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Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast postprocessing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for postprocessing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware postprocessing method are expected to boost user confidence in seasonal precipitation forecasts.

Global Warming Potentials (GWPs) of refrigerants and related compounds are calculated by complex simulation and periodically updated listed by World Meteorological Organisation (WMO). A new attempt has been made to predict GWPs of such compounds based on their molecular structure using Artificial Neural Network (ANN) and Group Contribution Method (GCM). The main focus is on refrigerants, including CFCs, HCFCs, HFCs, HFOs, HCs, chlorocarbons, fluorocarbons, bromocarbons, iodocarbons, oxygenated and unsaturated halocarbons. In order to give some statistical advantage to the ANN model, many other related families of compounds, totally about 495 compounds, have also been considered. Only GWPs for the time horizon of 100 years are used in this analysis. Considering the complexities of the problem, the confidence limits on GWP data published by WMO (2018), the proposed ANN with GCM model is able to predict the GWP within reasonable accuracy. A comparative analysis with another complex prediction method establishes the superiority of this direct GWP predictions from the molecular structure of a compound.

This paper aims to fully tap the value of the tourism brand of Changbai Mountains scenic area, give play to the radiation effect of the brand, and create the Greater Changbai Mountains (GCM) tourist area, thereby enhancing the coordinated development of regional tourism. Specifically, symbiosis correlation and symbiosis density were adopted to recognize the tourism symbiosis units in the core region of GCM tourist area, and an evaluation model was established for the tourism symbiosis quality in the core region of GCM tourist area. Based on the evaluation results, the places within the core region were divided into multiple levels. The results show that the core region of GCM tourist area covers Yanji City, Dunhua City, Hunchun City, Antu County, Baishan City, Fusong County, Changbai County, and Tonghua City, and these places fall on three different development levels.

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

Understanding the devastating nature of drought, this work has assessed the variability in the Severity-Area-Frequency (SAF) curve using Standardised Precipitation Evapotranspiration Index (SPEI) as meteorological drought indicator over Maharashtra, India. The future meteorological outputs from 19 Global Circulation Models (GCMs) of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under two Representative Concentration Pathway (RCP) 4.5 and 8.5 are used. The SAF curves are developed for five different seasons namely pre-monsoon, monsoon, post-monsoon, Kharif and Rabi. The uncertainty associated with GCMs and scenarios is assessed using possibility theory. The results reveal that the precipitation magnitude is expected to increase in pre-monsoon, monsoon, and Kharif seasons over most of the areas in Maharashtra. However, the temperature is likely to increase during all the seasons in future. The frequency of extreme drought condition during post-monsoon, pre-monsoon, and Rabi seasons shows an increment as compared to historical period. The Rabi season drought is noticed to be most pronounced and likely to affect significant portions of Maharashtra during all return periods. The SAF curve reveals that, in most of the cases, the percentage of drought affected area is expected to increase for high magnitude of severity.

Extreme precipitation events exhibit an increasing trend for both the frequency and magnitude on global and regional scales and it has already proven the impact of man-made global warming on the extreme precipitation amplification. Based on the observed datasets and global climate model (GCM) output, this study has evaluated the impact from anthropogenic forcing on the trend and temporal non-uniformity (i.e. increase in unevenness or disparity) of the precipitation amounts (PRCPTOT), extremes (R95p and RX5day) and intensity (SDII) in Central Asia (CA) from 1961 to 2005. Results indicate that radiative forcing changes, mainly driven by human activities, have significantly augmented the extreme precipitation indices in CA. The median trend with the influence of anthropogenic activities for the PRCPTOT, SDII, R95p and RX5day amounted to 2.19 mm/decade, 0.019 mm/decade, 1.39 mm/decade and 0.21 mm/decade during the study period, respectively. A statistically insignificant decrease in non-uniformity was noticed for the PRCPTOT, SDII and RX5day in Central CA (CCA) and Western CA (WCA), while Eastern CA (ECA) was the only region with a statistically significant increase in non-uniformity of the PRCPTOT, SDII, R95p and RX5day by 4.22%, 3.98%, 3.73% and 3.97%, respectively from 1961 to 2005 due to anthropogenic forcing. These results reflect the difference in various regions regarding the impact of anthropogenic forcing on the non-uniformity of extreme precipitation events in CA, which might help to fully understand the role of anthropogenic forcing in the changes of the precipitation extremes in CA and contribute to the development of water resource management strategies.

Anthropogenic climate change is expected to cause an increase in fire danger over south-eastern Australia during the 21st century, primarily driven by increased surface temperature. Studies of future fire weather in Victoria, Australia, have so far mostly utilised direct output from general circulation models, which have inadequate resolution for resolving the dynamics of local fire danger and are prone to substantial biases that may affect the seasonality of dry fuels. In this paper, we assess the ability of the Multivariate Adaptive Constructed Analogs (MACA) method to downscale output from general circulation models over Victoria, and replicate statistical attributes of fire danger indices. We find that climatological descriptors of meteorological variables of wind, temperature and humidity are captured extremely well, and fields on extreme fire days are well captured. We find that the method works very well for statistically downscaling fire weather elements over Victoria and provides a vehicle to assess the regional variation of fire weather projections over Victoria.

This paper applies the Multivariate Adaptive Constructed Analogs (MACA) statistical downscaling method to 12 general circulation models to produce 21st century projections of fire weather variables over Victoria, Australia, under two emissions scenarios. The statistically downscaled model data accurately replicate the observed distributions of meteorological variables over the contemporary period, but underestimate fire danger extremes in some models. Under each climate scenario, both mean and extreme fire danger are expected to increase. Though there is variation across Victoria, the 12-model average by year for RCP8.5 indicates a 10–20% increase in extreme (99th percentile) Forest Fire Danger Index across the state, with the greatest change projected in the north-west region. At five geographically and climatologically different locations across Victoria, there is a 50–200% increase in the number of days per year exceeding the threshold for the Victorian Very High or higher fire danger rating by the end of the century compared with the start. The high-end warming (RCP8.5) scenario shows increased temperature to be the main driver of heightened fire danger. Changes in temperature, humidity and precipitation during spring and early summer both increase the length of the fire season and may reduce springtime opportunities for prescribed burning.

The accuracy of the climate impact models depends on the representative power of the climate model used in the analysis. Many problems in these impact models can be eliminated by the selection of the appropriate climate models as an input. The main purpose of this research is to find a methodology to select the more representative climate models. In recent years, the quantity and quality of climate models have significantly improved. In several projects, many different Global Climate Model (GCM)–Regional Climate Model (GCM) combinations have been carried out. One of these recent comprehensive projects is CORDEX. The monthly temperature outputs of various GCM-RCM combinations over Europe between the years 1976 and 2005 were used in this paper for the determination of the more representative combinations. E-OBS is used as the observation data set in this research. Using the proposed method, a clear difference between the more, moderately, and less representative model combinations could be obtained. To model the future temperature climatology of Europe, the best choice of the analysis method is using MPI as GCM and either REMO or CCLM5 as RCM over the whole domain. According to our analysis, the best representative climate model combination can be performed by running MPI-CCLM5 combination over the EURO-CORDEX domain.

The health status monitoring of special transformers is of great significance to ensure the secure operation of the distribution network and the power quality of special transformer users. Given this background, an operation health status monitoring algorithm of special transformers is proposed in this paper based on balanced iterative reducing and clustering using hierarchies (BIRCH) and Gaussian Cloud methods (GCM). The algorithm is composed of two parts, i.e., the offline and online parts. For the offline part, the operating indexes of special transformers are extracted based on historical operating data, and Gaussian clouds of normal operating conditions of the special transformers are determined by BIRCH clustering and Gaussian cloud methods. For the online part, Gaussian clouds of real-time operating conditions of special transformers are determined by BIRCH clustering and Gaussian cloud methods based on real-time operation data. Then, the monitoring results of operating health status are determined by the distance between the standard Gaussian clouds and the real-time Gaussian clouds of special transformers. Finally, case studies for actual special transformers are performed to verify the proposed method, and the results show that the proposed model can effectively identify the abnormal operation of the special transformer.

Observational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

Aims This study aims to demonstrate the characteristics of late gadolinium enhancement (LGE) assessed by cardiovascular magnetic resonance (CMR) imaging in patients with giant cell myocarditis (GCM). Methods and results Six patients histologically diagnosed with GCM were retrospectively recruited in this study. All of them underwent CMR during hospitalization. The distribution and extent of LGE were assessed on both ventricles, and the AHA‐17 segment model was used for left ventricular (LV) analysis. Nine case reports with CMR in GCM were reviewed and summarized to investigate the features of LGE further. LGE was detected on both ventricular walls in all subjects. For a detailed analysis of LGE in the LV, the extent ranged from 21.6% to 56%. Among 70 segments (68.6%) involved by LGE, the subendocardial LGE was the most common pattern (46/102, including 24 segments located in the right‐sided septum), followed by the subepicardial pattern (23/102). The right‐sided septum, the subepicardial anterior wall, and the subendocardial right ventricular (RV) wall were observed in all subjects. To summarize the results of the present study with these case reports, the three most common patterns of LGE are the right‐sided septum (73%), the subepicardial anterior wall (60%), and the subendocardial RV wall (53%). Conclusions Extensive LGE seems to be common in GCM, affecting both LV and RV walls. Apart from subepicardial LGE, subendocardial LGE, which was used to be implicated in ischaemic disease, was frequently presented in GCM. The right‐sided subendocardial septum, the subepicardial anterior wall, and the subendocardial RV wall might be the vulnerable areas of LGE in GCM.

Purpose: This study examined the effects of heel insoles on the static balance and leg muscle activity and posture control strategy dur ing external perturbation. Methods: Thirty healthy young men participated in the study. The subjects underwent two experimental conditions: 1) no heel insole condition (0cm) and 2) wearing heel insole condition (5cm). The static balance was measured using an I-Balance device, which measured the change in the center of gravity (COG). The onset time of muscle activation and muscle activation of the erector spinae (ES), ham string (HAM), gastrocnemius (GCM) were measured using surface EMG electrodes to determine the change in posture control strategy during external perturbation. Results: The speed and distance of COG were significantly higher in the wearing heel insoles condition than the no heel insole condition (p

Marine Heatwave (MHW) events have been increasing all around the world, causing severe impacts on marine ecosystems and on the economy of the aquaculture, fishing and tourism industries. In this study, the occurrence and characteristics of MHW events in the North Atlantic are analyzed for a recent period (1971–2000) and the two future periods (2041–2070 and 2071–2100). The analysis is based on Sea Surface Temperature (SST) data obtained from observations and from Global Climate Models (GCM) in the CMIP5 archieve. The results show that the GCMs present significant shortcomings in reproducing the SST and MHWs. The model results regarding the recent trends in MHW are in agreement with the ones observed, although they underestimate the values of MHW intensity and overestimate the values of both frequency and duration of events. The MHWs observed occur at a mean frequency of 1.90 events per year and are characterized by ~ 13 days of mean duration and 0.37 oC of mean intensity, while the multi-model ensemble mean characterizes the events with 12 additional days and 0.15 oC less intensity. Under climate change scenarios, when considering a stationary threshold, the models project noticeable increases in MHW event intensity that could reach 2 oC above the 90th percentile and a quasi-permanent state of MHW by the end of the century. When a non-stationary threshold is considered, the characteristics of the events are similar to those obtained during the historical period. The GCMs significant biases in simulating the SST and leading to extremes like MHWs highlight the importance of improving GCM performance.

The main objective of this research is to use AES 256 GCM encryption and decryption of a web application system database called Accounting Information System (AIS) for achieving more privacy and security in a cloud environment. A cloud environment provides many services such as software, platform, and infrastructure. AIS can use the cloud to store data to achieve accounting with more performance, efficiency, convenience, and cost reduction. On the other hand, cloud environment is not secure because data is kept away from the organization. This paper focuses on how we deal with secure sensitive data such as accounting data AIS web application at web level encryption by using AES 256 GCM encryption to store data as encrypted data at cloud in a secure manner? Accounting Information System (AIS) has very sensitive data and its need to be more secure and safe specially in cloud because it’s not saved at local servers but at another cloud service provider. The storage of encryption and decryption keys are stored in locations and devices different from those in which the database is stored in the cloud for ensuring more safety.

Current rainfall Intensity-Duration-Frequency (IDFs) curves generally were developed based on stationary extreme value assumption. However, climate change is expected to alter extreme rainfalls and invalidate the stationary assumption. So, it is crucial to develop future rainfall IDFs taking into account the impacts of climate change. Difficulty in preparing reliable rainfall time series with fine time step and capturing extremes for future climate scenarios has led to limited studies aimed at investigating the change of IDF curves due to climate change. In this paper, a robust stochastic rainfall model (NSRP) is employed to produce future rainfall IDFs for five stations across Karkheh and Karun basins in Iran. NSRP can generate long-term realistic rainfall series containing extremes. Moreover, it applies changes in different rainfall statistical characteristics, projected by GCMs, in the downscaling procedure. For each station, the model was calibrated using observed rainfalls series. Then, 3000-year daily rainfall series were generated, and historical IDFs were developed. Consequently, NSRP parameters were perturbed based on GCM projections. Then, 3000-year future rainfall series were generated, and future IDFs were developed. The climate change uncertainties were represented by employing two GCM (CanESM2 and HadGem2) and three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results showed NSRP is able to reproduce observed extreme rainfalls in a wide range of time scales. Also, climate change will lead to a considerable increase in future extreme rainfall intensity in the study basins. As averages of all considered scenarios, rainfall intensities will increase between 22 and 206% in the future.

This essay examines the decision of States to affirm the Global Compact on Refugees (GCR) and the Global Compact for Safe, Orderly and Regular Migration (GCM) in 2018, in light of the normative and...

This study compares radiation-circulation coupling over tropical and subtropical oceans by examining ice water path, radiation, low-level wind, and precipitation fields from the uncoupled prescribed sea surface temperature AMIP6 and the fullycoupled CMIP6 historical runs. Ice water path of the CMIP6 ensemble shows a closer agreement than the AMP6 ensemble, particularly in the subtropics. The inclusion of falling ice (snow) radiative effects (FIREs), in general, improves simulation of radiation, low-level wind, and precipitation fields over the northwest Pacific, Southeast Pacific Convergence Zone (SPCZ), equatorial eastern Pacific and Atlantic Intertropical Convergence Zone (ITCZ). When both AMIP6 and CMIP6 models are divided into two groups with inclusion and exclusion of FIREs, the impact of FIREs is most pronounced in ITCZ and the subtropical trade-wind regions in CMIP6 but over the tropical Pacific and SPCZ in AMIP6. This suggests that active ocean plays a significant role in radiation-circulation coupling. The CMIP6 models with FIREs have less over-estimated biases in upward longwave radiation over the convective zones in Pacific and Atlantic and less low-level divergence of anomalous flows over convective zones, i.e., stronger trade winds. The circulation changes stronger in CMIP6 over the trade wind regions than in AMIP6, which suggests that the role of active ocean is important in testing an improved physical process in models. This conclusion is also supported by more systematic improvements in groups of models with inclusion of FIREs in CMIP6 than in AMIP6, hinting the important roles of FIREs in radiationcirculation coupling.

The changes and fluctuations in climate variables, especially temperature, that subsequently affect human activities and natural environments are one of the critical topics in scientific societies. Therefore, time variability analysis of average temperature is an important concept in climate studies, particularly in environmental planning and management at various levels. The main purpose of the present study is to detect the temporal and spatial variability of average monthly temperature in Iran during the period 2015–2060 based on the RCP4.5 and RCP 8.5 scenarios simulated by the CMIP5 atmospheric general circulation models. For this purpose, the monthly average temperature data relative to four CMIP5 models, including CMCC-CM, CESMI-BGC, CCSM4, and MRI-CGCM3 models, for the period 1987–2060, and the observed monthly average temperature for the period 1987–2014 measured at 88 synoptic stations distributed all over Iran were used. The accuracy of the CMIP5 models in simulating the historical data for the period 1987–2005 was evaluated against the observed data at the synoptic stations using R, R2, RMSE, bias, EF, NARMSE, slope, and IA statistics. To study the temporal and spatial variation of temperature relative to the historical and future periods across Iran, the statistical spatial variance model was applied. The result showed that the historical temperatures estimated by all CMIP5 models are highly correlated with the observed temperatures all over Iran, but the accuracy of the MRI-CGCM3 model was found slightly higher than the other three models in most areas of Iran. In general, the results showed that the temperature estimated by the selected models and scenarios have a very high correlation with the observed data in most parts of Iran, especially in the mountainous areas of the western country. In the coastal areas of southern and northern Iran, the accuracies of the models somehow decreased which can be attributed to the complex topographical structure of these areas and/or the other effective local features that have not been incorporated in the models. The results showed that the highest temporal variability of temperature has occurred in winter months and partly in the autumn, and the spatial variability of temperature has been observed primarily in the mountainous areas of Iran. The investigation of the temperature variability in the future decades (2015–2059) was found in parallel with the temporal variations of temperature in the present period, considering that the highest temperature variability will occur in winter and somehow in the autumn, mostly in the mountainous areas of Iran. Generally, in most parts of the country, the air temperature in future decades will have an increasing tendency in all four seasons of the year.

The falling ice (snow) radiative effects (FIREs) have previously been shown to contribute substantially to reduced discrepancies in simulations of present-day climatology of radiation, skin temperatures and sea ice concentration and thickness over the Southern Ocean. This study extends to examine the impacts of FIREs on simulation of sea ice changes under a scenario of gradual increase of atmospheric CO2 concentration. We perform a pair of sensitivity experiments including (CESM1-SoN) and excluding (CESM1-NoS) FIREs using Community Earth System Model version 1. The differences in the annual and seasonal means between the initial and warmer periods are examined. Relative to CESM1-SoN, CESM1-NoS simulates more surface reflected shortwave and less downward longwave radiative warming, as well as colder surface temperature, resulting in larger annual-mean sea ice extent and thickness and slower seasonal and long-term sea ice melting and thinning. Over the Southern Ocean of CESM1-SoN, reduced downwelling longwave radiation in austral winter (June-July-August: JJA) leads to sea-ice growth with colder skin temperature while reduced net radiation resulting from increased shortwave reflection in austral summer reduces the melting of sea ice with little change in skin temperature. CESM1-NoS shows seasonal and long-term trends similar to those in CMIP5 models that exclude FIREs, hinting slower future warming-driven changes and larger amplitude of the annual cycle in sea ice concentration and thickness. The ice-free Southern Ocean in peak melting season is simulated at approximately year 130 for CESM1-NoS but year 100 for CESM1-SoN, about 30 years later than that of the Arctic.

Teachers’ beliefs inform their instructional practice, which in turn has direct bearing on students’ success. The current study utilised Trochim’s Group Concept Mapping (GCM) to explore the experie...

This essay interrogates the production of knowledge about migration in a prominent UN instrument, the Global Compact for Migration (GCM). To articulate which worlds of mobility it privileges and de...

Statistical prediction of tropical sea surface temperatures (SSTs) is performed using linear inverse models (LIMs) that are constructed from both observations and general circulation model (GCM) output of SST. The goals are to establish a baseline for tropical SST predictions, to examine the extent to which the skill of a GCM-derived LIM is indicative of that GCM’s skill in forecast mode, and to examine the linkages between mean state bias and prediction skill. The observation-derived LIM is more skillful than a simple persistence forecasts in most regions. Its skill also compares well with some GCM forecasts except in the equatorial Pacific, where the GCMs are superior. The observation-derived LIM is matched or even outperformed by the GCM-derived LIMs, which may be related to the longer data record available for GCMs. The GCM-derived LIMs provide a fairly good measure for the skill achieved by their parent GCMs in forecast mode. In some cases, the skill of the LIM is actually superior to that of its parent GCM, indicating that the GCM predictions may suffer from initialization problems. A weak-to-moderate relation exists between model mean state error and prediction skill in some regions. An example is the eastern equatorial Atlantic, where an erroneously deep thermocline reduces SST variability, which in turn affects prediction skill. Another example is the equatorial Pacific, where skill appears to be linked to cold SST biases in the western tropical Pacific, which may reduce the strength of air–sea coupling.

Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude, compared with anthropogenic climate change, in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof-of-concept with an idealized general circulation model (GCM). Uncertainty quantification is performed using the calibrate-emulate-sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect-model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the size of the parameter posterior distributions leads to a reduction in the uncertainty of climate model predictions., 21 pages, 11 figures, 2 tables

One of the most debated and criticized characteristics of the Global Compact for Safe, Orderly and Regular Migration (GCM) is its legally non-binding form. This article analyzes the GCM’s legal form, mechanisms of effectiveness, and legitimacy and finds that while the legal form of the GCM is important, the available mechanisms of effectiveness and legitimacy are equally or, perhaps, more important factors to determine the compact’s relevance and future impact. Although non-binding, the GCM does possess the relevance, capacities, and legitimacy to become a normative force in the field of international migration governance. Compliance with its outlined commitments will, however, be strongly dependent on the political will of the participating states. Perhaps its key function will be to fill the existing gaps in hard law in global migration governance by fostering cooperation and consolidating international obligations, standards, and stakeholders of a crosscutting topic into one instrument.

Managing crisis challenges the ability to make numerous decisions under great uncertainty. This study address the decision-making process, and how the mix of involved individuals, prior knowledge, and available decision-makers forms the decisions made during a crisis. A large-scale exercise with a cyberattack scenario was chosen as the study’s case. The organization studied have highly skilled crisis management personnel; however, they are not used to manage a large-scale cyber-attack scenario. The garbage can model (GCM) of Organizational Choice with a few modifications is used as the analytical framework in the study.

The climate features over Central America and the Caribbean are simulated with the regional climate model RegCM4 to analyze the performance of the model in reproducing precipitation and temperature patterns over the region. Results from RegCM4 and the driving global climate model (GCM) HadGEM2-ES (HadG) are compared against gridded observations for a period from January 1980 to December 2004 using a perturbed physics ensemble formed by four configurations of the RegCM4 using different combinations of four convection schemes run over land and ocean areas: Kain Fritsch (Kf), Emanuel (Em), Grell (Ge), and Tiedtke (Tk). The added value of RegCM4 relative to the GCM simulation is also estimated, with focus over four subregions, using several metrics calculated on the RegCM4 as well as the GCM grids. The RegCM4 downscaling, expressed by the ensemble mean ENS, adds significant details to the HadG simulation of the temperature field patterns, showing smaller biases of up to ± 1.6 °C at the RCM resolution. Regarding the spatial patterns of precipitation, the HadG has overall higher correlation values with observations than ENS. However, the regional model provides more detailed spatial distributions, decreasing the bias by more than 1 mm/day over some of the islands. It also captures extreme precipitation events that are underestimated in the HadG simulation, even after upscaling to the GCM resolution grid. Concerning the performance of the different RegCM4 convection scheme combinations, KfEm and EmEm show the highest skill values for precipitation, while for temperature TkEm and GeEm are the best performing. This highlights that no individual scheme outperforms the others in all respects, while the application of the averaged ensemble technique provides the best results.

Comparing outputs of multiple climate and crop models is an option to assess the uncertainty in simulations in a changing climate. The use of multiple wheat models under five plausible future simulated climatic conditions is rarely found in literature. CERES-Wheat, DSSAT-Nwheat, CROPSIM-Wheat, and APSIM-Wheat models were calibrated with observed data form eleven sowing dates (15 October to 15 March) of irrigated wheat trails at Faisalabad, Pakistan, to explore close to real climate changing impacts and adaptations. Twenty-nine GCM of CMIP5 were used to generate future climate scenarios during 2040–2069 under RCP 8.5. These scenarios were categorized among five climatic conditions (Cool/Wet, Cool/Dry, Hot/Wet, Hot/Dry, Middle) on the basis of monthly changes in temperature and rainfall of wheat season using a stretched distribution approach (STA). The five GCM at Faisalabad and Layyah were selected and used in the wheat multimodels set to CO2 571 ppm. In the future, the temperature of both locations will elevate 2–3 °C under the five climatic conditions, although Faisalabad will be drier and Layyah will be wetter as compared with baseline conditions. Climate change impacts were quantified on wheat sown on different dates, including 1 November, 15 November, and 30 November which showed average reduction at semiarid and arid environment by 23.5%, 19.8%, and 31%, respectively. Agronomic and breeding options offset the climate change impacts and also increased simulated yield about 20% in all climatic conditions. The number of GCMs was considerably different in each quadrate of STA, showing the uncertainty in possible future climatic conditions of both locations. Uncertainty among wheat models was higher at Layyah as compared with Faisalabad. Under Hot/Dry and Hot/Wet climatic conditions, wheat models were the most uncertain to simulate impacts and adaptations. DSSAT-Nwheat and APSIM-Wheat were the most and least sensitive to changing temperature among years and climatic conditions, respectively.

Gutters are used to drain water way off to the Inlet. This process significantly reduces the phenomenon of hydroplaning and thus reduces traffic accidents. The main objective of this study is to evaluate the effect of the characteristics road (manning's coefficient, longitudinal slope, and cross slope) on the capacity of gutter, and spread as well as the change which was clarified for all gutters types. Two simulation models have been developed in this study, which represent the major components of the system. These models are spread Model (SM), Gutter Capacity Model (GCM), and the first model studies spread if rain information is available, while the second model studies a gutter capacity if no rain information is available. The study reached at the conclusion: gutter capacity is inverse proportion with manning’s coefficient and directly proportional with cross slope and longitudinal slope for road. Affect spread is different according to form gutter. http://dx.doi.org/10.30572/2018/kje/110403

The regional climate of the Arabian Gulf region is modeled using a set of simulations based on the Weather Research and Forecasting (WRF) Model, including a 30-yr benchmark simulation driven by reanalysis data, and two bias-corrected Community Earth System Model (CESM)-driven (BCD) WRF simulations for retrospective and future periods that both include 10-yr convection-permitting nested simulations. The modeled precipitation is cross-validated using Tropical Rainfall Measuring Mission data, rain gauge data, and the baseline dataset from the benchmark simulation. The changes in near-surface temperature, precipitation, and ambient conditions are investigated using the BCD WRF simulations. The results show that the BCD WRF simulation well captures the precipitation distribution, the precipitation variability, and the thermodynamic properties. In a warmer climate under the RCP8.5 scenario around the year 2070, the near-surface temperature warms by ~3°C. Precipitation increases over the Arabian Gulf, and decreases over most of the continental area, particularly over the Zagros Mountains. The wet index decreases while the maximum dry spell increases in most areas of the model domain. The future changes in precipitation are determined by both the thermodynamics and dynamics. The thermodynamic impact, which is controlled by the warming and moistening, results in more precipitation over the ocean but not over the land. The dynamic impact, which is controlled by changes in the large-scale circulation, results in decrease in precipitation over mountains. The simulations presented in this study provide a unique dataset to study the regional climate in the Arabian Gulf region for both retrospective and future climates.

The single-column model (SCM) functionality of the Energy Exascale Earth System Model version 1 (E3SMv1) is described in this paper. The E3SM SCM was adopted from the SCM used in the Community Atmosphere Model (CAM) but has evolved significantly since then. We describe changes made to the aerosol specification in the SCM, idealizations, and developments made so that the SCM uses the same dynamical core as the full general circulation model (GCM) component. Based on these changes, we describe and demonstrate the seamless capability to “replay” a GCM column using the SCM. We give an overview of the E3SM case library and briefly describe which cases may serve as useful proxies for replicating and investigate some long-standing biases in the full GCM runs while demonstrating that the E3SM SCM is an efficient tool for both model development and evaluation.

In this contribution, I examine the links between the human rights basis of the UN Global Compact for Safe, Orderly and Regular Migration (GCM) and its embeddedness in the UN Sustainable Development Agenda 2030. While the GCM grew out of a development framework, it was rapidly incorporated into the UN human rights system. Even during the negotiation of the GCM, human rights took priority over development. The resistance that was manifested against the GCM on its endorsement by the UN General Assembly was directed not against its development links, but rather concerns about its human rights impact. This paper examines the placing of migration in this dual framework and the ways in which outcomes compatible with both are achievable.

Recent decades have seen improvements in our understanding of the gendered dynamics of migration and how they affect women migrant workers. Whilst characterised by precarity, women's labour migration is recognised as contributing to positive developmental outcomes. The full dimensions of these contributions are not yet well understood, but the need to improve the situation of women migrant workers is. Through analysis of the Sustainable Development Goals (SDGs) and the Global Compact for Safe, Orderly and Regular Migration (GCM), this paper examines the global development agenda to assess the extent to which it recognises the gendered dimensions of migration and seeks to realise the rights of women migrant workers as development actors. Drawing on content analysis of the SDGs and a review of the GCM, the paper finds that, while their content provides potential for states to strengthen their evidence base and establish progressive policy agendas and practice interventions to realise women migrant workers’ rights, the measures for implementation and monitoring of these frameworks reduce the likelihood of this potential being realised.

A large majority of the direct observational record for glacier changes falls within the industrial period, from the 19th century onward, associated with global glacier retreat. Given this availability of data and the significant focus in contemporary glacier modelling falling on recent retreat, glacier models are typically calibrated using – and validated with – only observations of glaciers that are considerably out of equilibrium. In order to develop a broader picture of the skill of one glacier model – the Open Global Glacier Model (OGGM) – we model glaciers for extended historical timescales of 850–2004 CE using a selection of six general circulation model (GCM) outputs. We select glaciers for which long-term length observations are available in order to compare these observations with the model results, and we find glaciers with such observations in almost all glacierised regions globally. In many regions, the mean modelled glacier changes are consistent with observations, with recent observed retreat in these regions typically at the steeper end of the range of modelled retreats. However, on the scale of individual glaciers, performance of the model is worse, with overall correlation between observed and modelled retreat weak for all of the GCM datasets used to force the model. We also model the same set of glaciers using modified climate time series from each of the six GCMs that keep temperature or precipitation constant, testing the impact of each individually. Temperature typically explains considerably more variance in glacier lengths than precipitation, but results suggest that the interaction between the two is also significant within OGGM and neither can be seen as a simple proxy for glacier length changes. OGGM proves capable of reproducing recent observational trends on at least a qualitative level in many regions, with a modelling period over a considerably larger timescale than it is calibrated for. Prospects are good for more widespread use of OGGM for timescales extending to the pre-industrial period, where glaciers were typically larger and experience less rapid (and less globally consistent) geometry changes, but additional calibration will be required in order to have confidence in the magnitude of modelled changes, particularly on the scale of individual glaciers.

While migration is a policy field that is fairly state-centric, the prominent role of the EU in the development of international migration law and policy has been acknowledged, to some extent, by the international community. This paper scrutinises the EU's role and impact during the preparatory and inter-governmental talks leading to the adoption of the Global Compact for Safe, Orderly and Regular Migration (GCM). A central question is the degree to which the EU influenced negotiations and their outcome; and how the international community received the EU's external action in this matter. Next to mapping the EU's substantive input shaping the process, the EU's internal machinery to formulate its position and the challenges faced within the bloc are also explored. The GCM process also illustrates the willingness of the international community – or the lack of it – to elevate European standards to the global level in the highly complex and politicised domain of migration.

This paper explores the role played by the International Labour Organization (ILO) in the consultations and stocktaking during 2017 and the negotiations during 2018 leading up to the adoption of the Global Compact for Safe, Orderly and Regular Migration (GCM). It examines selected parts of the text of the GCM, with particular reference to the ILO's mandate of securing social justice and decent work, as well as the protection of migrant workers and governance of labour migration. The final part of the paper looks ahead to the ILO's role in the implementation of the GCM, with specific reference to the Arab states region, where migration for employment is significant and the governance challenges, particularly in relation to the protection of low-wage and low-skilled workers, are especially acute.

In this mixed‐methods study, we examined the employment trajectories of immigrant employees in the construction sector in Milan and the role of unions in promoting their labour market inclusion. Drawing on a unique dataset of 417,004 contracts representing more than 166,000 construction workers over a 12‐year period (2000–2011), we employed Growth Curve Modelling (GCM) to explore national group differences in contract, firm and sector tenure trajectories. We found Egyptian and Romanian workers suffer from lowest tenure levels. To investigate these results, we conducted 15 interviews with key informants. Results suggested firm characteristics and position along the production process (mono‐task), pervasive immigrant hiring queues (mono‐national) and union's use of class strategies are interlocking forces that shape deep labour market segregation. We recommend unions develop and apply tailored ethnic strategies to empower highly segregated immigrant groups.

Global climate models (GCMs) are used by major climate centers worldwide for global climate forecasting, and predictive performance is one of the most important issues in GCM forecast applications. In addition to spatial plotting that illustrates anomaly correlation at individual grid cells, this study proposes a novel local indicator of spatial association (LISA) of anomaly correlation (herein, LISAAC) for GCM seasonal forecasts of global precipitation. LISAAC is built upon local Moran’s I by relating anomaly correlation at neighboring grid cells to one another. While local Moran’s I takes the grand mean of anomaly correlation as the benchmark, LISSAC considers the original value of anomaly correlation in the mathematical formulation. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts, which are initialized in January, February,…, and June, of the global precipitation in June, July, and August. Three metrics—LISAAC, local Moran’s I, and original anomaly correlation—are applied to investigate the predictive performance. In comparison with local Moran’s I, LISAAC can identify clusters of positive, neutral, and negative anomaly correlations. In comparison with anomaly correlation, LISAAC can capture outliers of positive (negative) anomaly correlation surrounded by negative (positive) anomaly correlation. Overall, the results highlight that LISAAC can serve as a useful tool for evaluating the predictive performance of GCM seasonal forecasts of global precipitation.

The precipitation behaviour at different timescales (interannual, intraseasonal and synoptic) and extreme events in a warmer scenario over South America is analysed. This study is based on a set of Regional Climate Models (RCM) from the CORDEX database and their driving Global Climate Models (GCM) from the CMIP5 Project. Historical simulations for 1979–2005 and from the RCP4.5 scenario for 2071–2100 are used. The projected changes in the precipitation behaviour are evaluated in terms of the consistency between the pairs RCM-GCM aiming to explore the added value of RCMs. The agreement between projected changes from RCMs is also evaluated as a measure of confidence of the regional climate change signal. The analysis is carried out for two extended seasons (April to September and October to March). The projected change in the mean precipitation over subtropical latitudes is associated with both changes in the frequency of rainy days and in the intensity of extreme events, while for the tropics the changes are mainly associated with changes in the wet day frequency. The increase of extreme precipitation events over the subtropics arises as a robust signal among models, while for tropical latitudes the dispersion among models is high, which reduces the confidence of the projections. In general, the consistency of the projections in the pairs RCM-GCM and the agreement among models are higher for the low frequency variability patterns during winter, while for summertime a better agreement in the projected changes of the precipitation behaviour at different timescales among RCMs is found.

Although many studies have sought to characterize future meteorological droughts, a few efforts have been done for quantifying the uncertainty, inter-model variability, arises from global circulation models (GCM) ensemble. A clear understanding of the uncertainty in multiple GCMs should be preceded before future meteorological droughts are projected. Therefore, this study evaluates the uncertainty in future meteorological drought characteristics that are induced by GCM ensemble using the custom measure “the degree of GCM spreading”. Future meteorological drought indices, the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), were computed to five different time scales: 3, 6, 9, 12 and 24 months using statistically downscaled 28 GCMs under Representative Concentration Pathway (RCP) 4.5 and 8.5 at 60 weather stations in South Korea. The frequency, duration, and severity of drought events were estimated for three different future periods; F1 (2010–2039), F2 (2040–2069), and F3 (2070–2099). It was found that the uncertainty increases as the time scale lengthens regardless of a choice of drought indices or RCP scenarios. It also turned out that the SPI exhibits larger uncertainty rather than the SPEI, because temperature data exhibit a relatively much smaller variability comparing to precipitation data. Moreover, there was a shift of regions having larger values of the increasing rate between F1 and F2, which is shift from the north-western to southern region of South Korea.

The possible future changes in temperature over Nigeria were projected in this study. Using Climate Research Unit (CRU) temperature as the reference data, gain ratio (GR), entropy gain (EG), and symmetrical uncertainty (SU) feature selection methods and a multi-criteria decision-making (MCDM) approach were used in selecting the most suitable GCMs for Nigeria from 20 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). The biases in selected GCMs were corrected using power transformation (PT) method. Multi-model ensembles (MMEs) were generated for the selected GCMs for the different temperature classes’ maximum, average, and minimum for all RCPs. The MMEs were used for the projection of temperatures over the country during 2010–2039, 2040–2069, and 2070–2099. The GCMs HadGEM2-ES, CESM1-CAM5, CSIRO-Mk3.6.0, and MRI-CGCM3 were the best performing in replicating temperature characteristics of the observed temperature in Nigeria. The MME mean projections of bias-corrected (BC) GCMs using PT revealed that there will be an increase in temperature of 4.0 °C at the semi-arid and 5.0 °C at the arid regions during dry and wet seasons respectively under RCP 4.5. In the same regions, the maximum temperature is expected to increase up to 5.5 °C under RCP 8.5 during 2070–2099 in the dry season. In the wet season, temperatures are expected to be higher under RCP 8.5, with an increase of 0.0–4.0 °C in the southern region and 3.0–6.9 °C in the northern region.

Suitable global climate models (GCMs) of precipitation P, maximum temperature Tmax, and minimum temperature (Tmin) are evaluated for 14 grid points in Telangana State, India. Three standard statist...

This study investigates how subgrid cloud water inhomogeneity within a grid spacing of a general circulation model (GCM) links to the global climate through precipitation processes. The effect of the cloud inhomogeneity on autoconversion rate is incorporated into the GCM as an enhancement factor using a prognostic cloud water probability density function (PDF), which is assumed to be a truncated skewed-triangle distribution based on the total water PDF originally implemented. The PDF assumption and the factor are evaluated against those obtained by global satellite observations and simulated by a global cloud-system-resolving model (GCRM). Results show that the factor implemented exerts latitudinal variations, with higher values at low latitudes, qualitatively consistent with satellite observations and the GCRM. The GCM thus validated for the subgrid cloud inhomogeneity is then used to investigate how the characteristics of the enhancement factor affect global climate through sensitivity experiments with and without the factor incorporated. The latitudinal variation of the factor is found to have a systematic impact that reduces the cloud water and the solar reflection at low latitudes in the manner that helps mitigate the too-reflective cloud bias common among GCMs over the tropical oceans. Due to the limitation of the factor arising from the PDF assumption, however, no significant impact is found in the warm rain formation process. Finally, it is shown that the functional form for the PDF in a GCM is crucial to properly characterize the observed cloud water inhomogeneity and its relationship with precipitation.