Influence of Tropical Cyclones and Cold Waves on the Eastern Guangdong Coastal Hydrodynamics: Processes and Mechanisms

Climate describes how the atmosphere behaves over a long period of time informing us on the “average of weather”, as well as the description of other aspects of weather patterns and meteorological parameters’ distribution, including anomalous, rare and extreme events. Some examples of extreme weather or climate events include heat waves, cold waves, floods, extreme precipitation, drought, tornadoes and tropical cyclones (WMO-CCL, 2018).Drought is a natural weather phenomenon that occurs in all climates and can differ greatly from other extreme events. Unlike other extreme events, such as floods, which are weather events that are immediately detectable, droughts develop slowly, making it difficult to determine the onset and end.Portugal is a country with recurrent phenomena’s of drought, which creates the strong need for impact reduction strategies as an integral part of drought preparedness plans.To better understand the risk of droughts in mainland Portugal, was used indices that rely on precipitation deficits and allows us to characterize and monitor drought in Portugal, determining drought onset, duration and intensity. In this study was used the PDSI (palmer Drought Severity Index), SPI (Standardized Precipitation Index), SMI (Soil Moisture Index).These indices were computed using daily and monthly data and based on the recommendations of CCL task team on the definition of extreme weather and climate events: Threshold (is determined based on historical values of the index); Temporal (Station-level information on starting date, ending date, and duration of the event); Spatial (calculates the area affected, by providing the distribution of stations where the threshold was surpassed and uses a geographical information system (GIS) to determine the area affected by the event, the magnitude, and severity).In order to characterize the drought events was used the following properties:Magnitude: measures the departure from the threshold and reflects unusualness or extremity of the event. Duration: defined by the time at which the event begin and end. Extent: defined as the geographical area affected by the extreme event. Severity: severity should indicate the potential damages and impacts that can be associated to the event. It is a combination of magnitude and persistence of a drought. It is also aimed the framing of this extreme event with climate changes based on trend analyses and simulation of scenarios with variables such as precipitation, temperature and evapotranspiration.

Smallholder farmers in Nepal are vulnerable to climate change-related extreme weather events. Adaptation in the agriculture sector is needed to mitigate social, economic, and ecological impacts from increasing levels of hazard activity. To examine this issue, a household survey of 350 farmers in the Terai region of Nepal was carried out to assess farmers’ risk perceptions towards three common extreme weather events (floods, cold spells, and heat waves) and to explore their intended responses to cope with future impacts. The intended common adaptation strategies include changes in farm management, seeking off-farm employment, emergency management planning, purchasing crop insurance, and the raising of awareness. Threat appraisal is the strongest predictor of the number of intended adaptation strategies adopted in response to slow-onset hazards (heat waves and cold spells), while coping appraisal is the major predictor of the number of intended adaptation strategies adopted to mitigate flood risk, a rapid onset hazard. Crop insurance and off-farm employment are farmers’ most preferred flood adaptation strategies, while crop insurance is the most preferred adaptation strategy for heat waves and cold spells. Other variables such as the number of past implemented strategies, experience with extreme events, community organisation membership, and access to credit and extension services were also significantly associated with farmers’ choices for adaptation strategies in response to the three extreme events. This information can be used to tailor community-centred communication about potential threats from different extreme weather events and government technical and financial support, which will be crucial for farmers to adapt effectively to climate change-related weather extremes.

This study examined the trend in extreme weather event related mortality reported in India during 2001–14 and explored the regional, age and sex differentials in such fatalities. We used age and sex-segregated data on the number of accidental deaths by natural causes released by Government of India under the National Data Sharing and Accessibility Policy. Various extreme weather events were grouped into five broad categories: cold wave, extreme precipitation, heat wave, lightning, and tropical cyclone. During 2001–14, 25% of all accidental deaths due to natural causes happened as a result of extreme weather events. Deaths due to extreme precipitation and tropical cyclones declined over time, whereas increasing trend was observed for lightning, and extreme temperature conditions. Most of the extreme weather event induced deaths were due to lightning, followed by extreme precipitation and temperature extremes. The burden of death was highest in the central part of India. States of Andhra Pradesh, Bihar, Uttar Pradesh, Maharashtra and West Bengal were affected the most by extreme weather events. More males and older population died than their counterparts. Findings suggest that people are adaptive to some extreme weather events such as cold wave and cyclones; whereas adaptation and coping with the heat wave and extreme precipitation seems to be less. Building awareness on the adaptive mechanisms to different extremes and a community-centered alert system can help in reducing fatalities due to extreme weather events.

Extreme weather events such as persistent high temperatures, heavy rains or sudden cold waves in Shanxi Province in China have brought great losses and disasters to people’s production and life. It is of great practical significance to study the temporal and spatial distribution characteristics of extreme weather events and the circulation background field. We selected daily high temperature data (≥35°C), daily minimum temperature data and daily precipitation data (≥50 mm) from 109 meteorological stations in Shanxi Province, China from 1981 to 2010, then set the period in which the temperature is ≥35°C for more than 3 days as a high temperature extreme weather event, define the station in which 24 hour cumulative precipitation is ≥50 mm precipitation on a certain day (20 - 20 hours, Beijing time) as a rainstorm weather, and determine the cold air activity with daily minimum temperature dropped by more than 8°C for 24 hours, or decreased by 10°C for 48 h, and a daily minimum temperature of ≤4°C as a cold weather process. We statistically analyze the temporal and spatial characteristics and trends of high temperature, heavy rain and cold weather and the circulation background field. We count the number of extreme weather events such as persistent high temperatures, heavy rains and cold weather frosts in Shanxi, and analyze the temporal and spatial distribution characteristics, trends and general circulation background of extreme weather events. We analyze and find out the common features of the large-scale circulation background field in various extreme weather events. Through the study of the temporal and spatial distribution characteristics of extreme weather events in Shanxi, including persistent high temperature, heavy rain or sudden cold wave frost weather, we summarize the large-scale circulation characteristics of such extreme weather events. It will provide some reference for future related weather forecasting.

An assessment of long-term changes in mortalities due to extreme weather events in India: A study of 50 years’ data, 1970–2019

Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

Between 1980 and 2020, extreme weather and climate change events have caused, in Europe, up to 520 billion euros in economic losses, according to a 2022 report released by the European Environment Agency. Many weather extreme events such as heatwaves, droughts, tropical cyclones and cold spells will change in frequency and/or intensity due to the ongoing anthropogenic climate change, driving our planet to unprecedented threats. From a theoretical point of view, such extreme events are difficult to characterize because they are rare and they do occur at specific spatiotemporal scales of the dynamics, not necessarily the largest or the smallest of the climate system. Their study requires a framework capable of tracking their probability of occurrence, predictability and persistence. In this manuscript I will describe how dynamical systems theory helps in building numerical and theoretical tools for weather extreme events, using recurrences of patterns termed analogues. These tools can be used to study whether climate change do already affect the dynamics of extremes. Three recent examples will be analysed in details, namely the Argentinian heatwave, the Greek cold spell and the windstorm Malik, all occurred in January 2022.

<p><span>Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for selected cases of extreme events that are linked to remote drivers and large-scale teleconnections. We here present a range of case studies, including heatwaves, cold spells, and tropical cyclones, where precursors and global linkages may have improved sub-seasonal predictability. These linkages include teleconnections from the tropics as well as the stratosphere, in addition to circumglobal teleconnections. For example, cold extremes can be induced by stratospheric or tropical teleconnections, while the predictability of tropical cyclones tends to be increased when an active Madden-Julian Oscillation signal is present. </span>Prediction can further be improved through increased physical process understanding of the drivers of extremes, an improved representation of these processes in prediction systems, as well as the development of post-processing techniques that improve model output. While the case studies presented only represent some of the major types of extreme events, they provide an informative overview of the current ability of prediction models<span> to benefit from teleconnections. </span><span>These case studies clearly illustrate the potential for event – dependent advance warnings for a wide range of extreme events globally.The subseasonal predictability of extreme events allows for an extension of warning horizons, can provide advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events. </span></p>

To investigate the values of 10-m drag coefficient (CD) in different coastal areas under the influence of tropical cyclones, the present study used the observational data from four towers in different coastal areas of the South China Sea (SCS) during six tropical cyclone (TC) passages, and employed the eddy covariance method and the flux profile method. The analysis of footprint showed that the fluxes at Zhizai Island (ZZI), Sanjiao Island (SJI) and Donghai Island (DHI) were influenced basically by the ocean, and the flux at Shangyang Town (SYT) was influenced mainly by the land. The results showed that the dependence relationships of CD on 10-m wind speed (U10) in four different coastal areas under the influence of TCs were different. CD at ZZI and SJI initially increased and then decreased as U10 increased, similar to the pattern over the ocean. CD at ZZI and SJI represented the values over shallow water with seawater depths of ~7 m and ~2 m, respectively. Moreover, the critical wind speed at which CD peaked gradually decreased as the seawater depth became shallower in the coastal areas. CD at DHI and SYT decreased monotonously as U10 increased, similar to the pattern over the land. CD at DHI represented the value over the transition zone from shallow water to coastal land, and CD at SYT represented the value over the coastal land. Meanwhile, the eddy covariance method and the flux profile method were compared at ZZI and SYT during TC passages. It was found that their CD values obtained by the two methods were close. Finally, the parameterizations of observed u* and CD as a function of U10 over four different coastal areas were given under the influence of high winds. These parameterizations of observed CD may be used in high-resolution numerical models for landfalling TC forecast.

It is a perilous time to be a farmer. Across the world, 2015 broke records for unseasonal, unprecedented, and unexpected weather. The combination of El Nino and climate change produced conditions with devastating effects for the agriculture sector around the globe. This article examines the impacts of unseasonal weather on farmers around the world, in losses to yield quality and quantity but also in economic, physical and psychological effects for farmers coping with the “new normal” in weather. It considers regional differences in farmers’ susceptibility to unseasonal weather, and presents the implications of the lack of resiliency of the major crop producers for the future of food security, and by extension, political stability. Finally, it looks at how the international community is addressing this situation, concluding with practical and achievable means for farmers and cooperatives to start to build resiliency to climate change today.

In the first paper in this series, a variational data assimilation of ideal tropical cyclone (TC) tracks was performed for the statistical-dynamical prediction model SD-90 by the adjoint method, and a prediction of TC tracks was made with good accuracy for tracks containing no sharp turns. In the present paper, the cases of real TC tracks are studied. Due to the complexity of TC motion, attention is paid to the diagnostic research of TC motion. First, five TC tracks are studied. Using the data of each entire TC track, by the adjoint method, five TC tracks are fitted well, and the forces acting on the TCs are retrieved. For a given TC, the distribution of the resultant of the retrieved force and Coriolis force well matches the corresponding TC track, i.e., when a TC turns, the resultant of the retrieved force and Coriolis force acts as a centripetal force, which means that the TC indeed moves like a particle; in particular, for TC 9911, the clockwise looping motion is also fitted well. And the distribution of the resultant appears to be periodic in some cases. Then, the present method is carried out for a portion of the track data for TC 9804, which indicates that when the amount of data for a TC track is sufficient, the algorithm is stable. And finally, the same algorithm is implemented for TCs with a double-eyewall structure, namely Bilis (2000) and Winnie (1997), and the results prove the applicability of the algorithm to TCs with complicated mesoscale structures if the TC track data are obtained every three hours.

The Asia Pacific region is regarded as the most disaster-prone area of the world. Since 2000, 1.2 billion people have been exposed to hydrometeorological hazards alone through 1215 disaster events. The impacts of climate change on meteorological phenomena and environmental consequences are well documented. However, the impacts on health are more elusive. Nevertheless, climate change is believed to alter weather patterns on the regional scale, giving rise to extreme weather events. The impacts from extreme weather events are definitely more acute and traumatic in nature, leading to deaths and injuries, as well as debilitating and fatal communicable diseases. Extreme weather events include heat waves, cold waves, floods, droughts, hurricanes, tropical cyclones, heavy rain, and snowfalls. Globally, within the 20-year period from 1993 to 2012, more than 530 000 people died as a direct result of almost 15 000 extreme weather events, with losses of more than US$2.5 trillion in purchasing power parity.

Assessment and planning for emerging impacts of climate change on species

The increasing intensity and frequency of extreme weather events resulting from climate change have led to grid outages and other negative consequences. To ensure the resilience of buildings which serve as primary shelters for occupants, resilient strategies are being developed to improve their ability to withstand these extreme events (e.g., building upgrades and renewable energy generators and storage). However, a crucial step towards creating a resilient built environment is accurately estimating building performance during such conditions using historical extreme climate change-induced weather events. To conduct Building Performance Simulation (BPS) in extreme conditions, such as weather events induced by climate change, it is essential to utilize Actual Meteorological Year (AMY) weather files instead of Typical Meteorological Year (TMY) files. AMY files capture the precise climatic conditions during extreme weather events, enabling accurate simulation of such scenarios. These weather files provide valuable data that can be used to assess the vulnerabilities and resilience of buildings to extreme weather events. By analyzing past events and their impacts using BPS tools, we can gain insights into the specific weaknesses and areas that require improvement. This approach applies to both existing buildings needing climate change-resilient retrofits and new building designs that must be compatible with future climatic conditions. Moreover, the intensification and frequency increase of these extreme weather events makes developing adaptation and resilient-building measures imperative. This involves understanding the potential losses that households may experience due to the intensification of extreme events and developing farsighted coping strategies and climate-proof resilient-building initiatives. However, addressing the knowledge gap caused by the absence of an AMY weather file dataset of extreme events is essential. This will allow for accurate BPS during past extreme climate change-induced weather events. To fill this gap, this article introduces a comprehensive .epw format weather file dataset focusing on historical extreme weather events in Canada. This collection encompasses a diverse array of past extreme climate change occurrences in various locations, with potential for future expansion to include additional locations and countries. This dataset enables energy simulations for different types of buildings and considers a diverse range of historical weather conditions, allowing for better estimation of thermal performance.

Extreme weather events in europe and their health consequences - A systematic review.