Model Simulations Research Articles

Deciphering the Variations and Mechanisms of Global Land Monsoons during Marine Isotope Stage 3

Abstract Marine Isotope Stage 3 (MIS 3) is characterized by significant millennial-scale climatic oscillations between cold stadials and mild interstadials, which presents a valuable case for understanding hydrological response to abrupt climate change. Through a set of coupled model simulations, our results broadly show an antiphased interhemispheric change in land monsoonal precipitation during the present-day relative to MIS 3 interstadial and the stadial–interstadial transition, with a general decrease in the Northern Hemisphere but an increase in the Southern Hemisphere. The antiphased pattern is largely caused by the change in orbital insolation during the present-day relative to MIS 3 interstadial, whereas by the weakened Atlantic meridional overturning circulation during the interstadial–stadial transition. However, there are obvious discrepancies in precipitation response and underlying mechanisms among individual monsoon domains and across different periods. Based on the moisture budget analysis, we indicate that the dynamic factor mainly explains the decreased monsoonal rainfall in the Northern Hemisphere during the present-day relative to the MIS 3 interstadial, whereas the thermodynamic term is largely responsible for the increased precipitation in the Southern Hemisphere. In contrast, the dynamic factor plays an important role in the variation of precipitation over all the monsoon zones from the MIS 3 interstadial to stadial states, with the thermodynamic term mainly contributing to the decreased tropical monsoonal precipitation in the colder Northern Hemisphere. Our results help improve the understanding of global monsoon variations under intermediate glacial climate conditions and shed light on their behaviors under potentially rapid climate change in the future.

El Niño-like tropical pacific ocean cooling pattern during the last glacial maximum

Many state-of-the-art climate models are unable to reproduce the observed 20th century surface warming pattern in the tropical Pacific Ocean, casting doubt on the robustness of future projections. Here, we examine past changes in the tropical Pacific upper ocean spatial pattern using paleoclimate reconstructions from proxies and simulations from a multi-model ensemble. The proxy results demonstrate that during the Last Glacial Maximum, when atmospheric carbon dioxide concentrations were lower than present, temperatures in the western tropical Pacific Ocean decreased more than in the east, leading to an El Niño-like cooling pattern. This result contrasts with the zonally uniform cooling pattern observed in model simulations, highlighting the common issue of models overestimating the sensitivity of eastern tropical Pacific sea surface temperatures to greenhouse forcing. Our proxy results imply that the western Pacific may warm more than the east in response to future climate change, producing a La Niña-like surface warming pattern.

Shear-driven magnetic buoyancy in the solar tachocline: Dependence of the mean electromotive force on diffusivity and latitude

Abstract The details of the dynamo process that is responsible for driving the solar magnetic activity cycle are still not fully understood. In particular, whilst differential rotation provides a plausible mechanism for the regeneration of the toroidal (azimuthal) component of the large-scale magnetic field, there is ongoing debate regarding the process that is responsible for regenerating the Sun’s large-scale poloidal field. Our aim is to demonstrate that magnetic buoyancy, in the presence of rotation, is capable of producing the necessary regenerative effect. Building upon our previous work, we carry out numerical simulations of a local Cartesian model of the tachocline, consisting of a rotating, fully compressible, electrically conducting fluid with a forced shear flow. An initially weak, vertical magnetic field is sheared into a strong, horizontal magnetic layer that becomes subject to magnetic buoyancy instability. By increasing the Prandtl number we lessen the back reaction of the Lorentz force onto the shear flow, maintaining stronger shear and a more intense magnetic layer. This in turn leads to a more vigorous instability and a much stronger mean electromotive force, which has the potential to significantly influence the evolution of the mean magnetic field. These results are only weakly dependent upon the inclination of the rotation vector, i.e. the latitude of the local Cartesian model. Although further work is needed to confirm this, these results suggest that magnetic buoyancy in the tachocline is a viable poloidal field regeneration mechanism for the solar dynamo.

The evolving three-dimensional landscape of human adaptation.

Over the past 3 million years, humans have expanded their ecological niche and adapted to more diverse environments. The temporal evolution and underlying drivers behind this niche expansion remain largely unknown. By combining archeological findings with landscape topographic data and model simulations of the climate and biomes, we show that human sites clustered in areas with increased terrain roughness, corresponding to higher levels of biodiversity. We find a gradual increase in human habitat preferences toward rough terrains until about 1.1 million years ago (Ma), followed by a 300 thousand-year-long contraction of the ecological niche. This period coincided with the Mid-Pleistocene Transition and previously hypothesized ancestral population bottlenecks. Our statistical analysis further reveals that from 0.8 Ma onward, the human niche expanded again, with human species (e.g., H. heidelbergensis, H. neanderthalensis, and H. sapiens) adapting to rougher terrain, colder and drier conditions, and toward regions of higher ecological diversity.

Overlooked branch turnover creates a widespread bias in forest carbon accounting

Most measurements and models of forest carbon cycling neglect the carbon flux associated with the turnover of branch biomass, a physiological process quantified for other organs (fine roots, leaves, and stems). Synthesizing data from boreal, temperate, and tropical forests (184,815 trees), we found that including branch turnover increased empirical estimates of aboveground wood production by 16% (equivalent to 1.9 Pg Cy-1 globally), of similar magnitude to the observed global forest carbon sinks. In addition, reallocating carbon to branch turnover in model simulations reduced stem wood biomass, a long-lasting carbon storage, by 7 to 17%. This prevailing neglect of branch turnover suggests widespread biases in carbon flux estimates across global datasets and model simulations. Branch litterfall, sometimes used as a proxy for branch turnover, ignores carbon lost from attached dead branches, underestimating branch C turnover by 38% in a pine forest. Modifications to field measurement protocols and existing models are needed to allow a more realistic partitioning of wood production and forest carbon storage.

A Jupyter Notebook for teaching mathematical modeling with experiments

AbstractMathematical modeling and numerical simulation have a considerable presence in the vast universe of engineering disciplines, given their usefulness in explaining, comprehending, and simulating phenomena and processes with which engineers are in contact in their daily creative and problem‐solving work. For this reason, engineering study programs have at least one course dedicated to dealing with the mathematical modeling of dynamic systems as an essential complement to subsequent courses such as automatic control, structural dynamics, and mechanical vibrations. Nowadays, many technological tools illustrate the applications of mathematical modeling interactively through experiments that offer an incomparable motivation to the students to corroborate with real‐world examples, the utility and veracity of the theory presented to them in the classroom and that in many occasions seems lacking utility and direct relation with the world in which they develop. Based on those mentioned above, this paper presents an example of applying the Laplace transform in modeling physical systems, using a second‐order circuit attached to an Arduino Due board in conjunction with the Jupyter Notebook environment. The numerical and experimental results can be obtained through three optional kernels: Python, Octave, or MATLAB®. For educational purposes, the resulting computer application was presented to undergraduate students of Mechatronics Engineering as an illustrative complement to two courses entitled Signals and Systems Analysis, part of the second semester, and Mathematical Modeling, part of the fifth semester.

A Microscopic On-Ramp Model Based on Macroscopic Network Flows

While macroscopic traffic flow models adopt a fluid dynamic description of traffic, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena accurately remains a challenge for microscopic models, especially in complex road sections. Based on a macroscopic network flow model calibrated to real traffic data and new rules for the acceleration and merging behavior on the on-ramp, we propose a microscopic model for on-ramps. To evaluate the performance of the new flow-based model, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model exhibits some limitations, its performance is superior to the Intelligent Driver Model in the evaluated aspects. While the Intelligent Driver Model simulations are almost free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and therefore can be considered for safety analysis.

Enhanced relationship between seasonal soil moisture droughts and vegetation under climate change over China

Climate change could intensify seasonal soil moisture droughts and subsequently degrade vegetation, but it can also facilitate vegetation growth with the rising temperature and CO2 concentrations. Therefore, the change of the vegetation-drought relationship under climate change remains elusive. Here, we investigate the effects of vegetation greening (i.e., increasing leaf area index (LAI)) on seasonal soil moisture drought trends by performing a set of land surface model simulations with the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), as well as the impact of seasonal soil moisture drought change on vegetation productivity via a statistical fitting model. Our results indicate that both the impact of increasing LAI on exacerbating seasonal droughts and the impact of increasing seasonal droughts on reducing vegetation productivity have been enhanced over China during 1961–2018. Specifically, increased LAI accounts for 48 % of the rising drought frequency through escalated evapotranspiration losses, with more pronounced effects in northern semiarid regions. The decrease in vegetation stomatal conductance caused by rising CO2 concentrations limits transpiration and alleviates 11 % of the increase in drought frequency, but it cannot fully offset the drought aggravation induced by increased vegetation water consumption under climate warming. Meanwhile, increased drought frequency and intensity have reduced the upward trend in vegetation productivity by 5 % over China, and by 8 % in water-limited northern regions. Our study shows the relationship between seasonal soil moisture droughts and vegetation over China has strengthened, indicating that vegetation greening will lead to increased water shortages and further impact vegetation growth and carbon sequestration. This challenges water resources management and environmental sustainability, especially in semiarid regions.

Present-day methane shortwave absorption mutes surface warming relative to preindustrial conditions

Abstract. Recent analyses show the importance of methane shortwave absorption, which many climate models lack. In particular, Allen et al. (2023) used idealized climate model simulations to show that methane shortwave absorption mutes up to 30 % of the surface warming and 60 % of the precipitation increase associated with its longwave radiative effects. Here, we explicitly quantify the radiative and climate impacts due to shortwave absorption of the present-day methane perturbation. Our results corroborate the hypothesis that present-day methane shortwave absorption mutes the warming effects of longwave absorption. For example, the global mean cooling in response to the present-day methane shortwave absorption is -0.10±0.07 K, which offsets 28 % (7 %–55 %) of the surface warming associated with present-day methane longwave radiative effects. The precipitation increase associated with the longwave radiative effects of the present-day methane perturbation (0.012±0.006 mm d−1) is also muted by shortwave absorption but not significantly so (-0.008±0.009 mm d−1). The unique responses to methane shortwave absorption are related to its negative top-of-the-atmosphere effective radiative forcing but positive atmospheric heating and in part to methane's distinctive vertical atmospheric solar heating profile. We also find that the present-day methane shortwave radiative effects, relative to its longwave radiative effects, are about 5 times larger than those under idealized carbon dioxide perturbations. Additional analyses show consistent but non-significant differences between the longwave versus shortwave radiative effects for both methane and carbon dioxide, including a stronger (negative) climate feedback when shortwave radiative effects are included (particularly for methane). We conclude by reiterating that methane remains a potent greenhouse gas.

Time scale analysis of enzymatic reduction of uric acid in a microfluidic biomedical device

Time Scale Analysis (TSA) is an investigative tool used in engineering design to identify locations in processes that should be a focus of Process Intensification (PI). Furthermore, TSA points to process variables and parameters that could be used to advance and measure PI improvement. However, TSA cannot suggest any specific design solution to intensify process performance. Instead, design engineers should use their fundamental knowledge and creative intelligence to specify detailed design transformations. TSA will then provide a specific quantitative measure of the improvement. TSA implementation improves an explicitly defined process performance, thus helping achieve process intensification goals. TSA is based on first principles, and it utilizes Characteristic Times (CT) such as diffusion, mean residence, and reaction times to improve an existing process. In this study, we specifically consider microfluidic biomedical devices. To illustrate the genesis of CT and TSA, we start by developing a mathematical model of an enzymatic degradation process in a biomedical device called iCore based on mass, momentum, and kinetic equations. After introducing user-defined scaling parameters, we extract CTs pertinent to the enzymatic degradation of uric acid in this microfluidic biomedical device. Diffusion coefficients, microchannel architectural characteristics, enzyme loading, hydrogel thickness, and characteristic parameters of enzyme kinetics are the parameters and process variables incorporated in this analysis. Finally, we compared the extracted CTs with a COMSOL Multiphysics parametric study to demonstrate how time scale analysis as a design tool and adjusting design parameters, such as diffusion coefficient, hydrogel layer thickness, substrate concentration, and enzyme concentration, can enhance the enzymatic reaction process without a need for complex computational modeling. It is crucial to recognize that pertinent CTs can be determined by understanding the type and nature of the observed process, previous experience, published data, and other foundational engineering design work. There is no need for mathematical modeling and numerical simulations to identify and acknowledge the CTs relevant and essential to the observed process; in this work, we only illustrate the principal origin of CTs via a detailed mathematical model of the process, as previously reported by Jovanovic et al. Therefore, in a routine application of TSA, it is important to remember that mathematical modeling and detailed numerical simulations are not necessary. This is a very comforting fact when TSA is deployed as a tool in higher-level process design functions. The investigations on how best to apply TSA in these higher level design functions such as Process Intensification, scale-up/numbering-up, change of device architecture, change of operating conditions, change of process feed characteristics, change of material physical and chemical properties, parametric optimization of the system for various objective functions, and techno-economic analysis, are yet to be studied and reported.

A Forensic Investigation of Climate Model Biases in Teleconnections: The Case of the Relationship Between ENSO and the Northern Stratospheric Polar Vortex

AbstractTeleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively.

Thermally tunable phonon–plasmon polariton modes at hexagonal boron nitride (hBN) and indium antimonide (InSb) interfaces

Abstract This work examines the propagation of thermally tunable phonon–plasmon modes at the interfaces of hexagonal boron nitride (hBN) and isotropic indium antimonide (InSb). Both theoretical modeling and numerical simulations are carried out to analyze the effect of temperature on surface wave behavior. hBN is realized as a polar material via the Lorentzian model, while InSb ismodeled as a temperature-sensitive material (TSM) in the framework of Drude’s model. The possible plasmon–phonon polaritonic interactionsarestudied for the TSM–elliptic type interface and TSM– hyperbolic type interface. It is reported that by varying the temperature, the surface modes can be tuned for the lower and upper Reststrahlen (RS) bands of hBN. The dispersion curve, effective mode index, propagation length, and phase speed are computed for each case under different temperatures. It is concluded that the hBN–InSb-based phonon–plasmon polariton modes are actively tuned by changing the external temperature in the lower and upper RS bands. Surface waves propagating across the interface can be modulated from the terahertz (THz) region to the infrared (IR)region by changing the temperature of InSb. This study will help researchers to design innovative thermo-optical sensors, plasmonic platforms, detectors, and surface waveguides in the THz and IR regions.

Enhancing Energy Harvesting Efficiency from Ambient Sources through Advanced Materials and Nanotechnology

The utilization of ambient energy sources for powering small-scale electronic devices and sensors has garnered significant attention due to its potential for sustainable and autonomous operation. This research focuses on enhancing the efficiency of energy harvesting from ambient sources, such as vibrations, heat differentials, and electromagnetic fields, through the integration of advanced materials and nanotechnology into energy harvesting systems. The study begins with a comprehensive review of existing energy harvesting technologies, highlighting their limitations in terms of efficiency, scalability, and adaptability to various ambient energy sources. It identifies the key challenges faced in achieving high energy conversion rates and explores the potential of advanced materials and nanoscale structures to address these challenges. One aspect of the research involves the development and characterization of novel materials with superior energy conversion properties. This includes the synthesis of piezoelectric materials for vibration energy harvesting, thermoelectric materials for heat-to-electricity conversion, and nanomaterials for enhancing electromagnetic energy harvesting efficiency. Advanced characterization techniques, such as scanning electron microscopy (SEM) and X-ray diffraction (XRD), are employed to analyze the structural and electrical properties of these materials. Furthermore, the study investigates the design and fabrication of nanostructured energy harvesting devices optimized for specific ambient energy sources. This involves the integration of nanoscale components, such as nanostructured electrodes, into energy harvesting systems to improve energy capture and conversion rates. Finite element analysis (FEA) simulations and experimental testing are conducted to evaluate the performance and efficiency of these nano-enhanced energy harvesting devices under real-world conditions. The research also addresses the optimization of energy harvesting system parameters, including resonance tuning, impedance matching, and energy management circuits, to maximize energy extraction and utilization. Computational modeling techniques, such as multiscale modeling and finite element simulations, are utilized to optimize system configurations and improve overall energy harvesting efficiency. Overall, this research contributes to the advancement of energy harvesting technologies by leveraging advanced materials and nanotechnology, paving the way for sustainable and autonomous power generation from ambient energy sources for a wide range of applications.

Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models

AbstractNorthwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

Attribution of Excess Methane Emissions Over Marine Environments of the Mediterranean and Arabian Peninsula

AbstractTo accurately assess the current atmospheric methane budget and its future trends, it is essential to apportion and quantify the anthropogenic methane emissions to specific sources. This poses a significant challenge in the under‐sampled Middle East, where estimates predominantly depend on remote sensing observations and bottom‐up reporting of national emissions. Here, we present in situ shipborne observations of greenhouse gases (GHGs) and non‐methane hydrocarbons (NMHCs) collected along a >10,000‐km route from Vigo, Spain, to Abu Dhabi, UAE. By comparing our observations with Lagrangian dispersion model simulations, coupled with two methane emission inventories, we identify periods of considerable mismatch and apportion the responsible sources. Employing interspecies relationships with NMHCs has enabled the characterization of methane emissions from oil and gas (O&G) operations, urban centers, Red Sea deep water, enteric fermentation, and agriculture across diverse atmospheric environments. Our analysis reveals that the Suez area is a regional emission hotspot, where simulations consistently underestimate the methane emission sources. Importantly, the Middle Eastern O&G sector has been identified as an additional source of considerable uncertainty. Here, methane emissions were alternately underestimated and overestimated by the two inventories, exposing significant gaps in our understanding of fuel exploitation‐related emissions in the Middle East. This underscores the need for further targeted field campaigns and long‐term observations to improve the accuracy of emission data in the inventories.

The Seasonality of PM and NO2 Concentrations in Slovakia and a Comparison with Chemical-Transport Model

The air quality (AQ) of a given location depends mostly on two factors: emissions and meteorological conditions. For most places on Earth, the meteorology of an area changes seasonally. For central Europe, winters are associated with poor dispersion conditions, which, in combination with high emissions from local heating systems, lead to significantly higher concentrations than during summer. In this study, the seasonality of AQ is analysed using hourly measurements from 44 monitoring stations in Slovakia for the years 2007–2023 for NO2, PM10 and PM2.5. Two factors are used to evaluate the seasonality—the difference and ratio of the winter and summer mean concentrations. It was found that the seasonal difference has been gradually decreasing for all pollutants since 2017. In the case of PM2.5, the seasonal ratio drops from a value of around 2.5 in 2018 to approximately 1.7 in 2023. While in the past, the seasonal ratio was the highest for PM2.5, in the last three years it is the highest for NO2 with values larger than 2. Our results imply that summer sources of PM emissions start to play a more important role for the AQ than in the past. The observed seasonality was compared with two full-year chemical-transport model simulations.

Performance Evaluation of a Bioinspired Geomagnetic Sensor and Its Application for Geomagnetic Navigation in Simulated Environment

For geomagnetic navigation technology, taking inspiration from nature and leveraging the principle of animals’ utilization of the geomagnetic field for long-distance navigation, and employing biomimetic technology to develop higher-precision geomagnetic sensors and more advanced navigation strategies, has emerged as a new trend. Based on the two widely acknowledged biological magnetic induction mechanisms, we have designed a bioinspired weak magnetic vector (BWMV) sensor and integrated it with neural networks to achieve geomagnetic matching navigation. In this paper, we assess the performance of the BWMV sensor through finite element model simulation. The result validates its high measurement accuracy and outstanding adaptability to installation errors with the assistance of specially trained neural networks. Furthermore, we have enhanced the bioinspired geomagnetic navigation algorithm and proposed a more advanced search strategy to adapt to navigation under the condition of no prior geomagnetic map. A simulated geomagnetic navigation platform was constructed based on the finite element model to simulate the navigation of the BWMV sensor in geomagnetic environments. The simulated navigation experiment verified that the proposed search strategy applied to the BWMV sensor can achieve high-precision navigation. This study proposes a novel approach for the research of bioinspired geomagnetic navigation technology, which holds great development prospects.

Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout

Abstract. Remote sensing radars from airborne and spaceborne platforms provide critical observations of clouds to estimate precipitation rates across the globe. The ability of these radars to detect changes in precipitation properties is advanced by Doppler measurements of particle fall speed. Within mixed-phase clouds, precipitation mass and its fall characteristics are especially sensitive to the effects of riming. In this study, we quantified these effects and investigated the distinction of riming from aggregation in Doppler radar vertical profiles using quasi-idealized particle-based model simulations. Observational constraints of a control simulation were determined from airborne in situ and remote sensing measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) for a wintry–mixed precipitation event over the northeastern United States on 4 February 2022. From the upper boundary of a one-dimensional column, particle evolution was simulated through vapor deposition, aggregation, and riming processes, producing realistic Doppler radar profiles. Despite a modest observed amount of supercooled liquid water (0.05 g m−3), riming accounted for 55 % of the ice-phase precipitation mass, cumulatively increasing reflectivity by 44 % and Doppler velocity by 68 %. Independent evaluation of process-based sensitivities showed that, while radar reflectivity is comparably sensitive to either riming- or aggregation-based particle morphology, the Doppler velocity profile is uniquely sensitive to particle density changes during riming. Thus, Doppler velocity profiles advance the diagnosis of riming as a dominant microphysical process in stratiform clouds from single-wavelength radars, which has implications for quantitative constraints of particle properties in remote sensing applications.

Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning.

Global measurements of ocean pCO2 are critical to monitor and understand changes in the global carbon cycle. However, pCO2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on direct learning, have been used to upscale and extrapolate sparse point data to dense estimates using globally available input features. However, these estimates tend to exhibit spatially heterogeneous performance. As a result, we propose a physics-informed transfer learning workflow to generate dense pCO2 estimates that are grounded in real-world measurements and remainphysically consistent. The models are initially trained on dense input predictors against pCO2 estimates from Earth system model simulation, and then fine-tuned to sparse SOCAT observational data. Compared to the benchmark direct learning approach, our transfer learning framework shows major improvements of up to 56-92%. Furthermore, we demonstrate that using models that explicitly account for spatiotemporal structures in the data yield better validation performances by 50-68%. Our strategy thus presents a new monthly global pCO2 estimate that spans for 35 years between 1982-2017.

Real-Time Simulation of the Reaction Kinetics of Supported Metal Nanoparticles.

A common issue with supported metal catalysts is the sintering of metal nanoparticles, resulting in catalyst deactivation. In this study, we propose a theoretical framework for realizing a real-time simulation of the reactivity of supported metal nanoparticles during the sintering process, combining density functional theory calculations, microkinetic modeling, Wulff-Kaichew construction, and sintering kinetic simulations. To validate our approach, we demonstrate its feasibility on α-Al2O3(0001)-supported Ag nanoparticles, where the simulated sintering behavior and ethylene epoxidation reaction rate as a function of time show qualitative agreement with experimental observation. Our proposed theoretical approach can be employed to screen out the promising microstructure feature of α-Al2O3 for stable supported Ag NPs, including the surface orientation and promoter species modified on it. The outlined approach of this work may be applied to a range of different thermocatalytic reactions other than ethylene epoxidation and provide guidance for the development of supported metal catalysts with long-term stability.