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In 2018/19 winter, the Yangtze–Huai River Basin (YHRB, one of the most developed areas in China) experienced an extremely pluvial condition with excessive and long-duration precipitation. Other three extremely pluvial winters during 1979/80–2018/19: 1997/98, 2002/03 and 1989/90 winters were also found. In 2018/19, 2002/03 and 1997/98 winters, similar low-level circulation pattern of anomalous southwesterlies (southeasterlies) over the southern (northern) YHRB was found. However, we found different tropical ocean states for the events. In 2018/19 winter, a combination of a moderate El Niño in the tropical Pacific and warm sea surface temperature (SST) anomalies in the tropical western Indian Ocean (IO) and Atlantic was seen. In 1997/98 winter, we found an extreme eastern Pacific El Niño with warm SST anomalies in the western tropical IO. In 2002/03 winter, a central Pacific El Niño occurred. In contrast, no significant warm SST anomalies were seen in 1989/90 winter. Based on coupled model numerical experiments, the tropical SST anomalies contributed almost half of the YHRB precipitation anomalies in 2018/19 and 1997/98 winters. In particular, the tropical IO warm SST anomalies induced anomalous anticyclones over the South China Sea and northeastern China, with moisture convergence over the YHRB. In 2002/03, the tropical Pacific SST anomalies contributed almost half of the YHRB precipitation anomalies, although its contribution could be reduced due to the inter-basin interactions. For 1989/90 winter, we discussed possible impacts of mid-high latitude wave trains. The results indicate the complexity and diverse potential predictability among the four extreme pluvial winters.kindly check and conform the processed affliations was correct [1,2]Yes, we have checked that. The affliations are correct. [ABSTRACT FROM AUTHOR]

The 2015/16 El Niño displayed a distinct feature in the SST anomalies over the far eastern Pacific (FEP) compared to the 1997/98 extreme case. In contrast to the strong warm SST anomalies in the FEP in the 1997/98 event, the FEP warm SST anomalies in the 2015/16 El Niño were modest and accompanied by strong southeasterly wind anomalies in the southeastern Pacific. Exploring possible underlying causes of this distinct difference in the FEP may improve understanding of the diversity of extreme El Niños. Here, we employ observational analyses and numerical model experiments to tackle this issue. Mixed-layer heat budget analysis suggests that compared to the 1997/98 event, the modest FEP SST warming in the 2015/16 event was closely related to strong vertical upwelling, strong westward current, and enhanced surface evaporation, which were caused by the strong southeasterly wind anomalies in the southeastern Pacific. The strong southeasterly wind anomalies were initially triggered by the combined effects of warm SST anomalies in the equatorial central and eastern Pacific (CEP) and cold SST anomalies in the southeastern subtropical Pacific in the antecedent winter, and then sustained by the warm SST anomalies over the northeastern subtropical Pacific and CEP. In contrast, southeasterly wind anomalies in the 1997/98 El Niño were partly restrained by strong anomalously negative sea level pressure and northwesterlies in the northeast flank of the related anomalous cyclone in the subtropical South Pacific. In addition, the strong southeasterly wind and modest SST anomalies in the 2015/16 El Niño may also have been partly related to decadal climate variability. [ABSTRACT FROM AUTHOR]

This study examined the predictive skill of a statistical Generalised Additive Model (GAM) by considering the impact of the Indian Ocean Dipole (IOD). The proposed technique is powerful but simple and considers both the linear and non-linear relations hidden in the data. The model considers tropical cyclogenesis through kernel density estimation, trajectories by velocity field, and landfall through a country mask approach. A lead–lag analysis for TC forecast potential confirms that the IOD is a good predictor for 2-month lead forecast. Result shows that TC occurrence increase (decrease) during the negative (positive) IOD events and that the landfall probability vary for each IOD phase. Altering convection, steering flow, and low-level vorticity influence the NIO TC activity. Result also highlights the importance and potential of the GAM approach (approximately 72% skill) in matching predicted landfall with the observations. [ABSTRACT FROM AUTHOR]

The present study analyzed a long-term record of major floods over Bangladesh under the influence of El Niño–Southern Oscillation (ENSO). Bangladesh is highly vulnerable to floods, and ENSO has a great influence on it. El Niño (La Niña) represents the warm (cold) phase of the ENSO cycle. Southern Oscillation Index data and a long-term flood records have been identified from National Oceanic and Atmospheric Administration, Bureau of Meteorology, Australia, and historical archives, respectively. Synoptic features—precipitation, wind, geopotential height, precipitable water, relative humidity, and surface runoff data—were used to examine the influence of ENSO. Result shows that the Indian summer monsoon contributes the major flood events and largely influenced by both El Niño and La Niña events, with a slightly higher frequency for the latter. Physical mechanisms (distribution of precipitation, wind, geopotential height, precipitable water, relative humidity, and surface runoff) supported these flood events; however, they are quite distinct during El Niño and La Niña condition. Moreover, ENSO's impacts to the Bangladesh region are different to the west part of the monsoon region. Therefore, if ENSO forecast is reliable, the predictability of the monsoon-related flood events can be improved. [ABSTRACT FROM AUTHOR]

The extreme floods in the Middle/Lower Yangtze River Valley (MLYRV) during June−July 2020 caused more than 170 billion Chinese Yuan direct economic losses. Here, we examine the key features related to this extreme event and explore relative contributions of SST anomalies in different tropical oceans. Our results reveal that the extreme floods over the MLYRV were tightly related to a strong anomalous anticyclone persisting over the western North Pacific, which brought tropical warm moisture northward that converged over the MLYRV. In addition, despite the absence of a strong El Niño in 2019/2020 winter, the mean SST anomaly in the tropical Indian Ocean during June−July 2020 reached its highest value over the last 40 years, and 43% (57%) of it is attributed to the multi-decadal warming trend (interannual variability). Based on the NUIST CFS1.0 model that successfully predicted the wet conditions over the MLYRV in summer 2020 initiated from 1 March 2020 (albeit the magnitude of the predicted precipitation was only about one-seventh of the observed), sensitivity experiment results suggest that the warm SST condition in the Indian Ocean played a dominant role in generating the extreme floods, compared to the contributions of SST anomalies in the Maritime Continent, central and eastern equatorial Pacific, and North Atlantic. Furthermore, both the multi-decadal warming trend and the interannual variability of the Indian Ocean SSTs had positive impacts on the extreme floods. Our results imply that the strong multi-decadal warming trend in the Indian Ocean needs to be taken into consideration for the prediction/projection of summer extreme floods over the MLYRV in the future. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is an important variable for understanding interactions between the ocean and the atmosphere. SST fusion is crucial for acquiring SST products of high spatial resolution and coverage. This study introduces a Bayesian maximum entropy (BME) method for blending daily SSTs from multiple satellite sensors. A new spatiotemporal covariance model of an SST field is built to integrate not only single-day SSTs but also time-adjacent SSTs. In addition, AVHRR 30-year SST climatology data are introduced as soft data at the estimation points to improve the accuracy of blended results within the BME framework. The merged SSTs, with a spatial resolution of 4 km and a temporal resolution of 24 hours, are produced in the Western Pacific Ocean region to demonstrate and evaluate the proposed methodology. Comparisons with in situ drifting buoy observations show that the merged SSTs are accurate and the bias and root-mean-square errors for the comparison are 0.15°C and 0.72°C, respectively. [ABSTRACT FROM AUTHOR]