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The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a "good model". This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models. [ABSTRACT FROM AUTHOR]

Water scarcity is a major challenge facing many regions worldwide, especially arid and semi‐arid areas that are increasingly vulnerable to climate change. This study aimed to project water availability in the Amu Darya Basin (ADB) of Central Asia under four Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) during two upcoming periods (2020–2059 and 2060–2099). The study used a robust machine learning approach, namely a Random Forest (RF) model, to simulate Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) data from precipitation and maximum and minimum temperatures (Tmax and Tmin). It then incorporated precipitation, Tmax and Tmin from four selected CMIP6 GCMs, into a water storage model to project spatiotemporal changes in water availability across the basin. The study also evaluated the relative impacts of land use and population on TWS. Results indicate an increase in TWS by approximately 4 cm in the basin's eastern, northwestern and southwestern regions in both future periods, while a decrease by approximately −4 cm in the remaining areas. These projections suggest that TWS will decline in densely populated regions and increase in certain intensively cultivated areas. The most pronounced increase in TWS is anticipated in the snow‐covered Tundra climate zone of the basin. This is attributed to the melting of glaciers, which contributes to runoff in the tributaries of the Amu River. The findings highlight the importance of considering climate change and socioeconomic factors when projecting water availability in arid and semi‐arid regions. The projected changes in TWS have important implications for water resources management in the ADB, particularly in densely populated and intensively cultivated areas. [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally. Plain Language Summary: The STAR‐ESDM tool is able to quickly and accurately generate future climate projections for weather stations and high‐resolution grids anywhere in the world. It does this by breaking down global or regional climate model output into different components, from the long‐term trend to the day‐to‐day variability, then merging projected changes with observations. When tested against projections generated by a complex and computationally expensive dynamical global model, STAR‐ESDM produced almost the same output, even for extreme temperature and precipitation values, at a fraction of the computational cost. Moreover, unlike most statistical downscaling models, this method isn't tied to any specific geographic area or predictand and/or predictor data set. It can be applied to any regional or global data set, whether generated by a climate or reanalysis model, derived from satellite observations, recorded at weather stations, and more. As climate impacts escalate, STAR‐ESDM offers a flexible and effective way to generate the high‐resolution climate projections needed to better gauge climate risk and enhance resilience anywhere in the world where reliable observational or quasi‐observational data, including reanalysis or satellites, are available. This is particularly relevant to under‐resourced regions, which are often most vulnerable to climate impacts as well as most lacking in future projections. Key Points: STAR‐ESDM is a rapid, flexible and generalizable approach to bias‐correct and downscale climate model output to any finer‐resolution data setPredictors/predictands can be derived from global or regional models, satellites, reanalysis, gridded observations and weather stationsProjected changes in temperature and precipitation mirror those of a high‐resolution global model at a fraction of the computational cost [ABSTRACT FROM AUTHOR]

The medicinal potential of Coelogyne suaveolens, a traditional medicinal plant, was investigated through in vivo and molecular docking studies. The ethyl acetate fraction of the plant's acetonic extract was subjected to various bioactivity tests to assess its analgesic, anxiolytic, and sedative effects on Swiss albino mice. Furthermore, we used GCMS to identify the bioactive chemicals in the extract's ethyl acetate fraction. The root and bulb extracts demonstrated significant analgesic activity in acetic acid‐induced writhing, hot plate, and tail immersion tests in a dose‐dependent manner when compared to the control. Again, the extract exhibited moderate anxiolytic activity in the elevated plus maze test at a dosage of 400 mg/kg body weight, while the root extract showed significant anxiolytic activity in the hole board test at the same dosage. Significant sedative activity was observed in the hole cross, open field, and rotarod tests at a dosage of 400 mg/kg. According to molecular docking studies, the extract has the potential to serve as an analgesic medication by reducing the enzymatic activity of cyclooxygenases 1 and 2. Overall, the findings suggest that C. suaveolens has substantial therapeutic potential for the development of novel treatments for pain, anxiety, and sleep disorders. [ABSTRACT FROM AUTHOR]

BACKGROUND: Betel leaf is an essential oil (EO)‐rich plant from the Piperaceace family used as traditional herbal medicine. The minimum EO yield by the conventional extraction method was increased by adopting cold plasma (CP) as pretreatment. Thus, the present study involved optimizing the CP conditions and analyzing the EO's qualities. RESULTS: Optimization of the CP parameters like electric voltage (A = 25–35 kV), treatment duration (B = 4–12 min) and extraction time (C = 60–180 min) was done for maximum EO yield (R1) and total phenolic content (TPC; R2) using response surface methodology with Box–Behnken design. Maximum EO yield (20.76 ± 1.15 g kg−1) and TPC (29.43 ± 1.7 mg GAE mL−1) were derived under optimal conditions: A = 34 kV, B = 10 min and C = 110 min. A quadratic polynomial model developed by multiple regression analysis revealed that the three independent variables significantly influenced the oil yield and TPC with R2 values of 0.9909 and 0.9962, respectively. The CP treatment significantly altered the betel leaf powder morphology and increased the EO's radical scavenging capacity and bioactive compounds like chavibetol, chavibetol acetate, hydroxychavicol and γ‐muurolene. Conversely, the functional groups, refractive index and specific gravity were unaffected by CP treatment. CONCLUSION: The EO yield and its qualities were improved by applying CP under optimal conditions which can be helpful for scaled‐up industrial processes with further studies. The identified bioactive compounds are valuable in the food and pharmaceutical industries. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Morocco is located in a region vulnerable to the impacts of climate change, which can have profound effects on its social, economic and environmental systems. This makes studies aimed at forecasting these impacts in future using climate models particularly important. However, the generally coarse spatial resolution of models, combined with a large number of models, imposes a limitation on the models, allowing the selection of the most appropriate ones for climate change impact assessments in a specific region. In this study, 38 GCMs and GCM‐RCMs from CMIP5 ensemble and CORDEX project were downscaled and bias‐corrected for use in projecting climate change over Morocco under the RCP4.5 and RCP8.5 scenarios. A three‐step sequential process was adopted, involving in that order the selection of models based on: (i) projection of climate means; (ii) projection of climate extremes; and (iii) ability of the models to simulate the baseline climate. Climate projections show precipitation decreases of up to 10% by the beginning of the century, with decreases of more than 20% under RCP8.5 projected by 2100, with the central and northern mountainous regions of the country being the most affected. Seasonal projections showed autumn months likely to experience the greatest decline in precipitation, up to 36.56% by the end of the century. Temperature projections revealed an upward trend in mean, maximum and minimum temperatures, with increases of up to 3°C predicted by mid‐century over most of the country, particularly in the winter months. Our results point to a concerning future, with impacts related to decreased precipitation and increased temperatures expected to be many and varied across the country. Nevertheless, they can help provide a knowledge base for efforts to mitigate and adapt to expected changes. [ABSTRACT FROM AUTHOR]

In this study, the formation of dicarbonate degradation products of ethylene carbonate‐based carbonate mixtures containing dimethyl carbonate, ethyl methyl carbonate or diethyl carbonate that were combined with lithium or potassium metal, is investigated. It is shown by NMR and GCMS that the dicarbonate products dimethyl dioxahexane dioate, ethyl methyl dioxahexane dioate and diethyl dioxahexane dioate are formed from the reactants to different extents and, in particular, the potassium surface initiates the fast formation of the corresponding dicarbonate products. Experiments with deuterated DMC suggest an intermolecular mechanism of the dicarbonate formation. In cell tests, namely potassium vs. graphite, it is shown that the electrolyte formulation with the lowest tendency to dicarbonate formation (EC/DEC) exhibited the best cell stability respectively lowest cell aging. [ABSTRACT FROM AUTHOR]

BACKGROUND: A simulation study was performed for assessing climate change impact on maize under Representative Concentration Pathways (RCPs 2.6 and 8.5) for Punjab, India. The study area comprised five agroclimatic zones (AZs) including seven locations. The bias corrected temperature and rainfall data from four models (CSIRO‐Mk‐3‐6‐0, FIO‐ESM, IPSL‐CM5A‐MR and Ensemble) were used as input in CERES‐Maize model which was run with constant management practices for two Punjab maize hybrids (PMH 1 and PMH 2). The maize yield for upcoming 70 years (2025–2095) was simulated and its deviations from the baseline (2010–2021) yield were computed under optimized sowing (early‐May to early‐July) and current sowing (end‐May to end‐June) period. RESULTS: With current sowing dates, the maize yield declined under both RCP 2.6 and RCP 8.5 scenarios, respectively in all the AZs, that is, by 4–23% and 60–80% in AZ II, by 5–60% and 60–90% in AZ III, by 9–30% and 50–90% in AZ IV and by 13–40% and 30–90% in AZ V. Though yield decline was lesser under RCP 2.6 as compared to RCP 8.5, but still it indicates that adaptive strategy such as shifting of sowing dates may be helpful in stabilizing the maize yield. CONCLUSION: The results for iterative combinations of sowing period revealed that early June sowing in AZ II for both the hybrids, mid‐ to end‐June (Ludhiana and Amritsar) and end‐May to mid‐June (Patiala) sowings for PMH 1 were able to nullify the negative impact of climate change. Maize cultivation in AZ IV and AZ V would not be a suitable venture for farmers of the region. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Mushrooms, like other fungi, are unique in the living world They have been assigned to the kingdom of Myceteae. Lentinula edodes(shiitake), is second‐largest and most often consumed edible mushroom in the world. Shiitake mushroom possess large capability to be used as food as it contains carbohydrates, proteins, vitamins, minerals, fibers, fatty acids, amino acids, etc. In current study powdered form mushroom used to form semisolid extract in selected fractions (Butanol, ethyl acetate, hexane, chloroform and aqueous). The aim of the current study was to explore phytoconstituents which have biological activities in selected fractions (n. hexane chloroform, and ethyl acetate) of methanolic extract of Lentinula edodes.GC‐MS, and HPLC analysis presented the secondary metabolites in all the selected fractions under taken. Chloroform fraction had the highest total phenolic, and total flavonoid which exhibited the maximum in vitro antioxidant ABTS potential (74.78±0.83) potential at 350 μg/mL while ethyl acetate has moderate and n‐hexane shows least concentration. Hemolysis essay showed less than 10 % lysis of RBC, s which indicates that fractions under study are active, and safe to use as herbal medicine. Biomolecules detected through HPLC and GC‐MS in fractions under study showed significant inhibition in paw inflammation (1.18±0.06) and yeast induced pyrexia (36.8±0.68) in dose dependent manner. Furthermore, In‐silico study showed that 4H‐Furo[3,2‐b] pyrrole‐5‐carboxylic acid have a strong interaction to NADPH oxidase, Myeloperoxidase as compared to standard Melatonin which shows best antioxidant compound which was confirmed in chloroform fraction.The outcomes of this research indicated that L. edodes significantly reduced inflammation and have ability to scavenge free radicals, prevent protein denaturation, also indicate the possible use in food and nutraceutical applications as an antioxidant, antipyretic, anti‐inflammatory, and antidiabetic agent. [ABSTRACT FROM AUTHOR]

The purpose of the current study piece is to use short branches rather than stem bark to take a conservative approach to the Bombax ceiba plant. Similarities were found in the HPLC, LCMS, and GCMS data of the plant's stem bark and tiny branches. Analysis using liquid chromatography‐mass spectrometry (LC‐MS) of abundant medicinal phytochemicals of Bombax ceiba and its Molecular Docking Interaction with AKT Protein Kinase, Human Acetyl Choline Esterase, and Pancreatic Lipase Colipase Target Protein. According to the HPLC study results, Bombax ceiba stem bark contains 0.359 % lupeol, whereas tiny branches have 0.039 %. Three compounds have good binding energy (Kcal/mol) with Human acetyl choline esterase, while seven compounds have very good binding energy (Kcal/mol) with AKT Protein kinase and Pancreatic Lipase Colipase. Small branches are essentially identical to stem bark in terms of their chemical composition that makes them active against certain target proteins. [ABSTRACT FROM AUTHOR]

N,N‐Diformylmescaline, a novel analogue of mescaline, has recently been detected in Australia in two unrelated seizures. To confirm the identification, a three‐step synthesis from 3,4,5‐trimethoxyphenylacetic acid was devised. However, purification of the final product proved problematic with the compound prone to degradation in solution. Analysis of the compound by LC–MS indicated that the compound was unstable under acidic and basic conditions, breaking down to N‐formylmescaline. Further degradation to mescaline was observed when the compound was dissolved in hydrochloric acid for an extended period of time suggesting that N,N‐diformylmescaline may be a prodrug for mescaline. The GC–MS, NMR and FTIR data for the seized compound are presented along with details of the synthesis and studies of the compound's stability in solution. [ABSTRACT FROM AUTHOR]

Appraisal of the long‐term precipitation trends and variability is crucial for sustainable water resources management. This research intended to evaluate Bangladesh's monthly, seasonal, and annual spatiotemporal rainfall variability using 40 global climate models for two representative concentration pathways (RCPs), RCP4.5 and RCP8.5. Statistical downscaling climate model (SimCLIM) was used for downscaling and ensemble projection of rainfall in near (2011–2040), middle (2041–2070), and far (2071–2100) futures. Modified Mann–Kendall test was applied to detect future rainfall trends. The results revealed a significant increasing trend in rainfall in near and middle futures for RCP4.5 and in all three future periods for RCP8.5 at all meteorological stations of Bangladesh during significant rainfall months (May–October). The results also showed a decreasing trend in rainfall in dry months (December–January) at many stations. The highest increase in rainfall was projected in June at a rate of 0.10–1.11 mm·year−1 for RCP4.5 and 3.34–4.98 mm·year−1 for RCP8.5 in different future periods. Monsoon rainfall showed the highest increase, and winter rainfall the lowest increase for all RCPs and future periods. The increase in annual precipitation over Bangladesh was projected 2.76–5.98% in three future periods for RCP4.5 and 6.98–26.44% for RCP8.5. These outcomes indicate a possible increase in floods severity and frequency in Bangladesh in the future. [ABSTRACT FROM AUTHOR]

BACKGROUND: The protection of grains from insect infestation is critical during storage. Insect repellent pellets (IRPs) are a potential technique to repel insects by hindering insect movement toward the grains. The basic principle of IRPs is the use of active components found in the oils of lemongrass, eucalyptus, and neem leaves for the controlled release of fumes, thereby avoiding the need for reapplication after a few days. Here, we examined the antifungal activity, the lethal dose, and the repelling effect of IRPs against red flour beetle, Tribolium castaneum, over a 30 day period. RESULTS: We observed that IRPs possessed antifungal properties and were able to repel the adults of T. castaneum. These insects ultimately died from the fumes if they manage to stay near the IRPs (LD50 = 2 and LD99 = 7 days). The active components (phenol, 2,4‐di‐tert‐butyl‐, citral, neral, geraniol, n‐hexadecanoic acid) present in IRP during the initial stage were also found after a storage period of 35 days. CONCLUSION: The active components present in IRPs have antifungal, repellent, and fumigant properties. The IRPs can thus be termed potent botanical insecticides and are an alternative to synthetic insecticides. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Pre‐race dosing of horses with alkalinising agents to manipulate performance has been evident in racing worldwide for over 30 years. To regulate the use of alkalinising agents, racing authorities adopted thresholds for total plasma carbon dioxide (TCO2) in racehorses. Traditionally, racing laboratories have measured plasma TCO2 using ion selective electrode (ISE) technology, with the Association of Official Racing Chemists (AORC) approving the use of only three ISE instruments for measurement. Because of the manufacture and support of these instruments ceasing, racing laboratories have explored alternative techniques to measure plasma TCO2. In this study, headspace gas chromatography mass spectrometry (HSGCMS) with fully automated sample preparation was investigated as an alternative technique to ISE. Sample preparation was carried out online on a Gerstel robot, where plasma was aspirated directly from sealed vacutainer tubes before further treatment and headspace injection into a GCMS. The method was successfully cross validated against a Beckman Unicel DxC®600, meeting all criteria stipulated in the AORC cross‐validation protocol. The method achieved an accuracy of 99.8%, within‐run relative standard deviation of 0.22% and interday reproducibility of 0.04 mM, all significant improvements on the authors ISE method. A population study was also conducted to ensure the plasma TCO2 threshold, established with ISE methodology, did not change with the developed HSGCMS method. The concentrations and standard deviations for the two methods were almost identical, HSGCMS mean 30.62 mM, standard deviation 1.65 mM, and ISE 30.65 and 1.55 mM. The results indicate that the fully automated HSGCMS method is suitable for measurement of equine plasma TCO2 for regulatory purposes. [ABSTRACT FROM AUTHOR]

Downscaling techniques are effective to bridge the scale gap between global circulation models and regional studies. Statistical downscaling methods are prevalent due to their advantages in high computational efficiency and accuracy. However, an implicit assumption of most statistical techniques is that time series should be relatively stationary after certain function transformations. Otherwise, statistics of nonstationary time series may be meaningless for describing future behavior. In this study, a hybrid statistical downscaling framework was developed through integrating bivariate empirical mode decomposition (BEMD) and a machine learning method to extract multi‐timescale features from nonstationary data to enhance downscaling performance. The proposed framework can reduce the effects of non‐stationarity in data‐driven models by using the BEMD method, which can decompose time series into independent and stationary components at multiple time‐frequency resolutions. It was applied to downscale monthly precipitation and temperature of Canadian Earth System Model in multiple stations with different climate types in the Central Valley of California, USA, to verify its accuracy and generalization ability. The performance of downscaling maximum and minimum temperatures (R2 > 0.9) was more accurate than that of precipitation. The potential reason is that precipitation is more sensitive to transient weather phenomena, which can only be extracted from data with higher temporal resolution. The proposed model was further compared with models based on discrete wavelet transform and models without time series decomposition. The results showed that a decomposition strategy of the proposed framework can improve the downscaling accuracy, potentially providing a viable option to deal with the nonstationary of data in statistical downscaling models. Key Points: A hybrid statistical downscaling framework based on a nonstationary time series decomposition method and machine learning is proposedThe framework realizes nonstationary time series decompositions of temperature and precipitationThe proposed framework outperforms downscaling methods without decomposition and wavelet‐based methods [ABSTRACT FROM AUTHOR]

We compare the response of the Quasi‐Biennial Oscillation (QBO) to a warming climate in eleven atmosphere general circulation models that performed time‐slice simulations for present‐day, doubled, and quadrupled CO2 climates. No consistency was found among the models for the QBO period response, with the period decreasing by 8 months in some models and lengthening by up to 13 months in others in the doubled CO2 simulations. In the quadrupled CO2 simulations, a reduction in QBO period of 14 months was found in some models, whereas in several others the tropical oscillation no longer resembled the present‐day QBO, although it could still be identified in the deseasonalized zonal mean zonal wind timeseries. In contrast, all the models projected a decrease in the QBO amplitude in a warmer climate with the largest relative decrease near 60 hPa. In simulations with doubled and quadrupled CO2, the multi‐model mean QBO amplitudes decreased by 36 and 51%, respectively. Across the models the differences in the QBO period response were most strongly related to how the gravity wave momentum flux entering the stratosphere and tropical vertical residual velocity responded to the increases in CO2 amounts. Likewise it was found that the robust decrease in QBO amplitudes was correlated across the models to changes in vertical residual velocity, parametrized gravity wave momentum fluxes, and to some degree the resolved upward wave flux. We argue that uncertainty in the representation of the parameterized gravity waves is the most likely cause of the spread among the eleven models in the QBO's response to climate change. [ABSTRACT FROM AUTHOR]

Two chalcedonite artefacts from the Magdalenian site of Ćmielów 95 (Poland), with macroscopic features suggestive of thermal treatment, were subjected to a multi‐instrumental analysis. The red upper layer of the objects consists of "protohematite", implying temperature‐driven, goethite‐to‐hematite transition. The red layer shows traces of carbonized matter with saccharides and levoglucosan (from burning wood) as well as fatty acids. PXRD data suggest a source of higher temperatures (up to ~800°C) within the bottom layer, with ~200–300°C range ascribed to the red layer. On the basis of the collected data the artefacts are proposed to be relics of cooking stones. [ABSTRACT FROM AUTHOR]

In experimental evolutionary studies, altering sex ratio in populations imposes sex‐biased intrasexual and intersexual interactions, which is useful to study the effect of sexual selection and sexual conflict in such populations. We studied cuticular hydrocarbon (CHC) patterns of 170 generations‐old operational sex ratio‐altered Drosophila melanogaster population to investigate the evolutionary outcomes of such selection lines. Our results indicate that the competing sex in each of the selection lines has less variable CHC profile than the less abundant sex, potentially due to sexual selection. The intensity of sexual selection is possibly influenced by intense competition among the abundant sex as well as potential mate choice due to easy availability of mates for the rarer sex in the population. This result is even more striking since the male‐biased replicates were previously shown to have diverged in terms of mate preferences due to sexually antagonistic coevolution (SAC). The precopulatory isolating mechanism underlying such divergent mate preference could be sexual signals such as CHCs since they evolve rapidly and are involved in D. melanogaster mate recognition. Therefore, we also investigate whether CHC profiles diverged in male‐biased replicates in comparison to female‐biased replicates. We found no evidence that cuticular hydrocarbon profiles of male‐biased and female‐biased populations have evolved due to SAC. This study indicates that the differentiation of sexual traits may not be credited to sexual conflict despite populations isolated due to high sexual conflict evolve divergent cuticular hydrocarbon profiles. [ABSTRACT FROM AUTHOR]

Trends of 15 extreme and non‐extreme daily rainfall indices for Mexico and the southern United States are analyzed for the extended boreal winter (November–March) and summer (June–October) during 1981–2010. The analysis includes gridded observations (CHIRPS and Livneh), reanalyses (ERA5, ERA‐Interim, MERRA2, and CFSR), the regional climate model RegCM4.7 (forced by ERA‐Interim), and four general circulation models (GCMs) from the CMIP5 (HadGEM2‐ES, CNRM‐CM5, MPI‐ESM‐MR, and GFDL‐ESM2M). The domain is divided into 13 regions with similar seasonal rainfall patterns according to the k‐means algorithm applied to CHIRPS monthly precipitation, as a reference. Gridded observations and reanalyses (O&R) show similar regional annual cycles and metrics of precipitation. The two observational data sets show the largest differences in the magnitude of some indices in the wettest region (southeastern Mexico) and in the monsoon. During summer, O&R show positive significant trends in extreme rainfall indices in regions commonly affected by tropical cyclones (southern Baja California Peninsula, northeastern Mexico, and the southeastern United States), while negative significant trends of seasonal accumulation of rainfall are seen in the Mediterranean Californias and the Sonoran Desert region. There is an agreement among most data sets that the frequency and intensity of extreme winter precipitation indices have declined in northwest Mexico and the southwestern United States, but trends are not statistically significant, while southeastern Mexico shows significant increases in 5‐days accumulation (rx5d). ERA5 followed by MERRA2 show the best performance. RegCM4.7 reproduces the annual cycle of precipitation in most of the regions and captures the main trend signals especially in winter, but it has difficulties in reproducing the summer interannual variability of few indices. The GCMs tend to overestimate (underestimate) the annual cycle of precipitation and the median values of extreme indices in the driest (wettest) regions. They are not able to capture the regional trends of the indices. [ABSTRACT FROM AUTHOR]

Assessments of climate change impacts on streamflow and sediment processes are essential for developing science‐based sustainable watershed management plans. We assessed climate change impacts on streamflow and sediment load in the upstream of the Mekong River Basin, as a case study. Future climate scenarios including an ensemble‐mean climate scenario (EnM scenario) were generated based on 20 GCMs in CMIP5, using a stochastic weather generator (LARS‐WG) coupled with a distribution‐free shuffle procedure. The SWAT model was applied to simulate changes in streamflow and sediment load for the future period 2071–2100 under RCP8.5 with respect to the baseline period 1971–2000. Results show that mean monthly maximum and minimum temperature were projected to increase by all the 20 GCMs, with an ensemble‐mean increase of 4.6–5.7°C and 4.2–5.8°C across the 12 months, respectively. An increase in mean annual precipitation (3.4–55.8%) and streamflow (1.0–72.7%) was also projected by all GCMs. However, projected changes in sediment load were not consistent. One half of the GCMs projected an increase (5.2–53.2%) in annual sediment load while the other half projected a decrease (5.1%–43.2%). In each month, at least three‐quarters of the GCMs projected an increase in monthly streamflow. For monthly sediment load, an increase in May to July was projected by over half of the GCMs, while a decrease was projected by a majority of the GCMs for other months. Our results indicate large uncertainties in streamflow and sediment projections under climate change, demonstrating the need to use multi‐model ensembles in climate change impact studies. Moreover, it was found that the streamflow and sediment loads simulated using the EnM scenario were often close to the ensemble means simulated using the 20 GCMs, which implies that the single EnM scenario has the potential of effectively and efficiently estimating the ensemble means of projections in a multi‐model ensemble. [ABSTRACT FROM AUTHOR]

Climate change significantly influences characteristics of rainfall events including rainfall depth, rainfall duration, inter‐event time and temporal patterns that directly affect water resources management, flood defence and hydraulic structure design. In this study, a framework is proposed to analyse daily‐scale rainfall event characteristics based on global climate model (GCM) simulations. This framework includes bias correction of raw GCM‐simulated rainfall series, selection of good‐performing bias‐corrected GCMs based on the mean absolute percentage error (MAPE) and evaluation of selected GCMs' skills in simulating rainfall event characteristics and finally assessment of changes in rainfall event characteristics in the future. In this study, 17 GCMs, four representative concentration pathways (i.e., RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and two future periods (i.e., 2041–2070 and 2071–2100) are considered. After bias correction of the GCMs using the monthly‐scale double gamma distribution, 9 out of 17 GCMs with MAPE values smaller than 20% in the historical period 1971–2000 are selected. In general, these selected GCMs well capture the rainfall characteristics of different rainfall event classes. The multi‐model ensembles suggest that compared to the historical period, the frequency of rainfall events with an extreme depth, short duration and long inter‐event time will increase in the two future periods and the change in 2071–2100 is generally larger than that in 2041–2070, indicating that more extreme climate conditions may occur in Qu River basin in the future. Moreover, the temporal patterns of heavy rainfall events will become more non‐uniform with more concentrated peak rainfall. The frequency of the delayed rainfall type (i.e., peaks occurring at the end of the rainfall event) will increase in the future, which can probably cause more severe floods and is very detrimental to flood defence in this study area. [ABSTRACT FROM AUTHOR]

Summer monsoon rainfall forecasting in the Yangtze River basin is highly valuable for water resource management and for the control of floods and droughts. However, improving the accuracy of seasonal forecasting remains a challenge. In this study, a statistical model and four dynamical global circulation models (GCMs) are applied to conduct seasonal rainfall forecasts for the Yangtze River basin. The statistical forecasts are achieved by establishing a linear regression relationship between the sea surface temperature (SST) and rainfall. The dynamical forecasts are achieved by downscaling the rainfall predicted by the four GCMs at the monthly and seasonal scales. Historical data of monthly SST and GCM hindcasts from 1982 to 2010 are used to make the forecast. The results show that the SST‐based statistical model generally outperforms the GCM simulations, with higher forecasting accuracy that extends to longer lead times of up to 12 months. The SST statistical model achieves a correlation coefficient up to 0.75 and the lowest mean relative error of 6%. In contrast, the GCMs exhibit a sharply decreasing forecast accuracy with lead times longer than 1 month. Accordingly, the SST statistical model can provide reliable guidance for the seasonal rainfall forecasts in the Yangtze River basin, while the results of GCM simulations could serve as a reference for shorter lead times. Extensive scope exists for further improving the rainfall forecasting accuracy of GCM simulations. [ABSTRACT FROM AUTHOR]

In this study, the performances of 31 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) were evaluated using observed temperature and precipitation data sets for mainland Southeast Asia (MSA). In addition, the technique for order preference by similarity to ideal solution model (TOPSIS), which is an effective multi‐objective decision method and has been widely applied in systems engineering, was used to comprehensively evaluate the GCMs on a regional scale. The meteorological variables employed included monthly mean, maximum, and minimum air temperature series and annual/seasonal precipitation during the period 1961–2004. Results showed the overall Ci values ranged from 0.56 to 0.80. Overall, ACCESS1.0, HadGEM2‐A, HadGEM2‐CC, and CESM1 (BGC) performed better than the other models with respect to air temperature and precipitation over MSA. For meteorological variables, the Ci of extreme temperature by GCMs was 0.65 and higher than mean temperature at 0.61. The performance of GCMs in simulating precipitation during wet season was superior to that for annual and dry season precipitation. With respect to evaluation indicators, most of the GCMs assessed in this study failed to capture observed mean annual air temperature and annual precipitation (they underestimated annual air temperature and overestimated mean annual precipitation). When evaluating the performance of the GCMs with respect to reproducing individual climatic variables, the use of various statistical evaluation criteria provided inconsistent results. These results demonstrate the need to consider the influence of different statistical eigenvalues and to employ multiple indicators when possible to enable a comprehensive evaluation of GCMs. The results of this study provide useful information for climate change, water resource, and agricultural management research in MSA. [ABSTRACT FROM AUTHOR]

We analyze physical processes leading to daily wintertime (December, January, and February) extreme precipitation events in Alaska between 1986 and 2005. This is done by applying self‐organizing maps to environmental conditions corresponding to National Centers for Environmental Information precipitation, using the European Centre for Medium‐Range Weather Forecasts reanalysis (ERA‐Interim) and Coupled Model Intercomparison Project 5 (CMIP5) global climate selected Global climate model (GCM; selected GCMs). We focus on widespread extreme events, defined as the top 0.1% of daily precipitation occurring on at least six grid boxes on the same day. The self‐organizing maps methodology allows identifying large‐scale circulations conducive to extreme events. This methodology identifies distinctive circulation patterns conducive to producing extreme events with a trough west of Alaska leading to south or southwest flow across the state. Extreme events occur along the windward (southern) side of the Alaska Range due to uplift by the mountains in the ERA‐Interim and in all models. In the National Centers for Environmental Information observations, precipitation rates are greater than in any of the selected GCMs. Simulated extreme precipitation decreases as model resolution decreases, and our study suggests that the smoothness of model topography is a reason for the scaling between model precipitation rate and model resolution. Key Points: Self‐organizing maps help identify large‐scale circulations conducive to precipitation extremes in a subset of Global Climate ModelsGlobal climate models can simulate large‐scale processes conducive to daily precipitation extremesGlobal climate model topography can be too coarse to properly simulate orographically forced extreme precipitation [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) serve as an important tool to perform high resolution climate simulations for a limited area. However, there remains a crucial issue regarding the efficacy of RCMs that whether they are able to produce added value (AV) in representing the regional climatic features compared to their coarse scale driving Global climate models (GCMs). The present study aims at assessing the AV of a set of RCM simulations performed under the Coordinated Regional Climate Downscaling Experiments‐South Asia (CORDEX‐SA) compared with their respective driving GCMs in representing the Indian summer monsoon (ISM) precipitation and its dynamical features. For this purpose, the performance of RCMs and GCMs is compared for the present day climate (1970–2005) against observations and reanalysis datasets in terms of various ISM features, that is, the spatial distribution of precipitation, the evolution of vertical shear of zonal wind and the onset phase of ISM in terms of delayed or early arrival of monsoon. The RCMs show a clear improvement upon their corresponding driving GCMs in simulating spatial features of precipitation distribution that are characteristically associated with ISM. Regionally, the RCMs also show skill in capturing the precipitation amount as a reduction in bias is found compared with their driving GCMs. There is a significant improvement in the simulation of onset dates of ISM by only some of the experiments particularly, RegCM4 driven with IPSL‐CM5A‐LR. The study concludes that the present set of CORDEX‐SA RCM simulations do indeed add value to their driving GCMs for a number of features associated with the ISM precipitation but this value varies with region, driving GCM and the particular feature of ISM under focus. Simulated climatology (1970–2005) of JJAS mean precipitation (mm day−1) by (A) IMD observation, (a1–l1) GCMs and their corresponding (a2–l2) CORDEX‐SA RCMs grouped together in a box. The orographic feature in spatial variability of ISM is evident in IMD—a characteristic which seems to be better captured here by RCMs as compared to their parent GCMs. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi‐model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables. The relative change of RMSE for different size of the CMIP5 subset compared with full number of GCMs using the random forest (RF) ensemble simulations. Our results demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. [ABSTRACT FROM AUTHOR]

Biodiesel is considered as an alternative fuel which can replace the traditional fuels easily with its less hazardous effects and economical pricing. This study compares an ultrasonic‐assisted catalytic transesterification using ZnO nanocatalyst for waste cooking oil (WCO) with the conventional stirring method. The structural and morphological characterizations of prepared catalysts were done by XRD and FESEM with EDX methods. The effect of reaction parameters such as oil to methanol ratio, catalyst amount and reaction time was investigated to optimize the transesterification conditions. GC/MS and FTIR analysis have been carried out to identify and confirm the presence of fatty acid methyl esters (FAME) under optimum conditions. The FAME conversion yield up to 96% under ultrasonication method was calculated. Two important fuel properties like density and viscosity were analyzed and compared with the standard biodiesel. These studies provided the clear evidence that WCO can be employed as a substantial source of biodiesel as fuel in diesel engines. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 1176–1182, 2018 [ABSTRACT FROM AUTHOR]

ABSTRACT: Assessing warming over the Western Himalayan Region (WHR) of India is challenging due to its limited station data availability and poor data quality. The missing values in the station data were replaced using the Multiple Imputation Chained Equation technique. Finally, 16 stations having continuous records during 1969–2009 were considered as the ‘reference stations’ for assessing the warming/cooling trends in addition to evaluate the Coupled Model Intercomparison, phase 5 (CMIP5), Global Circulation Model (GCM). Station data indicates winter (DJF) warming is higher and rapid (1.41 °C) than the other seasons and less warming was observed in the post‐monsoon (0.31 °C) season. Overall mean annual warming over WHR is ∼0.84 °C during 1969–2009. The performance of 34 CMIP5 models was evaluated based on three different criteria namely (1) mean seasonal cycle, (2) temporal trends and (3) spatial correlation between simulated and observed signals for common available period of 1969–2003 over the study area. Models are provided a final rank on the basis of the cumulative rank obtained in each of three approaches. CMCC‐CM, GISS‐E2‐H and MIROC 5 are three top‐ranked models while MIROC‐ESM, MIROC‐ESM‐CHEM and bcc‐csm1‐1 are three bottom‐ranked models over the WHR. The study also extended to judge whether the selected top‐ranked models perform well through two alternative data sources namely European Reanalysis (ERA)‐interim and Climate Research Unit (CRU), which have not used in the process of model evaluation. The spatial patterns of top‐ranked GCM are similar to the spatial pattern obtained through ERA‐interim and CRU while zoomed in to WHR but bottom‐ranked models fail to reproduce such spatial patterns indicating the top‐ranked GCMs would offer more reliability for projecting future climate over WHR. [ABSTRACT FROM AUTHOR]

Abstract: A set of idealized experiments are developed using the Community Atmosphere Model (CAM) to understand the vertical velocity response to reductions in forcing scale that is known to occur when the horizontal resolution of the model is increased. The test consists of a set of rising bubble experiments, in which the horizontal radius of the bubble and the model grid spacing are simultaneously reduced. The test is performed with moisture, through incorporating moist physics routines of varying complexity, although convection schemes are not considered. Results confirm that the vertical velocity in CAM is to first‐order, proportional to the inverse of the horizontal forcing scale, which is consistent with a scale analysis of the dry equations of motion. In contrast, experiments in which the coupling time step between the moist physics routines and the dynamical core (i.e., the “physics” time step) are relaxed back to more conventional values results in severely damped vertical motion at high resolution, degrading the scaling. A set of aqua‐planet simulations using different physics time steps are found to be consistent with the results of the idealized experiments. [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

We define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. We find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias. [ABSTRACT FROM AUTHOR]

ABSTRACT Multi-model ensembles ( MMEs) have been demonstrated to be useful for improving the results of models. Furthermore, previous research suggests that weighted MMEs outperform unweighted multi-model results in climatological simulations, with the degree of difference in model performance dependent on the suitability of the weighting scheme. The goal of this research is to improve the results of MMEs by optimizing the weighting for each model for predicting the distribution of Köppen-Geiger climatic types. Results suggest that the correspondence between general circulation model- ( GCM-) based and Climate Research Unit- ( CRU-) based output of the geographic distribution of the Köppen-Geiger climatic types is inadequate for nine GCMs simulations, with only 40-52% of the total land area having agreement for the Köppen climatic type. An unweighted ensemble average GCM-based simulation produced only marginally improved model performance. However, the terrestrial area with disagreement between the MMEs and CRU was reduced to about 35% by using the nonlinear-weighted ensemble average. Inconsistent regions between the MMEs and CRU are concentrated along the climatic boundaries, confirming that ecotones are simulated poorly by these models. Because, the Köppen classification is designed so that climatic regions correspond to biomes, this research may have implications for improving simulation of agricultural, forestry, and other biotic realms under past and future climatic conditions. [ABSTRACT FROM AUTHOR]

The cloud-to-precipitation transition process in warm clouds simulated by state-of-the-art global climate models (GCMs), including both traditional climate models and a high-resolution model, is evaluated against A-Train satellite observations. The models and satellite observations are compared in the form of the statistics obtained from combined analysis of multiple-satellite observables that probe signatures of the cloud-to-precipitation transition process. One common problem identified among these models is the too-frequent occurrence of warm precipitation. The precipitation is found to form when the cloud particle size and the liquid water path (LWP) are both much smaller than those in observations. The too-efficient formation of precipitation is found to be compensated for by errors of cloud microphysical properties, such as underestimated cloud particle size and LWP, to an extent that varies among the models. However, this does not completely cancel the precipitation formation bias. Robust errors are also found in the evolution of cloud microphysical properties from nonprecipitating to drizzling and then to raining clouds in some GCMs, implying unrealistic interaction between precipitation and cloud water. Nevertheless, auspicious information is found for future improvement of warm precipitation representations: the adoption of more realistic autoconversion scheme in the high-resolution model improves the triggering of precipitation, and the introduction of a sophisticated subgrid variability scheme in a traditional model improves the simulated precipitation frequency over subtropical eastern ocean. However, deterioration in other warm precipitation characteristics is also found accompanying these improvements, implying the multisource nature of warm precipitation biases in GCMs. [ABSTRACT FROM AUTHOR]

The numerical simulation of atmospheric blocking, in particular over the Euro-Atlantic region, still represents a main concern for the climate modeling community. We discuss the Northern Hemisphere winter atmospheric blocking representation in a set of 30 year atmosphere-only simulations using the EC-Earth Earth System Model with several ensemble members at five different horizontal resolutions (from 125 to 16 km). Results show that the negative bias in blocking frequency over Europe becomes negligible at resolutions of about 40 and 25 km. A combined effect by the more resolved orography and by a change in tropical precipitation is identified as the source of an upper tropospheric planetary wave. At the same time, a weakening of the meridional temperature gradient reduces the upper level baroclinicity and the zonal mean winds. Following these changes, in the high-resolution configurations the Atlantic eddy-driven jet stream is weakened favoring the breaking of synoptic Rossby waves over the Atlantic ridge and thus increasing the simulated European blocking frequency. However, at high-resolution the Atlantic jet stream is too weak and the blocking duration is still underestimated. This suggests that the optimal blocking frequencies are achieved through compensation of errors between eddies found at upper levels (too strong) and eddies at lower levels (too weak). This also implies that eddies are not necessarily better represented at higher resolutions. [ABSTRACT FROM AUTHOR]

We investigate the effects of physical parameterizations on cloud feedback uncertainty in response to climate change. For this purpose, we construct an ensemble of eight aquaplanet simulations using the Weather Research and Forecasting (WRF) model. In each WRF-derived simulation, we replace only one parameterization at a time while all other parameters remain identical. By doing so, we aim to (i) reproduce cloud feedback uncertainty from state-of-the-art climate models and (ii) understand how parametrizations impact cloud feedbacks. Our results demonstrate that this ensemble of WRF simulations, which differ only in physical parameterizations, replicates the range of cloud feedback uncertainty found in state-of-the-art climate models. We show that microphysics and convective parameterizations govern the magnitude and sign of cloud feedbacks, mostly due to tropical low-level clouds in subsidence regimes. Finally, this study highlights the advantages of using WRF to analyze cloud feedback mechanisms owing to its plug-and-play parameterization capability. [ABSTRACT FROM AUTHOR]

Climatic elasticity has been widely applied to assess streamflow responses to climate changes. To fully assess impacts of climate under global warming on streamflow and reduce the error and uncertainty from various control variables, we develop a four-parameter (precipitation, catchment characteristics n, and maximum and minimum temperatures) climatic elasticity method named PnT, based on the widely used Budyko framework and simplified Makkink equation. We use this method to carry out the first comprehensive evaluation of the streamflow response to potential climate change for 372 widely spread catchments in China. The PnT climatic elasticity was first evaluated for a period 1980-2000, and then used to evaluate streamflow change response to climate change based on 12 global climate models under Representative Concentration Pathway 2.6 (RCP2.6) and RCP 8.5 emission scenarios. The results show that (1) the PnT climatic elasticity method is reliable; (2) projected increasing streamflow takes place in more than 60% of the selected catchments, with mean increments of 9% and 15.4% under RCP2.6 and RCP8.5 respectively; and (3) uncertainties in the projected streamflow are considerable in several regions, such as the Pearl River and Yellow River, with more than 40% of the selected catchments showing inconsistent change directions. Our results can help Chinese policy makers to manage and plan water resources more effectively, and the PnT climatic elasticity should be applied to other parts of the world. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper examines the skill of seasonal precipitation forecasts over Iran using one two-tiered model, three National Multi-Model Ensemble ( NMME) models, and two coupled ocean-atmosphere or one-tiered models. These models are, respectively, the ECHAM4.5 atmospheric model that is forced with sea surface temperature ( SST) anomalies forecasted using constructed analogue SSTs ( ECHAM4.5- SSTCA); the IRI-ECHAM4.5-DirectCoupled, the NASA-GMAO-062012 and the NCEP-CFSv2; and the ECHAM4.5 Modular Ocean Model version 3 ( ECHAM4.5- MOM3-DC2) and the ECHAM4.5- GML-NCEP Coupled Forecast System ( CFSSST). The precipitation and 850 hPa geopotential height fields of the forecast models are statistically downscaling to the 0.5° × 0.5° spatial resolution of the Global Precipitation Climatology Centre ( GPCC) Version 6 gridded precipitation data, using model output statistics ( MOS) developed through the canonical correlation analysis ( CCA) option of the Climate Predictability Tool ( CPT). Retroactive validations for lead times of up to 3 months are performed using the relative operating characteristic ( ROC) and reliability diagrams, which are evaluated for above- and below-normal categories and defined by the upper and lower 75th and 25th percentiles of the data record over the 15-year test period of 1995/1996 to 2009/2010. The forecast models' skills are also compared with skills obtained by (a) downscaling simulations produced by forcing the ECHAM4.5 with simultaneously observed SST, and (b) the 850 hPa geopotential height NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) reanalysis data. Downscaling forecasts from most models generally produce the highest skill forecast at lead times of up to 3 months for autumn precipitation - the October-November-December ( OND) season. For most seasons, a high skill is obtained from ECHAM4.5- MOM3-DC2 forecasts at a 1-month lead time when the models' 850 hPa geopotential height fields are used as the predictor fields. For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January ( NDJ), December-January-February ( DJF) and January-February-March ( JFM) seasons were 0.68, 0.62, 0.42 and 0.43, respectively. [ABSTRACT FROM AUTHOR]

Future changes in water supply are likely to vary across catchments due to a river basin's sensitivity to climate and land use changes. In the Santiam River Basin ( SRB), Oregon, we examined the role elevation, intensity of water demands, and apparent intensity of groundwater interactions, as characteristics that influence sensitivity to climate and land use changes, on the future availability of water resources. In the context of water scarcity, we compared the relative impacts of changes in water supply resulting from climate and land use changes to the impacts of spatially distributed but steady water demand. Results highlight how seasonal runoff responses to climate and land use changes vary across subbasins with differences in hydrogeology, land use, and elevation. Across the entire SRB, water demand exerts the strongest influence on basin sensitivity to water scarcity, regardless of hydrogeology, with the highest demand located in the lower reaches dominated by agricultural and urban land uses. Results also indicate that our catchment with mixed rain-snow hydrology and with mixed surface-groundwater may be more sensitive to climate and land use changes, relative to the catchment with snowmelt-dominated runoff and substantial groundwater interactions. Results highlight the importance of evaluating basin sensitivity to change in planning for planning water resources storage and allocation across basins in variable hydrogeologic settings. [ABSTRACT FROM AUTHOR]

The ability of 17 global circulation models to simulate daily rainfall of the Pampas region is assessed for winter and summer, key seasons for crop production in the region. Principal Component Analysis combined with k-means Cluster Analysis is employed to examine the models' representation of the relationship between daily sea level pressure of southern South America and rainfall. Models represent this relationship better for winter, reflecting their ability to reproduce winter synoptic scale patterns associated with rainfall. They precipitate too frequently at low intensity and less frequently at high intensity. This characteristic is more accentuated in winter. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT A gas chromatography-mass spectrometry method was developed and validated for the simultaneous automated solid-phase extraction and quantification of cotinine and trans-3-hydroxycotinine in human urine. Good linearity was observed over the concentration ranges studied ( R2 > 0.99). The limit of quantification was 10 ng/mL for both analytes. The limits of detection were 0.06 ng/mL for cotinine (COT) and 0.02 ng/mL for trans-3-hydroxycotinine (OH-COT). Accuracy for COT ranged from 0.98 to 5.28% and the precision ranged from 1.24 to 8.78%. Accuracy for OH-COT ranged from −2.66 to 3.72% and the precision ranged from 3.15 to 7.07%. Mean recoveries for cotinine and trans-3-hydroxycotinine ranged from 77.7 to 89.1%, and from 75.4 to 90.2%, respectively. This analytical method for the simultaneous measurement of cotinine and trans-3-hydroxycotinine in urine will be used to monitor tobacco smoking in pregnant women and will permit the usefulness of trans-3-hydroxycotinine as a specific biomarker of tobacco exposure to be determined. © 2014 The Authors. Biomedical Chromatography published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT It is important to investigate potential changes in temperature, precipitation and solar radiation for assessing the impacts of future climate change on agricultural production for specific regions. In this study, climate scenarios of precipitation, temperature and solar radiation for the North China Plain ( NCP) were constructed in terms of stochastic daily weather sequences. A nonhomogeneous hidden Markov model ( NHMM) was used to downscale daily precipitation projections at 32 stations during winter wheat and summer maize growing seasons for a baseline (1966-2005) and a 21st century (2080-2099) A1B scenario, using selected general circulation models ( GCMs). A climatological seasonal cycle of regional-averaged daily reanalysis precipitation was used as input to the down-scaling for the baseline simulation; this input was then scaled by the precipitation changes from GCM projections to generate down-scaled stochastic simulations of precipitation in the 21st century. Temperature was generated using a weakly stationary generating process, conditional on precipitation occurrence, with 21st century additive changes taken from the GCMs at the regional scale. Three hypotheses about changes in solar radiation (−20%, 0% and 20%) were made considering the large uncertainty in its future change. The down-scaled simulations exhibit station increases in the mean daily rainfall of 13.9-69.7% in the scenarios driven by the GCM with the projected largest and multi-model mean precipitation increase for the wheat season, with changes of 0.4-29.9% for the maize season. In the scenario driven by the GCM with the largest projected precipitation decrease, the simulated rainfall decreases at all stations, with changes ranging from −24.6 to −0.1% for the wheat and maize seasons, respectively. Temperature increases by about 3.7 °C for the wheat season and 3.6 °C for the maize season. [ABSTRACT FROM AUTHOR]

This comparative study disclosed the diverse catalytic activities of Brevibacillus laterosporus on two different azo dyes. It decolorized 100% of Remazol red and 95% of Rubine GFL within 30 and 48 h respectively, under static condition at 50 mg l−1 dye concentration. Significant increase was observed in azo reductase, NADH-DCIP reductase, veratryl alcohol oxidase and tyrosinase in cells obtained after decolorization of Remazol red; whereas these values were much different with complete inhibition of azo reductase during decolorization of Rubine GFL. The plausible pathway of dye degradation obtained from Gas chromatography-Mass spectroscopy (GC-MS) data confirmed the different metabolic fate of these structurally unidentical dyes. FTIR and HPTLC analysis of extracted metabolites confirmed the biodegradation, while phytotoxicity study assured the detoxification of both the dyes studied. The results obtained in this study suggests, i) sulpho and hydroxyl group present at ortho position to azo group stimulated reduction of azo bond by azo reductase in Remazol red, ii) the same reduction was totally hampered due to presence of ethyl-amino propanenitrile group at para position to azo group in Rubine GFL. [ABSTRACT FROM AUTHOR]

Despite its many health benefits, many consumers avoid fish oil supplements due to fishy tastes and odors. Common chemical measures of oxidation have little correlation with sensory properties, making it difficult to determine the sensory quality of fish oil without the use of an expensive sensory panel. Here we investigate an alternative method to assess oxidation using solid phase microextraction and gas chromatography-mass spectrometry. Fish oils containing different amounts of eicosapentaenoic acid and docosahexaenoic acid were oxidized, and headspace volatiles were monitored over time and compared to sensory evaluations by a taste panel. Peroxide value and anisidine value were also measured. Sensory panel scores and headspace volatile data were analyzed using principal component analysis and linear regression to identify key volatiles responsible for changes in sensory degradation of oils over time. A total of eight compounds were identified, primarily aldehydes and ketones. By monitoring these volatiles, it may be possible to create a simple method to assess oxidation in fish oils that correlates well with sensory properties of the oil without the use of a sensory panel. [ABSTRACT FROM AUTHOR]

Groundwater temperature is a key parameter regulating the ecological balance of the ecosystems in groundwater dominated wetlands, estuaries and ponds. This study evaluated the potential impacts of climate change on groundwater temperature and proposed a methodology for use in areas with limited hydrological and metrological data. Groundwater temperatures were measured in 1 m intervals in five observation wells and used for groundwater recharge estimation. Three different techniques, the water balance method, the water level fluctuation (WLF) method and Darcy's method, were performed to verify the estimated recharge rates from the temperature-depth profiles. Of the six sets of global climate model (GCM) predictions analysed, three of them were selected by considering a range of potential climate changes in the future. The transfer function method was used to downscale the GCM outputs of precipitation and temperature of the Sendai plain. Raw GCM data for nine scenarios (A2, A1B, and B1 from HADCM3, MRI, and ECHAM5 models) and observed data from 1967 to 2006 were used to develop the transfer functions. Derived functions, which were tested for 1927-1966, were used to downscale GCM data for 2060-2099. These predictions were used in a one-dimensional heat transport model, which was calibrated to the existing site conditions by the water budget technique. There are marked differences among the scenarios and GCMs. However, all the model scenarios projected increasing trends in temperature and precipitation for the 2060-2099 time period. HADCM3 A2 scenario shows the strongest effect compared with the base line climate (1967-2006), which involves an increase in the air temperature of 3·9 °C and an increase in annual precipitation of 345 mm. When considering all GCM scenarios compared with observations in 2006, the aquifer temperature at 8 m depth from the ground surface could possibly increase within the range of 1·2-3·3 °C by 2080. The above findings from the methodology developed here will be important for estimating the impact of climate change and will be useful for environmental management programs. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large-scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south-east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI-ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO-MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS-AOM, MIROC-M, NCAR-PCM1, IAP and NCAR-CCSM. However, the reduction in the combined score as we move from the best- to the worst-performing GCMs is gradual, and there is no evident cut-off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Using cryo-SEM with EDX fundamental structural and mechanical properties of the moss Ceratodon purpureus (Hedw.) Brid. were studied in relation to tolerance of freezing temperatures. In contrast to more complex plants, no ice accumulated within the moss during the freezing event. External ice induced desiccation with the response being a function of cell type; water-filled hydroid cells cavitated and were embolized at −4 °C while parenchyma cells of the inner cortex exhibited cytorrhysis, decreasing to ∼20% of their original volume at a nadir temperature of −20 °C. Chlorophyll fluorescence showed that these winter acclimated mosses displayed no evidence of damage after thawing from −20 °C while GCMS showed that sugar concentrations were not sufficient to confer this level of freezing tolerance. In addition, differential scanning calorimetry showed internal ice nucleation occurred in hydrated moss at ∼−12 °C while desiccated moss showed no evidence of freezing with lowering of nadir temperature to −20 °C. Therefore the rapid dehydration of the moss provides an elegantly simple solution to the problem of freezing; remove that which freezes. [ABSTRACT FROM AUTHOR]

The Putranjivaceae is an enigmatic family, notable for being the only lineage outside the Capparales to possess the glucosinolate biochemical pathway, which forms the basis of an induced chemical defense system against herbivores (the "mustard oil bomb"). We investigated the pollination biology and floral scent chemistry of Drypetes natalensis (Putranjivaceae), a dioecious subcanopy tree with flowers borne on the stem (cauliflory). Flowering male trees were more abundant than female ones and produced about 10-fold more flowers. Flowers of both sexes produce copious amounts of nectar on disc-like nectaries accessible to short-tongued insects. The main flower visitors observed were cetoniid beetles, bees, and vespid wasps. Pollen load analysis indicated that these insects exhibit a high degree of fidelity to D. natalensis flowers. Insects effectively transfer pollen from male to female plants resulting in about 31% of female flowers developing fruits with viable seeds. Cetoniid beetles showed significant orientation toward the scent of D. natalensis flowers in a Y-maze olfactometer. The scents of male and female flowers are similar in chemical composition and dominated by fatty acid derivatives and isothiocyanates from the glucosinolate pathway. The apparent constitutive emission of isothiocyanates raises interesting new questions about their functional role in flowers. [ABSTRACT FROM AUTHOR]

A method was developed for collecting isothiocyanates (ITCs) from compost and their detection by gas chromatography–mass spectrometry. Identification of the compounds was based on retention time, molecular weight and previously published information (Wiley Database, Hewlett Packard ChemStation Library). The release of ITCs from freeze-dried plant material of Brassica carinata, Cleome spinosa and Tropaeolum majus cultivars was investigated by incorporation in compost contained in pots. Allyl isothiocyanate, methyl isothiocyanate and benzyl isothiocyanate, respectively, were released after hydrolysis of plant tissues. Abundance of ITCs was monitored for 12 days and was shown to decrease rapidly following incorporation until the fourth day for all types of plant tissue. [ABSTRACT FROM AUTHOR]

Allergenic ingredients extracted from used denture base resin were quantitatively analysed using a gas chromatograph/mass spectrometer and high performance liquid chromatography. Methyl methacrylate (MMA), hydroquinone (HQ), formaldehyde (FMA), benzoyl peroxide (BPO), benzoic acid (BA) and methyl benzoate (MB) were detected in a eluate from all of the dentures, while ethyleneglycol dimethacrylate (DME) was detected in the eluate from 87 dentures in use for <15 years. MMA, HQ, FMA, BPO and MB showed a decrease in correlation to the period of denture use, but continuing to be evident even after 29 years of use. The MMA showed the highest level of elution and a relatively moderate decrease over time. The elution of BA, on the contrary, showed an increase with the period of denture use up to about 10 years and subsequently reached a plateau. Our results indicate that purported allergens exist in the resin base and can be eluted into the oral cavity, even in patients using an old denture for a period of nearly 30 years. [ABSTRACT FROM AUTHOR]

• Carbon transfer from fungus to plant in the arbuscular mycorrhizal (AM) symbiosis has been reported, but its significance and even its existence have been called into question and the issue remains controversial. We investigated carbon movement from fungus to plant and from one mycorrhizal root system to another via a common AM fungal network in monoxenic cultures to avoid limitations of some previous studies. • 13C and 14C labeled substrates were supplied to functioning in vitro AM mycorrhizas between Ri T-DNA transformed carrot ( Daucus carota) roots and Glomus intraradices to follow carbon movement into and between host and fungal metabolite pools. • Fungal triacylglycerol and trehalose were labeled when permeant substrates were supplied to the extraradical mycelium (ERM), but host-specific compounds in the roots did not become labeled. When labeled glucose was provided to a donor root system, label moved to recipient roots via a common AM fungal network but remained in fungal compounds. • We conclude that carbon flow in the AM symbiosis is normally unidirectional from plant to fungus and that while carbon is translocated by the fungus from one metabolically active root system to another, it remains within the intraradical mycelium (IRM). New Phytologist (2004) 163: 617–627 © New Phytologist (2004) doi: 10.1111/j.1469-8137.2004.01152.x [ABSTRACT FROM AUTHOR]