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The paper reports recent developments of Open Innovation Environments in a focus of the European Union research projects. It presents a new extension of the open access computational electromagnetics platform to the modeling of coupled electrochemical phenomena occurring at electrolyte/electrode interfaces. The problems are solved using coupled Laplace / Poisson and drift-diffusion equations, which create a basic model of ion transport process in the electrolyte, as in e.g. in popular Li-ion batteries. The developed coupled FDTD solver is validated against analytical solutions for the electrostatics and independent FEM solutions for the electrochemistry. It is prepared to be openly used for the modeling of industrially representative test-fixtures for battery materials, such as those defined in the H2020 NanoBat project.

Beach profile change in the swash zone on gravel beaches is characterised by enhanced onshore sediment transport and berm formation, and infiltration loss in the swash zone is often given as the reason why gravel beaches are steeper than sand beaches. In this paper, we report on field measurements conducted on a gravel beach, Slapton Sands, Devon, UK, in April 2001. Collected data included surface pressures under uprush and backwash, subsurface pore water pressures, uprush and backwash velocities, and bed elevations. The field data were compared with predictions of the BeachWin model, which simulates interacting wave run-up/run-down, beach groundwater flow, swash sediment transport, and resulting beach profile changes. With a relatively large value of hydraulic conductivity, the model was able to predict the observed berm formation at the upper part of the beach. In contrast, the berm feature was absent in the simulation without swash infiltration effects included. The predicted erosion at the middle section of the beach, however, took place at a location landward of that of the measurements. Simulations were carried out to investigate the sensitivity of the model to changes in the hydraulic conductivity, friction factor, and coefficient ratio of uprush/backwash sediment transport. All parameters were shown to affect the simulation results—in particular, the formation and extent of the berm.

This paper presents the development and implementation of a distributed model of coupled water nutrient processes, based on the representative elementary watershed (REW) approach, to the Upper Sangamon River Basin, a large, tile-drained agricultural basin located in central Illinois, mid-west of USA. Comparison of model predictions with the observed hydrological and biogeochemical data, as well as regional estimates from literature studies, shows that the model is capable of capturing the dynamics of water, sediment and nutrient cycles reasonably well. The model is then used as a tool to gain insights into the physical and chemical processes underlying the inter- and intra-annual variability of water and nutrient balances. Model predictions show that about 80% of annual runoff is contributed by tile drainage, while the remainder comes from surface runoff (mainly saturation excess flow) and subsurface runoff. It is also found that, at the annual scale nitrogen storage in the soil is depleted during wet years, and is supplemented during dry years. This carryover of nitrogen storage from dry year to wet year is mainly caused by the lateral loading of nitrate. Phosphorus storage, on the other hand, is not affected much by wet/dry conditions simply because the leaching of it is very minor compared to the other mechanisms taking phosphorous out of the basin, such as crop harvest. The analysis then turned to the movement of nitrate with runoff. Model results suggested that nitrate loading from hillslope into the channel is preferentially carried by tile drainage. Once in the stream it is then subject to in-stream denitrification, the significant spatio-temporal variability of which can be related to the variation of the hydrologic and hydraulic conditions across the river network.

A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI‐ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low‐level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two‐layer model. Key Points An updated version of the Max Planck Institute for Meteorology Earth System Model (MPI‐ESM1.2) is presented The model includes both code corrections and parameterization improvements Despite this, the model maintains an equilibrium climate sensitivity, which rises with warming

With the aim of developing a fully coupled atmosphere‐hydrology model system, the Weather Research and Forecasting (WRF) model was enhanced by integrating a new set of hydrologic physics parameterizations accounting for lateral water flow occurring at the land surface. The WRF‐Hydro modeling system was applied for a 3 year long simulation in the Crati River Basin (Southern Italy), where output from the fully coupled WRF/WRF‐Hydro was compared to that provided by original WRF model. Prior to performing coupled land‐atmosphere simulations, the stand‐alone hydrological model (“uncoupled” WRF‐Hydro) was calibrated through an automated procedure and validated using observed meteorological forcing and streamflow data, achieving a Nash‐Sutcliffe Efficiency value of 0.80 for 1 year of simulation. Precipitation, runoff, soil moisture, deep drainage, and land surface heat fluxes were compared between WRF‐only and WRF/WRF‐Hydro simulations and validated additionally with ground‐based observations, a FLUXNET site, and MODIS‐derived LST. Since the main rain events in the study area are mostly dependent on the interactions between the atmosphere and the surrounding Mediterranean Sea, changes in precipitation between modeling experiments were modest. However, redistribution and reinfiltration of local infiltration excess produced higher soil moisture content, lower overall surface runoff, and higher drainage in the fully coupled model. Higher soil moisture values in WRF/WRF‐Hydro slightly influenced precipitation and also increased latent heat fluxes. Overall, the fully coupled model tended to show better performance with respect to observed precipitation while allowing more water to circulate in the modeled regional water cycle thus, ultimately, modifying long‐term hydrological processes at the land surface. Key Points: Fully coupled model includes lateral surface and subsurface water fluxes Lateral redistribution increases soil moisture content compared to control run Precipitation and long‐term land surface hydrological processes are influenced

A new climate model, HadGEM3 N96ORCA1, is presented that is part of the GC3.1 configuration of HadGEM3. N96ORCA1 has a horizontal resolution of ~135 km in the atmosphere and 1° in the ocean and requires an order of magnitude less computing power than its medium‐resolution counterpart, N216ORCA025, while retaining a high degree of performance traceability. Scientific performance is compared to both observations and the N216ORCA025 model. N96ORCA1 reproduces observed climate mean and variability almost as well as N216ORCA025. Patterns of biases are similar across the two models. In the northwest Atlantic, N96ORCA1 shows a cold surface bias of up to 6 K, typical of ocean models of this resolution. The strength of the Atlantic meridional overturning circulation (16 to 17 Sv) matches observations. In the Southern Ocean, a warm surface bias (up to 2 K) is smaller than in N216ORCA025 and linked to improved ocean circulation. Model El Niño/Southern Oscillation and Atlantic Multidecadal Variability are close to observations. Both the cold bias in the Northern Hemisphere (N96ORCA1) and the warm bias in the Southern Hemisphere (N216ORCA025) develop in the first few decades of the simulations. As in many comparable climate models, simulated interhemispheric gradients of top‐of‐atmosphere radiation are larger than observations suggest, with contributions from both hemispheres. HadGEM3 GC3.1 N96ORCA1 constitutes the physical core of the UK Earth System Model (UKESM1) and will be used extensively in the Coupled Model Intercomparison Project 6 (CMIP6), both as part of the UK Earth System Model and as a stand‐alone coupled climate model. Plain Language Summary In this article, a new version of the climate model currently used in the United Kingdom (HadGEM3) is presented and analyzed. The circulation of the atmosphere and the oceans is simulated on a relatively coarse spatial grid with a grid cell size of about 120 km. The advantage of using a coarse spatial grid is that less computing power (on a supercomputer) is needed compared to using a finer grid. This gives an opportunity to do many more simulations of the ways in which Earth's climate may evolve in the decades and centuries ahead. We have carefully compared a simulation of the climate around the year 2000 with climate observations from that time and with a simulation from the same model with a finer spatial grid. Our results show that our new, coarse‐grid version is representing the current climate reasonably well, for instance, with regards to climate variability in the tropics and major ocean currents. However, there are clear differences between the two models. In the coarse‐grid model, the ocean surface is too cold in the northwest Atlantic, while in the fine‐grid version it is too warm in the Southern Ocean around Antarctica. We look into explanations for these inaccuracies. Key Points A low‐resolution, traceable version of the current Met Office Hadley Centre climate model HadGEM3 GC3.1 is presented The scientific performance is comparable to the medium‐resolution version, while requiring much less computational resources In the low‐resolution version the Southern Ocean warm bias is reduced, linked with a more realistic ocean circulation

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2xCO2 is slightly higher than previously at 2.7‐‐3.1°C (depending on version), and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

With the ease of gene sequencing and the technology available to study and manipulate non-model organisms, the extension of the methodological toolbox required to translate our understanding of model organisms to non-model organisms has become an urgent problem. For example, mining of large coral and their symbiont sequence data is a challenge, but also provides an opportunity for understanding functionality and evolution of these and other non-model organisms. Much more information than for any other eukaryotic species is available for humans, especially related to signal transduction and diseases. However, the coral cnidarian host and human have diverged over 700 million years ago and homologies between proteins in the two species are therefore often in the gray zone, or at least often undetectable with traditional BLAST searches. We introduce a two-stage approach to identifying putative coral homologues of human proteins. First, through remote homology detection using Hidden Markov Models, we identify candidate human homologues in the cnidarian genome. However, for many proteins, the human genome alone contains multiple family members with similar or even more divergence in sequence. In the second stage, therefore, we filter the remote homology results based on the functional and structural plausibility of each coral candidate, shortlisting the coral proteins likely to have conserved some of the functions of the human proteins. We demonstrate our approach with a pipeline for mapping membrane receptors in humans to membrane receptors in corals, with specific focus on the stony coral, P . damicornis . More than 1000 human membrane receptors mapped to 335 coral receptors, including 151 G protein coupled receptors (GPCRs). To validate specific sub-families, we chose opsin proteins, representative GPCRs that confer light sensitivity, and Toll-like receptors, representative non-GPCRs, which function in the immune response, and their ability to communicate with microorganisms. Through detailed structure-function analysis of their ligand-binding pockets and downstream signaling cascades, we selected those candidate remote homologues likely to carry out related functions in the corals. This pipeline may prove generally useful for other non-model organisms, such as to support the growing field of synthetic biology.

Flash flooding has become more prominent under climate change, threatening people's life and property. Post‐event investigations of recent events emphasize the role of floating debris, such as vehicles, in exacerbating damage. Few modeling methods and tools have been developed to simulate the full‐process dynamics of floating debris driven by large‐scale flood waves in real world. In this work, a fully coupled model is developed for simulating the full‐process interactive movements of vehicles driven by flash flood hydrodynamics, from entrainment, transport to deposition. The proposed coupled modeling system consists of a finite volume shock‐capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. The proposed two‐way coupling approach estimates the hydrostatic and hydrodynamic forces acting on solid objects using the water depth and velocity predicted by the hydrodynamic model; the resulting counter forces on the fluid flow are then considered by adding extra source terms in the hydrodynamic model. A multi‐sphere method is further embedded in the DEM model to better represent vehicle shapes. New calculation modules are further implemented to represent the vehicle entrainment, contact and stopping motions. The coupled model is applied to reproduce a flash flood event hit Boscastle in the UK in 2004. Over 100 vehicles were moved and carried downstream by the highly transient flood flow. The model well predicts the hydrodynamics, interactive transport process and the final locations of vehicles. The proposed coupled model provides a new tool for simulating large‐scale flash flooding processes, including debris dynamics. Key Points A new coupled model for simulation of entrainment, transport and deposition of vehicles driven by and interacting with flood hydrodynamics The model is used to reproduce a flash flood event that moved over 100 vehicles, with results consistent with post‐event report and survey Increasing number of floating vehicles alters flood hydrodynamics and intensifies debris‐debris and debris‐fluid interactions