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Long-term trends and dependences on solar activity were derived for wave disturbances at mesopause altitudes (80−100 km) based on temperature variations obtained by spectral observations of the hydroxyl airglow at the Zvenigorod Science Station, Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, in 2000−2024, as well as by methods of statistical analysis. Their activities were determined by digital frequency filtering in three regions of wave periods, such as 0.7−2.0, 1.4−4.1, and 2.7−8.2 h with maxima at 1, 2, and 4 h. The root-mean-square values of temperature half-differences served as an indicator of wave activity. Both year-round and average seasonal (winter and summer) values were analyzed. According to the study results, the wave activity is characterized by positive trends with their dependence on the frequency band of disturbances (in winter, the trend is greater in the high-frequency band, and in summer, the trend is greater in the low-frequency band). The dependence on solar activity is positive. Its values are greater for the high-frequency band of disturbances, as well as in winter. [ABSTRACT FROM AUTHOR]

Due to the weak radiation generated by positive leaders, our understanding of how positive leaders are initiated and reach the ground remains limited. This study investigated positive cloud‐to‐ground (CG) lightning induced after the long intracloud lightning based on high‐speed video and fast antenna mapping results. Below the cloud base in the field of view, re‐breakdowns of decayed negative branches occurred consecutively beneath the horizontal negative channel in the form of bidirectional leaders. These bidirectional leaders advanced along the same channel, and eventually, the third reached the ground, generating a positive return stroke (RS) with a peak current of 157 kA. Following the RS, new negative discharges emerge adjacent to the vertical return stroke channel, persisting and propagating to form a continuing current (CC) lasting over 200 milliseconds. Plain Language Summary: Positive cloud‐to‐ground flash attracts more attention than its negative counterpart because it is more hazardous. Previous studies have confirmed the close relationship between intracloud (IC) lightning and positive cloud‐to‐ground (+CG) lightning. Downward positive leaders forming +CG can originate from branching of intracloud lightning or even from negative polarity leader channels. However, due to the weak radiation from positive leaders during their development within the cloud, common lightning detection devices, such as LF/VLF fast antennas and VHF antennas, find it very difficult to detect positive leaders. Consequently, our understanding of how positive leaders initiate within the cloud and reach the ground remains limited. In this study, we comprehensively utilized lightning channel mapping results and high‐speed video to investigate a case of +CG. We found that the occurrence of downward positive leaders around the negative leader channel is facilitated through multiple side breakdowns, events that continue even after the return stroke. Therefore, we propose that the multiple positive side breakdowns beneath the reactivated negative leader channel are responsible for generating positive leaders from the negative lightning channel. These side discharges play a crucial role in the generation of +CG lightning. Key Points: A positive cloud‐to‐ground lightning flash was analyzed based on synchronous high‐speed video and lightning mapping resultsDownward positive leader initiated, reached ground via three bidirectional side discharges beneath horizontal negative leader channelSide negative breakdowns from vertical return stroke channel and reactivation of decayed negative branches were found [ABSTRACT FROM AUTHOR]

Three-body recombination reactions, in which two particles form a bound state while a third one bounces off after the collision, play significant roles in many fields, such as cold and ultracold chemistry, astrochemistry, atmospheric physics, and plasma physics. In this work, the dynamics of the recombination reaction for the N3 system over a wide temperature range (5000–20,000 K) are investigated in detail using the quasi-classical trajectory (QCT) method based on recently developed full-dimensional potential energy surfaces. The recombination products are N2(X) + N(4S) in the 14A″ state, N2(A) + N(4S) in the 24A″ state, and N2(X) + N(2D) in both the 12A″ and 22A″ states. A three-body collision recombination model involving two sets of relative translational energies and collision parameters and a time-delay parameter is adopted in the QCT calculations. The recombination process occurs after forming an intermediate with a certain lifetime, which has a great influence on the recombination probability. Recombination processes occurring through a one-step three-body collision mechanism and two distinct two-step binary collision mechanisms are found in each state. And the two-step exchange mechanism is more dominant than the two-step transfer mechanism at higher temperatures. N2(X) formed in all three related states is always the major recombination product in the temperature range from 5000 K to 20,000 K, with the relative abundance of N2(A) increasing as temperature decreases. After hyperthermal collisions, the formed N2(X/A) molecules are distributed in highly excited rotational and vibrational states, with internal energies mainly distributed near the dissociation threshold. Additionally, the rate coefficients for this three-body recombination reaction in each state are determined and exhibit a negative correlation with temperature. The dynamic insights presented in this work might be very useful to further simulate non-equilibrium dynamic processes in plasma physics involving N3 systems. [ABSTRACT FROM AUTHOR]

To bridge the gaps between meteorological large‐eddy simulation (LES) models and computational fluid dynamics (CFD) models for microscale urban climate simulations, the present study has developed a meteorological LES model for urban areas. This model simulates urban climates across both mesoscale (city scale) and microscale (city‐block scale). The paper offers an overview of this LES model, which distinguishes itself from standard numerical weather prediction models by resolving buildings and trees at the microscale simulations. It also differs from standard CFD models by accounting for atmospheric stratification and physical processes. Noteworthy features of this model include: (a) the calculation of long‐ and short‐wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three‐dimensional manner, and the model can also run on a graphics processing unit cluster. Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. The model developed in this study is intended to serve as a community tool for addressing both fundamental and applied studies in urban climatology. Plain Language Summary: This study introduces a new multi‐scale urban climate model, City‐LES, which is a hybrid model combining meteorological modeling and engineering computational fluid dynamics (CFD) approaches. This model simulates urban climates across both mesoscale (city scale) and microscale (city‐block scale). Developed through collaboration among urban climatologists, CFD modeling researchers, and high‐performance computing experts, City‐LES simulates airflow in urban areas, conducts three‐dimensional radiation calculations in the urban canopy, incorporates various atmospheric physics schemes, and operates on supercomputers equipped with graphics processing units, as well as CPUs. Furthermore, the model enables the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roof strategies. Validation results indicate that City‐LES accurately simulates urban atmospheric dynamics, the thermal environment, and heat stress indices. The model will serve as a valuable community tool for both fundamental and applied studies in urban climatology. Key Points: This study introduces a multi‐scale urban climate model that combines meteorological and computational fluid dynamics modeling approachesThe model resolves buildings, simulates 3D airflow and radiation in urban areas, and runs on supercomputers equipped with graphics processing units and/or CPUsThe model accurately simulates urban atmospheric dynamics, thermal environments, and heat stress indices [ABSTRACT FROM AUTHOR]

Aerosol and aerosol-cloud radiation interactions significantly influence Earth's radiative balance, hydrological cycle, global monsoons, atmospheric circulation, and climate, attracting substantial scientific attention. This study employs bibliometric and quantitative trend analyses to evaluate the development, knowledge structure, and research trends in aerosol and aerosol-cloud radiation interactions from 1999 to 2023 using Web of Science Core Collection data. Results reveal a consistent increase in publications and citations, indicating sustained attention in this field. The USA and China are identified as the most prolific countries, with significant contributions from institutions like the National Aeronautics and Space Administration and the Chinese Academy of Sciences. However, while the USA shows a recent decline in growth, China has demonstrated a significant upward trend in research contributions. Productive journals include Atmospheric Chemistry and Physics and the Journal of Geophysical Research-Atmospheres, with prolific authors such as Babu S. Suresh and Li Zhanqing. A co-occurrence analysis of keywords identifies research topics focused on aerosol optical properties, aerosol types, aerosol radiation interactions, and aerosol-cloud interactions. Emerging trends emphasize advanced methodologies such as remote sensing, model simulation, and artificial intelligence, with growing attention to regions like the Southern Ocean and the Arctic. This comprehensive analysis provides valuable insights for researchers, identifying knowledge gaps and guiding future research directions in aerosol and aerosol-cloud radiation interactions, which are crucial for understanding their climatic and atmospheric impacts. [ABSTRACT FROM AUTHOR]

Quantifying differences in secondary organic aerosols (SOAs) between the preindustrial period and the present day is crucial to assess climate forcing and environmental effects resulting from anthropogenic activities. The lack of vegetation information for the preindustrial period and the uncertainties in describing SOA formation are two leading factors preventing simulation of SOA. This study calculated the online emissions of biogenic volatile organic compounds (VOCs) in the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) by coupling the Model of Emissions of Gases and Aerosols from Nature (MEGAN), where the input vegetation parameters were simulated by the IAP Dynamic Global Vegetation Model (IAP-DGVM). The volatility basis set (VBS) approach was adopted to simulate SOA formation from the nontraditional pathways, i.e., the oxidation of intermediate VOCs and aging of primary organic aerosol. Although biogenic SOAs (BSOAs) were dominant in SOAs globally in the preindustrial period, the contribution of nontraditional anthropogenic SOAs (ASOAs) to the total SOAs was up to 35.7%. In the present day, the contribution of ASOAs was 2.8 times larger than that in the preindustrial period. The contribution of nontraditional sources of SOAs to SOA was as high as 53.1%. The influence of increased anthropogenic emissions in the present day on BSOA concentrations was greater than that of increased biogenic emission changes. The response of BSOA concentrations to anthropogenic emission changes in the present day was more sensitive than that in the preindustrial period. The nontraditional sources and the atmospheric oxidation capability greatly affect the global SOA change. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is a synoptic-scale phenomenon that consists in an obstruction of the normal easterly progression of weather patterns in the midlatitudes, leading to persistent atmospheric conditions sometimes associated with extreme weather. State-of-the-art climate models systematically underestimate winter atmospheric-blocking frequency, especially over Europe. This is often attributed to a poor representation of small-scale processes that are fundamental for the onset and maintenance of blocking events. Here, we explore how the implementation of two stochastic parameterizations, namely the stochastically perturbed parameterization tendencies (SPPTs) and the stochastic kinetic energy backscatter (SKEB) schemes, influences the representation of Northern Hemisphere winter blocking in EC-Earth3. We show that the activation of the two stochastic schemes has moderate detrimental effects on blocking representation, when assessed through a gradient reversal index. Using a zonal–blocked flow linear decomposition, we attribute such modification to changes in the mean winter atmospheric circulation, primarily manifested in a strengthening of the midlatitude jet stream and an intensification of the Hadley cell. Ultimately, an analysis of the meridional transport of zonal momentum by stationary and transient eddies reveals that these circulation differences arise from changes in tropical stationary-eddy activity. Our findings reconnect with earlier literature on similar experiments and suggest that the activation of stochastic parameterizations may require a retuning of the model to account for the resulting significant changes in the mean atmospheric circulation. [ABSTRACT FROM AUTHOR]

Atlantic Meridional Overturning Circulation (AMOC) variability originates from a large number of interacting processes with multiple time scales, with dominant processes dependent on both the latitude and timescale of interest. Here, we isolate the optimal atmospheric modes driving climate‐relevant decadal AMOC variability using a novel approach combining dynamical and statistical attribution (dynamics‐weighted principal component, or DPC analysis). We find that for both the subpolar (55°N) and subtropical (25°N) AMOC, the most effective independent mode of heat flux forcing closely resembles the North Atlantic Oscillation, and drives meridionally coherent AMOC anomalies through western boundary density anomalies. Conversely, established modes of wind stress variability possess limited quantitative similarity to the optimal wind stress patterns, which generate low‐frequency AMOC fluctuations by rearranging the ocean buoyancy field. We demonstrate (by running a modified version of the ECCOv4r4 state estimate) that most AMOC variability on decadal time scales can be explained by the DPCs. Plain Language Summary: The heat‐redistributing north‐south "overturning" circulation of the Atlantic Ocean is uniquely influential on climate, driven in part by chaotic atmospheric patterns of heat and wind that impart unpredictable variability into its strength. This study uses a novel modeling technique that combines the statistics of historical atmospheric data with the physics of the overturning circulation to uncover the distinct atmospheric patterns that most effectively stimulate this variability. We deduce that slow fluctuations in the overturning circulation are best forced by heat inputs that follow the shape of the well‐known North Atlantic Oscillation pattern. However, we also uncover two new patterns of wind (one most effective nearer the equator, one nearer the Arctic) that lead to a strong, delayed response in the overturning circulation. Removing these atmospheric patterns from the a simulation of recent ocean history results in a substantial drop in the intensity of overturning fluctuations. Key Points: The leading surface patterns forcing decadal AMOC variability are identified using a hybrid dynamical‐‐statistical attribution techniqueThe leading heat flux pattern is independent of AMOC latitude, drives up to 81% of AMOC variability, and is closely related to the NAOLeading wind stress patterns forcing AMOC are distinct from the NAO and differ for subtropical and subpolar AMOC [ABSTRACT FROM AUTHOR]

Understanding optical and radiative properties of aerosols and clouds is critical to reducing uncertainties in climate models. For over 10 years, the Observatory of Atmospheric Physics in Reunion (OPAR; 21.079° S, 55.383° E) has been operating three active lidar instruments, named lidar 1200 (Li1200), stratospheric ozone lidar (LiO3S), and tropospheric ozone lidar (LiO3T), providing time series of vertical profiles from 3 to 45 km of the aerosol extinction and backscatter coefficients at 355 and 532 nm as well as the linear depolarization ratio at 532 nm. This work provides a full technical description of the three systems, the details about the methods chosen for the signal preprocessing and processing, and an uncertainty analysis. About 1737 nighttime averaged profiles were manually screened to provide cloud-free and artifact-free profiles. Data processing consisted of Klett inversion to retrieve aerosol optical products from preprocessed files. The measurement frequency was lower during the wet season and the holiday periods. There is a good correlation between the Li1200 and LiO3S instruments in terms of stratospheric aerosol optical depth (AOD) at 355 nm (0.001–0.107; R=0.92±0.01) and with LiO3T in terms of Ångström exponent 355/532 (0.079–1.288; R=0.90±0.13). The lowest values of the averaged uncertainty in the aerosol backscatter coefficient for the three time series are 64.4 ± 31.6 % for LiO3S, 50.3 ± 29.0 % for Li1200, and 69.1 ± 42.7 % for LiO3T. These relative uncertainties are high for the three instruments because of the very low values of extinction and backscatter coefficients for background aerosols above Maïdo observatory. Uncertainty increases due to the signal-to-noise ratio (SNR) decrease above 25 km for LIO3S and Li1200 and above 20 km for LiO3T. The lidar ratio (LR) is responsible for an uncertainty increase below 18 km (10 km) for LiO3S and Li1200 (LiO3T). LiO3S is the most stable instrument at 355 nm due to fewer technical modifications and fewer misalignments. Li1200 is a valuable addition meant to fill in the gaps in the LiO3S time series at 355 nm or for specific case studies about the middle and low troposphere. Data described in this work are available at https://doi.org/10.26171/rwcm-q370 (Gantois et al., 2024). [ABSTRACT FROM AUTHOR]

Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a crucial physical parameter in meteorology and oceanography. This study demonstrates that the influence of earth incidence angle (EIA) on the SST retrieved from the microwave radiation imager (MWRI) onboard FengYun-3 (FY-3) meteorological satellites should not be ignored. Compared with algorithms that do not consider the influence of EIA in the regression, those that integrate the EIA into the regression can enhance the accuracy of SST retrievals. Subsequently, based on the recalibrated Level 1B data from the FY-3/MWRI, a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression. The reprocessed SST data, including FY-3B/MWRI SST during 2010-2019, FY-3C/MWRI SST during 2013-2019, and FY-3D/MWRI SST during 2018-2020, were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis (OSTIA). The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset. Compared with the Copernicus Climate Change Service V2.0 SST, the absolute deviation of the reprocessed SST, with a quality flag of 50, was less than 1.5°C. The root mean square errors of the FY-3/MWRI orbital, daily, and monthly SSTs, with a quality flag of 50, were approximately 0.82°C, 0.69°C, and 0.37°C, respectively. The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current. Overall, this reprocessed SST product is recommended for El Niño and La Niña events monitoring. [ABSTRACT FROM AUTHOR]

'My fascination with meteorology and its related sciences stems from my love for remote sensing technology. This enthusiasm ignited in my youth when my father, a radiology doctor, showed me [...]

The North Pacific Meridional Mode (PMM) is the strongest interannual air‐sea coupled system in the subtropical northeastern Pacific, which can significantly impact the development of El Niño and Southern Oscillation (ENSO). This study examines performance of the second version of the Chinese Academy of Sciences Earth System Model (CAS‐ESM2), developed primarily at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS), in simulating the PMM, ENSO, and their relationship. It reveals that CAS‐ESM2 can well reproduce the tropical climate mean states, including sea surface temperature (SST), surface winds, and precipitation. Furthermore, the model shows a good ability in reproducing the seasonal evolutions of the PMM and ENSO. Moreover, CAS‐ESM2 effectively simulates the influence of the PMM on subsequent ENSO and the underlying physical mechanisms, including the wind‐evaporation‐SST feedback process, the trade wind charging mechanism and summer deep convection mechanism. However, some improvements are still needed, particularly in representing the periodicity of the PMM, an overestimation of the ENSO intensity and westward extension of ENSO‐related SST anomalies in the tropical Pacific. The results obtained from the CAS‐ESM2 showcase significant progress in understanding the interaction between air‐sea interaction systems over the tropics and subtropics. Plain Language Summary: El Niño and Southern Oscillation (ENSO) is the strongest interannual atmosphere‐ocean coupling variability in the tropical Pacific. ENSO can trigger extreme weather and climate events worldwide, and lead to substantial economic losses. While tropical processes are acknowledged to influence ENSO, many recent studies have emphasized the important role of extratropical air‐sea variabilities in modulating the occurrence and development of ENSO events. The Pacific Meridional Mode (PMM) has been identified as a key channel in transmitting the impact of extratropical forcing on ENSO. Therefore, it is crucial to evaluate the performance of current climate models in representing the PMM and its relationship with the ENSO. This evaluation holds significant scientific value as it can enhance our understanding of the dynamics underlying the ENSO and help improve the prediction of ENSO. The second version of the Chinese Academy of Sciences Earth System Model (CAS‐ESM2), developed primarily at the Institute of Atmospheric Physics, Chinese Academy of Sciences, represents a significant milestone in advancing atmospheric science in China. This study aims to assess the capabilities of CAS‐ESM2 in simulating the ENSO, PMM, and the impact of PMM on ENSO. Our findings indicate that CAS‐ESM2 well reproduces these two air‐sea coupling systems and captures their relation. Key Points: Chinese Academy of Sciences Earth System Model (CAS‐ESM2) has eliminated the double Intertropical Convergence Zone bias to some extentCAS‐ESM2 can reasonably well simulate the spatial‐temporal features of Pacific Meridional Mode (PMM) and El Niño and Southern Oscillation (ENSO)The influence of the spring PMM on ENSO and the underlying mechanism can be captured by the CAS‐ESM2 [ABSTRACT FROM AUTHOR]

The Madden–Julian Oscillation (MJO) is a crucial predictability source on a subseasonal-to-seasonal (S2S) timescale. Therefore, the models participating in the World Weather Research Programme and the World Climate Research Programme (WWRP/WCRP) S2S prediction project focus on accurately predicting and analyzing the MJO. This study provides a detailed description of the configuration within the Institute of Atmospheric Physics at the Chinese Academy of Sciences (IAP-CAS) S2S forecast system. We assess the accuracy of the IAP-CAS model's MJO forecast using traditional Real-time Multivariate MJO (RMM) analysis and cluster analysis. Then, we explain the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 d. However, the ensemble spread still has potential for improvement. To examine the MJO structure in detail, we use cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to stronger forecasted convection and coupled waves, especially in the fast MJO events. The overestimation of the strength and length of MJO-coupled waves results in a faster MJO mode and quicker dissipation in the IAP-CAS model. These findings show that the IAP-CAS skillfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast. [ABSTRACT FROM AUTHOR]

Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitime-scale high-efficiency filtering algorithm which includes a deep-ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously established CDA system within the Coupled Model, version 2.1 (CM2-MSHea-CDA), that is developed by the Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past 4 decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep-ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, El Niño–Southern Oscillation (ENSO), and Pacific decadal oscillation (PDO). Particularly, both CDA systems generate similar time mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations. Significance Statement: A coupled climate model simulates the interactions of the atmosphere, ocean, sea ice, and land processes and derives change and variations of the climate system by combining these components. Due to imperfect numerical schemes, a model usually has systematic biases to the real world. Coupled data assimilation (CDA) combines coupled model dynamics and physics with atmospheric and oceanic observations to reconstruct the historical states of Earth's climate system, forming the analysis of climate change and variability. However, because of the existence of model errors and individual data assimilation algorithm behaviors, currently, the ocean variability of reanalysis produced by different numerical systems is basically divergent. Here, we first apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely used IPCC coupled models to establish two CDA systems. Then, we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past 4 decades. We found, by some degree, that the results of these multimodel coupled climate reanalyses converged to what historical states ought to be in observational estimation. These convergent climate reanalyses are expected to significantly increase our understanding of CDA and climate assessment. [ABSTRACT FROM AUTHOR]

Ocean observational gridded products are vital for climate monitoring, ocean and climate research, model evaluation, and supporting climate mitigation and adaptation measures. This paper describes the 4th version of the Institute of Atmospheric Physics (IAPv4) ocean temperature and ocean heat content (OHC) objective analysis product. It accounts for recent developments in quality control (QC) procedures, climatology, bias correction, vertical and horizontal interpolation, and mapping and is available for the upper 6000 m (119 levels) since 1940 (more reliable after ∼ 1957) for monthly and 1°×1° temporal and spatial resolutions. IAPv4 is compared with the previous version, IAPv3, and with the other data products, sea surface temperatures (SSTs), and satellite observations. It has a slightly stronger long-term upper 2000 m OHC increase than IAPv3 for 1955–2023, mainly because of newly developed bias corrections. The IAPv4 0–2000 m OHC trend is also higher during 2005–2023 than IAPv3, mainly because of the QC process update. The uppermost level of IAPv4 is consistent with independent SST datasets. The month-to-month OHC variability for IAPv4 is desirably less than IAPv3 and the other OHC products investigated in this study, the trend of ocean warming rate (i.e., warming acceleration) is more consistent with the net energy imbalance at the top of the atmosphere than IAPv3, and the sea level budget can be closed within uncertainty. The gridded product is freely accessible at 10.12157/IOCAS.20240117.002 for temperature data (Cheng et al., 2024a) and at 10.12157/IOCAS.20240117.001 for ocean heat content data (Cheng et al., 2024b). [ABSTRACT FROM AUTHOR]

This study looked at the behaviour of Equatorial Total Electron Content (TEC) in connection to geomagnetic storms that occurred between January and March of 2020. The data used in the study came from the Global Positioning System (GPS). Vertical Total Electron Content (VTEC) data was collected at three different ground stations to conduct the investigation: NKLG (GMAGLat. -8.04°S, GMAGLong. 81.05°E), ADIS (GMAGLat. 1.13°N, GMAGLong. 110.47°E), and DJIG (GMAGLat. 3.36°N, GMAGLong. 11457°E). The locations were carefully chosen in order to offer a comprehensive picture of the equatorial ionosphere's response to magnetic storms. The study's findings show that the equatorial region undergoes both amplification and depletion effects in the reaction of TEC to geomagnetic storms. The paper also proposed possible approaches to clarify these findings. The three stations' different latitudinal positions were recognised as the principal source of the considerable variations in GPS-TEC responses during storm periods. Furthermore, during the storm periods, the peak positive percentage deviation of TEC displayed an ascending trend with rising altitude for each monitoring station. This finding suggests the existence of complex height-related interactions between the equatorial ionosphere and geomagnetic storms. The current study examines the complicated factors that influence the behaviour of TEC in equatorial regions during geomagnetic storms. It highlights the need of adding latitudinal and altitude factors in such studies. [ABSTRACT FROM AUTHOR]

Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to predict air quality, e.g., pollutant concentration, efficiently and accurately is a core problem in environmental research. Developing a robust air quality predictive model has become an increasingly important task, holding practical significance in the formulation of effective control policies. Recently, deep learning has progressed significantly in air quality prediction. In this paper, we go one step further and present a neat scheme of masked autoencoders, termed as masked air modeling (MAM), for sequence data self-supervised learning, which addresses the challenges posed by missing data. Specifically, the front end of our pipeline integrates a WRF-CAMx numerical model, which can simulate the process of emission, diffusion, transformation, and removal of pollutants based on atmospheric physics and chemical reactions. Then, the predicted results of WRF-CAMx are concatenated into a time series, and fed into an asymmetric Transformer-based encoder–decoder architecture for pre-training via random masking. Finally, we fine-tune an additional regression network, based on the pre-trained encoder, to predict ozone (O   3 ) concentration. Coupling these two designs enables us to consider the atmospheric physics and chemical reactions of pollutants while inheriting the long-range dependency modeling capabilities of the Transformer. The experimental results indicated that our approach effectively enhances the WRF-CAMx model's predictive capabilities and outperforms pure supervised network solutions. Overall, using advanced self-supervision approaches, our work provides a novel perspective for further improving air quality forecasting, which allows us to increase the smartness and resilience of the air prediction systems. This is due to the fact that accurate prediction of air pollutant concentrations is essential for detecting pollution events and implementing effective response strategies, thereby promoting environmentally sustainable development. [ABSTRACT FROM AUTHOR]

As part of a project aimed at exploring the use of next-generation high-performance computing technologies for numerical weather prediction, we have ported two physics modules from the Common Community Physics Package (CCPP) to Graphics Processing Unit (GPU) and obtained accelerations of up to 10× relative to a comparable multi-core CPU. The physics parameterizations accelerated in this work are the aerosol-aware Thompson microphysics (TH) scheme and the Grell–Freitas (GF) cumulus convection scheme. Microphysics schemes are among the most time-consuming physics parameterizations, second to only radiative process schemes, and our results show better acceleration for the TH scheme than the GF scheme. Multi-GPU implementations of the schemes show acceptable weak scaling in a single node with 8 GPUs, and perfect weak scaling on multiple nodes using one GPU per node. The lack of inter-node communication for column physics parameterizations contributes to their scalability, however, physics parameterizations are run along with dynamics, so the overall multi-GPU performance is often governed by the latter. In the context of optimizing CCPP physics modules, our observations underscore that the extensive use of automatic arrays within inner subroutines hampers GPU performance due to serialized memory allocations. We have used the OpenACC directive programming language for this work because it allows for easy porting of large amounts of code and makes code maintenance more manageable compared to low-level languages like CUDA and OpenCL. [ABSTRACT FROM AUTHOR]

A set of decadal simulations has been completed and evaluated for gains using the Regional Arctic System Model (RASM) to dynamically downscale data from a global Earth system model and two atmospheric reanalyses. RASM is a fully coupled atmosphere-land-ocean-sea ice regional Earth system model. Nudging to the forcing data is applied to approximately the top half of the atmospheric domain. RASM simulations were also completed with a modification to the atmospheric physics for evaluating changes to the modeling system. The results show that for the top half of the atmosphere, the RASM simulations follow closely to that of the forcing data, regardless of the forcing data. The results for the lower half of the atmosphere, as well as the surface, show a clustering of atmospheric state and surface fluxes based on the modeling system. At all levels of the atmosphere the imprint of the weather from the forcing data is present as indicated in the pattern of the annual means. Biases, in comparison to reanalyses, are evident in the Earth system model forced simulations for the top half of the atmosphere but are not present in the lower atmosphere. This suggests that bias correction is not needed for fully coupled dynamical downscaling simulations. While the RASM simulations tended to go to the same mean state for the lower atmosphere, there are a differences in the variability and changes of weather patterns across the ensemble of simulations. These differences in the weather result in variances in the sea ice and oceanic states. [ABSTRACT FROM AUTHOR]

This article delves into the cultural and meteorological significance of Groundhog Day, tracing its roots back to the Christian holiday of Candlemas and its connection to the pagan holiday Imbolc. It explores the folklore surrounding Groundhog Day and its supposed ability to predict the arrival of spring, emphasizing that these predictions are not scientifically accurate but can stimulate discussions about weather and climate. The article also discusses the historical and cultural origins of the Groundhog Day tradition and its diffusion from Europe to America. It raises questions about what constitutes a "fair" Candlemas and the paradox of global warming in relation to the Groundhog Day predictions, highlighting the role of atmospheric circulation and climate patterns in understanding the folklore. [Extracted from the article]

Idealized numerical experiments have been performed with the earth system model developed at the Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (IAP RAS ESM), under anthropogenic CO2 emissions into the atmosphere, which increase initially and decrease afterwards. These numerical experiments revealed the inertia of various components of the earth system, leading to a delay in the response of various components of the carbon cycle relative to the anthropogenic emissions by several decades. The inertia of the carbon cycle components leads to a hysteretic response of its characteristics to anthropogenic CO2 emissions into the atmosphere that are nonmonotonic in time and, noticeable, in particular, for the gross primary production and respiration of plants and soil. In turn, the hysteretic response of the characteristics of the global carbon cycle indicates the irreversibility of its changes on the scale of (at least) several decades. The latter should be taken into account when planning for adapting and/or mitigating climate change. [ABSTRACT FROM AUTHOR]

The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s. [ABSTRACT FROM AUTHOR]

Abstract Due to the weak radiation generated by positive leaders, our understanding of how positive leaders are initiated and reach the ground remains limited. This study investigated positive cloud‐to‐ground (CG) lightning induced after the long intracloud lightning based on high‐speed video and fast antenna mapping results. Below the cloud base in the field of view, re‐breakdowns of decayed negative branches occurred consecutively beneath the horizontal negative channel in the form of bidirectional leaders. These bidirectional leaders advanced along the same channel, and eventually, the third reached the ground, generating a positive return stroke (RS) with a peak current of 157 kA. Following the RS, new negative discharges emerge adjacent to the vertical return stroke channel, persisting and propagating to form a continuing current (CC) lasting over 200 milliseconds.

Understanding optical and radiative properties of aerosols and clouds is critical to reduce uncertainties in climate models. For over 10 years, the Observatory of Atmospheric Physics of La Réunion (OPAR) has been operating three active lidar instruments (named Li1200, LiO3S and LiO3T) providing time-series of vertical profiles from 3 to 45 km of the aerosol extinction and backscatter coefficients at 355 and 532 nm, as well as the linear depolarization ratio at 532 nm. This work provides a full technical description of the three systems, details about the methods chosen for the signal preprocessing and processing, and an uncertainty analysis. About 1737 night-time averaged profiles were manually screened to provide cloud-free and artifact-free profiles. Data processing consisted in Klett inversion to retrieve aerosol optical products from preprocessed files. The measurement frequency was lower during the wet season and the holiday periods. There is a good correlation between the Li1200 and LiO3S in terms of stratospheric AOD at 355 nm (0.001–0.107; R = 0.92 ± 0.01), and with the LiO3T in terms of Angström exponent 355/532 (0.079–1.288; R = 0.90 ± 0.13). The lowest values of the averaged uncertainty of the aerosol backscatter coefficient for the three time-series are 64.4 ± 31.6 % for the LiO3S, 50.3 ± 29.0 % for the Li1200, and 69.1 ± 42.7 % for the LiO3T. These relative uncertainties are high for the three instruments because of the very low values of extinction and backscatter coefficients for background aerosols above Maïdo observatory. Uncertainty increases due to SNR decrease above 25 km for the LIO3S and Li1200, and 20 km for the LiO3T. The LR is responsible for an uncertainty increase below 18 km (10 km) for the LiO3S and Li1200 (LiO3T). The LiO3S is the most stable instrument at 355 nm due to less technical modifications and less misalignments. The Li1200 is a valuable addition to fill in the gaps in the LiO3S time-series at 355 nm or for specific case-studies about the middle and low troposphere. Data described in this work are available at https://doi.org/10.26171/rwcm-q370 (Gantois et al., 2024). [ABSTRACT FROM AUTHOR]

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions. [ABSTRACT FROM AUTHOR]

The Pyrenean Platform for Observation of the Atmosphere (P2OA) is a coupled plain-mountain instrumented platform in southwest France. It is composed of two physical sites: The 'Pic du Midi' mountain top observatory (2877 m a.s.l.) and the 'Centre de Recherches Atmosphériques' (600 m a.s.l). Both sites are complementarily instrumented for the monitoring of climate variables and the study of meteorological processes in a mountainous region. The scientific topics covered by the P2OA include surface-atmosphere interactions in heterogeneous landscape and complex terrain, physics and chemistry of atmospheric trace species at large scale, influence of local and regional-scale emissions and transport on the atmospheric composition, and transient luminous events above thunderstorms. With a large number of instruments and a high hosting capacity, the P2OA contributes to atmospheric sciences in (i) building long-term series of atmospheric observations, (ii) hosting experimental field campaigns and instrumental tests, (iii) educational training in atmospheric observation techniques. Here we present the complete instrumentation of the P2OA and the associated datasets, give a meteorological characterization of the platform, and illustrate the potential of the P2OA and its dataset with past or ongoing studies and projects. [ABSTRACT FROM AUTHOR]

This paper describes the technical specifications of the extensions made to the middle-atmospheric lidar facility at the Leibniz Institute of Atmospheric Physics in Kühlungsborn, Germany (54.12° N, 11.77° E). The upgrade complements the existing daylight-capable Rayleigh–Mie–Raman (RMR) temperature lidar with a nighttime-only RMR wind–temperature lidar. The new system comprises an independent lidar with laser, telescopes, and detectors, which is synchronized with and adapted to the (old) temperature lidar. As a result, with the combination of RMR lidars the atmosphere is probed with three (vertical and tilted) beams. This work intends to highlight the recent innovations in the construction of a Doppler–Rayleigh lidar system using the single-edge iodine-cell technique, which allows for the simultaneous measurement of wind, temperature, and aerosols. We will detail supporting subsystems that allow for a high degree of lidar automation and concisely provide key technical information about the system that will support readers in the development of additional RMR wind–temperature lidar systems. We show an example of time-resolved temperature and wind soundings reaching up to ∼ 90 km. These data agree well with ECMWF-IFS profiles between 35 and ∼ 50 km but show a much larger variability above. In the companion article, we will present the algorithm design and uncertainty budgets associated with the data processing chain. [ABSTRACT FROM AUTHOR]

The distributed hydrologic model WRF‐Hydro can operate in a fully‐coupled mode with the atmospheric Weather, Research and Forecasting (WRF) model. WRF‐Hydro enhances the modeling of terrestrial hydrologic processes in coupled WRF/WRF‐Hydro by simulating lateral surface and subsurface water flows. The objectives of this study are (a) to examine the effect of WRF‐Hydro on the surface energy and water balance in fully‐coupled WRF/WRF‐Hydro simulations and (b) to examine the impact of five WRF physics parameterizations on WRF‐Hydro streamflow. The study area is the Mediterranean island of Cyprus and 31 mountainous watersheds. The domain‐average soil moisture was 20% higher in the coupled WRF/WRF‐Hydro, relative to the standalone WRF model, during a 1‐year simulation. The higher soil moisture could explain the increase in latent heat (36%) and evapotranspiration (33%). The increase in these fluxes was less with a modification in the model transpiration parameterization to represent nocturnal transpiration and the use of remote sensing leaf area index data. The simulated precipitation of the coupled model increased up to 3%, relative to WRF. Two‐year long WRF‐Hydro simulations gave a median Nash‐Sutcliffe Efficiency for daily streamflow of the 31 watersheds of 0.5 for observed precipitation forcing and between −1.9 and 0.2 for the forcing of the five WRF parameterizations. This study showed that the enhancement of the standalone WRF model with lateral water flow processes in the coupled mode with WRF‐Hydro modifies the terrestrial energy and water balance. The improved terrestrial process representation should be considered for future hydrological cycle studies with WRF. Plain Language Summary: We tested the widely used Weather Research and Forecasting (WRF) model against the coupled atmospheric ‐ hydrologic WRF/WRF‐Hydro model, which adds the simulation of the processes of horizontal surface and subsurface flow of water. We conducted 1‐year long simulations for the Mediterranean island of Cyprus and 31 small mountainous river basins. We found higher soil moisture (20%), higher evapotranspiration (33%) and a small increase in rainfall (3%) for the coupled WRF/WRF‐Hydro model, compared to the WRF model without horizontal flows. We also examined the streamflow modeled with WRF‐Hydro using observed rainfall and five different set‐ups of the WRF model. We found that WRF‐Hydro with observed rain underestimated the average streamflow by 6%, during a 2‐year simulation. The best of the five WRF set‐ups showed a 19% underestimation of the average streamflow. Our study suggests that the coupling of WRF with the WRF‐Hydro model can improve land‐atmosphere simulations. However, an optimized ensemble of WRF set‐ups is needed to model the streamflow. Key Points: Fully‐coupled simulations of the Weather Research and Forecasting model with WRF‐Hydro and the effect of WRF ensemble forcings were testedThe process of lateral routing of water in coupled simulations increases soil moisture, latent heat, evapotranspiration and precipitationDifferent WRF atmospheric physics parameterizations used as forcing significantly affect WRF‐Hydro streamflow (bias between −37% and 119%) [ABSTRACT FROM AUTHOR]

In the boreal summer and autumn of 2023, the globe experienced an extremely hot period across both oceans and continents. The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter. In this report, as shown in the multi-model ensemble mean (MME) prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences, a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2–3 months, which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection, thus serving to modulate the winter climate in East Asia and North America. Despite some uncertainty due to unpredictable internal atmospheric variability, the global mean surface temperature (GMST) in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend. Specifically, the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter, and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991. Moreover, the necessary early warnings are still reliable in the timely updated medium-term numerical weather forecasts and sub-seasonal-to-seasonal prediction. [ABSTRACT FROM AUTHOR]

A realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future. Plain Language Summary: A realistic representation of the radiation balance at the top of the Earth's atmosphere (TOA) by coupled climate models is essential for trust in future climate projections. Despite that relevance, it is still not clear whether the models correctly simulate the coupling between surface warming and TOA radiation because of the short observational record. We show that climate models systematically underestimate the observed increase in global TOA radiation during 2001–2022 in 552 simulations. Locally, even if a simulation reproduces observed changes in surface temperature, changes in TOA radiation are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale patterns of surface warming and from errors in the atmospheric physics which suppress the communication of the surface information to the TOA. Models that better represent the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity, that is, a weak global‐mean surface warming in response to a doubling of the atmospheric CO2 concentration above pre‐industrial levels. Our new bias metric links a model's current response to climate change to its behavior in the future. Key Points: Climate models underestimate the observed global TOA radiation trend during 2001–2022Surface warming patterns and atmospheric physics matter for the observation‐model discrepancyModels with a small bias in the coupling between surface warming and TOA radiation trends have a relatively low climate sensitivity [ABSTRACT FROM AUTHOR]

We present the first measurements of simultaneous horizontal and vertical winds using a new lidar system developed at the Leibniz Institute of Atmospheric Physics in Kühlungsborn, Germany (54.12° N, 11.77° E), for the concept of Vertical And Horizontal COverage by LIdars (VAHCOLI). We describe the technical details of a multi-field-of-view (MFOV) upgrade, which allows the measurement of wind dynamics in the transition region from microscale to mesoscale (103 – 104 m). The method was applied at the edge of a developing high-pressure region, covering altitudes between 3 and 25 km. Comparisons between the lidar measurements and data from the European Centre for Medium-Range Weather Forecasts (ECMWF) show excellent agreement for the meridional wind component along the north beam of the lidar, which is better than 0.30±0.33 m s -1 , while along the south beam, a higher deviation with -0.93±0.73 m s -1 is observed. Measurements of vertical wind show a significant underestimation of this component by ECMWF. Comparison of Aeolus winds to the lidar winds projected to the Aeolus viewing direction shows good agreement, with results better than -0.12±3.31 m s -1. The capability of the MFOV lidar to explore small-scale asymmetries in the wind field is shown by a comparison of the north and south field of view, where we observe a wind asymmetry in the meridional winds, which is also present in ECMWF but underestimated by a factor of approximately 4. [ABSTRACT FROM AUTHOR]

Chen et al. (2022) analyzed the event of rapid nocturnal O 3 enhancement (NOE) observed on 31 July 2021 at the surface level in the North China Plain and proposed transport of substantial stratosphere ozone to the surface by Typhoon In-fa followed by downdraft of shallow convection as the mechanism of the NOE event. The analysis seems to be valid from the viewpoint of atmospheric physics. This comment revisits the NOE phenomenon on the basis of the China National Environmental Monitoring Center (CNEMC) network data used in Chen et al. (2022), together with the CNEMC data from Zibo (ZB) and O 3 , NO x , PAN (peroxyacetic nitric anhydride), and VOC (volatile organic compound) data from the Zibo supersite operated by the China Research Academy of Environmental Sciences (CRAES). We found (a) O x (O 3 + NO 2) levels during the NOE period approaching those of O 3 during 14:00–17:00 LT, (b) levels of PAN and the relationship between O 3 and PAN consistent with dominance of chemical and physical processes within the boundary layer, and (c) estimated photochemical ages of air mass shorter than 1 d and showing no drastic increases during the NOE. We argue that the NOE was not caused by typhoon-induced stratospheric intrusion but originated from fresh photochemical production in the lower troposphere. Our argument is well supported by the analysis of atmospheric transport as well as ground-based remote sensing data. [ABSTRACT FROM AUTHOR]

This study aimed to utilize hybrid physics-based models that allow for a comprehensive evaluation of how solar–meteorological variables influence the fraction of absorbed photosynthetically active radiation (FAPAR). By integrating both physical and statistical approaches, these models provided a novel perspective on understanding the complex relationship between solar radiation and FAPAR dynamics. The integration of URE and CLC in studying ecosystem efficiency in the nations of China and India is crucial, owing to their interdependent nature. To achieve this, the coupled model intercomparison project phase 6 (CMIP6) was utilized, which involved interpolating data from the most recent five GCMs from the six-phase CMIP6. By integrating advanced modeling techniques with observational data, this innovative approach enabled a holistic assessment of the intricate connections between urban development, climate dynamics, and the productivity of surrounding ecosystems in these regions. The reported decline in FAPAR productivity in China and India highlights the growing concern over the impact of climate change on the health and resilience of vegetation. China experienced negative changes of −1.425, −0.414, and −0.018, while India reported negative changes of −2.463, 2.199, and 2.915 under different scenarios. Interestingly, despite these variations, both countries observed similar tidal variations in their seasonal bases, suggesting a consistent pattern in the fluctuations of vegetation productivity throughout the year. The authors of this study collected and analyzed extensive data on CLC and URE in order to investigate their impact on FAPAR fluctuations in China and India. The higher impact of URE compared to CLC in terms of effective contributions suggests that URE plays a crucial role in shaping the atmospheric physics of both China, accounting for 45.26%, and India, registering 33.95%. Conversely, the effects of CLC and PAR residual factor (PRF) were more pronounced in India compared to China, with impacts of 8.21% and 53.03% compared to 4.21% and 46.70%, respectively. [ABSTRACT FROM AUTHOR]

In 1963, at the height of the Cold War, R. W. Stewart visited the Institute of Atmospheric Physics in Moscow for three months, including a side trip to a field station on the River Don. The visit followed on from discussions at scientific meetings on the topic of turbulence in fluid flows. Major theoretical advances had been made in the USSR, while Stewart's group in Canada had conducted a key observational programme in turbulent tidal currents off the coast of British Columbia. During his visit, Stewart was able to show how some Soviet observations were in accord with others after correction for instrument size and, most importantly, with Evgeny Novikov he pioneered a new approach to the theory of turbulence to allow for intermittency. His report contains observations of the style of science in the USSR in that era, as well as interesting comments on food and culture. The visit produced lasting dividends in scientific collaboration, particularly in international programmes related to climate. [ABSTRACT FROM AUTHOR]

Ocean observational gridded products are vital for climate monitoring, ocean and climate research, model evaluation, and supporting climate mitigation and adaptation measures. This paper describes the 4th version of the Institute of Atmospheric Physics (IAPv4) ocean temperature and ocean heat content (OHC) objective analysis product. It accounts for recent developments in quality control (QC) procedures, climatology, bias correction, vertical and horizontal interpolation, and mapping and is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1957) for monthly and 1° × 1° temporal and spatial resolutions. The IAPv4 is compared with the previous version, IAPv3, and to the other data products, sea surface temperatures (SSTs), and satellite observations. It has a slightly stronger long-term upper 2000 m OHC increase than IAPv3 for 1955-2023, mainly because of newly developed bias corrections. IAPv4 OHC 0-2000 m trend is also higher during 2005-2023 than IAPv3. The first level of IAPv4 is consistent with independent SST datasets. The month-to-month OHC variability for IAPv4 is desirably less than IAPv3 and other OHC products investigated in this study; annual variations are consistent with the net energy imbalance at the top of the atmosphere, and the sea level budget can be closed within uncertainty. [ABSTRACT FROM AUTHOR]

The paper deals with studying the influence of air pressure fluctuations on the health of patients. Since August 2008, the Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences and the Central Clinical Hospital of the Russian Academy of Sciences (CCH RAS) observed air pressure fluctuations with a simultaneous registration of patients who were urgently hospitalized to the CCH RAS and classified as weather-sensitive. Weather-sensitive patients included patients diagnosed with myocardial infarction, angina, hypertension, extrasystole, stroke, and cerebrovascular disease. The data on 6078 patients over a four-year period (January 1, 2009–November 31, 2012) were processed. The results have shown an increase in the cases of patients' hospitalization with an enhancement of the external effects of air pressure. [ABSTRACT FROM AUTHOR]

Unlike in controlled settings, social scientists' performance when predicting real-world societal trends is indistinguishable from that of laypeople or naive statistical methods. There is no conclusive evidence that domain-specific expertise improves predictive accuracy of societal change. Causal models in social sciences are oversimplified, confusing levels of analysis, and generally lack specificity about the magnitude or distribution of effects, limiting prediction accuracy. Engaging in counterfactual thought experiments to go beyond the most obvious factors specified in a theory, predictive accuracy in social sciences can be improved. Integrating broad foundational models with context-specific time series data, as in meteorology, can enhance the accuracy of social science predictions. Enhancing analytical training and understanding of temporal dynamics can yield more precise, riskier predictions, the practical application of which requires the fostering of intellectual humility in scientists. We examine the opportunities and challenges of expert judgment in the social sciences, scrutinizing the way social scientists make predictions. While social scientists show above-chance accuracy in predicting laboratory-based phenomena, they often struggle to predict real-world societal changes. We argue that most causal models used in social sciences are oversimplified, confuse levels of analysis to which a model applies, misalign the nature of the model with the nature of the phenomena, and fail to consider factors beyond the scientist's pet theory. Taking cues from physical sciences and meteorology, we advocate an approach that integrates broad foundational models with context-specific time series data. We call for a shift in the social sciences towards more precise, daring predictions and greater intellectual humility. [ABSTRACT FROM AUTHOR]

The document titled "E3SM Chemistry Diagnostics Package (ChemDyg) Version 0.1.4" describes an open-source software called ChemDyg that supports the Department of Energy's Energy Exascale Earth System Model (E3SM). ChemDyg generates diagnostic plots and tables for model-to-model and model-to-observation comparisons, including contour mapping plots, diurnal and annual cycle plots, time-series plots, and processing tables. The software is modular and each diagnostic set has its own driving script for calculation and plotting. The document provides a comprehensive description of ChemDyg, including details of each diagnostics set and its required input data formats. [Extracted from the article]

This paper documents the methodology and preliminary results from a Perturbed Parameter Ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2-CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations are analyzed and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks. The use of various emulators is explored in the multi-dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that using PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2-CAM6 PPE is publicly available, and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g. coupling to the land surface). [ABSTRACT FROM AUTHOR]

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is severely limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. 'reservoir age' offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age/delta-R estimates therefore have limited applicability. The construction of regional marine calibration curves could provide a solution to this challenge. However, the spatial reach of such calibrations, and their robustness subject to temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R-ages). We investigate the performance of multiple algorithms (K-Means, K-Medoids, hierarchical clustering) applied to outputs from 2 different numerical models, spanning a range of climate states and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics (i.e. locations of deep mixing, gyres, fronts, divergence etc.). At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins (Arctic, Atlantic, Southern, Indo-Pacific). By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations that cohere with known modern oceanographic processes, such as increased high latitude R-ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the Eastern Equatorial Pacific. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This is found to hold when cluster assignments obtained from one model are applied to simulated R-age time series from another. The proposed clusters are also found to be broadly consistent with existing reservoir age reconstructions that span the last ~30 ka. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which marine radiocarbon calibration curves may eventually be generated. [ABSTRACT FROM AUTHOR]

Paul Jozef Crutzen was a pioneer in the atmospheric sciences, a kind-hearted and humorous person with empathy for the private lives of his colleagues and students, and a man who upheld the highest scientific standards for himself and for others. He made fundamental scientific contributions to a wide range of scientific topics in all parts of the atmosphere, from the mesosphere to the stratosphere and from the stratosphere to the troposphere. In particular, he was the first to describe the NOx -driven ozone depletion cycle in the stratosphere, he was among the first to develop the idea of chemical formation of ozone in the troposphere, he provided key concepts to explain the "ozone hole", and he made fundamental discoveries about the effects of biomass burning on the troposphere. Understanding and addressing the causes of anthropogenic air pollution and climate change were the driving motivations for his scientific work. In his work, he did not shy away from challenge and provocation. His work on smoke from fires after a potential nuclear war inspired new research on a concept now known as "nuclear winter". He also initiated the reopening of the debate on "geoengineering" – a concept now referred to as "climate intervention". He also brought the term "Anthropocene" to the popular debate. Moreover, he had a strong influence on atmospheric science through his educational role; there is a very large number of outstanding scientists who started their career with scientific work with Paul. In 2000, Paul was among the founders of the journal Atmospheric Chemistry and Physics, which was unique at the time in providing public discussion of published preprints as well as what we now call "open access" to published articles. Paul's work on human impacts on atmosphere and climate has had a profound impact on the environmental policies of many countries for decades. In the future, his work will continue to be a guide for generations of scientists and environmental policymakers to come. [ABSTRACT FROM AUTHOR]

Scientific studies concentrated in Antarctica hold great value and importance for humanity. Research projects are conducted in various disciplines on the continent, where nearly 100 research stations from 30 countries are established. Despite the challenges posed by Antarctica's natural structure, unique environment, difficult working conditions, and limited logistics opportunities, scientific research conducted there is indispensable due to the valuable information it offers. In pursuit of accessing this information, Turkish Antarctic expeditions have been organized since 2017. In this study, a polar data center was established through a web-based geographic information system (WebGIS), a significant infrastructure for the continuity of scientific studies. The implemented projects and raw/processed data from previous expeditions in diverse subjects and disciplines, including geology, geophysics, geography, hydrology, meteorology, oceanography, glaciology, atmospheric physics, chemistry, microbiology, and remote sensing, were incorporated into this information system. Studies in Antarctica have primarily focused on observing the effects of global warming, necessitating the monitoring of past and current data related to sea ice, sea level, and coastal changes. The data also encompasses seismic, volcanic, tectonic, and Global Navigation Satellite System (GNSS) monitoring. As suggested by the Antarctic Treaty, a user-friendly website was designed and made accessible for research projects, data, and publications. An entity relationship diagram was created for the fields to be included in the database. These fields were structured as tables using structured query language (SQL) queries in PgAdmin. A workspace and storage area were created in GeoServer, and a connection was established with PostGIS. Layers of spatial and nonspatial data from the PgAdmin application were added to the GeoServer workspace. The data transferred to GeoServer were presented in an OpenLayers web map with Web Map Service (WMS) and Web Feature Service (WFS) according to Open Geospatial Consortium (OGC) standards. Additionally, a mini web map was designed using the desktop Quantum GIS (QGIS) application and GIS Cloud collaboration, displaying GNSS and meteorological (weather) observation stations on the continent. A dynamic web project was employed for coding in Eclipse software, where backend coding was done with JavaScript. SQL was used as a part of the backend to integrate data from the database per request. Frontend coding for the website's visual side was completed with HTML, CSS, and JavaScript. As a result, a WebGIS platform was developed where researchers can register, add projects, and publications, and view data on a map. Users can download, analyze, and utilize the available data. The data center's existence underscores the importance of polar science and serves as an incentive for further research. [ABSTRACT FROM AUTHOR]