Dynamics of Atmospheres and Oceans最新文献

The Andaman Islands in the Bay of Bengal were affected by a Very Severe Cyclone Storm event in November 2013. A study was conducted to evaluate the surge heights and wave characteristics at six locations along the east and west coasts of these islands. The study used the MIKE 21 HD/SW coupled model to simulate the effects of this cyclone on the wave and surge dynamics. The results showed that the locations on the east coast experienced higher surges before and during the landfall, with maximum values reaching up to 0.38 m, compared to the lower surges observed on the leeward side. The analysis of Significant Wave Heights (Hs) and the Peak Wave Period (Tp) revealed that the strongest waves were observed on the right side of the cyclone, with the maximum Hs increasing from 2.3 m to 8.65 m as the wind speed increased. During the time of landfall, the East coast experienced higher waves with Hs ranging from 5.0 m to 6.0 m, while the West coast saw comparatively lower waves with Hs ranging from 0.8 m to 2.0 m.

This study highlights the importance of conducting comprehensive evaluations of the impacts of severe weather events on coastal regions, and the need to consider the spatial and temporal variability of such events when developing risk assessment and disaster management strategies.

Coastal upwelling is the dominant physical process triggering high biological productivity in Eastern Boundary Upwelling Systems (EBUS). These regions are characterized by intense upwelling events driven by Equatorward alongshore winds. In the Humboldt current system off central-southern Chile (30–40°S) the coastal upwelling process has been studied from several approaches including biogeochemical, fisheries and physical studies. Yet, the phenology of wind-driven upwelling along the meridional gradient has been poorly inspected. Using reanalysis data from the ERA5 product (1966–2020), we calculated the Cumulative Upwelling Index (CUI, m² s⁻¹ × 1000 m) to characterize the phenology of coastal upwelling off central-southern Chile, identifying the beginning (STI), the maximum (MAX) and the end (END) of the upwelling season. In addition, we quantified the duration (LUSI) and the total magnitude (TUMI) of the upwelling season. The response of the water column to cumulative wind stress was determined using in situ hydrographic data (2002–2020) from a middle shelf station off Concepción, which showed marked seasonal and interannual variability. In general, the onset, duration, and intensity of Ekman transport were highly variable. At 36.5°S (off Concepción), the STI occurred on August 6 ± 25.4 days and the duration of the upwelling season (LUSI) was $\sim$9 months $\pm$ 32.5 days. On the other hand, the TUMI at this latitude was −1.97 × 10⁸ $\pm$ 4.88 × 10⁷. The CUI climatology during El Niño years showed weak and late upwelling (STI = August 29 ± 16.2 days) while upwelling was strong and early (STI = July 13 ± 30.6 days) during La Niña compared to the mean climatology. The water column showed a direct response to cumulative wind-driven upwelling conditions during El Niño 2015-2016 and La Niña 2007-2008. The rise of cold ($\le$11 °C), saline (34.5 isohaline), dense ($>$25.8 kg m⁻³), and oxygen-poor ($\le$ 1 ml L⁻¹) subsurface waters corresponded to stronger upwelling winds during La Niña 2007–2008. In contrast, coastal upwelling was substantially weak, with a warmer water column and the isotherm of 11.5 °C located below 30 m depth during El Niño 2015–2016.

A GPU-accelerated 3D smooth particle hydrodynamics (SPH) scheme is developed and applied to a coastal multi-phase liquid-sediment interaction and sediment transport. The SPH scheme's meshless design and the sediment's particle structure enable the modeling of the waves' interactions with the sediment particles beyond the limitation of the mesh-based methods. A Newtonian constitutive model is used to model the liquid phase, and the sediment transport is formulated based on the Herschel-Bulkley-Papanastasiou (HBP) model. The yield characteristics of the sediment phase are estimated using the Drucker-Prager yield criterion. Due to the parallelization of the solution on graphics processing units, the 3D SPH scheme's performance, which uses millions of particles, is improved. Good correlations were observed in the SPH predictions and experimental measurements, with a maximum difference of 4.85 %. The validated scheme is applied to formulate forecasting models for the coastline sediment transport. It is found that erosion and scouring are expected at the coastline region inclined to the direction of the sea waves, with a predicted mass erosion of about 60e3 kg in four years. The wave's velocity is also established to be directly proportional to the sediment transport. The proposed multi-phase SPH methodology is proven effective for sediment transport prediction.

Accurate lightning prediction stands as a pressing global challenge, demanding robust solutions for safeguarding lives and valuable assets. In this study, we employ the mesoscale numerical model, Weather Research and Forecasting (WRF-ARW, version 4.0.3), to conduct numerical simulations of lightning occurrences during the 2021 monsoon season in Hooghly (on 07 June) and Jaipur (on 11 July). These events resulted in 31 and 11 casualties, respectively. The WRF model is integrated at horizontal resolutions of 9 km and 3 km for both regions, utilizing six-hourly NCEP-FNL datasets at a 0.25º resolution. The primary objective of this inquiry is to identify the most suitable microphysics scheme among WSM-6, NSSL-2, and MORRISON for a comprehensive assessment of lightning activity. Model performance is meticulously evaluated through skill scores, including Equitable Threat Score (ETS), False Alarm Rate (FAR), Accuracy (ACC), and Probability of Detection (POD), focusing specifically on hourly rainfall. Furthermore, a comprehensive spatial evaluation assesses the Lightning Potential Index and lightning flash count using the McCaul Method. The model-simulated results effectively depict lightning conditions in both regions, showing slight spatial and temporal discrepancies compared to observational datasets. Validation of the simulated lightning flash count is accomplished using data from the Lightning Detection Network (LDN) operated by the Indian Institute of Tropical Meteorology (IITM). The NSSL-2 microphysical scheme demonstrates noteworthy efficacy in identifying lightning occurrences in both regions. Rainfall representations correspond remarkably well with Indian Monsoon Data Assimilation and Analysis (IMDAA) data, indicating precipitation levels of 20–40 mm and 70–80 mm in Jaipur and Hooghly, respectively. The NSSL-2 microphysics scheme exhibits commendable proficiency, with consistently high model skill scores (ACC and POD ∼0.9) for lightning events. This study signifies a significant step toward the development of an operational lightning warning system, offering the potential to substantially reduce the risks associated with lightning occurrences. Consequently, such a system has the capacity to enhance safety and preparedness measures for regions affected by lightning phenomena.

Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble-ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations. The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 °C and an average coefficient of determination (R²) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10°S to 10°N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean. This indicates the potential of machine learning models, particularly ensemble models, for enhancing OSTS estimation in the Pacific Ocean and other regions, offering valuable insights for future research and applications in physical oceanography.

Using multifractal detrended fluctuation analysis (MF-DFA), the pre-monsoon rainfall and Indian summer monsoon rainfall (ISMR) from 1871 to 2016 are examined in this work for northeast India. According to observations, rainfall during the pre-monsoon and summer monsoons both exhibit persistence and quasi-randomness. The generalized Hurst Exponent value indicates that the pre-monsoon rainfall time series and the summer monsoon rainfall time series have long-term positive auto-correlation. The fluctuation function obtained through MF-DFA by means of the slopes of ln(Fq(s)) against ln(s) plots is dominated by segments with significant variance, which leads us to believe that the rainfall time series related to the pre- and summer monsoon exhibit multifractal behaviour. The presence of multifractality in the pre- and summer monsoon rainfall time series is further substantiated through the association between the qth-order variance and the length scale.

The Sea Surface Temperature (SST) of the Arabian Sea undergoes large seasonal variations owing to the monsoonal forcing and upwelling. Warming of the ocean adversely affects its biological productivity. The present study examined the role of the rapid warming of the tropical Indian Ocean and associated changes in physical forcing parameters on phytoplankton biomass in the Arabian Sea. SST during the summer monsoon period (June-September) of 1971–2020 showed an increasing trend of 0.6 °C in the southeastern Arabian Sea (SEAS) and 1.6 °C in the northwestern Arabian Sea. During the recent summer monsoon period of 1998–2020, surface chlorophyll-a (chl-a) concentration showed a decreasing trend of – 0.32 mg m⁻³ in the SEAS and an increasing trend of 0.56 mg m⁻³ in the northwestern Arabian Sea. High-resolution satellite and reanalysis data of physical forcing parameters such as surface winds, SST, Sea Level Anomaly (SLA) that influence the surface chl-a concentration at two distinct upwelling locations in the Arabian Sea, were analysed for the recent two decades (1998–2020). Multiple linear regression (MLR) analysis showed that SLA was the most important parameter that determines the surface chl-a variability in the SEAS, whereas alongshore wind stress was dominant in the northwestern Arabian Sea. An epochal analysis showed that in the most recent decade, SLA in the SEAS became less favourable for upwelling, whereas summer monsoon winds became increasingly favourable for upwelling in the northwestern Arabian Sea. These differences corroborate the contrasting trends in surface chl-a in the two locations in the Arabian Sea. The present study has shown that the inconsistent response of surface chl-a at distinct locations within the Arabian Sea depends on the relative strength of the influencing physical forcing mechanisms.

Subsurface Layer Temperature Inversion (SLTI) is a prominent physical process occurring in the Bay of Bengal (BoB) during winter. BoB also witnesses intense cyclones during winter (post-monsoon). Sidr is a category-5 cyclone that occurred in the BoB during 11–15 November 2007. The present study emphasises bringing out the effect of SLTI on SST warming. This paper uses observations and modelling to present SLTI's role in surface layer post-cyclone warming. In the absence of SLTI, the cyclone induces surface cooling. However, in the case of a surface layer with SLTI, instead of cooling, sea surface warming of < 0.5 °C is observed near the head bay, where SLTI is prominent. Model results and observations suggest the role of entrainment and warming of the surface layer.

The Western Ghats (WG) of Peninsular India, an integral part of the Indian summer monsoon rainfall, receives three times the average of all India rainfall. The averaged rainfall over WG is characterized by an intense tropospheric biennial oscillation (TBO) of 2–3 years of periodicity. The rainfall anomalies over WG are almost uncorrelated to rainfall over Central India (CI) in the TBO window. This study characterizes the WG and CI biennial rainfall variability and their governing mechanisms using observation and reanalysis datasets for 1980–2020. A zonally symmetric build-up of heat anomalies from the Iranian Plateau (IP) to the Tibetan Plateau (TP) extending from the surface to the mid-troposphere governs the TBO rainfall of CI. On the other hand, localized heating (cooling) over the IP and Pak-Afghanistan region (PA) and cooling (heating) over the TP governs the phases of TBO rainfall over WG. An increase (decrease) in anomalous heat build-up in the vertical column causes an increase (decrease) in anomalous moist static energy during positive (negative) WG TBO years extending over the IP (TP) region. Increased heating (cooling) over the IP and PA (TP) during positive WG TBO years can shift the center of near-surface cyclonic circulation, anchored over the Indian subcontinent and surrounding areas during the positive CI TBO years, to move westward. This shift in rainfall anomalies and the center of cyclonic circulation is because of the westward shift in anomalous moisture convergence from CI and significant moisture loss along southeastern Peninsular India. Considering the growing number of extreme rainfall events over the WG regions recently, the present work is an attempt to understand the mechanism through which TBO modulates WG and CI rainfall.

In the spring of 2022, a stratospheric final warming (SFW) event occurred on March 19 at 10 hPa in the Northern Hemisphere, ranking the third earliest SFW event during 1979–2022. Meanwhile, Central Asia recorded the highest surface air temperature (SAT) in the ensuing April in recent 44 years. Based on the fifth generation reanalysis data from European Centre for Medium-Range Weather Forecasts, this study reports an intimate linkage between the SFW onset and the extremely high SAT in Central Asia in April 2022. Specifically, after the SFW onset, positive geopotential height anomalies in the stratospheric polar region persist for more than a month, which propagate downward to the troposphere in about one week, resulting in a negative phase of the Northern Annular Mode (NAM) in the upper troposphere during the period from late March to mid-April. Such a negative NAM tends to excite an anomalous Rossby wave train propagating southeastward from the North Atlantic to Central Asia, causing an anomalous high over Central Asia in the middle and upper troposphere. As a result, the associated anomalies in descending motion and adiabatic heating would lead to the in situ unprecedented high SAT in April.