<p>Several studies highlight the relevance of considering polar winter stratospheric information such as the occurrence of Sudden Stratospheric Warmings (SSWs) for skillful Subseasonal to Seasonal (S2S) surface climate predictions. However, current S2S forecast systems can only predict these events about two weeks in advance. A potential way of increasing their predictability is to improve the models' representation of the triggering mechanisms of SSWs. Traditional theories indicate that SSWs follow sustained wave dissipation in the stratosphere, but the relative role of tropospheric versus stratospheric conditions in the enhancement of stratospheric wave activity remains unclear.</p>
<p>This study aims to quantify the role of the stratospheric state in wave activity preceding SSWs by analyzing three recent SSWs: the boreal SSWs of 2018 and 2019 and the austral minor SSW of 2019, using specific sets of S2S experiments. These ensembles follow the SNAPSI (Stratospheric Nudging And Predictable Surface Impacts) guidelines and include free-evolving atmospheric runs and nudged simulations, where the zonally-symmetric stratospheric state is nudged to either observations of a certain SSW or a climatological state. Our results show that the models struggle to capture the strong enhancement of wave activity preceding the 2018 SSW, limiting predictability beyond 10 d. In contrast, both SSWs of 2019 are better predicted, consistent with a more accurate simulation of the wave activity. Nudging the zonal mean stratospheric state does not drastically influence the upward wave activity flux or tropospheric circulation anomalies prior to these SSWs, but it has some impact on the stratospheric wave activity, although this modulation depends on the event characteristics. The boreal 2019 SSW appears to be primarily driven by tropospheric processes. In contrast, stratospheric contributions may have also played an important role in triggering the boreal 2018 SSW and the austral 2019 SSW. Understanding these variations is key to improving SSW predictability in S2S models.</p>
In recent advancements, deep learning-based methods for change detection have demonstrated rapid capabilities to identify alterations across extensive regions, underscoring significant research and application potential in remote sensing change detection. Nonetheless, these methods currently encounter limitations in feature extraction, often leading to blurred edges and challenges in identifying small-scale changes. To overcome these challenges, we introduce the Edge-Synergy and Multidimensional Information Interaction Network (ESMII-Net) specifically designed for remote sensing change detection. We achieve feature enhancement through the Multidimensional Information Interaction Fusion Module (MIIFM) and, by integrating the edge aware decoder and the Edge-Synergy Module (ESM), guide the model to acquire effective edge information, thereby improving change detection performance. Furthermore, during the loss function formulation, we have incorporated a Small Object Enhancement Factor (SOEF) to prioritize small object detection. An edge-awareness map is also utilized within the model to accurately delineate change edges and assess their influence on adjacent changed pixels. The efficacy of our model and its innovative components has been validated through experimental results on two public datasets, showcasing improved capabilities in detecting edges and small objects.
Roderic Gilles Claret Diabankana, Ernest Nailevich Komissarov, Daniel Mawuena Afordoanyi
et al.
Microorganisms are fundamental drivers of soil productivity, mediating nutrient cycling and pathogen suppression. In this study, we evaluated changes in the fungal community in the soil of barley (<i>Hordeum vulgare</i> L.) in a field experiment involving the application of a consortium of <i>Paenibacillus pabuli</i>, <i>Priestia megaterium</i>, <i>Pseudomonas koreensis</i>, and <i>Pseudomonas orientalis</i>. Seed pretreatment and seed pretreatment followed by rhizosphere drenching at different growth stages were implemented. Regarding fungal communities in bulk soil, the rhizospheres of untreated and treated plants were characterized based on full-length ribosomal RNA gene (18S-5.8S-28S) metabarcoding sequencing. Despite the compositional shifts, no statistical differences were observed among the alpha diversity metrics. Seed treatment resulted in long-term, targeted suppression of <i>Fusarium graminearum</i>, <i>Fusarium fujikuroi</i>, <i>Fusarium musae</i>, and <i>Fusarium verticillioides</i> from the booting through flowering and dough development stages, outperforming seed pretreatment followed by rhizosphere drenching. A low-modularity network was observed in the rhizosphere of untreated plants. Seed treatment fostered a highly interconnected and uniform network with low hub-betweenness scores. Rhizosphere drenching of pretreated seeds shifted the network topology toward higher hub-betweenness scores, reducing their connectivity by up to 10% in the rhizosphere and bulk soil. These findings provide a framework for optimizing the soil ecosystem for sustainable agriculture.
Li‐Wei Chao, Mark D. Zelinka, Christopher R. Terai
et al.
Abstract Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub‐grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE's Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio‐temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12 km) are examined, and both depict a realistic spatial structure of mean‐state cloud cover but underestimate its global mean magnitude. SCREAM 3 km reasonably reproduces the distribution of mean‐state cloud properties across various cloud optical thickness and cloud‐top pressure regimes, with performance comparable to CMIP5 and CMIP6 ensemble and marginally outperforming SCREAM 12 km. Still, SCREAM 3 km tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean‐state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases.
Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range, as well as significant and prolonged hazards. In this work, we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts, global models and regional models with lead times varying from 1 to 5 days. The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration (CMA). The typhoon also caused significant wind and rainfall impacts, with precipitation at several stations reaching historical extremes, ranking eighth in terms of total rainfall impact during the event. The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method (SSTC) and Fengwu Model are the most effective for short-term track forecasts. Meanwhile, the forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) and United Kingdom Meteorological Office (UKMO) are optimal for long-term predictions. It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity. The objective forecast is terribly poor when there is a sudden change in intensity. CMA-National Digital Forecast System (CMA-NDFS) provides a better reference value for typhoon accumulated rainfall forecasts, and regional models perform well in forecasting extreme rainfall. The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model. This improves the effective application of model products.
Dagmara Helena Brzeziecka, Bartosz Piziak, Karolina Thel
The aim of the article is to analyse the activities of Urban Living Labs (ULL) in Poland from
the perspective of supporting the realization of sustainable development goals at the local level. The article is based on an analysis of Internet materials (1,907 research units from social media and websites) of
Polish Urban Labs on various types of activities they perform. The analysis of the materials helped to assess the way in which Sustainable Development Goals (SDGs) concepts are implemented as part of the
urban innovations developed at Urban Labs. It helped to identify the most important directions of SDG
implementation, as well as to propose a typology of urban labs in this regard. The main conclusions of the
research concern the different strategies for concentrating ULL activities around the SDGs, as well as the
emergence of three speeds of ULL in terms of their involvement in SDG implementation. The “great absentee,” i.e. the undervaluing of sustainable energy topics in ULL activities in Poland, was also revealed.
Alexandra B. Holland, Achituv Cohen, Afik Faerman
et al.
Aim: This pilot study’s aim was to determine the feasibility of examining the effects of an environmental variable (i.e., tree canopy coverage) on mental health after sustaining a brain injury. Methods: A secondary data analysis was conducted leveraging existing information on mental health after moderate to severe traumatic brain injury (TBI) from the TBI Model System. Mental health was measured using PHQ-9 (depression) and GAD-7 (anxiety) scores. The data were compared with data on tree canopy coverage in the state of Texas that was obtained from the Multi-Resolution Land Characteristics (MRLC) Consortium using GIS analysis. Tree canopy coverage as an indicator of neighborhood socioeconomic status was also examined using the Neighborhood SES Index. Results: Tree canopy coverage had weak and non-significant correlations with anxiety and depression scores, as well as neighborhood socioeconomic status. Data analysis was limited by small sample size. However, there is a higher percentage (18.8%) of participants who reported moderate to severe depression symptoms in areas with less than 30% tree canopy coverage, compared with 6.6% of participants who endorsed moderate to severe depression symptoms and live in areas with more than 30% tree canopy coverage (there was no difference in anxiety scores). Conclusion: Our work confirms the feasibility of measuring the effects of tree canopy coverage on mental health after brain injury and warrants further investigation into examining tree canopy coverage and depression after TBI. Future work will include nationwide analyses to potentially detect significant relationships, as well as examine differences in geographic location.
Near-surface temperatures, such as air, land surface, and soil temperatures, play significant roles in surface radiation and energy balance. This study assessed nine gridded near-surface temperature products and analyzed the spatial heterogeneity and clear-sky bias of these temperature variables, using extensive measurements collected at Heihe River Basin. The MXD21 (MOD21 and MYD21) product had the lowest root mean square error (RMSE) (3.35 K) among all skin temperature products but a high percentage of missing values (48.4 %). All-weather skin temperature products had comparable accuracy for the interpolated cloudy-sky cases (RMSE 4.92 K) and observed clear-sky pixels (RMSE 3.42 K). For air temperature, AMSR2 had the lowest RMSE (2.48 K), but a high percentage of invalid data (32.5 %); and ERA5 had a worse accuracy (RMSE 3.87 K) but a high spatial resolution and gap-free data coverage. Comparing products from the same data source, air and soil temperatures had higher accuracies than skin temperature. Among the different variables of temperature, the 0 cm soil temperature and skin temperature had higher spatiotemporal heterogeneity than the air temperature and the soil temperatures at greater depths. The skin temperature, 0 cm soil temperature, and air temperature had higher clear-sky biases compared to soil temperatures.
Sedimentological and biogeochemical measurements were conducted on minerotrophic peat in a wilderness area on a granitic plateau to reconstruct the local ecosystem’s history and clarify the peat’s response to local and global changes. The peat is less than 1900 years old. Its clay and iron (Fe) concentration profiles revealed an increasing atmospheric influx over time, whereas the levels of its nutrients (P, K, Ca, Mg) have increased since the 19th century. Additionally, changes in the relative abundance of amorphous aluminium indicated a gradual decrease in soil weathering. The dominant metallic trace elements were cadmium during the Roman epoch and early Middle Ages, then lead and mercury during the modern and the industrial eras. Unexpectedly, the peat proved to be sub-modern and lacks wildfire proxies, probably indicating an absence of nearby woodlands over the last 1900 years. Its concentrations of Ca and Mg indicate that airborne transport of particles released by soil erosion in lowland agricultural plains has strongly affected the peat’s composition since the 18th–19th century. The site has also been heavily influenced by metallic contamination due to regional metallurgy and agriculture, producing a peat that has been modified by social imprints over several centuries.
Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (<50 m2). Using our method, 76 of 83 field-measured small patches were accurately detected and the delineated S. alterniflora patch perimeters agreed well with the field-measured patch perimeters (root mean square error [RMSE] = 8.29 m, mean absolute percentage error [MAPE] = 23.46 %). The invasion process showed fast expansion from 2012 to 2015 and slow growth from 2016 to 2019. We observed that the landward limits of S. alterniflora patches were influenced by elevation and vicinity to tidal creeks.
There is considerable interest in optimizing geothermal exploration techniques via the mapping of alteration and evaporate mineralisation, as well as of thermal emissions associated with geothermally active areas on the Earth’s surface. Optical and thermal satellite sensor technologies, improvements in processing algorithms and the means for large scale (e.g. 1:250,000) spatial data distribution are required for detecting both these attributes. The extensive visible, -near, -shortwave and thermal infrared (VNIR-SWIR-TIR) data archive acquired by the multi-spectral Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) provides a rich source of geoscience related imagery for geothermal exploration. Examples of generating large scale mosaicked ASTER imagery to provide province to continental mineral mapping have been undertaken in areas including such as Australia, western USA, Namibia and Zagros Mountains Iran. In addition, ASTER’s thermal infrared imagery also provides night time land surface temperature (LST) estimates relevant for detecting possible geothermal related anomalies.This study outlines existing methods for the application of ASTER data for geothermal exploration in East Africa. The study area encompasses a section of the East African Rift System across the Tanzanian and Kenyan border. The area includes rugged volcanic terrain which has had geological mapping of limited coverage at detailed scales, from various heritages and mapping agencies. This study summarizes the technology, the processing methodology and initial results in applying ASTER imagery for such compositional and thermal anomaly mapping related to geothermal activity. Fields observations have been used from the geothermal springs of Lake Natron, Tanzania, and compared with ASTER derived spectral composition and land surface temperature results. Published geothermal fields within the Kenyan portion of the study area have also been incorporated into this study.
Nurul Amirah ISA, Wan Mohd Naim WAN MOHD, Siti Aekbal SALLEH
et al.
Physical geography and urban characteristics influenced the urban climate conditions. Built-up areas, green urban parks, forest reserves, streets and terrain constitute the climatic interactions within urban areas. These have led to the variation of the urban climate condition throughout the world. Thus, in studying urban climate, the impacts of these factors are crucial to be examined. This study aims to examine the effects of six important factors, namely built-up areas, green covers, terrain elevation, building volume, surface roughness and land use type, which contribute to the variation of the urban climate condition within the Kuala Lumpur City. In this study, the effects of the six factors (urban parameters) towards the air surface temperature variation were statistically tested. Using the Weather Research and Forecasting (WRF) model and remote sensing technique, the data needed for the analyses were extracted. The Geographical Information System (GIS) was employed as the analysis platform during the study. Based on the Spearman’s rho and Mann-Whitney U tests, it was identified that the six urban parameters and the air surface temperature variation are correlated. The further investigation conducted using the Kruskall-Wallis test has identified that only five of the urban parameters showed significant effects toward the air surface temperature variation, which are built-up areas, green covers, terrain elevation, building volume and surface roughness while the land use type was excluded. The findings of this study are very crucial as a pioneer research to integrate the urban climatic information in the urban planning decision making in tropical cities like Kuala Lumpur.
Cities. Urban geography, Urban groups. The city. Urban sociology
Folasade K. Olagoke, Karsten Kalbitz, Cordula Vogel
Knowledge of how interactions of clay minerals and extracellular enzymes (EEs) influence organic matter turnover in soils are still under discussion. We studied the effect of different montmorillonite contents on EE activities, using two experiments—(1) an adsorption experiment with a commercially available enzyme (α-glucosidase) and (2) an incubation experiment (10 days) where microorganisms were stimulated to produce enzymes through organic carbon (OC) addition (starch and cellulose). Soil mixtures with different montmorillonite contents were created in four levels to a sandy soil: +0% (control), +0.1%, +1%, and +10%. The potential enzyme activity (pEA) of four enzymes, α-glucosidase, β-glucosidase, cellobiohydrolase, and aminopeptidase, involved in the soil carbon and nitrogen cycle were analysed. The adsorption experiment revealed a reduction in the catalytic activity of α-glucosidase by up to 76% with increasing montmorillonite contents. However, the incubation experiment showed an inhibitory effect on pEA only directly after the stimulation of in-situ EE production by OC addition. At later incubation stages, higher pEA was found in soils with higher montmorillonite contents. This mismatch between both experiments, with a transient reduction in catalytic activity for the incubation experiments, points to the continuous production of enzymes by soil microorganisms. It is conceivable that microbial adaptation is characterized by higher investment in EEs production induced by increasing clay contents and a stabilisation of the EEs by clay minerals. Our results point to the need to better understand EE-clay mineral-OC interactions regarding potential microbial adaptations and EE stabilisation with potentially prolonged activities.
Marta Pereira Llopart, Michelle Simões Reboita, Rosmeri Porfírio da Rocha
et al.
This work analyzed the 3.5 land surface scheme coupled in the Regional Climate Model RegCM4 (RegCLM),
seeking to identify the impact of this coupling in the climatology and in the interannual variability, mainly in the Southeast of Brazil. The climatology analysis showed that the RegCLM, for DJF, is more humid than the set of observations
used, overestimating the South Atlantic Convergence Zone (SACZ) due to the RegCLM simulate more intense the northeast trade winds, and with this, there is a transportation of moisture from the Tropical Atlantic Ocean to the continent
trhought the Low Level Jet (LLJ), intensifying the SACZ. For air temperature, RegCLM is colder than observations in
the SDE region. This result also occurs in the analysis of the annual cycle and, especially in winter, reaching the difference of 1.8o
C. During summer, lowest simulation errors were found for this variable. For the interannual variability
for the SDE, the simulated precipitation presents an intensify pattern of the signal and reverse the phase of the observed
anomaly. For air temperature, the simulation agrees with the observations, intensifying in some years the anomalies.
ABSTRACT: With modern infrastructures and effective warning systems, casualties, damages and losses due to tropical cyclones (TCs) have been significantly reduced over the years in Hong Kong. Nevertheless, densely populated coastal cities like Hong Kong will need to continuously enhance its resilience to high winds, heavy rain and storm surges brought by TCs, especially with the growing concern of the challenges induced by climate change and sea level rise. By embracing the advance of remote sensing, communication and numerical modelling technology, the Hong Kong Observatory (HKO) continues to improve its TC monitoring and forecasting techniques as well as forecasting and warning services to meet the needs of the society. This paper concisely reviews the major development and achievement of TC-related operation and services of HKO in recent decades, in aspects such as Numerical Weather Prediction (NWP) models, nowcasting techniques, warning communication and public education. Future thrusts on TC forecasting and warning services of HKO will also be discussed. Keywords: Hong Kong Observatory, tropical cyclones, forecasting and warning services
Of particular importance to the study of large-scale phenomena in physical geography is the modifiable areal unit problem ( MAUP). While often viewed as only a problem in human geography (particularly demographic studies), the MAUP is an issue for all quantitative studies in geography of spatial phenomena (Openshaw and Taylor, 1979). Increasingly, remote sensing and Geographic Information Systems ( GIS) are being used to assess the distribution of phenomena from a large scale. These phenomena are modelled using areal units that can take any shape or size resulting in complications with statistical analysis related to both the scale and method used to create the areal units. In this paper, we define the modifiable areal unit problem, present examples of when it is a problem in physical geography studies, and review some potential solutions to the problem. Our aim is to increase awareness of this complicated issue and to promote further discussion and interest in this topic.
This brief and commissioned paper reviews ten years of book reviews that have been published in Island Studies Journal (2006-2015). The paper discusses numbers and types of reviews, the nationality of the reviewers, the spatial, thematic and/or disciplinary focus of the books reviewed, using these observations to make critical comments on Island Studies Journal.
El principal aporte de la histología cuantitativa a la antropología ha sido la estimación de edad a la muerte en restos óseos humanos no documentados. Los procesos secuenciales de remodelación ósea permiten observar la asociación entre el número de osteonas y la edad cronológica, lo cual constituye la base primaria de los métodos histológicos de predicción de edad. El primer estudio sobre cambios en la microestructura ósea y su aplicación para el cálculo de la edad en esqueletos adultos fue desarrollado en 1965. Posteriormente, el mismo fue testeado en muestras independientes y modificado por varios investigadores que trataron de subsanar y ajustar algunos inconvenientes, sobre todo aquellos vinculados con la precisión y exactitud. El siguiente artículo de revisión tiene como objetivo discutir los principales métodos histológicos de estimación de edad aplicados a restos óseos humanos y sintetizar el estado actual del conocimiento al respecto.
PALABRAS CLAVE antropología forense; análisis histomorfométrico
The main contribution of quantitative histology to anthropology has been the estimation of age at death in undocumented human skeletal remains. Sequential bone remodeling processes allow us to observe the association between the number of osteons and chronological age, which constitutes the primary basis for histological age predicting methods. The first study on changes in bone microstructure and its application to age calculation in adult skeletons was developed in 1965. Subsequently, it was tested in independent samples and modified by several researchers with the intention of rectifying and adjusting some drawbacks, especially those related to precision and accuracy. The following review article aims to discuss major histological age estimation methods applied to human remains and summarize the current state of knowledge in this area.
KEY WORDS forensic anthropology, histomorphometric analysis