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General circulation models (GCMs) are limited in their representation of regional climates. Thus, the selection and downscaling of the most suitable models for regional/local studies are crucial prior to climate change impact studies. This study addressed the selection and downscaling of GCM models from 100 ensembles each from the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) emission scenarios from the CMIP6 archive using an advanced envelop-based selection approach for Northern Nigeria. We used 2021–2050 as the short-term and 2051–2080 as the long-term study periods. The selection approach revealed that CanESM5 models are more skilful in simulating the warm and wet season, while HadGEM3-GC31-LL in the warm and dry season, whereas MPI-ESM1-2-HR and MPI-ESM1-2-LR are skilful in the cold and dry season. Furthermore, we downscaled the three most skilled models from each season and calculated their spatial averages over Northern Nigeria to provide a more precise illustration of the temperature and precipitation patterns. Under the SSP4.5 emission scenario, the ensemble mean of the downscaled and the (raw) GCMs projected about 13% (8–17%) and 20% (11–35%) increase in average annual precipitation during the short-term and long-term periods, respectively. Similarly, for SSP8.5, the models projected about 23% (5–38%) and 41% (29–60%) increase in the average annual precipitation during short-term and long-term periods respectively. For the temperature, under SSP4.5, the GCMs projected a 1.1 °C (0.26–1.6 °C) and 2.5 °C (0.87–4.04 °C) increase in average annual temperature for short-term and long-term periods respectively. Similarly, an increase of 1.2 °C (0.01–1.78 °C) and 2.7 °C (0.01–4.3 °C) is projected for SSP8.5 during the short-term and long-term periods respectively. These findings can be used for climate impact studies in the region.

General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments. This is especially relevant for regions with complex orography and coastlines, such as in Chile. Downscaling techniques attempt to reduce the gap between global and regional/local scales; for instance, statistical downscaling methods establish empirical relationships between large-scale predictors and local predictands. Here, statistical downscaling was employed to generate climate change projections of daily maximum/minimum temperatures and precipitation in more than 400 locations in Chile using the analog method, which identifies the most similar or analog day based on similarities of large-scale patterns from a pool of historical records. A cross-validation framework was applied using different sets of potential predictors from the NCEP/NCAR reanalysis following the perfect prognosis approach. The best-performing set was used to downscale six different CMIP5 GCMs (forced by three representative concentration pathways, RCPs). As a result, minimum and maximum temperatures are projected to increase in the entire Chilean territory throughout all seasons. Specifically, the minimum (maximum) temperature is projected to increase by more than 2 °C (6 °C) under the RCP8.5 scenario in the austral winter by the end of the twenty-first century. Precipitation changes exhibit a larger spatial variability. By the end of the twenty-first century, a winter precipitation decrease exceeding 40% is projected under RCP8.5 in the central-southern zone, while an increase of over 60% is projected in the northern Andes.

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models’ agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result.

In recent years, many studies on the analysis of microplastics (MP) in environmental samples have been published. These studies are hardly comparable due to different sampling, sample preparation, as well as identification and quantification techniques. Here, MP identification is one of the crucial pitfalls. Visual identification approaches using morphological criteria alone often lead to significant errors, being especially true for MP fibers. Reliable, chemical structure-based identification methods are indispensable. In this context, the frequently used vibrational spectroscopic techniques but also thermoanalytical methods are established. However, no critical comparison of these fundamentally different approaches has ever been carried out with regard to analyzing MP in environmental samples. In this blind study, we investigated 27 single MP particles and fibers of unknown material isolated from river sediments. Successively micro-attenuated total reflection Fourier transform infrared spectroscopy (μ-ATR-FTIR) and pyrolysis gas chromatography-mass spectrometry (py-GCMS) in combination with thermochemolysis were applied. Both methods differentiated between plastic vs. non-plastic in the same way in 26 cases, with 19 particles and fibers (22 after re-evaluation) identified as the same polymer type. To illustrate the different approaches and emphasize the complementarity of their information content, we exemplarily provide a detailed comparison of four particles and three fibers and a critical discussion of advantages and disadvantages of both methods.

The hydroalcoholic extract of Sphaeranthus amaranthoides (Asteraceae) entire part was screened for hepatoprotective activity on carbon tetrachloride-induced hepatotoxic in Wistar rats. Plant extract was screened for phytochemicals and GC MS study was also carried out for identification of compounds. Further, hepatoprotective activity was performed where carbon tetrachloride and corn oil mixture (1:1 v/v) was injected at 2 ml/kg body weight on the 6th day. The rats were treated with hydroalcoholic extract of SA at the doses of 250 mg/kg and 500 mg /kg body weight daily for 14 days, orally. Results were compared with standard silymarin (10 mg/kg b.w). After completion of observation duration (up to 21 day), all rats were sacrificed and blood was collected. The level of biomarkers of liver injury viz. serum glutamate pyruvate transaminase (SGPT), oxaloacetate transaminase (SGOT), alkaline phosphate (ALKP), total protein (TP), total bilirubin was analysed. Further, change in body weight, liver weight, and histopathologic examination were conducted. Finally, the result concluded that SA extract is an useful therapeutic herb to prevent hepatic impairment due to the presence of various terpenoids, acid esters, phenolics, phytosterols etc. as major groups of compounds identified by GCMS in the plant.

We systematically evaluate the high‐latitude Joule heating of the recently released version 3.0 Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM) by comparison to EISCAT incoherent scatter radar measurements. The model performance is examined using normalized root mean square deviations derived from test runs driven by different convection patterns from empirical and data‐assimilated models. The following features are revealed: (a) Data‐assimilated geomagnetic forcing improves the agreement between modeled and EISCAT‐derived Joule heating rates by 8%, 28%, and 54% for low, moderate, and high geomagnetic activity. (b) Increasing model grid resolution from 2.5° to 1.25° leads to ∼${\\sim} $ 20% higher Joule heating rates. (c) AMIE‐driven runs better reproduce the magnitude of the Joule heating rates, AMGeO‐driven runs the vertical profile. (d) Internal model time step resolution has no effect on the Joule heating rates.

The aim of the current investigation was the isolation and characterization of two active constituents i.e. Gulonic acid and Shikimic acid from the leaf extract of the newly discovered medicinal plant of Crinum solapurense (Amaryllidaceae). The Crinum solapurense was collected from the local areas of Solapur, Maharashtra, India. The Gulonic acid and Shikimic acid were isolated using column-chromatography and TLC (Thin Layer Chromatography) and characterized by using UV spectroscopy, FTIR, 1H-NMR,13C-NMR, and GCMS. The isolated compounds showed positive results for various alkaloidal tests like Dragendroff’s, Mayer’s, Wagner’s and Hager’s tests. The first isolated active constituent i.e. Gulonic acid in UV spectroscopy showed ƛ max at 261 nm; in FTIR spectroscopy showed O-H stretching, aliphatic O-H, C=O carbonyl functional groups, in 1H-NMR showed C-H group attached to –OH, C-H & -OH groups; 13C-NMR showed O-H stretching and carbon attached to –OH group and adjacent to carbonyl group; GCMS showed 196g/mol molecular weight. The second isolated active constituents i.e. Shikimic acid in UV spectroscopy showed ƛ max at 235nm; in FTIR spectroscopy showed O-H stretching, aliphatic O-H, C=C functional groups; in 1H-NMR showed C-H group attached to methylene group, C-H group attached to C=C, C-H group attached to –OH, C-H group attached to methylene group; 13C-NMR showed C=O of –COOH group, C=C attached to –COOH group, carbon adjuvant to C=C; GCMS showed 181 g/mol molecular weight. These results showed that two isolated compounds i.e. Gulonic acid and Shikimic acid have never been reported from the leaves extract of the Crinum solapurense plant.

The ability of the climate models submitted to the Coupled Model Intercomparison Project 5 (CMIP5) database to simulate the Northern Hemisphere winter climate following a large tropical volcanic eruption is assessed. When sulfate aerosols are produced by volcanic injections into the tropical stratosphere and spread by the stratospheric circulation, it not only causes globally averaged tropospheric cooling but also a localized heating in the lower stratosphere, which can cause major dynamical feedbacks. Observations show a lower stratospheric and surface response during the following one or two Northern Hemisphere (NH) winters, that resembles the positive phase of the North Atlantic Oscillation (NAO). Simulations from 13 CMIP5 models that represent tropical eruptions in the 19th and 20th century are examined, focusing on the large‐scale regional impacts associated with the large‐scale circulation during the NH winter season. The models generally fail to capture the NH dynamical response following eruptions. They do not sufficiently simulate the observed post‐volcanic strengthened NH polar vortex, positive NAO, or NH Eurasian warming pattern, and they tend to overestimate the cooling in the tropical troposphere. The findings are confirmed by a superposed epoch analysis of the NAO index for each model. The study confirms previous similar evaluations and raises concern for the ability of current climate models to simulate the response of a major mode of global circulation variability to external forcings. This is also of concern for the accuracy of geoengineering modeling studies that assess the atmospheric response to stratosphere‐injected particles. Key Points Large volcanic eruptions cause a major dynamical response in the atmosphere CMIP5 models are assessed for their ability to simulate this response No models in the CMIP5 database sufficiently represent this response

Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.5° resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010-2039 and 2070-2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of near-term changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (∼62%) occurs at mid- to high-latitudes, while agreement on decreasing probabilities (∼20%) is mainly in the tropics. Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

According to the Intergovernmental Panel on Climate Change 5th Assessment Report, the broad-scale features of precipitation as simulated by Phase 5 of the Coupled Model Intercomparison Project (CMIP5) are in modest agreement with observations, however, large systematic errors are found in the Tropics. In this study, a new algorithm has been developed to define the North Pacific Intertropical Convergence Zone (ITCZ) through several metrics, including: the centerline position of the ITCZ, the width of the ITCZ, and the magnitude of precipitation along the defined ITCZ. These metrics provide a quantitative analysis of precipitation associated with the ITCZ over the equatorial northern Pacific. Results from 29 CMIP5 Atmospheric Model Intercomparison Project (AMIP) Global Circulation Model (GCM) runs are compared with Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) observations. Similarities and differences between the GCM simulations and observations are analyzed with the intent of quantifying magnitude-, location-, and width-based biases within the GCMs. Comparisons show that most of the GCMs tend to simulate a stronger, wider ITCZ shifted slightly northward compared to the ITCZ in GPCP and TRMM observations. Comparisons of CMIP and AMIP simulated precipitation using like-models were found to be nearly equally distributed, with roughly half of GCMs showing an increase (decrease) in precipitation when coupled (decoupled) from their respective ocean model. Further study is warranted to understand these differences.