Hasil untuk "Meteorology. Climatology"

Wen Zhang, Jianglong Cui, Weike Yao et al.

Abstract Climate change and rapid urbanization are intensifying flood vulnerability in highly urbanized delta regions. This study develops a Bayesian network model to assess flood control infrastructure vulnerability (including flood susceptibility and critical failure nodes) and cascading failures in the Pearl River Delta (PRD) under future climatic scenarios. To evaluate future flood hazards, downscaled climate projections from global climate models and a stochastic weather generator were employed to simulate extreme precipitation patterns. Results indicate that in certain PRD areas, 100-year return period design rainstorm values may double by the 2050 s under high-emission scenarios and the central/southeastern PRD face highest cascading flood failures due to dense hydrological interconnectivity and topographic constraints. These findings underscore the urgent need for climate-adaptive infrastructure planning, enhanced early-warning systems, and integrated watershed management. This study offers a systematic, data-driven framework to support resilient urban flood governance in deltaic megacities facing compounding environmental risks.

Keita Honjo, Masayuki Hara, Hiroaki Yamato

There is growing concern about heat waves caused by climate change. In Japan, the population is rapidly aging, and protecting older people from heatstroke is attracting public attention. Formulating evidence-based adaptation strategies is urgent for local governments, but quantitative information on future heatstroke risk is limited. Here, we assess the impact of climate and population changes on heatstroke incidence in Saitama City, Japan. We obtain anonymized emergency call records from the local government and classify heatstroke cases into 12 groups by location (indoor and outdoor), age group (0–14, 15–64, and 65+), and sex (female and male). Using this dataset, we develop statistical models of heatstroke risk and project changes in heatstroke incidence from 2010 to 2100. Key findings are as follows: Climate change in SSP5-8.5 increases heatstroke risk (cases per 1000 people per year), but the impact varies by group. Projections for the 2090s suggest that males have a higher heatstroke risk than females, regardless of the combination of location and age group. Males aged 65+ have a higher heatstroke risk than other groups both indoors and outdoors. Females are more tolerant of temperature increases than males, but the indoor heatstroke risk for females aged 65+ rapidly increases. Assuming a standard population scenario, the total heatstroke incidence (cases per year) for Saitama City increases by 6.00 times between the 2010s and 2090s. In the same period, the indoor heatstroke incidence increases by 5.47 times, and the outdoor heatstroke incidence increases by 6.48 times. The increase in heatstroke incidence is mainly due to climate change rather than population change. The impact of climate change is much smaller in SSP1-2.6 than in SSP5-8.5. Our results highlight the need for adaptation strategies that take into account the diversity of heatstroke risk functions.

Dan Ye, Xin Wang, Yong Han et al.

In the current environment of climate change, the precipitation situation of marine islands is particularly valued. So, this study explores precipitation characteristics and mechanisms over Sri Lanka in the background of the western Indian Ocean using satellite and reanalysis datasets based on 40 years (from 1981 to 2020). The results show that the highest precipitation occurs between October and December, accounting for 46.3% of the entire year. The Indian Ocean sea surface temperature warming after 2002 significantly influences precipitation patterns. Particularly during the Second Inter-Monsoon, the western Indian Ocean warming induces an east–west zonal sea surface temperature gradient, leading to low-level circulation and westerly wind anomalies. This, in turn, results in increased precipitation in Sri Lanka between October and December. This study used the Trend-Free Pre-Whitening Mann–Kendall test and Sen’s slope estimator to study nine extreme precipitation indices, identifying a significant upward trend in extreme precipitation events in the Jaffna, arid northern Sri Lanka, peaking on 9 November 2021. This extreme event is due to the influence of weather systems like the Siberian High and intense convective activities, transporting substantial moisture to Jaffna from the Indian Ocean, the Arabian Sea, and the Bay of Bengal during winter. The findings highlight the impact of sea surface temperature warming anomalies in the western Indian Ocean and extreme precipitation events, anticipated to be more accentuated during Sri Lanka’s monsoon season. This research provides valuable insights into the variability of tropical precipitation, offering a scientific basis for the sustainable development of marine islands.

L. G. Andrian, L. G. Andrian, L. G. Andrian et al.

<p>The combined influence of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on the extratropical circulation in the Southern Hemisphere (SH) during austral spring is examined. Reanalyses and the large ensemble of National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) model outputs were used to compute composites and linear regressions for relevant variables. The results show that a positive IOD can reinforce the El Niño-induced circulation by merging the Indian Ocean wave train with the Pacific South American (PSA) pattern over the Pacific Ocean. In addition, the results obtained with the CFSv2 model output shows that strong positive IODs can contribute to enhancing the circulation signal of the El Niño anomalies and the Indian Ocean wave train. On the other hand, negative IODs in combination with La Niña do not have that combined circulation response. While there is a moderate intensification of the circulation anomalies associated with La Niña, accompanied by some changes in the location of their main action centers, results vary considerably between linear regression, the observed composites, and model composites. Regarding the influence of the IOD activity (independent of ENSO), reanalysis-based results show that the IOD positive phase has a significant impact over the entire SH, while the negative phase is associated with weaker anomalies and less consistent atmospheric response.</p>

Teerachai Amnuaylojaroen, Atsamon Limsakul, Pavinee Chanvichit

This study aims to address an assessment of climate change’s impact on agricultural water management in mainland Southeast Asia (MSEA). We used several agroclimatic indices, such as consecutive dry days (CDDs), maximum number of consecutive wet days (CWDs), consecutive summer days (CSUs), cold spell duration index (CDSIs), and warm and wet days (WWs), based on the Geophysical Fluid Dynamics Laboratory Earth System Model to characterize the effect of climate on crop water need (CWN) in MSEA. The climate model shows monthly precipitation and temperature patterns with acceptable accuracy but with an underestimation of precipitation and a warm bias in temperature. CDDs show a significant increase in aridity and drought occurrences, particularly in northern Myanmar, Laos, Vietnam, and northern Thailand, across different representative concentration pathways. CSUs have been seen to have a substantial influence on the region’s agricultural economy. The CDSIs, on the other hand, show a decrease in the duration of cold spells, indicating the existence of milder climatic conditions that could potentially affect crop growth. The CWDs show a decreasing trend in most of the multiple regions in Thailand, Laos, Vietnam, Cambodia, and Malaysia. Though the WW index shows more wet days, this does not immediately imply improved crop growth; rather, it highlights possible changes in water availability that could affect agricultural practices. While negative CWNs in the dry season months like March and November suggest possible water shortages, which pose risks to agriculture and food security in Myanmar, northern and eastern Thailand, and Cambodia, especially at the end of the century, increases in CWNs during the rainy season correspond with anticipated higher water demands for agriculture.

Pinelopi Petropoulou, Ioli Artopoulou, Ioannis Kalemikerakis et al.

Aranzazú Arangui, Lucrecia Cerini, Laura Imbert et al.

This article analyzes institutional vulnerability in relation of emergency intervention teams, considering some of the main findings of previous investigations on emergencies and disasters. Such findings include: Subjective impact of actors that take actions in these situations; the limited training and insufficient care of workers that are part of mentioned teams; and the insufficient public policy regarding an accurate training to intervene. All these are considered as institutional vulnerability indicators. Authors have participated in these investigations about emergencies and disasters, as part of an interdisciplinary team in the Facultad de Trabajo Social (UNER), Argentina. Finally, we consider the importance of continuing (or further) education of intervention teams as a fundamental part of an integrated disasters risk management system. This also includes the relevance of incorporating the notions of care and self-care in such teams which could reduce the risks they are exposed to and reinforcing their capacities for intervention.

Javed Ali, Thomas Wahl, Alejandra R. Enriquez et al.

Natural hazards such as hurricanes, floods, and wildfires cause devastating socio-economic impacts on communities. In South Florida, most of these hazards are becoming increasingly frequent and severe because of the warming climate, and changes in vulnerability and exposure, resulting in significant damage to infrastructure, homes, and businesses. To better understand the drivers of these impacts, we developed a bottom-up impact-based methodology that takes into account all relevant drivers for different types of hazards. We identify the specific drivers that co-occurred with socio-economic impacts and determine whether these extreme events were caused by single or multiple hydrometeorological drivers (i.e., compound events). We consider six types of natural hazards: hurricanes, severe storm/thunderstorms, floods, heatwaves, wildfire, and winter weather. Using historical, socio-economic loss data along with observations and reanalysis data for hydrometeorological drivers, we analyze how often these drivers contributed to the impacts of natural hazards in South Florida. We find that for each type of hazard, the relative importance of the drivers varies depending on the severity of the event. For example, wind speed is a key driver of the socio-economic impacts of hurricanes, while precipitation is a key driver of the impacts of flooding. We find that most of the high-impact events in South Florida were compound events, where multiple drivers contributed to the occurrences and impacts of the events. For example, more than 50% of the recorded flooding events were compound events and these contributed to 99% of total property damages and 98% of total crop damages associated with flooding in Miami-Dade County. Our results provide valuable insights into the drivers of natural hazard impacts in South Florida and can inform the development of more effective risk reduction strategies for improving the preparedness and resilience of the region against extreme events. Our bottom-up impact-based methodology can be applied to other regions and hazard types, allowing for more comprehensive and accurate assessments of the impacts of compound hazards.

Jian Song, Yu Ying Yang

Many factors are affecting the downstream development of baroclinic waves, among which zonal shear flow is one of the factors that need to be considered. In this paper, the influence of zonal shear flow and 'β' on the downstream development of unstable chaotic baroclinic waves is studied from the two-layer model in a wide channel controlled by quasi-geostrophic potential vorticity equation. Through the obtained Lorentz equation, We concentrated on the influence of zonal shear flow (the second derivative of baseline zonal flow is not zero) on the downstream development of baroclinic waves. In the absence of zonal shear flow, chaotic behavior along feature points would occur, and the amplitude would change rapidly from one feature to another, that is, it would change very quickly in space. When zonal shear flow is introduced, the influence of zonal shear flow on the downstream development of unstable baroclinic waves is examined categorically. And from Lorentz’s final equation, we’re investigating a change in his solution. It is found that the zonal shear flow smoothes the solution of the equation and reduces the instability, and with the increase of zonal shear flow, the stability in space will increase gradually. The second derivative of the zonal shear flow (the quadrical shear flow) therefore has a major influence on the stability of space.

Donghui Jia, Binbin Zhao, Huaizhong Yu et al.

The coseismic geothermal changes of ground temperature observed at observatories near the epicenter of the 2020 Jiashi Ms = 6.4 earthquake in China, provide a unique opportunity to study heat generation and conduction in rock. Here, evolutions of rock temperature at the Xikeer, Jiashizongchang, and Gedaliang observatories, which are located at epicentral distances of 1.4, 27.42, and 50 km respectively, were analyzed. Significant coseismic geothermal changes of 0.0432 °C were observed at the Xikeer observatory at the depth of 33.38 m, at which clear diurnal variations can be observed. Smaller changes of ~0.0001 °C were observed at the depths of 12.3 and 22.8 m at the Xikeer observatory and 22.3 m at the Jiashizongchang observatory. The stress transfer induced by the coseismic rupture induced a rise in local ground temperature, but the magnitude of the change was relatively small. The larger amplitude change at the Xikeer observatory was caused by fluid infiltration. We note that diurnal variation has been recorded at the Gedaliang observatory, but the coseismic response is no longer in existence. The temperature increases at the hypocentral area were higher than expected in the ground due to the coseismic stress transfer, but the change attenuated rapidly with distance.

Haijun Liu, Dongxing Lei, Jing Yuan et al.

The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are as follows: (1) adding an L1 constraint to the LSTM-based TEC prediction model—an L1 constraint prevents excessive attention to the input sequence during modelling and prevents overfitting; (2) adding multiple-attention mechanism modules to the TEC prediction model. By adding three parallel attention modules, respectively, we calculated the attention value of the output vector from the LSTM layer, and calculated its attention distribution through the softmax function. Then, the vector output by each LSTM layer was weighted and summed with the corresponding attention distribution so as to highlight and focus on important features. To verify our model’s performance, eight regions located in China were selected in the European Orbit Determination Center (CODE) TEC grid dataset. In these selected areas, comparative experiments were carried out with LSTM, GRU and Att-BiGRU. The results show that our proposed MA-LSTM model is obviously superior to the comparison models. This paper also discusses the prediction effect of the model in different months. The results show that the prediction effect of the model is best in July, August and September, with the R-square reaching above 0.99. In March, April and May, the R-square is slightly low, but even at the worst time, the fitting degree between the predicted value and the real value still reaches 0.965. We also discussed the influence of a magnetic quiet period and a magnetic storm period on the prediction performance. The results show that in the magnetic quiet period, our model fit very well. In the magnetic storm period, the R-square is lower than that of the magnetic quiet period, but it can also reach 0.989. The research in this paper provides a reliable method for the short-term prediction of ionospheric TEC.

Philippe Quevauviller

The increasing severity and frequency of extreme weather and climate events (e.g., floods, heat and cold waves, storms, forest fires) resulting from climate change-compounded vulnerabilities and exposure require a specific research focus. Climate-related extreme events are part of disaster risk reduction policies ruled at international, EU, and national levels, covering various sectors and features such as awareness-raising, prevention, mitigation, preparedness, monitoring and detection, response, and recovery. A wide range of research and technological developments, as well as capacity-building and training projects, has supported the development and implementation of these policies and strategies. In particular, research and innovation actions support the paradigm shift from managing “disasters” to managing “risks” and enhancing resilience needs. In this respect, a huge body of knowledge and technology has been developed in the EU-funded Seventh Framework Programme (2007–2013) and Horizon 2020 (2014–2020), for example in the area of measures and technologies needed to enhance the response capacity to extreme weather and climate events affecting the security of people and assets. In addition, networking initiatives have been developed to connect scientists, policy-makers, practitioners, and industry and civil society representatives in order to boost research uptake, identify gaps, and elaborate research programs at EU level. Research and networking efforts are pursued within the newly starting framework program Horizon Europe (2021–2027), with a focus on supporting civil protection operations. This paper provides a general overview of relevant EU policies and examples of past and developing research in the area of weather and climate extreme events and highlights current networking efforts in this area.

Lina XU, Yanbo SHEN, Zhong LI et al.

As the basis of the forecasting of solar radiation and power generation， the quality of observed radiation data has gradually become the key problem that restricts the forecasts’ level.FY-4A SSI product are tried to apply in order to effectively solve the problem of solar radiation observation data shortage on the location of photovoltaic power stations in the weather forecast service of photovoltaic power generation.In this study， three photovoltaic power stations on the location of sparse ground-based radiation observations are selected as the research areas， where the deviation correction of FY-4A surface solar irradiance is carried out using the probability density function matching method （PDF）.The PDF method adjusts the satellite retrievals through matching it’s probability density function against that based on situ observations.One advantage of this technique is that it is capable of correcting retrieval errors that are range dependent.According to the location of photovoltaic power stations and the temporal and spatial distribution of solar energy resources in Inner Mongolia， the temporal and spatial windows of PDF model are determined and then the PDF model is established.By the tests of basing the observation data on the location of photovoltaic power stations， results indicate that： The correction results of PDF method in the three research areas are similar.It is feasible to correct FY-4A SSI product in the effective area by using ground-based radiation observation.After correction， the correlation coefficients improve from 0.83~0.84 to 0.84~0.85， the mean errors decrease from 50.8~113.5 W·m-2 to 12.6~68.4 W·m-2， the mean absolute errors decrease from 107.8~174.0 W·m-2 to 92.8~135.3 W·m-2， and the mean absolute percentage errors decrease from 44.4%~92.6% to 27.0%~57.4%.The correlation coefficients， mean errors， mean absolute errors and mean absolute percentage errors of the research areas improve in varying degrees after correction， and the improvement effect of mean errors and mean absolute percentage errors is particularly obvious.The PDF method can improve the FY-4A’s overestimations in the range of low value radiations nd underestimations in the range of high value radiations.It has a strong correction ability to FY-4A total irradiance under precipitation and cloudy weather conditions.In these two conditions， the mean absolute errors are reduced by 19.9%~62.2% compared with that before correction.And the PDF method can improve the total irradiance of FY-4A in all seasons， especially in spring， summer and autumn.Affected by the limitations of FY-4A inversion algorithm， the improvement effect is the least obvious in winter.

Valneli da Silva Melo, Lourivaldo Mota Lima

Resumo Séries pluviométricas obtidas entre 1910 e 2016 em onze localidades da região de Catolé do Rocha, Estado da Paraíba, Brasil, foram analisadas com o objetivo de traçar um melhor perfil climático da região. Para tanto, o índice de Anomalia de Chuvas (IAC) juntamente com ferramentas estatísticas foram utilizados. Os resultados obtidos sugerem que as variabilidades nas chuvas, nos padrões de umidade, assim como nos períodos normais, úmidos, secos, e seus extremos, estão em conexão com os anos de El Niño e La Niña. O regime pluviométrico foi caracterizado por irregularidades com uma tendência significativa de decréscimo de chuvas no período considerado. A distribuição de probabilidade Logística representa de forma adequada as chuvas da região, com o p-valor de 0,994 para um nível de significância de 0,05.

Viviana Vanesa Urbina Guerrero, Marcos Vinicius Bueno de Morais, Edmilson Dias de Freitas et al.

One of the central problems in large cities is air pollution, mainly caused by vehicular emissions. Tropospheric ozone is an atmospheric oxidizing gas that forms in minimal amounts naturally, affecting peoples’ health. This pollutant is formed by the NO₂ photolysis, creating a main peak during the day. Nighttime secondary peaks occur in several parts of the world, but their intensity and frequency depend on the local condition. In this sense, this works aims to study the local characteristics for tropospheric nocturnal ozone levels in the Metropolitan Area of São Paulo, in Brazil, using the Simple Photochemical Module coupled to the Brazilian Developments on the Regional Atmospheric Modeling System. For this, three different situations of nocturnal occurrence were studied. The results show that the nocturnal maximum of ozone concentrations is related to the vertical transport of this pollutant from higher levels of the atmosphere to the surface and is not related to the synoptic condition.

Dayo George Oladipo, Kai Wei, Lei Hu et al.

Agriculture’s goal to meet the needs of the increasing world population while reducing the environmental impacts of nitrogen (N) fertilizer use without compromising output has proven to be a challenge. Manure and composts have displayed the potential to increase soil fertility. However, their potential effects on nitrous oxide (N₂O) and methane (CH₄) emissions have not been properly understood. Using field-scaled lysimeter experiments, we conducted a one-year study to investigate N₂O and CH₄ emissions, their combined global warming potential (GWP: N₂O + CH₄) and yield-scaled GWP in a wheat-maize system. One control and six different organic fertilizer treatments receiving different types but equal amounts of N fertilization were used: synthetic N fertilizer (NPK), 30% pig manure + 70% synthetic N fertilizer (PM30), 50% pig manure + 50% synthetic N fertilizer (PM50), 70% pig manure + 30% synthetic N fertilizer (PM70), 100% pig manure (PM100), 50% cow manure-crop residue compost + 50% synthetic N fertilizer (CMRC), and 50% pig manure-crop residue compost + 50% synthetic N fertilizer (PMRC). Seasonal cumulative N₂O emissions ranged from 0.39 kg N ha⁻¹ for the PMRC treatment to 0.93 kg N ha⁻¹ for the NPK treatment. Similar CH₄ uptakes were recorded across all treatments, with values ranging from −0.68 kg C ha⁻¹ for the PM50 treatment to −0.52 kg C ha⁻¹ for the PM30 treatment. Compared to the NPK treatment, all the organic-amended treatments significantly decreased N₂O emission by 32–58% and GWP by 30–61%. However, among the manure-amended treatments, only treatments that consisted of inorganic N with lower or equal proportions of organic manure N treatments were found to reduce N₂O emissions while maintaining crop yields at high levels. Moreover, of all the organic-amended treatments, PMRC had the lowest yield-scaled GWP, owing to its ability to significantly reduce N₂O emissions while maintaining high crop yields, highlighting it as the most suitable organic fertilization treatment in Sichuan basin wheat-maize systems.

Nand Lal Kushwaha, Jitendra Rajput, Ahmed Elbeltagi et al.

Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EP_d) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EP_d were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EP_d time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

B. Wang, H. Rui

Chih-wen Hung, Ming-Fu Shih

Drought is one of the important issues in climate studies. A drought index, Taiwan Meteorological Drought index (TMD index), was previously proposed and is applied here to identify historical severe droughts in Taiwan in order to clarify the corresponding large-scale backgrounds as a potential alert to the society in future. Through the TMD index, several historical severe drought cases in Taiwan are detected and characterized by significant seasonal variability in the annual cycle. Composites for large-scale atmospheric and oceanic environments over different periods within the dry season are conducted. From October to December, the colder sea surface temperature (SST) pattern of Pacific Meridional Mode (PMM) and the PMM-induced local anomalous anticyclones over the South China Sea are both in charge of the extremely dry conditions in Taiwan. From January to February, cold SST in the South China Sea and its adjacent oceans dominates local atmospheric conditions above these regions and creates an unfavorable environment for convection systems. From March to May, a massive anomalous anticyclonic circulation centering beside Alaska and extending its properties to East Asia and Taiwan generates a descending environment and in turn suppresses convection systems to develop. Therefore, the extremely dry conditions under this system are expected.

Reza Ghazavi, Haidar Ebrahimi

Purpose - Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran. Design/methodology/approach - A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels. Various climate scenarios in Long Ashton Research Station Weather Generator were applied to predict weather data. Findings - HadCM3 climatic model and A2 emission scenario were selected as the best methods for weather data generation. Based on the results of these models, annual precipitation will decrease by 3 per cent during 2015-2030. For three emission scenarios, i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall, precipitation in 2030 is estimated to be 265, 257 and 247 mm, respectively. For the studied aquifer, predicted recharge will decrease compared to recharge calculated based on the average of long-term rainfall. Originality/value - The decline of groundwater level in the study area was 11.45 m during the past 24 years or 0.48 m/year. Annual groundwater depletion should increase to 0.75 m in the coming 16 years via climate change. Climate change adaptation policies in the basin should include changing the crop type, as well as water productivity and irrigation efficiency enhancement at the farm and regional scales.