Diurnal variation of cloudiness during southwest monsoon season using I NSAT-IB radiance data

An attempt has been made to study the diurnal variation of convective clouds. For this study 3 hourly full resolution infrared data of INSAT-IB have been used for the monsoon season (Jun-Sep) of 1987-89. The area of study extends from 35°N to 25°S and 40oE to l00oE, which is subdivided into small areas of 2.5x 2.5 Lat./Long. Mean temperature and the fractional area covered by clouds colder than a given threshold temperature over each sub area are the parameters used for this study. Two threshold temperatures. namely 265°K & 235oK are chosen to represent convective clouds and deep convective clouds respectively. Using the three hourly observations, times of maximum and minimum convective activity are also obtained. Maximum convective activity is observed over head Bay of Bengal at about noon and this maximum migrates westward onto land till midnight and swings back to oceanic area by morning. This eastwest oscillation is less over equatorial regions (open ocean).

The art of modelling climate change in time slice integrations: The ICON-ART experience

<p>Climate change is happening in a transient manner – with continuously increasing greenhouse gases in the atmosphere, humans have started a radiative imbalance that leads to rising near-surface temperatures. However, there are good reasons why it makes sense to look at quasi-equilibrium climate change simulations. In such simulations, we approximate climate change by “fixing” the amount of long-lived greenhouse gases and use recurring boundary conditions that are representative of a particular year - past, present or future. With such a setup any climate model should simulate a stable climate (after a spin-up phase) that reveals internal variability and does not show any trends. It is a necessary condition for the validity of the model - if no transience is provided in the boundary conditions – that the model does not drift. With such a model configuration, it is possible to estimate probability density functions, because each year of a multi-annual integration is an equally valid realisation for the meteorology of the pre-selected year.</p> <p>Using such a time-slice approach, sensitivities to well-specified individual changes can be assessed. Here, we provide a range of examples using the ICON-ART modelling system to investigate (idealised) climate change scenarios with respect to different threshold temperatures, jet variability and the climatic impact of the ozone hole. We illustrate how such integrations allow the unambiguous attribution of certain climate change effects, e.g. the change of jet stream variability under global warming or the contribution of the ozone hole to regional surface warming. However, we caution against a strict causality chain of processes in explaining the response, because given the nature of the quasi-equilibrium modelled, consistency might not always imply causality.</p>

Experimental Warming Hastens Physical Dormancy Break and Germination in Tropical Fabaceae

Climate warming may threaten the germination strategies of many plants that are uniquely adapted to today’s climate. For instance, species that employ physical dormancy (PY) – the production of seeds that are impermeable to water until high temperatures break them, consequently synchronizing germination with favorable growing conditions – may find that their seeds germinate during unfavorable or potentially fatal periods if threshold temperatures are reached earlier in the year. To explore this, we subjected the seeds of five species with physical dormancy (from the genera Abrus, Bauhinia, Cassia, Albizia, and Acacia) to “mild” (+2°C) and “extreme” (+4°C) future warming scenarios and documented their germination over 2 years relative to a control treatment. Under current climatic conditions, a proportion of seeds from all five species remained dormant in the soil for 2 years. A mild warming of 2°C had little to no effect on the germination of four of the five study species. Contrastingly, an extreme warming of 4°C dramatically increased germination in all five species within the first year, indicating a reduction in their ability to persist in the soil long-term. Cassia fistula was particularly susceptible to warming, exhibiting a similar increase in germination under both mild and extreme warming relative to control. Our findings suggest that climate warming in the tropics may cause the seeds of species that rely on physical dormancy to stagger the risk of unsuccessful germination across years to leave soil seed banks prematurely – the long-term implications of which remain unknown.

Embryonic Development of Grasshopper Populations Along Latitudinal Gradients Reveal Differential Thermoaccumulation for Adaptation to Climate Warming

Climate warming has a remarkable effect on the distribution, phenology, and development of insects. Although the embryonic development and phenology of non-diapause grasshopper species are more susceptible to warming than those of diapause species, the responses of developmental traits in conspecifically different populations to climate warming remain unknown. Here, we compared the mtDNA sequences and embryonic development of eight populations of grasshopper species (Chorthippus dubius) in field-based manipulated warming and laboratory experiments. The mtDNA sequences showed a significant genetic differentiation of the southernmost population from the other seven populations on the Mongolian Plateau. The embryonic development of the southernmost population was significantly slower than those of the northern populations at the same incubation temperatures. Interestingly, laboratory experiments showed that a significant difference exists in the effective accumulated degree days (EADD) but not in the lower development threshold temperatures (LDTT) among the different populations. The high-latitude populations required less EADD than the low-latitude populations. The warming treatments significantly accelerated the embryonic development in the field and decreased duration from embryos to hatchlings of all eight populations in the incubation. In addition, warming treatments in field significantly increased EADD requirement per stage in the incubation. Linear regression model confirmed that the embryonic development characteristics of eight populations were correlated with the annual mean temperature and total precipitation of embryonic development duration. The results indicated that grasshopper species have evolved a strategy of adjusting their EADD but not their LDTT to adapt to temperature changes. The variations in the EADD among the different populations enabled the grasshopper eggs to buffer the influences of higher temperatures on development and preserve their univoltine nature in temperate regions while encountering warmer climatic conditions. Thus, the findings of this study is valuable for our understanding species variation and evolution, and as such has direct implication for modeling biological response to climate warming.

Evaluation of linear and nonlinear models for temperature driven development of Spodoptera litura (Fabricius) on soybean crop

The tobacco caterpillar, Spodoptera litura, a major pest of soybean in India is under surveillance in all soybean growing areas in Maharashtra in order to issue alerts to farmers and prevent economic losses. In this context, two linear models were fitted to developmental data of S. litura life stages reared on soybean at five constant temperatures viz. 15, 20, 25, 30 and 35°C through laboratory experiments. Optimum temperature for development (Topt) and upper temperature threshold (Tmax) were estimated from three nonlinear models by additionally including developmental response at >35°C. Topt estimates for the total immature development were 34.5°C (Lactin-2), 33.7°C (Briere-1) and 33.2°C (Simplified Beta type function) while Tmax estimates were in the range of 38 to 40°C. Application of a thermodynamic non-linear model (Optim SSI) gave estimate ofintrinsic optimum temperature (Tφ) for development of egg (28.3°C), larva (27.5°C) and pupal stage (30.3°C). The phenology model of S. litura on soybean based on estimated developmental threshold temperatures and thermal constants was validated using available field surveillance data to facilitate informed pest management decisions.

Conditional Seed Dormancy Helps Silene hicesiae Brullo & Signor. Overcome Stressful Mediterranean Summer Conditions

Investigations on seed biology and ecology are of major importance for the conservation of threatened plants, both providing baseline information and suggesting practical approaches. In our study, we focused on the germination behavior of Silene hicesiae Brullo & Signor., a narrow endemic species to Panarea and Alicudi (Aeolian Archipelago, Italy), as well as one of the 50 most threatened Mediterranean island plants. Specifically, the effects of temperature, light, seed age, seed source, and collection year were evaluated; in addition, threshold temperatures and thermal–time parameters were estimated. The thermal range for fresh seed germination resulted between 5 and 15 °C, reaching up to 20 and 25 °C at increasing seed age, with 30 °C being clearly beyond the ceiling temperature. This behavior indicates that fresh seeds exhibit the Type 1 non-deep physiological dormancy, and that germination is regulated by conditional dormancy. This dormancy syndrome emerged as a highly efficient adaptation strategy for this species and, together with thermo-inhibition, would allow seeds to counteract or take advantage of Mediterranean environmental conditions. The comparison between the wild Panarea population and the corresponding ex situ cultivated progeny has enabled the identification of the latter as a suitable seed source for sustainable in situ reinforcement actions, at least in the short-term; indeed, plant cultivation for a single generation did not produce significant modifications in the germination behavior of the offspring.

Modelling the effects of cold temperature during the reproductive stage on the yield of chickpea (Cicer arietinum L.)

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R2 = 0.97), and grain yield (n = 22; R2 = 0.63–0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature–induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively.

Evolution of the threshold temperature definition of a heat wave vs. evolution of the minimum mortality temperature: a case study in Spain during the 1983–2018 period

Background An area of current study concerns analysis of the possible adaptation of the population to heat, based on the temporal evolution of the minimum mortality temperature (MMT). It is important to know how is the evolution of the threshold temperatures (Tthreshold) due to these temperatures provide the basis for the activation of public health prevention plans against high temperatures. The objective of this study was to analyze the temporal evolution of threshold temperatures (Tthreshold) produced in different Spanish regions during the 1983–2018 period and to compare this evolution with the evolution of MMT. The dependent variable used was the raw rate of daily mortality due to natural causes ICD X: (A00-R99) for the considered period. The independent variable was maximum daily temperature (Tmax) during the summer months registered in the reference observatory of each region. Threshold values were determined using dispersion diagrams (annual) of the prewhitened series of mortality temperatures and Tmax. Later, linear fit models were carried out between the different values of Tthreshold throughout the study period, which permitted detecting the annual rate of change in Tthreshold. Results The results obtained show that, on average, Tthreshold has increased at a rate of 0.57 ºC/decade in Spain, while Tmax temperatures in the summer have increased at a rate of 0.41 ºC/decade, suggesting adaptation to heat. This rate of evolution presents important geographic heterogeneity. Also, the rate of evolution of Tthreshold was similar to what was detected for MMT. Conclusions The temporal evolution of the series of both temperature measures can be used as indicators of population adaptation to heat. The temporal evolution of Tthreshold has important geographic variation, probably related to sociodemographic and economic factors, that should be studied at the local level.

Can we clinically identify pre-symptomatic and asymptomatic COVID-19?

Objectives: COVID-19 has had a severe impact on morbidity and mortality among nursing home (NH) residents. Earlier detection of SARS-CoV-2 may position us to better mitigate risk of spread. Both asymptomatic or pre-symptomatic transmission are common in outbreaks, and threshold temperatures, such as 38C, for screening for infection could miss timely detection in the majority. Design: Retrospective cohort study using electronic health records Methods: We hypothesized that in long-term care residents, temperature trends with SARS-CoV-2 infection could identify infection in pre-symptomatic and asymptomatic individuals earlier. We collected information about age and other demographics, baseline temperature, and specific comorbidities. We created standardized definitions, and an alternative hypothetical model to test measures of temperature variation and compare outcomes to the VA reality. Settings and participants: Our subjects were 6,176 residents of the VA NHs who underwent SARS-CoV-2 trigger testing. Results: We showed that a change from baseline of >0.4C identifies 47% of the SARS-CoV-2 positive NH residents early, and achieves earlier detection by 42.2 hours. Range improves early detection to 55% when paired with a 37.2C cutoff, and achieves earlier detection by 44.4 hours. Temperature elevation >0.4C from baseline, when combined with a 0.7C range, would detect 52% early, leading to earlier detection by more than 3 days in 22% of the residents. This earlier detection comes at the expense of triggering 57,793 tests, as compared to the number of trigger tests ordered in the VA system of 40,691. Conclusion and implications: Our model suggests that current clinical screening for SARS-CoV-2 in NHs can be substantially improved upon by triggering testing using a patient-derived baseline temperature with a 0.4C degree relative elevation or temperature variability of 0.7C trigger threshold for SARS-CoV2 testing. Such triggers could be automated in facilities that track temperatures in their electronic records.

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS2. The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.