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The prompt penetration electric field (PPEF) drives the DP2 currents composed of the two-cell Hall current vortices surrounding the Region-1 field-aligned currents (R1FACs), and the zonal equatorial electrojet (EEJ, Cowling current) at the dayside equator, which is connected to the R1FACs by the Pedersen currents at middle latitudes. The midlatitude H- and D-components of the disturbance magnetic field are caused by the DP2 currents, as well as by the magnetospheric currents, such as magnetopause currents, FACs, ring currents, and so on. If the DP2 current is the major source for the midlatitude geomagnetic disturbances, H and D are supposed to be caused by the Hall and Pedersen currents, respectively. The H-D correlation would be negative in both morning and afternoon sectors, and H/D-EEJ correlation would be negative/positive in the morning and positive/negative in the afternoon. We picked out 39 DP2 events in the morning and 34 events in the afternoon from magnetometer data at Paratunka, Russia (PTK, 45.58° N geomagnetic latitude (GML)), which are characterized by negative H–D correlation with correlation coefficient (cc) < −0.8. We show that the midlatitude H/D is highly correlated with EEJ at Yap, Micronesia (0.38° S GML) in the same local time zone, meeting the Pedersen–Cowling current circuit between midlatitude and equator in the DP2 current system. Using the global simulation, we confirmed that the ionospheric currents with north–south direction at midlatitude is the Pedersen currents developing concurrently with the Cowling current. We suggest that the negative H-D correlation provides a clue to detect the PPEF when magnetometers are available at middle latitudes.

In this Pseudo-global Warming study, potential future changes in the Southeast Asia tropical cyclone (TC) exposure climatology are quantified. One hundred and seventeen landfalling TCs in the last 20 years are simulated with their current climate conditions and also with the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble perturbed conditions under the SSP2-4.5 and SSP3-7.0 climate change scenarios. Our simulations suggest that landfalling TCs are projected to be 8% more intense at landfall, 2.8% faster and have smaller sizes by the end of the 21st century under the SSP3-7.0 scenario. In addition, TC landfall locations shift northward with tracks extending further inland toward Laos and Thailand. In particular, TC exposures, wind and rainfall impacts significantly increase in the northern Philippines, Taiwan, southwestern coast of China, and northern Vietnam; and significantly decrease in the southern areas of Southeast Asia and the southeastern coast of China.

Magnetosheath jets with enhanced dynamic pressure are common in the Earth's magnetosheath. They can impact the magnetopause, causing deformation of the magnetopause. Here we investigate the 3‐D structure of magnetosheath jets using a realistic‐scale, 3‐D global hybrid simulation. The magnetosheath has an overall honeycomb‐like 3‐D structure, where the magnetosheath jets with increased dynamic pressure surround the regions of decreased dynamic pressure resembling honeycomb cells. The magnetosheath jets downstream of the bow shock region with θBn ≲ 20° (where θBn is the angle between the upstream magnetic field and the shock normal) propagate approximately along the normal direction of the magnetopause, while those downstream of the bow shock region with θBn ≳ 20° propagate almost tangential to the magnetopause. Therefore, some magnetosheath jets formed at the quasi‐parallel shock region can propagate to the magnetosheath downstream of the quasi‐perpendicular shock region. Plain Language Summary Magnetosheath jets are high‐speed transient structures frequently observed in the magnetosheath, and they can impact and dent the magnetopause. However, their three‐dimensional (3‐D) structure is still under debt despite decade‐long research. By performing high‐resolution, 3‐D numerical simulation, we reveal that the magnetosheath has an overall honeycomb‐like 3‐D structure where the jets surround regions with lower plasma velocity resembling honeycomb cells. Key Points Magnetosheath jets are studied by a realistic‐scale, 3‐D global hybrid simulation under a radial interplanetary magnetic field (IMF) The magnetosheath has a honeycomb‐like 3D structure where regions of increased dynamic pressure surround those of decreased dynamic pressure The magnetosheath jets formed at the quasi‐parallel shock can propagate to the magnetosheath downstream of the quasi‐perpendicular shock

China has the largest number of villages in the world, and research on rural microclimate will contribute to global climate knowledge. A three-by-three grid method was developed to explore village microclimates through field measurement and ENVI-met simulation. A regression model was used to explore the mechanistic relationship between microclimate and spatial morphology, and predicted mean vote (PMV) was selected to evaluate outdoor thermal comfort. The results showed that ENVI-met was able to evaluate village microclimate, as Pearson’s correlation coefficient was greater than 0.8 and mean absolute percentage error (MAPE) was from 2.16% to 3.79%. Moreover, the air temperature of west–east road was slightly higher than that of south–north, especially in the morning. The height-to-width ratio (H/W) was the most significant factor to affect air temperature compared to percentage of building coverage (PBC) and wind speed. In addition, H/W and air temperature had a relatively strong negative correlation when H/W was between 0.52 and 0.93. PMV indicated that the downwind edge area of prevailing wind in villages was relatively comfortable. This study provides data support and a reference for optimizing village land use, mediating the living environment, and promoting rural revitalization.

Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting. Plain Language Summary Numerical models used for projecting climate change impacts must use ad‐hoc assumptions about the effects of unresolved small‐scale processes. These assumptions contribute to uncertainty in predicting how rainfall and temperature will change in the future. Expensive fine‐grid simulations which eliminate the need for some of these assumptions are possible to run for shorter (month‐to year‐long) duration. We use such a simulation to train a data‐driven representation of the effects of processes, like clouds, which are poorly simulated by a cheaper coarse‐grid model. The data‐driven representation (a neural network) predicts rates of temperature and moisture change in each column using inputs from that grid column. This approach has been previously shown to work for models with idealized boundary conditions, but not for the realistic setting we use. When this neural network is used in a coarse‐resolution model, the realism of many global skill metrics is as good or better than a baseline model with traditional representation of small‐scale processes. However, some features are degraded, such as the time‐evolving pattern of rainfall in the tropics and humidity in the upper atmosphere. This work is a first step toward the use of data‐driven representations of unresolved processes in realistic global atmospheric models. Key Points Effective sources of heat and moisture are computed from a global storm‐resolving simulation accounting for semi‐resolved dynamics A neural network is trained to predict columns of the effective sources using profiles of temperature and specific humidity When used online, stable month‐long simulations are possible although skill is not yet comparable to a previous corrective approach

Eleven 40-day long integrations of five different global models with horizontal resolutions of less than 9 km are compared in terms of their global energy spectra. The method of normal-mode function decomposition is used to distinguish between balanced (Rossby wave; RW) and unbalanced (inertia-gravity wave; IGW) circulation. The simulations produce the expected canonical shape of the spectra, but their spectral slopes at mesoscales, and the zonal scale at which RW and IGW spectra intersect differ significantly. The partitioning of total wave energies into RWs an IGWs is most sensitive to the turbulence closure scheme and this partitioning is what determines the spectral crossing scale in the simulations, which differs by a factor of up to two. It implies that care must be taken when using simple spatial filtering to compare gravity wave phenomena in storm-resolving simulations, even when the model horizontal resolutions are similar. In contrast to the energy partitioning between the RWs and IGWs, changes in turbulence closure schemes do not seem to strongly affect spectral slopes, which only exhibit major differences at mesoscales. Despite their minor contribution to the global (horizontal kinetic plus potential available) energy, small scales are important for driving the global mean circulation. Our results support the conclusions of previous studies that the strength of convection is a relevant factor for explaining discrepancies in the energies at small scales. The models studied here produce the major large-scale features of tropical precipitation patterns. However, particularly at large horizontal wavenumbers, the spectra of upper tropospheric vertical velocity, which is a good indicator for the strength of deep convection, differ by factors of three or more in energy. High vertical kinetic energies at small scales are mostly found in those models that do not use any convective parameterisation.

Aim: The importance of quantifying the contribution of historical processes in shaping current biodiversity patterns is now recognized, but quantitative approaches that explicitly link speciation, extinction and dispersal processes to palaeo-environmental changes are currently lacking. Here, we propose a spatial diversification model of lineages through time (SPLIT) based on the reconstruction of palaeo-environments. We illustrate our approach using mangroves as a case study and evaluate whether habitat changes caused by plate tectonics explain the current biodiversity patterns of this group. Innovations: The SPLIT model allows one to simulate the evolutionary dynamics of species ranges by spatially linking speciation, extinction and dispersal processes to habitat changes over geological time periods. The SPLIT model provides a mechanistic expectation of speciation and extinction assuming that species are ecologically identical and not interacting. The likelihood of speciation and extinction is equivalent across species and depends on two dispersal parameters interacting with habitat dynamics (d a maximum dispersal distance and ds a distance threshold beyond which gene flow is absent). Beyond classical correlative approaches, this model tracks biodiversity dynamics under palaeo-environmental changes and provides multiple expectations (i.e., α-, β-diversity, phylogenies) that can be compared to empirical patterns. Main conclusions: The SPLIT model allows a better understanding of the origin of biodiversity by explicitly accounting for habitat changes over geological times. The simulations applied to the mangrove case study reproduced the observed longitudinal gradient in species richness, the empirical pattern of β-diversity and also provided inference on diversification rates. Future developments may include niche evolution and species interactions to evaluate the importance of non-neutral mechanisms. The method is fully implemented in the InsideDNA platform for bioinformatics analyses, and all modelling results can be accessed via interactive web links.

Wheat ( Triticum aestivum ) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.

India, the world’s leading rice exporter, banned the export of non-Basmati white rice, accounting for 25% of its total exports (or 10% of the global rice trade). The ban aims to ensure availability to domestic Indian consumers and reduce domestic market prices, impacting global rice market accessibility, consumers, and producers across twelve regions. The study utilized the global simulation model (GSIM) to analyze the effects of trade restrictions on industries. The model uses national product differentiation to assess trade policy changes at global, regional, or national scales. It examined importer and exporter effects on trade values, tariff revenues, exporter surplus, and importer surplus. It found that India’s Voluntary Export Restraint (VER) ban on non-Basmati rice resulted in a higher local price and a negative global net welfare impact of USD 1.7 billion. The losses decreased to USD 1.4 billion when importing countries responded by reducing rice import tariffs by 25% and USD 1.1 billion when importing countries reduced tariffs by 75%. Sub-Saharan Africa, the Middle East, North Africa, and the Gulf Cooperation Council regions were most affected. The study also found minimal impact on consumer surplus in India due to inelastic rice demand.