The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4  km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5  km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

Radon-222 ( 222 Rn) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate 222 Rn using the GEOS-Chem chemical transport model to improve our understanding of 222 Rn emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global 222 Rn emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface 222 Rn concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of 222 Rn emission fluxes (1 atom cm −2  s −1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global 222 Rn emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface 222 Rn concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining 222 Rn emission flux rates. However, large model underestimates of surface 222 Rn concentrations still exist in Asia, suggesting unusually high regional 222 Rn emissions. We therefore propose a conservative upscaling factor of 1.2 for 222 Rn emission fluxes in China, which was also constrained by observed deposition fluxes of 210 Pb (a progeny of 222 Rn). With this modification, the model shows better agreement with observations in Europe and North America ( >  80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on 222 Rn emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed 222 Rn vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of 222 Rn concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of 222 Rn, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of 222 Rn concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.

Radon-222 ( 222 Rn) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate 222 Rn using the GEOS-Chem chemical transport model to improve our understanding of 222 Rn emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global 222 Rn emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface 222 Rn concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of 222 Rn emission fluxes (1 atom cm −2  s −1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global 222 Rn emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface 222 Rn concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining 222 Rn emission flux rates. However, large model underestimates of surface 222 Rn concentrations still exist in Asia, suggesting unusually high regional 222 Rn emissions. We therefore propose a conservative upscaling factor of 1.2 for 222 Rn emission fluxes in China, which was also constrained by observed deposition fluxes of 210 Pb (a progeny of 222 Rn). With this modification, the model shows better agreement with observations in Europe and North America ( >  80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on 222 Rn emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed 222 Rn vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of 222 Rn concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of 222 Rn, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of 222 Rn concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.

Optogenetics uses light-inducible protein-protein interactions to precisely control the timing, localization, and intensity of signaling activity. The precise spatial and temporal resolution of this emerging technology has proven extremely attractive to the study of embryonic development, a program faithfully replicated to form the same organism from a single cell. We have previously performed a comparative study for optogenetic activation of receptor tyrosine kinases, where we found that the cytoplasm-to-membrane translocation-based optogenetic systems outperform the membrane-anchored dimerization systems in activating the receptor tyrosine kinase signaling in live Xenopus embryos. Here, we determine if this engineering strategy can be generalized to other signaling pathways involving membrane-bound receptors. As a proof of concept, we demonstrate that the cytoplasm-tomembrane translocation of the low-density lipoprotein receptor-related protein-6 (LRP6), a membrane-bound coreceptor for the canonical Wnt pathway, triggers Wnt activity. Optogenetic activation of LRP6 leads to axis duplication in developing Xenopus embryos, indicating that the cytoplasm-to-membrane translocation of the membrane-bound receptor could be a generalizable strategy for the construction of optogenetic systems. (C) 2021 Elsevier Ltd. All rights reserved.

Humidified gas turbine (HGT) is a promising technology with several advantages compared to traditional thermal power plants, such as higher electrical efficiency, lower investment costs, and lower emissions. Using steam diluted, carbon neural bio-syngas as fuel in the HGT cycle leads to distributed wet combustion, often characterised by high Karlovitz number. This kind of combustion may be unstable if a small perturbation of biosyngas fuel composition occurs and it can lead to flame blow-off. Hence, quantifying wet bio-syngas fuel variability effects on the flame physicochemical behaviour is an important step. Using uncertainty quantification, it is found that a 0.75% perturbation of a typical wet bio-syngas composition can lead to 10% fluctuation of the flame speed, 7.5% fluctuation of the flame thickness and 2% fluctuation of flame temperature for stoichiometric combustion of steam diluted reactants at gas turbine conditions. Since near stoichiometric combustion is associated with highly steam-diluted bio-syngas to retain constant thermal efficiency of HGT, ultra-wet combustion has indeed suffered from strong combustion instability led by fuel variability. The main sensitivity study shows that hydrogen variability is responsible for the high fluctuation of flame speed while methane variability is responsible for the fluctuation of thermal efficiency and flame thickness. A high pressure (HP) burner running on a typical wet bio-syngas can suffer from a change of Karlovitz number by 20 (300% by fraction) and Reynolds number by 14,000 (10% by fraction), with potential impact on flame stability and cycle performance due to small perturbation of bio-syngas composition.

Physically based constitutive equations incorporating the key microstructural mechanisms e.g. dislocations, grain size, etc, have been used widely to predict the stress-strain behaviour of alloys at plastic and viscoplastic conditions. This enables an accurate prediction of the formed geometry as well as the final underlying microstructures. However, these physically based constitutive equations have not been practically validated due to the lack of systematic experimental data at microscopic scale. This leads to a large number of unknown constants required to be determined through various optimization algorithms. The aim of this paper is to provide direct and systematic experimental data by revealing the dislocation (geometrically necessary one) density and grain size evolution of AA6082, which is a widely used high-strength aluminium alloy for automobile structural panels, as functions of strain, strain rate and temperature, and is the first-time using Electron Back Scattering Diffraction (EBSD) technique to visualize the microstructures during the hot deformation. The evolution of the dislocation accumulation during the hot tensile deformation at 300, 450, 530 degrees C using various strain rates (i.e. 1/s, 0.1/s, 0.01/s) was achieved. EBSD maps were analysed on samples submitted to a true strain level of similar to 0.1 and similar to 0.3 under each condition. These maps cover >3000 grains and enable to capture the statistical nature of the geometrically necessary dislocation densities during hot deformation. Despite the rapidly plateaued flow stress curves at high temperatures, a continuously increased average GND density was observed in AA6082 with the imposed true strain levels under all conditions. Dislocation channel structures were observed in the hot deformed samples. Dynamic recrystallization was also observed, which coupled with the GNDs and affected the hardening behaviour of the flow stress-strain curves. This work is the first study, using EBSD, to visualize the high temperature and high strain rate induced dislocation distributions over a relatively large area, providing valuable data that may be used for subsequently improving and calibrating the physically based material models.