Dynamic Climate Conditions Research Articles

Bioinspired smart dual-layer hydrogels system with synchronous solar and thermal radiation modulation for energy-saving all-season temperature regulation

All-season thermal management with zero energy consumption and emissions is more crucial to global decarbonization over traditional energy-intensive cooling/heating systems. However, the static single thermal management for cooling or heating fails to self-regulate the temperature in dynamic seasonal temperature condition. Herein, inspired by the dual-temperature regulation function of the fur color changes on the backs and abdomens of penguins, a smart thermal management composite hydrogel (PNA@H-PM Gel) system was subtly created though an “on-demand” dual-layer structure design strategy. The PNA@H-PM Gel system features synchronous solar and thermal radiation modulation as well as tunable phase transition temperatures to meet the variable seasonal thermal requirements and energy-saving demands via self-adaptive radiative cooling and solar heating regulation. Furthermore, this system demonstrates superb modulations of both the solar reflectance (ΔR = 0.74) and thermal emissivity (ΔE = 0.52) in response to ambient temperature changes, highlighting efficient temperature regulation with average radiative cooling and solar heating effects of 9.6 °C in summer and 6.1 °C in winter, respectively. Moreover, compared to standard building baselines, the PNA@H-PM Gel presents a more substantial energy-saving cooling/heating potentials for energy-efficient buildings across various regions and climates. This novel solution, inspired by penguins in the real world, will offer a fresh approach for producing intelligent, energy-saving thermal management materials, and serve for temperature regulation under dynamic climate conditions and even throughout all seasons.

Active remote sensing data and dispersal processes improve predictions for an invasive aquatic plant during a climatic extreme in Great Lakes coastal wetlands

Invasive aquatic plants pose a significant threat to coastal wetlands. Predicting suitable habitat for invasive aquatic plants in uninvaded yet vulnerable wetlands remains a critical task to prevent further harm to these ecosystems. The integration of remote sensing and geospatial data into species distribution models (SDMs) can help predict where new invasions are likely to occur by generating spatial outputs of habitat suitability. The objective of this study was to assess the efficacy of utilizing active remote sensing datasets (synthetic aperture radar (SAR) and light detection and ranging (LiDAR) with multispectral imagery and other geospatial data in predicting the potential distribution of an invasive aquatic plant based on its biophysical habitat requirements and dispersal dynamics. We also considered a climatic extreme (lake water levels) during the study period to investigate how these predictions may change between years. We compiled a time series of 1628 field records on the occurrence of Hydrocharis morsus-ranae (European frogbit; EFB) with nine remote sensing and geospatial layers as predictors to train and assess the predictive capacity of random forest models to generate habitat suitability in Great Lakes coastal wetlands in northern Michigan, USA. We found that SAR and LiDAR data were useful as proxies for key biophysical characteristics of EFB habitat (emergent vegetation and water depth), and that a vegetation index calculated from spectral imagery was one of the most important predictors of EFB occurrence. Our SDM using all predictors yielded the highest mean overall accuracy of 88.3% and a true skill statistic of 75.7%. Two of the most important predictors of EFB occurrence were dispersal-related: 1) distance to the nearest known EFB population (m), and 2) distance to nearest public boat launch (m). The area of highly suitable habitat (pixels assigned ≥0.8 probability) was 74% larger during a climatically extreme high water-level year compared to an average year. Our findings demonstrate that active remote sensing can be integrated into SDM workflows as proxies for important drivers of invasive species expansion that are difficult to measure in other ways. Moreover, the importance of a proxy variable for endogenous dispersal (distance to nearest known population) in these SDMs indicates that EFB is currently spreading, and thereby less influenced by within-site dynamics such as interspecific competition. Lastly, we found that extreme climatic conditions can dramatically change this species’ niche, and therefore we recommend that future studies include dynamic climate conditions in SDMs to more accurately forecast the spread during early invasion stages.

A novel on high voltage gain boost converter with cuckoo search optimization based MPPTController for solar PV system

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I–V and P–V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified.

Data‐driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions

AbstractAltered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade‐offs between simple climate‐independent econometric‐based models and complex climate‐sensitive data‐driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. The climate‐independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate‐sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. −27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate‐independent model. The climate‐sensitive workflow increased model accuracy and characterized climate‐demand interactions, demonstrating a novel tool to enhance water system management.

Assessing the impact of particulate fouling on the long-term performance of energy recovery ventilators

Energy recovery ventilators (ERVs), which integrate fresh air supply, air purification, and energy recovery, are prominently employed in buildings. Particulate fouling and dynamic climate conditions in real-time scenarios could cause the deviation of operating parameters from the designed values, impacting the long-term performance of ERVs. In this study, a non-linear regression model of energy exchanger involving imbalanced airflow rate conditions was derived based on experimental data, considering the decreased supply air volume caused by dust-loading of fresh air filter. Model validation presented errors within ±10 %. Furthermore, an ERV model was established by integrating the energy exchanger, filter, and fan. Simulation results indicate that particulate fouling could decrease the instant supply airflow rate and recovered energy to maximums of 15 % and 12 % respectively, compared to the ideal performance in the clean state. Analysis throughout the heating season concludes that, the reduced recovered energy together with the unorganized air infiltration consequent on the imbalanced airflow rates could increase the seasonal fresh air load by 10.9 %, 13.1 %, and 24.1 % in three typical cities, Shanghai, Beijing, and Harbin, respectively. The proposed model provides a reliable platform for evaluating the performance and analyzing the maintenance strategies of ERVs, contributing to improving their practical effects.

An integrated agent-based simulation modeling framework for sustainable production of an Agrophotovoltaic system

The Agrophtovoltaic (APV) system is an alternative for sustainable crop production, where solar power is generated via Photovoltaic (PV) modules. Since both crop production and solar power generation activities are heavily dependent on dynamic environmental conditions, it is challenging to design an APV system based on accurate estimation of its performance. To this end, this study aims to introduce an agent-based simulation (ABS) framework integrated with polynomial regression and ridge regression. In particular, two agent types are devised, as follow: (1) The photovoltaic agent calculates electricity produced via PV modules, and estimates its profits; and (2) the crop production agent calculates crop harvests underneath the PV modules, and estimates their profits. To validate the proposed framework, field experiment data with five types of crops (i.e., corn, sesame, soybean, mung bean, and red bean) at the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea with three different shading ratios of 21.3%, 25.6%, and 32.0% have been used. In addition, for the sustainable operation of an APV system in terms of electricity generation as well as crop production, three climate change scenarios based on the shared socioeconomic pathway (SSP) are considered. The proposed framework identifies that the agrophotovoltaic system with 32% sharing ratio increases up to 20% of the total profit of normal farmland. As a result, the proposed framework enables the performance of an APV system under dynamic climate conditions to be accurately estimated, so that APV system designers can utilize it to identify a profitable long-term APV system.

Moisture stress limits radial mixing of non-structural carbohydrates in sapwood of trembling aspen.

Dynamics in non-structural carbohydrate (NSC) pools may underlie observed drought legacies in tree growth. We assessed how aridity influences the dynamics of different-aged NSC pools in tree sapwood at two sites with differing climate conditions ('wet' vs. 'dry') that also experienced widespread regional drought five years earlier. We used an incubation method to measure radiocarbon (Δ14C) in CO2 respired from Populus tremuloides (aspen) tree rings to evaluate NSC storage and mixing patterns, coupled with measurements of NSC (soluble sugars, starch) concentrations and respired δ13C-CO2. At a wet site, CO2 respired from rings formed during 1962-1967 was only ~ 11years old, suggesting deep sapwood mixing of NSCs as starch. At a dry site, total NSC was about one-third of wet site totals, maximum ages in deep rings were lower, and ages more rapidly increased in shallow rings then plateaued. These results suggest historically shallower mixing and/or relatively higher consumption of NSCs under dry conditions. Both sites, however, had similar aged NSC (<1yr) in the most recent six rings, indicative of deep radial mixing following relatively wet conditions during the sampling year. We suggest significant differences in NSC mixing among sites are driven by moisture stress, where aridity reduces NSC reserves and restricts the depth of radial mixing. However, dynamic climate conditions in the southwestern US resulted in more complex radial patterns of sapwood NSC age than previously described. We suggest a novel conceptual framework to understand how moisture variability might influence the dynamics of NSC mixing in the sapwood.

Catchment-scale carbon fluxes and processes in major rivers of northern Québec, Canada

Boreal rivers transport and process large amounts of organic and inorganic materials derived from their catchments, yet quantitative estimates and patterns of carbon (C) transport and emissions in these large rivers are scarce relative to those of high-latitude lakes and headwater streams. Here, we present the results of a large-scale survey of 23 major rivers in northern Québec sampled during the summer period of 2010, which aimed to determine the magnitude and spatial variability of different C species (carbon dioxide - CO2, methane - CH4, total carbon – TC, dissolved organic carbon - DOC and inorganic carbon - DIC), as well as to identify their main drivers. In addition, we constructed a first order mass balance of total riverine C emissions to the atmosphere (outgassing from the main river channel) and export to the ocean over summer. All rivers were supersaturated in pCO2 and pCH4 (partial pressure of CO2 and CH4), and the resulting fluxes varied widely among rivers, especially the CH4. There was a positive relationship between DOC and gas concentrations, suggesting a common watershed source of these C species. DOC concentrations declined as a function of % land surface covered by water (lentic + lotic systems) in the watershed, suggesting that lentic systems may act as a net sink of organic matter in the landscape. The C balance suggests that the export component is higher than atmospheric C emissions in the river channel. However, for heavily dammed rivers, C emissions to the atmosphere approaches the C export component. Such studies are highly important for the overall efforts to effectively quantify and incorporate major boreal rivers into whole-landscape C budgets, to determine the net role of these ecosystems as C sinks or sources, and to predict how these might shift under anthropogenic pressures and dynamic climate conditions.

A dynamic intelligent building retrofit decision-making model in response to climate change

Building energy-saving retrofitting has become an essential way for the building sector to cope with climate change. Furthermore, climate change affects building retrofit strategies. The current building stock is massive, which means there is a large demand for building retrofitting. Additionally, climatic conditions are changing, posing a significant challenge to the current time-consuming and labor-intensive decision-making process. To solve these problems, a dynamic intelligent building decision-making model was established in this study. The static and dynamic features of building retrofit decision-making were identified, four machine learning algorithms were considered, and a case base containing records for 301 retrofitted buildings was established for knowledge mining. The findings demonstrate that the XGBoost algorithm performs well in terms of building retrofit strategy prediction, with 77% accuracy for the prediction of building envelope retrofits and 76% accuracy for the prediction of HVAC system retrofits. In addition, the trends of building retrofit strategy decision-making considering dynamic climate conditions were observed. The demand for building envelope retrofitting and heating retrofitting has declined, while the demand for cooling retrofitting has increased. Some buildings are extremely sensitive to climate change, and some redundant retrofitting strategies should be avoided. The proposed intelligent decision-making model can provide valuable information for future building retrofit strategy decision-making.

Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems

Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R2 = 0.51), annual herbaceous plant cover (R2 = 0.687), exposed soil (R2 = 0.656) and fire occurrence (R2 = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.

Cable Propelled Gondola System Operation in Icing Conditions

The scope of this study comprehends problems associated with modern urban vehicles known as cable propelled gondolas system operations in icing conditions. The aspects under consideration are problems related to the operations, safety, and maintenance of cable car systems in harsh climate conditions. The geographical location of the gondola cars makes them vulnerable to severe weather conditions especially in cold climates of the northern hemisphere, where icing on its components is an operational, maintenance, and safety concern. The harsh climate conditions can cause unadorned malfunctions posing a threat to the integrity the of system as well as a high risk to human safety. The study basis on the identification of these problems in operational, maintenance and safety domain including implications the industry faces in the form of severe accidents costing precious lives and lost capital. Furthermore, it incorporates the ice detection, anti/de-icing approaches as well as the safety strategies in use nowadays. The massive increase in operations and dynamic climate conditions gondola cars require serious attention. This study unsheathes serious underlying problems that severely affect the gondola operations, makes them prone to major maintenance shutdowns and poses high risk to structural and human safety. The identified problems in this study and severity of risks draw attention to need for practicable solutions incorporating de-icing and ice removal techniques for safe operation of gondolas in cold climates saving time, effort, inconvenience, and prodigious lost capital.

Thermochromic smart windows with highly regulated radiative cooling and solar transmission

Radiative cooling (RC) is a technique that spontaneously radiates long-wave infrared (LWIR) to the cold outer space, which provides cooling power to buildings, however only preferred in hot seasons. RC has been widely researched on walls and roofs but rarely on windows while windows are the least energy-efficient part of buildings. Moreover, in the current smart window design, consideration of tunable RC is missing. For the first time, we proposed an ideal smart window with a switchable front side LWIR emissivity ( ε Front ) and solar modulation ability (Δ T sol ). Such window needs not only to have high luminous transmission ( T lum ) and Δ T sol , the two major conventional performance indexes but also possess a switchable ε Front to cater for the variable seasonal thermal performance requirements and energy-saving demands. We further fabricated a tunable emissivity thermochromic (TET) smart window with large Δ T sol (51%) and T lum (72%) and switchable ε Front (0.95–0.1) to prove the efficacy of the proposed ideal window. Compared with current smart window technology solely regulating solar transmission and other RC materials with fixed emissivity, the TET smart window panel gives a wide tunability in the selective solar spectrum for dynamic climate conditions in energy-saving buildings, achieving largely enhanced energy saving performance globally • Smart window to regulate both solar transmittance and radiative cooling. • Switchable emissivity to prompt cooling in summer and warm keeping in winter. • High luminous transmittance, good solar regulation. • Better energy efficiency than commercial low-E glass across climate zones.

KAJIAN HAK KEPEMILIKAN PROPERTI OLEH WARGA NEGARA ASING DI INDONESIA

In developing the Indonesian state, a large amount of money is required. One of the financing comes from investment or capital invested by foreign parties with an investment permit of 30-60 years. The impact of this period causes foreigners to need housing facilities that can support their activities and streamline their business in the context of PMA. In response to these conditions, the government issued Government Regulation No. 41/1996. Based on such regulatory conditions, foreigners generally get a house by renting. This allows foreigners to apply for a rental period of more than 10 years for Indonesians who own the house. However, in practice there are many houses whose conditions, room arrangement, layout, environment, or so on do not suit the tastes of foreigners. In addition, the condition of ownership of property on a lease basis also prevents foreigners from being able to renovate space. According to the UUPA, foreigners are not allowed to own HGB and can only obtain usage rights. This needs to be revised to adapt to the current and more dynamic investment climate conditions. In other countries the right to use property reaches 99 years. The proposal to extend the right to use housing for foreigners will contribute foreign exchange for the country.

Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis.

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

Incorporating cultivar-specific stomatal traits into stomatal conductance models improves the estimation of evapotranspiration enhancing greenhouse climate management

The effect of considering cultivar differences in stomatal conductance (g s ) on relative air humidity (RH)-related energy demand was addressed. We conducted six experiments in order to study the variation in evapotranspiration (ET c ) of six pot rose cultivars, investigate the underlying processes and parameterise a g s -based ET c model. Several levels of crop ET c were realised by adjusting the growth environment. The commonly applied Ball–Woodrow–Berry g s -sub-model (BWB-model) in ET c models was validated under greenhouse conditions, and showed a close agreement between simulated and measured ET c . The validated model was incorporated into a greenhouse simulator. A scenario simulation study showed that selecting low-g s cultivars reduces energy demand (≤5.75%), depending on the RH set point. However, the BWB-model showed poor prediction quality at RH lower than 60% and a good fit at higher RH. Therefore, an attempt was made to improve model prediction: the in situ -obtained data were employed to adapt and extend either the BWB-model, or the Liu-extension with substrate water potential (Ψ; BWB-Liu-model). Both models were extended with stomatal density (D s ) or pore area. Although the modified BWB-Liu-model (considering D s ) allowed higher accuracy (R 2 = 0.59), as compared to the basic version (R 2 = 0.31), the typical lack of Ψ prediction in greenhouse models may be problematic for implementation into real-time climate control. The current study lays the basis for the development of cultivar specific cultivation strategies as well as improving the g s sub-model for dynamic climate conditions under low RH using model-based control systems. • Relative air humidity control is a key but costly aspect of greenhouse climate. • Accurate estimation of evapotranspiration (ET c ) is essential for relative air humidity regulation. • Six pot rose cultivars and several cultivation regimes were used. • ET c estimation was improved using cultivar-specific stomatal conductance parameters. • The use of low-stomatal conductance cultivars secures energy saving (2.5–5.75%).

A new Dead Sea pollen record reveals the last glacial paleoenvironment of the southern Levant

The southern Levant is a key region for studying vegetation developments in relation to climate dynamics and hominin migration processes in the past due to the sensitivity of the vegetation to climate variations and the long history of different anthropogenic occupation phases. However, paleoenvironmental conditions in the southern Levant during the Late Pleistocene were still insufficiently understood. Therefore, we investigated the vegetation and fire history of the Dead Sea region during the last glacial period. We present a new palynological study conducted on sediments of Lake Lisan, the last glacial precursor of the Dead Sea. The sediments were recovered from the center of the modern Dead Sea within an ICDP campaign. The palynological results suggest that Irano-Turanian steppe and Saharo-Arabian desert vegetation prevailed in the Dead Sea region during the investigated period (ca. 88,000–14,000 years BP). Nevertheless, Mediterranean woodland elements significantly contributed to the vegetation composition, suggesting moderate amounts of available water for plants. The early last glacial was characterized by dynamic climate conditions with pronounced dry phases and high but unstable fire activity. Anatomically modern humans entered the southern Levant during a climatically stable phase (late MIS 4–MIS 3) with diverse habitats, constant moisture availability, and low fire activity. MIS 2 was the coldest phase of the investigated timeframe, causing changes in woodland composition and a widespread occurrence of steppe. We used a biome modeling approach to assess regional vegetation patterns under changing climate conditions and to evaluate different climate scenarios for the last glacial Levant. The study provides new insights into the environmental responses of the Dead Sea region to climate variations through time. It contributes towards our understanding of the paleoenvironmental conditions in the southern Levant, which functioned as an important corridor for human migration processes.

What Makes a Location into a “Favorable Habitat” under Changing Climate and Environmental Conditions? A Pilot Study Focused on Exploring the Differences between Natural and Non-natural Habitats using Airborne LiDAR

Recovery plans supported by conservation models are critical for the protection of endangered species. For developing these models, parameters are most commonly extracted via field survey or remote sensing based methods. However, at times, these models get narrowed down to specific habitat features associated with naturally occurring ecosystems, and thereby fail to detect suitable non-natural habitats that the animals have gotten adapted to in recent years - as a survival mechanism to cope up with the dynamic climate and environmental conditions. As a first step to address this issue, we considered the case of Red-cockaded woodpecker (RCW) species and undertook a pilot study to explore the characteristics of non-natural locations that make it favorable for RCW nesting. On exploring the differences in habitat characteristics of natural and non-natural ecosystems - by employing airborne laser scanning data and logistic regression analysis method - we identified new prominent forest attributes and their variations for each habitat types. Based on our findings, we provide fruitful interpretations, recommendations and encourage discussions on less-studied conservation aspects and hope to stretch the horizons of ongoing biodiversity conservation efforts in the wake of global environmental change.

Stratigraphic architecture of the Cenozoic succession in the McMurdo Sound region, Antarctica: An archive of polar palaeoenvironmental change in a failed rift setting

AbstractThe Victoria Land Basin forms part of the failed West Antarctic Rift, and preserves a Cenozoic succession up to 4 km thick that records the onset of Cenozoic glaciation, and the history of Antarctic glaciation over the past 34 Myr. This succession is relevant both to investigations of modern climate change and to studies of long‐term palaeoclimate change in general. This study provides a sedimentological and stratigraphic review of the Victoria Land Basin succession, based on analysis of several continuous drillcores acquired since the 1970s, and supported by seismic stratigraphic analysis of a large array of seismic reflection data. An array of fifteen lithofacies is recognized within the Victoria Land Basin Cenozoic succession, including fossiliferous and diversely bioturbated mudrocks and diatomites, texturally mature sandstones and conglomerates, mixed mudstones and sandstones with dispersed gravel with restricted bioturbation, and diamictites and associated lithologies. These facies record a variety of marine, glaciomarine, proglacial and subglacial environments. Locally, volcanic and volcaniclastic deposits are interbedded in the succession. Lithofacies are arranged in repetitive vertical stacking patterns (depositional sequences) that record glacial advance–retreat cycles with attendant relative sea‐level changes. Seven varieties of depositional sequences (stratigraphic motifs) are recognized within the succession as a whole, and interpreted to record a range of depositional settings from rifts unaffected by glacial ice (Motif 7), through varying degrees of glacial influence with abundant meltwater contributions (Motifs 6 to 3), to cold, polar glaciated environments such as that of today (Motifs 2 and 1). Overall, there is a gradual trend upward through the succession from Motif 7 at the base towards Motif 1 at the top, but the trend is not monotonic. A significant conclusion of this work is that a record of dynamic climate and glacial conditions is preserved through the entire 34 Myr period of the Cenozoic icehouse, at least in the Victoria Land Basin. Intervals characterized by consistent stratigraphic style (motifs) are recognized throughout the Victoria Land Basin succession. These intervals are of 1 to 6 Myr duration, each containing numerous depositional sequences; they are one to two orders of magnitude longer than glacial–interglacial cycles, and record periods during which environmental conditions varied in an internally consistent manner. These intervals are considered to reflect convolutions of orbital parameters that remained stable for periods of 106 a, and then switched to alternative configurations. Such intervals are directly analogous to 1 to 8 Myr intervals characterized by glaciogenic strata that are preserved within the late Palaeozoic of eastern Australia among other areas, and may be a recurring stratigraphic response to icehouse climate regimes through geological time.

Impact of Large-Scale Dynamic versus Thermodynamic Climate Conditions on Contrasting Tropical Cyclone Genesis Frequency

Significant advances have been made in understanding the key climate factors responsible for tropical cyclone (TC) activity, yet any theory that estimates likelihood of observed TC formation rates from mean climate states remains elusive. The present study investigates how the extremes of observed TC genesis (TCG) frequency during peak TC seasons are interrelated with distinct changes in the large-scale climate conditions over different ocean basins using the global International Best Track Archive for Climate Stewardship (IBTrACS) dataset and ERA-Interim for the period 1979–2014. Peak TC seasons with significantly high and low TCG frequency are identified for five major ocean basins, and their substantial spatial changes in TCG are noted with regionally distinct differences. To explore the possible climate link behind such changes, a suite of potentially relevant dynamic and thermodynamic climate conditions is analyzed. Results indicate that the observed changes in extreme TCG frequency are closely linked with distinct dominance of specific dynamic and thermodynamic climate conditions over different regions. While the combined influences of dynamic and thermodynamic climate conditions are found to be necessary for modulating TC formation rate over the North Atlantic, eastern Pacific, and southern Indian Oceans, significant changes in large-scale dynamic conditions appear to solely control the TCG frequency over the western Pacific and South Pacific basins. Estimation of the fractional changes in genesis-weighted climate conditions also indicates the coherent but distinct competing effects of different climate conditions on TCG frequency. The present study further points out the need for revising the existing genesis indices for estimating TCG frequency over individual basins.

Spacio-Temporal Distribution and Tourist Impact on Airborne Bacteria in a Cave (Škocjan Caves, Slovenia)

(1) Background: Airborne microbes are an integral part of a cave ecosystem. Cave allochtonous airborne microbiota, which occurs mainly during aerosolization from an underground river, from animals, and from visitors, is particularly pronounced in show caves. The impacts of tourists and natural river aerosolization on the cave air were estimated in large cave spaces within the Škocjan Caves; (2) Methods: Simultaneously with the measurements of atmospheric parameters, cultivable airborne bacteria were impacted, counted and identified using MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry); (3) Results: A mix of bacteria typically associated with humans and with natural habitats, including a large percentage of non-identified isolates, was found in the cave air. Few of the isolates were attributed to Risk Group 2. A strong positive correlation between tourist numbers and the rise in the concentration of airborne bacteria was indicated. Concentration of airborne bacteria rises to particularly high levels close to the underground river during periods of high discharge. A 10-times lower discharge reflected an approximately 20-times lower concentration of airborne bacteria; (4) Conclusions: Caves that are open and visited contain a diverse airborne microbiota originating from different sources. Enormous cave chambers that display relatively dynamic cave climate conditions do not normally support the enhancement of airborne bacterial concentrations.