Hasil untuk "Meteorology. Climatology"

Daniel J. Cecil, Dennis E. Buechler, Timothy J. Lang et al.

Abstract New gridded lightning climatology datasets are compiled and released from a series of low-Earth-orbiting NASA lightning sensors: the Optical Transient Detector (OTD), Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS), and International Space Station (ISS) LIS. OTD collected data during April 1995–March 2000, TRMM LIS collected data during December 1997–April 2015, and ISS LIS collected data during March 2017–November 2023. From these, gridded lightning datasets depict the near-global distribution of annual-mean flash rate on a 0.1° grid. Hourly mean and monthly mean averages allow examination of diurnal and annual cycles, although some smoothing of the released data is warranted. This paper describes the released datasets, thoroughly examines data quality and sources of bias, and makes recommendations for potential users. Examples from these datasets are shown, with well-known lightning hotspots in central Africa, northwestern South America, and along the base of the Himalayas. Comparison of how often thunderstorms are observed to those storms’ conditional mean flash rates shows some striking differences, with frequent storms but relatively low per-storm flash rates over the Maritime Continent. Conversely, storms are less frequent but produce higher flash rates over central North America, subtropical South America, Pakistan, and the coasts of northern Australia.

Junyi He, Qiu-Sheng Li

Katerina Bačeva Andonovska, Robert Šajn, Jasminka Alijagić et al.

Moss biomonitoring was conducted in 2002, 2005, 2010, 2015 and 2020 to evaluate atmospheric mercury (Hg) deposition across N. Macedonia as part of a comprehensive survey of potentially toxic elements (PTEs). More than 70 samples of the dominant moss species <i>Hypnum cupressiforme</i> and <i>Homalothecium lutescens</i> were collected during the summer field campaigns. Mercury concentrations were determined using cold vapour atomic absorption spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). The results revealed marked temporal fluctuations: median Hg content increased from 56 µg/kg in 2002 to 68 µg/kg in 2005, peaked at 93 µg/kg in 2010, then decreased to 84 µg/kg in 2015, and further to 52 µg/kg in 2020. Over the study period, Hg concentrations ranged from 10 to 595 µg/kg, with the highest variability observed in 2010. Spatial distribution maps and regional comparisons indicate that elevated Hg contents correspond predominantly to anthropogenic sources, particularly in industrialised zones and regions affected by mining and metallurgical activities. The 2020 dataset shows a significantly lower median value (52 µg/kg) compared to previous surveys, indicating a slight improvement in air quality, although local hotspots persist. These results highlight the importance of long-term moss biomonitoring as a cost-effective approach for tracking atmospheric mercury trends and informing national environmental policy.

Hiroyuki Sasaki, Tsukasa Takahashi, Mari Futami et al.

Microplastics (MPs) are emerging pollutants detected in diverse environments and human tissues. Among them, airborne MPs (AMPs) remain poorly characterized due to limited data and methodological inconsistencies. Although regarded as analogous to particulate matter (PM), detailed comparisons with its components are scarce. To address this gap, this study implemented a unified and seasonal protocol for simultaneous measurement of AMPs and PM across three sites in Japan. AMPs were identified using micro-Raman spectroscopy, enabling polymer- and morphology-resolved analysis. A total of 106 AMPs were identified across all sites and seasons. Polyethylene (PE) was consistently dominant, followed by polyethylene terephthalate (PET) and polyamide (PA). Site-specific variation was evident, with certain polymers being relatively more abundant depending on the local environment. Feret diameter analysis showed a modal range of 4–6 μm, with fragments predominating over granular and fibrous particles. Significant correlations between AMP concentrations and PM components were determined, including syringaldehyde (SYAL), tungsten (W), cobalt (Co), and chromium (Cr), suggesting links to local sources, while indicating that AMP dynamics are not always aligned with PM behavior. This study provides one of the first integrated datasets of AMPs and PM components, offering insights into their occurrence, sources, and atmospheric relevance.

Julia C. Fine

Abstract Many US residents are worried about the climate crisis, but few are involved in collective climate action. Relational climate conversations are a commonly recommended yet understudied means of encouraging action. This study examines the effects of conversations between US climate activists and non-activists they knew, most of whom were concerned about climate change. Non-activists reported increased knowledge, perceived efficacy, and intention to take action following the conversations, but did not participate in collective climate action more than control groups. Common barriers included low perceived efficacy, lack of knowledge about collective climate action, and psychological distance of action. Activists’ discussion of collective climate action was correlated with an increase in perceived efficacy among non-activists. Because perceived efficacy has been found to predict collective action, these results suggest that focusing on action, more so than solutions in the abstract, could enhance the effectiveness of relational climate conversations.

Iuliia Mukhartova, Andrey Sogachev, Ravil Gibadullin et al.

This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km² forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results.

Mansen Wang, Shuyang Xie, Xiaoxiu Lun et al.

As the predominant pollutant in North China during the summer months, ozone (O₃) exhibits strong oxidizing capabilities. Long-term exposure of crops to ozone will cause a decrease in various physiological indicators, affect crop yields, and pose a serious threat to food security. The North China Plain, the primary region for summer maize production in China, is afflicted by ozone pollution. In order to explore the effects of increasing O₃ concentration on the physiological characteristics and photosynthetic characteristics of summer maize, this study took summer-sown maize as the research object and carried out the ozone exposure experiment with open-top chamber (OTCs). The response of maize to O₃ exposure was studied by measuring the damage, physiological indexes and photosynthetic indexes in the silking stage (late July to late August) and filling stage (late August to mid-September). The results indicated the following: (1) Prolonged exposure to high O₃ concentrations exacerbated leaf chlorosis and damage. (2) The increase in O₃ concentration caused lipid peroxidation. The content of malondialdehyde was significantly increased by 32.6%~122.56%. At the same time, chlorophyll was destroyed and decreased by 2.17% to 4.86%. Under ozone exposure, ascorbic acid content was significantly increased by 7.58%~35.69%. The antioxidant indexes of maize were more sensitive during the filling stage. (3) Under O₃ exposure, photosynthetic rate, stomatal conductance and intercellular carbon dioxide concentration decreased significantly, indicating that the influence of O₃ on maize was mainly due to stomatal limitation. Water use efficiency and transpiration rate decreased significantly. The water use efficiency decreased by 12.84%~35.62%, which led to the weakening of the carbon fixation ability of maize and affected the normal growth and development of maize.

Penelope Godwin, Penelope Godwin, Siyuan Tian et al.

Woody vegetation restoration projects are an important feature of landscape function in Indonesian karst savannas. Understanding the relationship between available moisture and vegetation condition can assist with the planning and implementation of revegetation efforts. Working at vegetation restoration sites in East Nusa Tenggara, Indonesia, we applied a windowed cross-correlation method to mean values of NDVI to examine the lag between moisture input and NDVI response for both rainfall and soil moisture between 1999 and 2018. To test for increasing or decreasing trends in NDVI and rainfall time series, we undertook Mann–Kendall trend analyses. We identified increasing trends in Landsat 7 NDVI at two of four restoration sites, with annual increases in NDVI of 2.7 and 3.74 × 10−4 respectively. We found that rainfall dependent sites had significant Pearson’s correlations with NDVI ranging from 0.52 to 0.71, while NDVI was not correlated with rainfall at shallow groundwater sites. There was a clear negative effect of the very dry period on all sites, and this was less pronounced at shallow groundwater sites. Wet years resulted in a positive response to NDVI across all sites, while the response was lower in very wet years with annual rainfall above 1,200 mm. We found that between 2 and 4 months of antecedent rainfall gave the highest correlation with NDVI, while for soil moisture the closest relationship was found with no lag and 1 month lag. Through this study, we demonstrated the applicability of using NDVI, rainfall, and soil moisture trend analyses to identify groundwater-dependent vegetation patches and monitor the effectiveness of vegetation restoration.

Jenny Villalobos-Sequeira, Jacqueline Centeno-Morales, Stephanie Cordero-Cordero et al.

The increase in floods as a consequence of climate change is causing considerable concern in vulnerable localities. The community of Pandora Oeste in Costa Rica has experienced increasingly frequent flooding. Therefore, the aim of this study was to describe the social vulnerability to flood risk, associated with people and housing characteristics, as well as current conditions, to generate information that contributes to integrated risk management. The action-research methodology utilized allowed the development of joint activities between local actors and academia to better understand several variables that influence social vulnerability. To achieve this, a survey was administered to community residents, and a social mapping workshop was conducted with the participation of members of the Valle la Estrella Community Emergency Committee. This gathered information serves as an input for community decision-making, directing efforts toward the implementation of flood risk mitigation actions. At the same time, the results have established an approach to the flood problem, providing the methodological scenarios implemented, which could potentially be replicated in other communities, including new perspectives from community management to the articulation of actors, strengthening shared responsibility for social resilience in the face of future extreme weather events.

Cornelia Klein, Emily R Potter, Cornelia Zauner et al.

In the Peruvian Andes, the first light rainfalls towards the end of the dry season in August-September are known as pushpa . Softening soils and improving sowing conditions, these rains are crucial for planting dates and agricultural planning. Yet pushpa remains to date unexplored in the literature. This study uses observations and convection-permitting model simulations to describe the characteristics of pushpa in the Rio Santa valley (Peru). Comparing an observed pushpa case in August 2018 with a dry and wet event of the same season, we find pushpa to coincide with upper-level westerly winds that are otherwise characteristic for dry periods. These conditions impose an upper-level dry layer that favours small-scale, vertically-capped convection, explaining the low rainfall intensities that are reportedly typical for pushpa . Climatologically, we find 83% of pushpa -type events to occur under westerly winds, dominating in August, when 60% of the modelled spatial rainfall extent is linked to pushpa . Larger, more intense deep-convective events gradually increase alongside more easterly winds in September, causing the relative pushpa cloud coverage to drop to ̃20%. We note high inter-annual and -decadal variability in this balance between pushpa and intense convective rainfall types, with the spatial extent of pushpa rainfall being twice as high during 2000-2009 than for the 2010-2018 decade over the key sowing period. This result may explain farmers’ perception in the Rio Santa valley, who recently reported increased challenges due to delayed but more intense pushpa rains before the rainy season start. We thus conclude that the sowing and germination season is crucially affected by the balance of pushpa -type and deep-convective rain, resulting in a higher probability for late first rains to be more intense.

Alea Yeasmin, Savin Chand, Nargiz Sultanova

Southwest Pacific nations are highly vulnerable to extreme weather and climate events, particularly those associated with synoptic-scale systems such as tropical cyclones (TCs) and depressions (TDs). This study utilises the Okubo–Weiss–Zeta parameter (OWZP) method to reconstruct historical records of both TCs and TDs for the South Pacific basin using state-of-the-art NOAA-CIRES Twentieth Century Reanalysis (20CR) product. Extensive statistical assessments of these reconstructions are carried out using observational records for the satellite period (i.e., 1979–2014) as ‘ground-truths’. Results show that 20CR-derived TCs and TDs resemble several key characteristics of the observational records, including spatial distribution of genesis locations and track shapes. This gives us confidence that the 20CR-derived long-term records of TCs and TDs can serve as an effective tool for examining historical changes in various characteristics of TCs and TDs, particularly in the context of anthropogenic climate change.

Shu Quan, Miaohan Liu, Boxuan Chen et al.

To highlight the characteristics of PM_2.5–O₃ pollution in the Central Plains Urban Agglomeration, spatial and temporal characteristics, key meteorological factors, and source pollution data for the area were analyzed. These data from the period 2014–2020 were obtained from state-controlled environmental monitoring stations in seven major cities of the agglomeration. The results revealed the following: (1) Spatially, the PM_2.5–O₃ pollution days were aggregated in the central area of Xinxiang and decreased toward the north and south. Temporally, during the 2014–2020 period, 50 days of PM_2.5–O₃ pollution were observed in the major cities of the Central Plains Urban Agglomeration, with an overall decreasing trend. (2) A low-temperature, high-pressure environment appeared unfavorable for the occurrence of PM_2.5–O₃ pollution days. Wind speeds of 2.14–2.19 m/s and a southerly direction increased the incidence of PM_2.5–O₃ pollution days. (3) The external transport range in summer was smaller and mainly originated from within Henan Province. These results can provide important reference information for achieving a synergistic control of PM_2.5–O₃ pollution, determining the meteorological causes, as well as the potential sources, of PM_2.5–O₃ pollution in polluted areas and promoting air pollution control.

Jyotsna Puri, Martin Prowse, Emma De Roy et al.

Climate finance institutions have been tasked with effectively and efficiently allocating funds to spur the transition to low-carbon, climate-resilient economies. The Green Climate Fund (GCF) is expected to assist the most vulnerable adapt to and mitigate climate change because of its mandate to contribute to a paradigm shift. To understand if the GCF’s portfolio is on track to achieve this aim, we review the project documents of GCF investments through March 2020 (N = 125 projects). We examine attributes of these investments by applying a framework for potential transformational change, comprised of eight components. We use bivariate statistics and multivariate cluster analysis to examine the GCF’s project portfolio of mitigation, cross-cutting and adaptation projects. Bivariate tests find that adaptation projects show the greater intention to integrate policy change into national planning processes and that both adaptation and cross-cutting projects require a greater need for and expectation of behaviour change. Results from cluster analysis shows how adaptation projects dominate clusters with high and medium potential for transformational change (with 47% and 78% of projects, respectively). However, even the high potential cluster only displays the highest average scores for four of the eight components in our framework of transformational change. These findings present learning opportunities for the GCF’s future project selection. The GCF should leverage its current resources carefully to attain transformational impacts especially within adaptation where the Fund has a greater market share compared to mitigation projects.

Chakresh Kumar, Anwesha Ghosh, Yash et al.

The River Ganga is reeling from pressures of rapid urbanization and resulting anthropogenic forcings. In this study, phytoplankton community assemblages were deduced from the Dakshineswar site located in the lower stretch of River Ganga to quantify and understand the health status of this river. Surface water samples were collected from six pre-defined stations of Dakshineswar spanning across monsoon and post-monsoon seasons of 2019 and 2020. Stations were categorized into point source and surface water based on proximity to municipal discharges. Measurement of in situ environmental parameters showed significant differences in values for dissolved oxygen, total dissolved solids, electrical conductivity and suspended particulate matter between the two seasons during the study period. In particular, concentrations of dissolved nitrate and silicate were found to be higher in point source stations compared to surface water stations. The concentration of Chlorophyll-a (Chl-a) was found to be higher in post-monsoon compared to monsoon seasons. Phytoplankton communities consisted of 23 diatom taxa and 14 green algal taxa and they showed distinct seasonal and spatial variations in the study site. Phytoplankton communities were dominated by diatom taxa namely Aulacoseira , Bacillaria, Coscinodiscus , and green algal taxa such as Ulothrix, Chlorella, and Scenedesmus . There was a dramatic increase in cell abundance of Aulacoseira spp. in post-monsoon seasons indicating a bloom-like scenario. Moreover, the rapid increase in cell abundance of Aulacoseira spp. also coincided with an increase in Chl-a and a sharp fall in the concentration of dissolved silicate. Some of the encountered phytoplankton taxa such as Tetraedron , Cosmarium, Nitzschia and Scenedesmus showed strong co-occurrence patterns indicating possible association at ecological scales. Four distinct clusters were formed in nMDS ordination plot based on the influences of environmental variables on encountered phytoplankton taxa. Network analysis revealed evidence of co-occurrence patterns between several diatoms and green algal taxa.

Hsiao-Chung Tsai, Russell L. Elsberry, Wei-Chia Chin et al.

Typhoon Lekima (2019) with its heavy rains and floods is an excellent example of the need to provide the earliest possible warnings of the formation, intensification, and subsequent track before a typhoon makes landfall along a densely populated coast. To demonstrate an opportunity to provide early (10 days in advance) warnings of the threat of Typhoon Lekima, the ensemble models from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Predictions have been used to provide time-to-formation timing and positions along the weighted-mean vector motion track forecasts. In addition, the seven-day intensity forecasts after the formation using a weighted analog intensity prediction technique are provided. A detailed description of one European Center ensemble forecast is provided to describe the methodology for estimating the formation time and generating the intensity forecasts. Validation summary tables of the formation timing and position errors, and the intensity errors versus the Joint Typhoon Warning Center intensities, are presented. The availability of these ensemble forecasts would have been an opportunity to issue alerts/watches/warnings of Lekima even seven days in advance of when Lekima became a Tropical Storm. These ensemble forecasts also represent an opportunity to extend support on the 5–15 day timescale for the decision-making processes of water resource management and hydrological operations.

William E. Lewis, Timothy J. Wagner, Jason A. Otkin et al.

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors.

Jakub Małecki

Arctic glaciers have been, and are predicted to remain, an important sea‐level rise contributor over the 20th and 21st centuries. However, multi‐annual observations of their basic meteorological characteristics, which drive melt processes, are very sparse, contributing to uncertainties in sea‐level rise projections. This paper presents a methodology to process and analyse 6 years (September 2011–September 2017) of glacier meteorology data collected by an automatic weather station on the ablation zone of a small valley glacier in Svalbard (~79°N). The study focuses on the microclimate of the study glacier and its differences with that of large glaciers in the region, namely slightly increased summer air temperature with steep near‐surface lapse rates, frequently superadiabatic, reduced wind speed and incoming shortwave radiation flux. These differences are likely related to the adjacent complex relief and non‐ice‐covered surfaces within the basin of the study glacier and, as such, pose a challenge for accurate simulation of alpine glacier microclimates in regional melt assessments in the Arctic. The processing routine applied in this paper might serve as a reference for future similar studies.

Shaohui Li, Xuejin Sun, Shan Zhang et al.

To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.

Haley B. Williams, Michele N. Koppes

Glacier thinning and retreat drives initial acceleration of glacier sliding and erosion, de-buttressing of steep valley walls, and destabilization of ice-marginal deposits and bedrock, which can lead to massive rock avalanching and accelerated incision of tributary watersheds. A compelling example of these changes occurred in Taan Fjord in SE Alaska due to the rapid thinning and retreat of Tyndall Glacier over the past half century. Increased glacier sliding speeds led to both increased rates of subglacial erosion and the evacuation of subglacially stored sediments into the proglacial basins. The shrinking glacier also exposed proglacial tributary watersheds to rapid incision and denudation driven by >350 m of baselevel fall in a few decades. Moreover, in October 2015 a large tsunamigenic landslide occurred at the terminus of Tyndall Glacier, largely due to thinning exposing oversteepened, unstable slopes. Sediment yields from the glacier, the landslide and the tributary watersheds, measured from surveys of the sediments in the fjord collected in 1999 and 2016, are compared to ongoing changes in glacier and fjord geometry to investigate the magnitude of glacial and paraglacial denudation in Taan Fjord during retreat. In the last 50 years, sediment yields from the glacier and non-glacial tributaries kept pace with the rapid rate of retreat, and were on par with each other. Notably, basin-averaged erosion rates from the paraglacial landscape were twice that from the glacier, averaging 58 ± 9 and 26 ± 5 mm a−1, respectively. The sharp increases in sediment yields during retreat observed from both the glacier and the adjacent watersheds, including the landslide, highlight the rapid evolution of landscapes undergoing glacier shrinkage.

Shaohua Liu, Denghua Yan, Tianling Qin et al.

High temporal-spatial precipitation is necessary for hydrological simulation and water resource management, and remotely sensed precipitation products (RSPPs) play a key role in supporting high temporal-spatial precipitation, especially in sparse gauge regions. TRMM 3B42V7 data (TRMM precipitation) is an essential RSPP outperforming other RSPPs. Yet the utilization of TRMM precipitation is still limited by the inaccuracy and low spatial resolution at regional scale. In this paper, linear regression models (LRMs) have been constructed to correct and downscale the TRMM precipitation based on the gauge precipitation at 2257 stations over China from 1998 to 2013. Then, the corrected TRMM precipitation was validated by gauge precipitation at 839 out of 2257 stations in 2014 at station and grid scales. The results show that both monthly and annual LRMs have obviously improved the accuracy of corrected TRMM precipitation with acceptable error, and monthly LRM performs slightly better than annual LRM in Mideastern China. Although the performance of corrected TRMM precipitation from the LRMs has been increased in Northwest China and Tibetan plateau, the error of corrected TRMM precipitation is still significant due to the large deviation between TRMM precipitation and low-density gauge precipitation.