events of the SSSP and SSSI regions, the divergence of incoming moisture flux results in anomalously dry conditions over Australia with a negative soil moisture anomaly. We show from the random-forest regression analysis that the local soil moisture, El Niño–Southern Oscillation (ENSO), and SSSP are the most important precursors for the northeast Australian rainfall whereas for the Brisbane region ENSO, SSSP, and the Indian Ocean dipole are the most important. The prediction of Australian rainfall using random-forest regression shows an improvement by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.

This paper investigates the features of extreme rainfalls and associated floods during March to May (MAM) season in 2016 over Rwanda and the atmospheric circulation influencing these rainfalls. All stations over the study area received above the average rainfall during MAM (long rain) season in 2016. Among all the months in this season, the rainiest month was April when most parts of the country received highest amounts of rainfall (≥ 180 mm). The presence of anomalous high pressure over the southwestern Indian Ocean, with the help of intense Mascarene high, advected warm moist air through southeasterly winds toward the study area. During this rainy season, the study area was characterized by convergence (divergence) in the lower (upper) troposphere, implying ascending (descending) motion. The ascending moist air at lower level enhanced convection and hence enhanced rainfall over Rwanda. This is associated with more convective activities over central Indian Ocean, and central to eastern Pacific Ocean due to vertical stretching. Moreover, the heavy rainfall events occurred in 2016 are strongly connected to Indian Ocean Dipole Modoki and El Nino due to observed above normal Sea Surface Temperature anomalies over the central Indian Ocean and central to eastern Pacific Ocean, respectively.

The impact of the South China Sea summer monsoon (SCSSM) on the Indian Ocean dipole (IOD) has been systematically investigated in observations. This study focuses on the ability of climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to reproduce the observed relationship between the SCSSM and IOD and the relevant physical mechanisms. All 23 models reproduce significant correlations between the SCSSM and IOD during boreal summer [June–August (JJA)], whereas the influence of the SCSSM on the IOD varies considerably across the CMIP5 models. To explore the causes, all models are divided into two groups. Models that successfully simulated both the correlations between the SCSSM and JJA IOD and of the SCSSM and JJA IOD with precipitation over the western North Pacific and Maritime Continent are classified as Type I, and these produce stronger low-level wind anomalies over the tropical southeastern Indian Ocean. The stronger low-level wind anomalies enhance local sea surface temperature (SST) anomalies via positive wind–evaporation–SST (WES) and wind–thermocline–SST (Bjerknes) feedbacks. This corresponds to a strengthening of IOD events due to the increased zonal gradient of SST anomalies over the tropical Indian Ocean. In contrast, Type II models perform poorly in representing the relationship between the SCSSM and JJA IOD or relevant physical processes, corresponding to weaker WES and Bjerknes feedbacks, and produce weaker IOD events. These results demonstrate that the better the model simulation of the critical physical processes, the larger contribution of the SCSSM to the IOD.

The water resources of Bintan Island depend much on rainfall since the impervious granitic basement that bears a low water storage capacity dominates the geology of Bintan Island. Water demand in Bintan is increasing due to both population and economic growth. This paper aims to determine the spatial and temporal rainfall variability over Bintan Island using the daily corrected-CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) version two (v2.0) dataset to a ground-based observation dataset (Kijang station) using quantile-base bias correction for period 1 January 1981–31 August 2018. For the analysis process, we used monthly corrected-CHIRPS, NINO 3.4 anomaly index, the DMI (dipole mode index), zonal and meridional wind, and specific humidity from January 1981 to December 2017. Semi-annual, annual, and interannual variations influence monthly rainfall amounts over the island. Two peaks representing equatorial and monsoonal patterns characterize the temporal variability of rainfall. Spatially, the southern part of the island receives more rainfall than the northern part especially in MAM and SON related to the more convergence of moisture. The effects of interannual variation, ENSO (El Nino-Southern Oscillation) and IOD (Indian Ocean dipole), vary with season. ENSO contributes more to rainfall variability than IOD during the period of study.

We explore the causes and predictability of extreme low minimum temperatures (Tmin) that occurred across northern and eastern Australia in September 2019. Historically, reduced Tmin is related to the occurrence of a positive Indian Ocean Dipole (IOD) and central Pacific El Nino. Positive IOD events tend to locate an anomalous anticyclone over the Great Australian Bight, therefore inducing cold advection across eastern Australia. Positive IOD and central Pacific El Nino also reduce cloud cover over northern and eastern Australia, thus enhancing radiative cooling at night-time. During September 2019, the IOD and central Pacific El Nino were strongly positive, and so the observed Tmin anomalies are well reconstructed based on their historical relationships with the IOD and central Pacific El Nino. This implies that September 2019 Tmin anomalies should have been predictable at least 1–2 months in advance. However, even at zero lead time the Bureau of Metereorolgy ACCESS-S1 seasonal prediction model failed to predict the anomalous anticyclone in the Bight and the cold anomalies in the east. Analysis of hindcasts for 1990–2012 indicates that the model's teleconnections from the IOD are systematically weaker than the observed, which likely stems from mean state biases in sea surface temperature and rainfall in the tropical Indian and western Pacific Oceans. Together with this weak IOD teleconnection, forecasts for earlier-than-observed onset of the negative Southern Annular Mode following the strong polar stratospheric warming that occurred in late August 2019 may have contributed to the Tmin forecast bust over Australia for September 2019.

To examine the rain-fed Aman rice yield fluctuation due to climatic anomalies overtimes in Bangladesh, we used climate-induced yield index (CIYI), ensemble empirical mode decomposition (EEMD), step-wise multiple regression, isotopic signature, wavelet transform coherence (WTC) and random forest (RF) model. In this work, daily multiple source climatic data which were collected between 1980 and 2017, from 18 weather stations and five atmospheric circulation indices were used for this purpose. The key findings were as follows; by employing principal component analysis (PCA), six temporal variability modes were identified as six corresponding sub-regions with various Aman rice CIYI fluctuations. The Aman rice CIYI in different sub-regions represented a noteworthy 3–4-year quasi-oscillation using the EEMD. The key climate variables (KCVs) including the potential evapotranspiration and cloud cover in September, the minimum temperature in August, and precipitation in July, August, and October were the best rice yield prediction signals in these sub-regions. The results suggest that Aman rice yield could likely decline by 33.59%, and 3.37% in the southwestern and southeastern regions, respectively, if KCV increased by 1 °C or 1%. The RF model suggests that the Indian Ocean Dipole (IOD) significantly influenced the rice yield. Isotopic signatures were employed to confirm the fluctuation and anti-amount effect during the Aman rice-growing period in Bangladesh. The results obtained in this study could be used as a guideline for sustainable mitigation and adaptation measures in managing agro-meteorological hazards in Bangladesh.

The Indian Ocean Dipole (IOD) is a major mode of interannual climate variability, but its response to external climate forcings (i.e. solar forcing, volcanic radiative forcing (VRF) and greenhouse gas (GHG) radiative forcing) remains elusive. To improve our understanding of the variability of the IOD, it is necessary to investigate the IOD’s response to external forcings through multi-model simulations. Here a Granger causality test is used to examine the impact of external forcings on the IOD from past 1000 years simulations (850–1850 Common Era) derived from Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The results show significant causal effects of VRF on the IOD in preindustrial times of the past 1000 years from the MPI-ESM-P, MRI-CGCM3, GISS-E2-R and CCSM4 models and uncertainties in the IOD’s responses to volcanic eruptions from other six models. Additionally, the phase responses (i.e. positive or negative) of the IOD to large volcanic eruptions remain unclear even from models showing significant causal impacts of VRF on the IOD. This result shows that the IOD exhibits a more complex response to volcanic forcing than the El Niño-Southern Oscillation. The causal impact of solar forcing on the IOD is more likely to be weak in most models. The IOD’s response to GHG variations is not significant across all the models due to minor fluctuations in GHGs occurring during preindustrial times of the past 1000 years. Further analyses based on new, improved and higher resolution models might further our understanding of the IOD’s responses to external forcing.

Extreme ocean waves can have devastating impacts on many populous coastal regions or offshore islands. Yet, knowledge of how ocean waves are likely to respond to future climate change remains limited. To assess potential increases in risk associated with extreme ocean waves, future changes in seasonal mean and extreme significant wave height (SWH) are examined over the Indian Ocean (IO) using 18 Coupled Model Intercomparison Project Phase 5 (CMIP5) models forced with representative concentration pathway (RCP) 4.5 and 8.5 scenarios. The seasonal maxima are fit to the generalized extreme value (GEV) distribution and corresponding 10-year return values are estimated for the present-day (1981–2010) and future periods (2070–2099). Overall, projected changes in IO SWH exhibit noticeable seasonality. Under the high emissions RCP8.5 scenarios, mean and extreme SWH in the Arabian Sea (AS) and Bay of Bengal (BOB) are projected to increase during all seasons except December–February (DJF). In the western tropical IO (TIO), mean and extreme SWHs are projected to increase during June–August (JJA) and September–November (SON) in line with the projected circulation changes toward an Indian Ocean Dipole (IOD) positive phase-like mean state. Southern IO (SIO) SWHs exhibit a strong zonal shift, with large increases over high-latitudes and decreases over mid-latitudes, which is related to future changes in the Southern Annular Mode (SAM) toward its positive phase. Interestingly, some regions like the western TIO show significantly less increases in SWH under the lower emissions RCP4.5 scenarios, highlighting avoidable future risk through global warming mitigation efforts.

Heavy rainfall is one of the primary causes of flood during rainy season in Tanzania leading to severe socio-economic impacts. The study aimed at assessing and characterizing the variability of Heavy Rainfall Events (HREs) using Empirical Orthogonal Function (EOF), Mann-Kendal (MK) trend test, Correlation and Composite analysis methods. Based on the daily-observed precipitation and reanalysis data sets for the October to December (OND) rainfall season of 35 years (1981-2015), the spatial and temporal characteristics of HREs in Tanzania are studied. The relationship between heavy rainfall (HR) and large-scale circulation anomalies including the Indian Ocean dipole (IOD) and El Ni?o southern oscillation (ENSO) indices was assessed. The study found that, approximately 590 HREs were concentrated over northern sector and coastal belt of Tanzania. The monthly variability indicates that HREs are more pronounced in December followed by November while October being the least affected. The occurrence of HREs over the Lake Victoria, Kigoma and Tabora is largely attributed to low-level convergence of westerlies and enhanced moisture from Congo basin accompanied by a pronounced rising limb of Indian Walker circulation cell. A time-series analysis of HRE exhibits an inter-annual variation characterized by a slightly increasing trend, though the computed trends were not statistically significant at 95% confidence level. In most part of Tanzania HREs were positively correlated with both ENSO and IOD indices, underscoring the critical role of ENSO and Indian Ocean dynamic in modulating rainfall variability over the region. In general, it has been found that most of the HREs are generally triggered or amplified by large-scale circulation patterns such as ENSO and IOD.

The IOD can be measured using the Dipole Mode Index (DMI) which is calculated based on the sea surface temperature in the Indian Ocean. Therefore, DMI can be predicted using sea surface temperature forecasting data, such as data provided by the European Center for Medium-Range Weather Forecasts (ECMWF). However, the data still has a bias as compared to the actual data, so to get a more accurate prediction, corrected data is needed. Therefore, the aim of this study is to predict DMI based on sea surface temperature forecasting data that has been corrected for bias using the quantile mapping method, a method that connects the distribution of forecasting and actual data. The results showed that the DMI prediction using corrected data was more accurate than the DMI prediction using ECMWF data. DMI predictions using corrected data have high accuracy to predict IOD events in October-April.

Coffee is an annual crop sensitive to climate variability. Most Indonesian coffee is cultivated on marginal lands that are vulnerable to environmental changes, including climate. Indonesia's climate variability is influenced by several factors, including the monsoon, local aspects, and global climate oscillations such as the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). It is crucial to identify the impacts of climate variability on both the production and the economy to develop adaptative measures. This study aims to determine the effects of global climate variability, namely ENSO and IOD, on coffee production in several Indonesian production centers. It uses annual coffee production data in the five major production centers in Indonesia. The ENSO indicators used in this study were the Oceanic Nino Index (ONI) in the Nino 3.4 region and the IOD indicator in the Dipole Mode Index (DMI). The production anomaly analysis approach in neutral years and the extreme ENSO and IOD phases were applied in this study. The results showed that the effects of ENSO and IOD were different in each region. The highest decline in production occurred in the La-Nina year in almost all production centers. The decline in output in the La-Nina year ranged from 6 to 22%. Meanwhile, the IOD that had a decreasing effect on production was positive IOD with a decrease ranging from 1 to 15%. Key words: ENSO; IOD; Economy; Climate change.

The waters of West Sumatera that face directly into Indian Ocean is strongly influenced by Indian Ocean Dipole (IOD) phenomenon which caused an anomaly of sea surface temperature (SST) and affect rainfall intensity in the West Sumatera Province. This research was aimed to know the effect of IOD on the distribution and anomaly of SST and rainfall intensity in West Sumatera. Data processing methods in this research is using statistical and descriptive. The data used in this research are NOAA OI-SST, Dipole Mode Index (DMI), and rainfall data from BKMG. The results showed that IOD positive occured in October 2018 and the IOD negative occured in July 2016. During the positive IOD, SST distribution values were 28 ˚C – 28,8 ˚C and SST anomaly values were ​​-1,2 to -0,4, in the negative phase the distribution of SST values were 29,8 ˚C – 30,35 ˚C and the SST anomaly values were 0,15 to 0,7. The rainfall intensity during positive IOD phase is 157 mm/month and during negative IOD phase is 525 mm/month.

Until now, driving mechanisms behind recurring droughts and hydroclimate variations that controls the Nile River Basin (NRB) remains poorly understood. Our results show significant hydroclimatic changes that contributed to recent increasing aridity of NRB since the 1970s. Besides climate warming, the influence of stronger ENSO and Indian Ocean dipole (IOD) in NRB has increased after 1980s, which have significantly contributed to NRB’s drought severity at inter-annual to inter-decadal timescales. Our results demonstrate that warming, El Niño and IOD have played a crucial role on NRB’s inter-decadal hydroclimate variability, but IOD has played a more important role in modulating NRB’s hydroclimate at higher timescales than El Niño. Results also indicate that the impacts of positive phases of ENSO and IOD events are larger than the negative phases in the NRB hydroclimate. Further, the southward (westward) shift in stream functions and meridional (zonal) winds caused an enhancement in the blocking pattern, with strong anticyclonic waves of dry air that keeps moving into NRB, has resulted in drier NRB, given stream function, geopotential height and U-wind anomalies associated with El Niño shows that changes in regional atmospheric circulations during more persistent and stronger El Niño has resulted in drier NRB. After 1970s, El Niño, IOD, and drought indices shows significant anti-phase relationships, which again demonstrates that more frequent and severe El Niño and IOD in recent years has led to more severe droughts in NRB. Our results also demonstrate that IOD and and the western pole of the Indian Ocean Dipole (WIO) are better predictors of the Nile flow than El Niño, where its flow has decreased by 13.7 (upstream) and by 114.1 m3/s/decade (downstream) after 1964. In summary, under the combined impact of warming and stronger IOD and El Niño, future droughts of the NRB will worsen.

A strong positive Indian Ocean Dipole (pIOD) induces weather extremes such as the 2019 Australian bushfires and African floods. The impact is influenced by sea surface temperature (SST), yet models disagree on how pIOD SST may respond to greenhouse warming. Here we find increased SST variability of strong pIOD events, with strong equatorial eastern Indian Ocean cool anomalies, but decreased variability of moderate pIOD events, dominated by western warm anomalies. This opposite response is detected in the Coupled Model Inter-comparison Project (CMIP5 and CMIP6) climate models that simulate the two pIOD regimes. Under greenhouse warming, the lower troposphere warms faster than the surface, limiting Ekman pumping that drives the moderate pIOD warm anomalies; however, faster surface warming in the equatorial western region favours atmospheric convection in the west, strengthening equatorial nonlinear advection that forces the strong pIOD cool anomalies. Climate extremes seen in 2019 are therefore likely to occur more frequently under greenhouse warming. The strength of a positive Indian Ocean Dipole (pIOD) is set by sea surface temperature gradient across the equatorial Indian Ocean. Modelling shows warming will increase strong pIODs but decrease moderate pIODs, as faster surface warming in the west sets up conducive conditions for the strong events.

Indo-Pacific Ocean sea surface temperature (SST) anomalies in the form of Indian Ocean Dipole (IOD) and El Nino-Southern Oscillation (ENSO) have a major role in determining the climate of the tropics and extratropics. The tropical Pacific variability was dominated by canonical ENSO (EOF1) and El Nino Modoki (EOF2) during the 1980–1998 period (period 1), while during the 1999–2017 period (period 2), both the flavors of ENSO contributed to EOF1. This Pacific Ocean variability weakened the ENSO-IOD relationship also. The present study estimates the changes that occurred to the major modes of Asian summer monsoon (ASM) rainfall during these two periods and addresses the role of the recent changes in SST boundary forcing in ASM interannual variability. During period 1, the first mode (EOF1) of ASM rainfall was associated with ENSO and IOD with 32% variability. But after 1998, EOF1 depends only on the ENSO flavors (canonical and Modoki ENSO) and has slightly reduced variability (26%). The second mode was controlled by the decay phase of ENSO during the 1980–1998 period (12%), while the eastern Indian Ocean and central-northeast Pacific SST variability contributed to EOF2 (15%) during period 2. EOF3 is controlled by IOD in both periods. Further analysis indicates that the extended warming pattern from central to eastern Pacific of ENSO during period 2 weakened ENSO-IOD co-occurrence through the modification of tropical circulation. In the absence of IOD, ENSO-monsoon teleconnection becomes stronger during period 2 than in the previous period. Indian summer monsoon rainfall (ISMR) relationship with the developing phase of ENSO became stronger after 1998, and that of the decay phase of ENSO is decreased. The ISMR-ENSO Modoki relationship has a phase shift from summer maxima to winter maxima. Meanwhile, the ISMR-IOD relationship in the absence of ENSO became slightly stronger in the 1999–2017 period. Thus, the Pacific variability in these two periods influenced ASM rainfall variability through the modulation of its relationship with tropical Indo-Pacific SST anomalies.

Using reanalysis data and observations, interannual variations and long-term trends in the Somali Jet (hereafter, the Jet) and Indian Summer Monsoon Rainfall (ISMR) are characterized for drought vs flood, El Nino vs La Nina, and positive vs negative Indian Ocean Dipole (IOD) summer monsoon seasons. In flood years, the Jet is stronger over the upstream sector (i.e., Arabian Sea) and weaker over the downstream (i.e., Bay of Bengal), resulting in additional convergence and enhanced moisture transport over India, whereas the reverse occurs during drought years. The Jet and ISMR characteristics for El Nino (La Nina) years are similar to drought (flood) years, despite only around half of dry (wet) years, being associated with El Nino (La Nina). In +IOD years, the Jet shifts northward and intensifies over central and northern India, bringing more moisture and rainfall to those regions, while the −IOD has an opposite but relatively weak impact on ISMR. Over the last century, the Jet has become weaker over peninsular India, with reductions in rainfall across east India. Future projections under the SSP585 scenario from the CMIP6 multi-model mean suggest that the Jet will progressively shift northward and strengthen over the Arabian Sea, with associated rainfall increases over entire Indian region. The long-term changes in the Jet over the twentieth century are of comparable magnitude to interannual variations; however, the absolute magnitude of projected future changes is smaller than interannual variations observed in the past.