Strong Winds Research Articles

Spatiotemporal evolution of air-sea CO2 flux in the Northwest Pacific and its response to ENSO

Global warming, driven by human activities since the Industrial Revolution, has significantly elevated atmospheric carbon dioxide (CO2) levels, leading to higher global temperatures and a rise in extreme weather events. The ocean, as a major carbon sink, has absorbed about 30% of human-induced carbon emissions, helping mitigate global warming’s impacts. This study examines the spatiotemporal distribution of air-sea CO2 flux in the Northwest Pacific from 1982 to 2021 and its response to El Niño-Southern Oscillation, using Empirical Orthogonal Function and composite analysis. The seasonal patterns of air-sea CO2 flux and the influence of environmental factors were further evaluated. The results show that air-sea CO2 flux in the Northwest Pacific exhibits clear seasonal fluctuations. In winter, high-latitude areas act as significant carbon sources. Strong winds deepen the mixed layer, promoting CO2 release from the ocean into the atmosphere. In contrast, in summer, longer daylight hours, rising SST, and melting sea ice lead to upwelling, which brings nutrients to the surface and stimulates phytoplankton growth. This process turns the region into a carbon sink as phytoplankton growth, driven by intense sunlight, enhances the ocean’s CO2 absorption. The mid-latitude region consistently acts as a carbon sink year-round. During El Niño events, more negative air-sea CO2 flux anomalies appear in the eastern Northwest Pacific, enhancing carbon uptake. La Niña events have the opposite effect in the eastern regions. These findings highlight the Northwest Pacific’s critical role in modulating regional and global carbon cycles under varying climatic conditions. Understanding these dynamics is crucial for improving predictions of future climate impacts and for developing effective strategies to mitigate global warming.

Microbial richness and air chemistry in aerosols above the PBL confirm 2,000-km long-distance transport of potential human pathogens

The existence of viable human pathogens in bioaerosols which can cause infection or affect human health has been the subject of little research. In this study, data provided by 10 tropospheric aircraft surveys over Japan in 2014 confirm the existence of a vast diversity of microbial species up to 3,000 m height, which can be dispersed above the planetary boundary layer over distances of up to 2,000 km, thanks to strong winds from an area covered with massive cereal croplands in Northeast (NE) Asia. Microbes attached to aerosols reveal the presence of diverse bacterial and fungal taxa, including potential human pathogens, originating from sewage, pesticides, or fertilizers. Over 266 different fungal and 305 bacterial genera appeared in the 10 aircraft transects. Actinobacteria, Bacillota, Proteobacteria, and Bacteroidetes phyla dominated the bacteria composition and, for fungi, Ascomycota prevailed over Basidiomycota. Among the pathogenic species identified, human pathogens include bacteria such as Escherichia coli, Serratia marcescens, Prevotella melaninogenica, Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus saprophyticus, Cutibacterium acnes, Clostridium difficile, Clostridium botulinum, Stenotrophomonas maltophilia, Shigella sonnei, Haemophillus parainfluenzae and Acinetobacter baumannii and health-relevant fungi such as Malassezia restricta, Malassezia globosa, Candida parapsilosis and Candida zeylanoides, Sarocladium kiliense, Cladosporium halotolerans, and Cladosporium herbarum. Diversity estimates were similar at heights and surface when entrainment of air from high altitudes occurred. Natural antimicrobial-resistant bacteria (ARB) cultured from air samples were found indicating long-distance spread of ARB and microbial viability. This would represent a novel way to disperse both viable human pathogens and resistance genes among distant geographical regions.

Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain

Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.

Multi-wavelength spectroscopic analysis of the ULX Holmberg II X-1 and its nebula suggests the presence of a heavy black hole accreting from a B-type donor

Ultra-luminous X-ray sources (ULXs) are high-mass X-ray binaries with an X-ray luminosity above $10^ $. These ULXs can be powered by black holes that are more massive than $20M_ accreting in a standard regime, or lighter compact objects accreting supercritically. There are only a few ULXs with known optical or ultraviolet (UV) counterparts, and their nature is debated. Determining whether optical/UV radiation is produced by the donor star or by the accretion disc is crucial for understanding ULX physics and testing massive binary evolution. We conduct, for the first time, a fully consistent multi-wavelength spectral analysis of a ULX and its circumstellar nebula. We aim to establish the donor star type and test the presence of strong disc winds in the prototypical ULX Holmberg\,II X-1 ( Furthermore, we aim to obtain a realistic spectral energy distribution of the ionising source, which is needed for robust nebula analysis. We acquired new UV spectra of with the Hubble Space Telescope ( ) and complemented them with archival optical and X-ray data. We explored the spectral energy distribution of the source and analysed the spectra using the stellar atmosphere code and the photoionisation code Our analysis of the X-ray, UV, and optical spectra of and its nebula consistently explains the observations. We do not find traces of disc wind signatures in the UV and the optical, rejecting previous claims of the ULX being a supercritical accretor. The optical/UV counterpart of is explained by a B-type supergiant donor star. Thus, the observations are fully compatible with being a close binary consisting of an $ 66\,M_ black hole accreting matter from an $ 22\,M_ B-supergiant companion. Furthermore, we propose a possible evolution scenario for the system, suggesting that is a potential gravitational wave source progenitor.

Evaluation and Ranking of Energy Alternatives for Implementation in Different Geographic Scenarios using Decision Methods: Case Study of Colombia

In this research, several crucial aspects have been explored in the evaluation and ranking of energy alternatives in Colombia using advanced decision methods such as TOPSIS and Diffuse TOPSIS. The study analyzed how these techniques allow to effectively compare different renewable energy sources in various regions of the country, highlighting the importance of considering multiple approaches due to the unique geographical and energy characteristics of each region. Specifically, five regions were evaluated: Caribbean 1 and 2, Pacific 1 and 2, Andean, Amazonian and Orinoquia, observing significant differences in the evaluation of technologies between the models. In the evaluation of energy sources for each region, the potential of photovoltaic solar energy in the Caribbean and Orinoquia regions was emphasized due to the high solar radiation, wind energy in the Caribbean and Pacific regions due to its strong winds, and biomass in the Amazon and Andean regions thanks to the abundance of organic resources. These technologies not only contribute to the reduction of carbon emissions and the diversification of the energy matrix, but also promote a just energy transition by generating employment and improving the quality of life in local communities. The TOPSIS method and its diffuse variant make it possible to weigh criteria with different degrees of importance and manage uncertainty in decision-making, resulting in more precise evaluations adapted to specific contexts. This type of research is vital for decision-making in the Colombian electricity sector, as it provides a solid and replicable methodology for evaluating energy alternatives in an integral way. The results not only contribute to environmental sustainability, but also boost the socio-economic development of the regions, fostering an equitable and fair energy transition.

Application of machine learning methods for earthquake detection from high-density temporary observation seismic records on a volcanic island

We applied two machine learning models to detect earthquakes from records observed with seismometers temporarily installed on a volcanic island. The two models are based on different principles: one regards seismic waveforms as images, using a convolutional neural network (CNN) to determine the first arrival times of P-waves, S-waves. The other model regards seismic waveforms as series data. The model processes seismic waveforms as data in a specific order of noise, P-wave, and S-wave, similar to natural language.The purpose of this study is to present the results of using machine learning first arrival times identification models with two principles for noisy seismic waveforms, caused by sea waves and strong winds in volcanic islands, and to evaluate the effectiveness of machine learning models for noisy observation records.We created a Confusion Matrix using first arrival times determined by an expert and evaluated the detection performance of these two models using some metrics of the matrix. Additionally, we assessed accuracy of the model-identified first arrival times by generating a frequency distribution of the difference from the expert's detecting time.The study discovered that the model treating data as series had superior detection ability for noisy data compared to the one treating data as images and the accuracy of the first arrival time detection was also better for the series data model too.We compared the results obtained on this island with those obtained at the permanent station, which is considered to have less noise interference, described in Mousavi et al., 2020. It was found that the difference in detection ability between the two models is slight for data obtained at permanent stations with low noise interference, but that the difference in detection ability between the algorithms of the two models is significant in noisy environments.

The level, source, and health outcome of PM2.5 exposure in Southwest Iran

IntroductionDusty storms considerably increase airborne particles in dry and semi-dry locations, such as deserts with no plants and strong winds. Therefore, the environment and people are affected severely. Ahvaz, an important metropolis, is often polluted by neighboring nations. The present research studies the concentration, source, and calculation of these particles' effects. Material and methodFor health consequences evaluation, the WHO suggests the Air Quality Health Impact Evaluation Programmed (Air Q 2.2.3). Khuzestan Meteorology Office recorded particulate matter measurements on both hazy and clear days. The data was gathered voluminously in 2023. ResultAccording to data collected from Khuzestan province's meteorology documents, 49 days in 2023 had very unsafe air quality. The most polluted months in terms of the number of dust days are as follows: January (14 days) > December (12 days) > November (11 days) > August (5 days) > May (3 days) > September (2 days) > March, February, June, July, October (1 day) > April (0 days). HYSPLIT maps indicate that Iran causes dust in March, the Great Arabian Desert in December and August, Iraq in April, September, and October, Kuwait in January, Turkey in February and July, Egypt in June and May, and Oman in November. ConclusionThe meteorology database reveals that Ahvaz is highly polluted and that 49 days had unacceptable dust levels. Based on assessments obtained employing the Air Q+ programs, the people of Ahvaz encountered heart disease, respiratory disease, and stroke caused by their exposure to PM2.5 particulates.

Multivariate analysis of compound hail, wind and rainfall extremes in Alberta's hail alley

Hailstorms are severe weather events with the potential for devastating impacts. The consequences can be significantly worsened when hail events are accompanied by strong winds, intensifying both hail momentum and damage to property sidings and windows. Additionally, rainfall extremes during hailstorms can disrupt the drainage systems, potentially leading to flash flooding. Therefore, understanding the inter-dependencies and joint behaviour of these hazards is crucial for developing effective risk mitigation strategies. In this study, we conduct a multivariate probabilistic assessment of concurrent hail, wind, and rainfall extremes over the Alberta's “hail alley” using radar and ground-based observations. The analysis comprehensively explores individual hazards, as well as bivariate and trivariate scenarios using a vine copula approach. We quantify individual, conditional, and joint return periods (JRPs) for the various scenarios. Findings indicate that in both wind-driven hail and hail-rainfall extreme hazards, the joint occurrences based on JRP, can be underestimated by 20% and 70% when assuming independence, respectively, which has substantial implications for risk assessment and management, as well as infrastructure design and maintenance. The analysis of the trivariate case suggests the potential for the concurrent occurrence of multiple hazards in the region. Furthermore, results show that Archimedean copula families outperform elliptical copulas in simulating extreme variables related to compound events associated with hailstorms. The study stresses the importance of assessing the joint behaviour of these hazard components in hailstorms, with the objective of mitigating potential impacts on vulnerable regions.

Arctic Wind, Sea Ice, and the Corresponding Characteristic Relationship

In efforts to fulfill the objectives of taking part in pragmatic cooperation in the Arctic, constructing the “Silk Road on Ice”, and ensuring ships’ safety and risk assessment in the Arctic, the two biggest hazards, which concern ships’ navigation in the Arctic, are wind and sea ice. Sea ice can result in a ship being besieged or crashing into an iceberg, endangering both human and property safety. Meanwhile, light winds can assist ships in breaking free of a sea-ice siege, whereas strong winds can hinder ships’ navigation. In this work, we first calculated the spatial and temporal characteristics of a number of indicators, including Arctic wind speed, sea-ice density, the frequency of different wind directions, the frequency of a sea-ice density of less than 20%, the frequency of strong winds of force six or above, etc. Using the ERA5 wind field and the SSMI/S sea-ice data, and applying statistical techniques, we then conducted a joint analysis to determine the correlation coefficients between the frequencies of various wind directions, the frequency of strong winds and its impact on the density of sea ice, the frequency of a sea-ice concentration (SIC) of less than 20%, and the correlation coefficient between winds and sea-ice density. In doing so, we determined importance of factoring the wind’s contribution into sea-ice analysis.

Robust future projections of global spatial distribution of major tropical cyclones and sea level pressure gradients

Despite the profound societal impacts of intense tropical cyclones (TCs), prediction of future changes in their regional occurrence remains challenging owing to climate model limitations and to the infrequent occurrence of such TCs. Here we reveal projected changes in the frequency of major TC occurrence (i.e., maximum sustained wind speed: ≥ 50 m s−1) on the regional scale. Two independent high-resolution climate models projected similar changes in major TC occurrence. Their spatial patterns highlight an increase in the Central Pacific and a reduction in occurrence in the Southern Hemisphere—likely attributable to anthropogenic climate change. Furthermore, this study suggests that major TCs can modify large-scale sea-level pressure fields, potentially leading to the abrupt onset of strong wind speeds even when the storm centers are thousands of kilometers away. This study highlights the amplified risk of storm-related hazards, specifically in the Central Pacific, even when major TCs are far from the populated regions.

Cover Photograph

Front cover: Lightning strikes the ground close to a wind turbine near Sankt Pölten, Austria at 1958 local time on 25 July 2021. The temperature ranged from 25°C–28°C and there were very active storms in Upper Austria at the time. The storms were also accompanied by strong winds with gusts up to 100kmh‐1. Estofex issued a Category 3 warning for these storms, their highest warning. (© Matej Stegar WPOTY 2022 entry) image

Improvement of Wind Power Prediction by Assimilating Principal Components of Cabin Radar Observations

Abstract Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on the four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbines within a wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that compared to the simulations without assimilation, the predicted ultra-short-term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly and by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly and could obviously reduce prediction error. Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.

Possible influence of the large-scale environment on extreme waves over the Yellow Sea in boreal spring

The Yellow Sea (YS) is exposed to various weather systems, such as typhoons, monsoon activities, and extratropical cyclones, which can pose a major threat to the adjacent coastal regions through the development of energetic oceanic surface waves. Unusually severe surface wave events in the YS occur with considerable frequency during the boreal spring (March-April-May), but have received less attention compared to winter and summer. This study focuses on the characteristics of spring extreme wave height (EWH) events in the YS during 1979-2022, based on observational and long-term reanalysis datasets. Our analysis shows that extreme waves, i.e. daily maximum heights in the top 5% of all spring days, take about 12 h to build up to the peaks, while they decay more slowly after the peaks. During the extreme events, the Siberian High is found to extend anomalously eastward compared to spring climatology. Such an anomalous extension contributes to the increase of the sea level pressure gradient and the intensification of the surface wind speed in the YS. Meanwhile, in the range of 6 ∼ 24 h following the peaks of the EWH events in the YS, swells induced by strong northerly winds begin to have an impact in the YS. These swells contribute to maintaining higher wave energy levels in the YS for longer after the atmospheric source has been removed. We further explore the large-scale environmental conditions that could provide the predictability of extreme waves in the basin developed by these findings. This study presents implications for assessing the risks associated with extreme waves in coastal regions and for improving coastal management strategies in the YS.

Mitigating crosswind response of a high-speed train passing the end of windbreak walls

The sudden change in wind speed at the end of a windbreak wall poses a significant threat to train safety, as it amplifies train’s responses. This study aims to investigate countermeasures to reduce the maximum unsteady crosswind response of high-speed trains at the end of windbreaks. Wind flow around windbreak walls was systematically examined by particle image velocimetry (PIV), considering both constant and varying porosities. Subsequently, a gust model was proposed to evaluate the wind distributions behind windbreak walls, validated by experimental results. Aerodynamic forces on high-speed train, incorporating windbreak walls with diverse porosities, were assessed by integrating the gust model and the quasi-steady theory. Ultimately, the unsteady crosswind responses were computed employing multibody simulations (MBS), accounting for both solid windbreak walls and those with varying porosities. A noteworthy finding was the reduction of approximately 25 % in the maximum unsteady crosswind response when windbreak porosity varied from 0 % to 80 %, thus affirming the efficacy of windbreaks featuring varying porosities in diminishing unsteady crosswind response. Overall, this study provides valuable insights into the design of windbreaks for high-speed trains, which can improve their safety and performance in strong wind regions.

Application of ANFIS in Decision-Making on the Smart Control Early Warning System for Tornadoes

A tornado is one weather process that arises due to atmospheric instability. A tornado is a strong wind, but not all strong winds are tornadoes. Tornadoes have a short time frequency but can result in no minor disaster because they can blow objects away and uproot trees. Due to the consequences, an early warning system is needed as an anticipation for the community in the affected areas so that it can help the community by warning early on of the occurrence of a tornado. The ANFIS (Adaptive Neuro Fuzzy Inference System) method is used to forecast the event of a tornado, and the parameters used are wind speed, ambient temperature, and ambient humidity. This study will compare the ANFIS method using hybrid and backpropagation algorithms. Using the backpropagation algorithm, an error of 0.42385 was produced during training and testing, and an average error of 136.54 was obtained. When using the hybrid algorithm, the error during training is 2.0781 x 10-5, and the average error during testing is 0.015%.Keywords — ANFIS ; Anemometer; DHT22; Early Warning System; Tornado.

철도산업과 기후변화대응

The railroad industry is the best means of transportation to respond to climate change, as it is more energy efficient than the automobile and aircraft industries and emits about six times less greenhouse gases. As of 2018, the transportation sector of railroads emits 2.6% of greenhouse gases, but greenhouse gases should be reduced by converting diesel locomotives to new and renewable energy locomotives and electric locomotives. However, the Framework Act on the Railway Industry, the Railroad Safety Act, the Railroad Construction Act, and the Railroad Business Act do not have legal and institutional mechanisms to respond to climate change or adapt to the climate crisis. Review the legislation and precedents of France and the United States to seek implications for Korea. The Framework Act on the Railway Industry shall establish and implement the national freight and passenger transport sharing rate and the greenhouse gas reduction target, and shall be improved to include matters on climate change and response to the climate crisis in the national policies and basic plans. The Railway Construction Act should also monitor the effects of climate change, such as extreme heat waves, extreme cold waves, extreme strong winds (types), extreme floods, and extreme wildfires, analyze the cost-benefit analysis of adverse effects on railway infrastructure to build them in advance, and maintain them to improve the quality of railway infrastructure, and include climate change response and adaptation to the climate crisis in the railway network plan. The Railway Business Act and the Railroad Safety Act should be revised to ban the operation of diesel locomotives in stages, such as California and the United Kingdom, to raise funds for the conversion of diesel locomotives to environment-friendly locomotives, report their status to the Minister of Land, Infrastructure and Transport, and stipulate that diesel locomotives cannot be idled for more than 30 minutes. Finally, railway stations and structures should be installed with geothermal power plants and solar power plants, and certified as carbon-zero buildings.

Influence of grazing management strategy and data time scales on estimates of sensible heat flux in grasslands

Study regionInner Mongolia Autonomous Region, North China Study focusSensible heat flux (H) quantifies the intensity of water evapotranspiration, making the factors influencing H pivotal to water consumption. North China's semi-arid grasslands are characterized by widespread grazing and strong wind conditions. Grazing strategy modifies surface air heat transfer resistance via vegetation and consequently alters H. Nevertheless, the majority of studies overlook the impact of the observed variables' time scale on H estimation. This paper employs a "big-leaf" model to estimate H, incorporating both grazing management strategy and data time scale, validated using the Bowen ratio, and attempted to analyze the factors that affect H. New hydrological insights for the regionAerodynamic characteristics estimated from daily data typically exceed estimates derived from hourly data. The influence of surface radiative temperature and air temperature on H outweighs that of aerodynamic resistance, whereas aerodynamic resistance accounts for the disparity in estimating H between daily and hourly data. In grazing-prohibited grasslands, the daily H values estimated by the Big Leaf model closely match those obtained from the Bowen ratio. When estimating the hourly H value for grazing prohibited and grazing grasslands, the increase was 25.95 w/m2 and 52.27 w/m2 compared to the daily scale, respectively. For the estimation of water and heat fluxes across different regions, the temporal scale of input data is a pivotal factor.

Numerical modelling and design of cold-formed steel battens subject to pull-out failures under bushfire conditions

Thin and high strength cold-formed steel (CFS) battens and claddings serve as non-combustible elements in roof and wall cladding systems, aligning with the recommendations of Australian bushfire standards. However, they are susceptible to significant damage from heat and strong winds experienced during bushfire events, particularly in the form of batten pull-out failures. Such failures may occur at the screw connections between cladding and battens, or battens and trusses/rafters. However, pull-out failures often occur in the thinner CFS battens, when the screw fastener pulls out of the batten’s top flange, leading to the loss of entire cladding and compromising the integrity of the building envelope. This study used finite element modelling of a range of CFS batten configurations to investigate their pull-out failures under combined wind action and bushfire conditions. Finite element models were developed to simulate the pull-out failures at elevated temperatures, validated using experimental results and used in a parametric study to investigate the effects of relevant parameters. Using the results, this study has proposed suitable design equations for predicting the pull-out capacities of CFS battens at elevated temperatures. Engineers can use them to design bushfire safe CFS cladding systems.

Turbulence coherence in complex wind field: Focusing on its on-site characteristics based on multi-point clustering

Turbulence coherence in mountainous areas necessitates meticulous evaluation due to the complex terrain blocking. This study focuses on the turbulence coherence of the non-uniform wind field in a canyon via the anemometers positioned along a bridge span. Based on a proposed multi-point clustering, two categories of strong winds impacted by a nearby ridge are identified. The turbulence coherence of the two strong winds is then investigated with an extra focus on the phase spectrum. Several complex coherence functions are extended from commonly employed real forms, including the simplified Krenk's formula proposed. The fitting results of them all suggest that parameters such as the decay factor of the two strong winds show a negative linear correlation with separation but their relationship with wind speed is not significant. For the phase spectrum, in addition to skew winds, the contribution of horizontal wind shear is first emphasized in the two strong winds with piecewise fittings. This paper also discusses single-point coherence between the along-wind and vertical turbulence components. It is found to be unusually positive in sign due to the influence of the local topography and a formula with a constant term is suggested that substantially improves the fitting compared to Solari's formula.

The vulnerability of fisheries-based livelihoods to climate variability and change in coastal small pelagic fishing communities in Tanzania

Fisheries-based livelihood activities constitute an essential means of income and well-being for the majority living in coastal areas. This paper examines vulnerability of fisheries-based livelihoods caused by climate variability and change. It focuses on three small pelagic fishers’ communities including Kipumbwi, Kilindoni, and Kilwa-Kivinje, along the Tanzanian coast, as a case study. Little research has been done on the effect of climate variability and change on the livelihoods of small-scale fishing communities in developing countries, particularly along the Tanzanian coast. In collecting data, questionnaire, focused group discussion and an interactive governance approach were used. Results showed that the natural vulnerability issues include, reduced catches, and strong winds. Economic vulnerability, mentioned lack of credit facilities. For social vulnerability, respondents mentioned an increased number of fishers and fishing vessels On the aspect of technology, respondents mentioned lack of modern fishing methods. As findings suggest, these vulnerability aspects, are possibly associated with anthropogenic factors such as illegal fishing, may not be related to climate change, but climate change may intensify them. The findings further showed that climate variability has negatively affected the fisheries on which fisher communities depend for income and for food security thus affecting their local economy. Climate change has negatively affected small pelagic fishers. The decline in fish catches and economic- un-matching changes in fish prices have negatively affected the fishing community's income, employment, and food security. The recommended means for reducing vulnerability include reducing taxes and levies, availability of facilities for reducing post-harvesting loss and availability of reliable markets.