Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

Evaluation of WRF-CHIMERE coupled models for the simulation of PM_2.5 in large East African urban conurbations

Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of capacity to forecast air quality make it difficult to quantify the real level of air pollution in this area. The chemistry-transport model CHIMERE has been coupled with the meteorological model WRF and used to simulate hourly concentrations of Particulate Matter PM2.5 for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality tool for a target monthly period of 2017 and the results compared against observed data from urban and rural sites. The results show that the model is able to reproduce hourly and daily temporal variability of aerosol concentrations close to observations both in urban and rural environments. CHIMERE’s performance as a tool for managing air quality was also assessed. The analysis demonstrated that despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66–99 % of the daily PM2.5 exceedances above the WHO 24-hour mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24-hour mean levels of PM2.5 was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a low air quality index for PM2.5 in the unhealthy category for human health exposing between 10000 to 30000 people/km2 to harmful levels of air contamination.

Dark confinement and chiral phase transitions: gravitational waves vs matter representations

We study the gravitational-wave signal stemming from strongly coupled models featuring both, dark chiral and confinement phase transitions. We therefore identify strongly coupled theories that can feature a first-order phase transition. Employing the Polyakov-Nambu-Jona-Lasinio model, we focus our attention on SU(3) Yang-Mills theories featuring fermions in fundamental, adjoint, and two-index symmetric representations. We discover that for the gravitational-wave signals analysis, there are significant differences between the various representations. Interestingly we also observe that the two-index symmetric representation leads to the strongest first-order phase transition and therefore to a higher chance of being detected by the Big Bang Observer experiment. Our study of the confinement and chiral phase transitions is further applicable to extensions of the Standard Model featuring composite dynamics.

Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms

Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series of the studied stations were modeled through the other effective variables comprising minimum air temperature (Tmin), extraterrestrial radiation (Ra), vapor pressure deficit (VPD), sunshine duration (n), and relative humidity (RH). The correlation coefficients between the input and output parameters were utilized to determine the most effective inputs. Furthermore, novel hybrid models were proposed in this study in order to increase the estimation accuracy of Tdew. For this purpose, two optimization algorithms named bee colony optimization (BCO) and dragonfly algorithm (DFA) were coupled on the classic ANFIS. It was concluded that the hybrid models (i.e., ANFIS-BCO and ANFIS-DFA) demonstrated better performances compared to the classic ANFIS. The full-input pattern of the coupled models, specifically the ANFIS-DFA, was found to present the most accurate results for both the selected stations. Therefore, the developed hybrid models can be proposed as alternatives to the classic ANFIS to accurately estimate the daily Tdew.

A Coupled Model of Temperature and Pressure for Managed Pressure Cementing in Deep-Water Region

The narrow density window in deep-water environment brought great challenges to well drilling and completion by causing well control issues. Managed Pressure Cementing (MPC) is a new technology developed from Manage Pressure Drilling (MPD), which can precisely control the annular fluid pressure profile. Accurate calculation of wellbore temperature and pressure is the key to MPC. This paper focus on coupled models of temperature and pressure for MPC in deep-water region. The well cementing process can be divided into two stages: fluid displacement stage and cement setting stage, which displays different characteristics. During the cementing displacement stage, the cement is in a flowable slurry state and is circulated into the annulus. During this process, the rheology of fluids if effected by temperature in wellbore. On basis of the fluid rheology model, a coupled model of temperature and pressure in wellbore is established considering the transient flow characteristics during cementing displacement stage. During cement setting stage, the cement slurry stops flowing and the significant cement hydration reaction starts. A large amount of hydration heat and obvious pressure reduction can be observed. On basis of the cement hydration kinetics model, a coupled model of temperature and pressure in wellbore during cementing setting stage is established. Based on the models established in this paper, a series of numerical simulations are conducted using a deep-water well. Simulation results show that neglecting the complicated interactions between temperature and pressure can cause a big error. During the cementing displacement stage, higher temperature in the deep part of wellbore reduces the fluid viscosity, which leads to a smaller friction. On the contrary, larger friction is observed near seabed as a result of the low temperature in deep-water environment. The pressure in wellbore changes frequently due to the coexistence of multiple fluids in wellbore. Therefore, a frequent control of annular fluid pressure is required using the MPC technology. During the cement setting stage, an obvious temperature increase is observed as a result of cement hydration heat. The pressure decreases with the depending of cement hydration. An addition back pressure at wellhead has to be added using the MPC technology. The transient temperature and pressure have impact on the rate of cement hydration in turn. Cement in the deep part of wellbore have a faster rate of cement hydration. The low temperature at mudline slows the cement hydration process. Considering the complicated interactions between temperature, pressure, cement hydration and fluid rheology, coupled models between temperature and pressure based on hydration kinetics during well cementing in deep-water region is established in the manuscript. The new model established in this paper plays an important role in the MPC technology.

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

A variety of fractional soliton solutions for three important coupled models arising in mathematical physics

This paper deals with the optical solitons of fractional coupled Boussinesq, Burgers-type and mKdV equations by the hypothesis of traveling wave and [Formula: see text]-expansion scheme. These equations are important in different fields such as propagation of long water waves, fluid dynamics, and shallow water wave propagation. In comparison to other analytical procedures, the analytical methodology [Formula: see text] is an incredibly beneficial approach. This technique can also be used with other nonlinear fractional models. The suggested method generates three distinct solutions such as trigonometric, hyperbolic, and rational. Moreover, graphical representation has been used to portray the physical significance of the constructed solutions. Finally, a comprehensive study is made by using a definition of Beta fractional derivative and obtained solutions are represented graphically to understand considered models.