Convective cloud development during the Indian monsoon helps moisten the atmospheric environment and drive the monsoon trough northwards each year, bringing a large amount of India’s annual rainfall. Therefore, an increased understanding of how monsoon convection develops in observations will help inform model development. In this study, 139 days of India Meteorological Department Doppler weather radar data is analysed for 7 sites across India during the 2016 monsoon season. Convective cell-top heights (CTH) are objectively identified through the season, and compared with near-surface (at 2 km height) reflectivity. These variables are analysed over three time scales of variability during the monsoon: monsoon progression, active-break periods and the diurnal cycle. We find a modal maximum in CTH around 6–8 km for all sites. Reflectivity increases with CTH, at first sharply, then less sharply above the freezing level. Bhopal and Mumbai exhibit lower CTH for monsoon break periods compared to active periods. A clear diurnal cycle in CTH is seen at all sites except Mumbai. The phase of the diurnal cycle depends on the surface type being land or ocean for south-eastern India, with the frequency of oceanic cells typically exhibiting an early morning peak compared to those over land, consistent with the observed diurnal cycle of precipitation. The cell characteristics discovered are discussed in light of the differences in large-scale synoptic and mesoscale mechanisms responsible for different cell regimes. Our findings confirm that Indian monsoon convective regimes are partly regulated by the large-scale synoptic environment within which they are embedded.

An analysis of concurrent extreme events of continental precipitation and Integrated Water Vapour Transport (IVT) is crucial to our understanding of the role of the major global mechanisms of atmospheric moisture transport, including that of the landfalling Atmospheric Rivers (ARs) in extratropical regions. For this purpose, gridded data on CPC precipitation and ERA-5 IVT at a spatial resolution of 0.5 º were used, covering the period from Winter 1980/1981 to Autumn 2017. For each season, and for each point with more than 400 non-dry days, several copula models were fitted to model the joint distribution function of the two variables. At each of the analysed points, the best copula model was used to estimate the probability of a concurrent extreme. At the same time, within the sample of observed concurrent extremes, the proportion of days with landfalling ARs was calculated for the whole period and for two 15-year subperiods, one earlier period and one more recent (warmer) period. Three metrics based on copulas were used to analyse carefully the influence of IVT on extreme precipitation in the main regions of occurrence of AR landfall. The results show that the probability of occurrence of concurrent extremes is strongly conditioned by the dynamic component of the IVT, the wind. The occurrence of landfalling ARs accounts for most of the concurrent extreme days of IVT and continental precipitation, with percentages of concurrent extreme days close to 90% in some seasons in almost all the known regions of maximum occurrence of landfalling ARs, and with percentages greater than 75% downwind of AR landfall regions. This coincidence was lower in tropical regions, and in monsoonal areas in particular, with percentages of less than 50%. With a few exceptions, the role of landfalling ARs as drivers of concurrent extremes of IVT and continental precipitation tends to show a decrease in recent (warmer) periods. For almost all the landfalling AR regions with high or very high probabilities of achieving a concurrent extreme, there is a general trend towards a lower influence of IVT on extreme continental precipitation in recent (warmer) periods.

A Large Eddy Simulation (LES) model that simulates the aerosol lifecycle, including aerosol sources and sinks, was used to study the stratocumulus to cumulus transition (SCT). To initialize, force, and evaluate the LES, we used a combination of reanalysis, satellite, and aircraft data from the Cloud System Evolution in the Trades field campaign in summer 2015 over the Northeast Pacific. The simulations follow two Lagrangian trajectories from initially overcast stratocumulus to the tropical shallow cumulus region near Hawaii. The first trajectory is characterized by an initially clean, well-mixed stratocumulus-topped marine boundary layer (MBL), then continuous MBL deepening and precipitation onset followed by a clear SCT and a consistent reduction of aerosols that ultimately leads to an ultra-clean layer in the upper MBL. The second trajectory is characterized by an initially polluted and decoupled MBL, weak precipitation, and a late SCT. Overall, the LES simulates the general MBL features seen in observations. Sensitivity studies with different aerosol initial and boundary conditions reveal aerosol-induced changes in the transition, and albedo changes are decomposed into the Twomey effect and adjustments of cloud liquid water path and cloud fraction. Impacts on precipitation play a key role in the sensitivity to aerosols: for the first case, runs with enhanced aerosols exhibit distinct changes in microphysics and macrophysics such as enhanced cloud droplet number concentration, reduced precipitation, and delayed SCT. Cloud adjustments are dominant in this case. For the second case, enhancing aerosols does not affect cloud macrophysical properties significantly, and the Twomey effect dominates.

Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NN), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach which is slower but more robust than full emulation is to use NNs to predict optical properties, without abandoning the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP gas optics scheme, and shown to be accurate while improving speed.However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using a hybrid loss function designed to reduce radiative forcing errors, and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark, new NN models were trained on RRTMGP k-distributions with reduced spectral resolutions. Offline evaluation shows a very high level of accuracy for clear-sky fluxes and heating rates; for instance the RMSE in shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for 5 important greenhouse gases across a wide range of concentrations such as 8x CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean-atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster), and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS

The January 2022 Hunga Tonga–Hunga Haʻapai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for >12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

Survey evidence has indicated that a significant percentage of the population does not fully embrace the scientific consensus regarding climate change. This paper assesses whether the hourly temperature data support this denial. Specifically, this paper examines the relationship between hourly CO2 atmospheric concentration levels and temperature using hourly data from the NOAA-operated Barrow observatory in northern Alaska. At this observatory, the average annual temperature over the 2015-2020 period has been about 3.37 oC higher than in the 1985-1990 period. A time-series model to explain hourly temperature is formulated using the following explanatory variables: the hourly level of total downward solar irradiance, the hourly CO2 value lagged by one hour, proxies for the diurnal variation in temperature, proxies for the seasonal temperature variation, and proxies for possible non-anthropomorphic drivers of temperature. A time-series modeling specification is employed to capture the data’s heteroskedastic and autoregressive nature. The model is estimated using hourly data from 1 Jan 1985 through 31 Dec 2015. The results are consistent with the hypothesis that increases in CO2 concentration levels have nontrivial consequences for hourly temperature. The estimated annual contributions of factors exclusive of CO2 and downward total solar irradiance are very small. The model was evaluated using out-of-sample hourly data from 1 Jan 2016 through 31 Aug 2017. The model’s out-of-sample hourly temperature predictions are highly accurate, but this accuracy is significantly degraded if the estimated CO2 effects are ignored. In short, the results are consistent with the scientific consensus on climate change.

Concerns about water security often inform climate risk-related decisions made by environmentally focused investors (Porritt, 2001; Stern, 2006). Yet, potential liabilities for damage caused by extreme flood and drought events linked to global warming present risks that are not always reflected in share prices (Krosinsky et al., 2012). Considering the highly destructive nature of such events, we query whether companies, or specific sectors, could and should be held at least partially liable for their emission-releasing business activities. Recent articles (Rayer & Millar, 2018; Rayer et al., 2020) estimate that under a hypothetical climate liability regime, North Atlantic hurricane seasons might increasingly generate 1-2% losses on market capitalizations (or share prices) for the top seven carbon-emitting, publicly listed companies. In this paper, we extend the concept of the climate liability regime to estimate the impact of global flood- and drought-related damages on the share prices of nine fossil-fuel firms (including the seven mentioned by Rayer et al. (2020)). Following Rayer et al. (2020), we use incremental climate impacts and historical corporate emissions to estimate that climate change-related global flood and drought damages for the period of 2012 to 2016 amount to approximately 2-3% of the top nine carbon-emitting companies’ market capitalizations. We also include a discussion of moral responsibility and the proportion of obligations between producers and users. Quantifying impacts from extreme weather events increases salience and serves as an example of how science can identify and address the important business questions, pertinent to both investors and companies, that arise from a changing climate. References Krosinsky, C., Robins, N., & Viederman, S. (2012). Evolutions in sustainable investing. John Wiley & Sons. Porritt, J. (2001). The world in context. HRH The Prince of Wales’ Business and the Environment Programme, Cambridge. Rayer, Q. G., & Millar, R. J. (2018). Investing in Extreme Weather Conditions. Citywire Wealth Manager®, (429) 36. Rayer, Q., Pfleiderer, P., & Haustein, K. (2020). Global Warming and Extreme Weather Investment Risks. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-38858-4_3 Stern, N. (2006). Stern Review executive summary. London.

In atmospheric modeling, superparameterization has gained popularity as a technique to improve cloud and convection representations in large scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in superparameterization lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large scale model. We demonstrate this phenomenon in a regional superparameterization experiment with the global model OpenIFS coupled to the local model DALES (the Dutch Atmospheric Large Eddy Simulation), as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. To mitigate the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model’s total water content is enhanced in order to achieve the correct cloud condensate amount.

The Indo-Gangetic Plain (IGP) is one of the dominant sources of air pollution worldwide. During winter, the variations in planetary boundary layer (PBL) height, driven by a strong radiative thermal inversion, affect the regional air pollution dispersion. To date, measurements of aerosol-water vapour interactions, especially cloud condensation nuclei (CCN) activity, are limited in the Indian sub-continent, causing large uncertainties in the radiative forcing estimates of aerosol-cloud interactions. We present the results of a one-month field campaign (February-March 2018) in the megacity, Delhi, a significant polluter in the IGP. We measured the composition of fine particulate matter (PM1) and size-resolved CCN properties over a wide range of water vapour supersaturations. The analysis includes PBL modelling, backward trajectories, and fire spots to elucidate the influence of PBL and air mass origins on the aerosols. The aerosol properties depended strongly on the PBL height, and a simple power-law fit could parameterize the observed correlations of PM1 mass, aerosol particle number, and CCN number with PBL height, indicating PBL induced changes in aerosol accumulation. The low inorganic mass fractions, low aerosol hygroscopicity and high externally mixed weakly CCN-active particles under low PBL height (<100 m) indicated the influence of the PBL on aerosol aging processes. In contrast, aerosol properties did not depend strongly on air mass origins or wind direction, implying that the observed aerosol and CCN are from local emissions. An error function could parameterize the relationship between CCN number and supersaturation throughout the campaign.

Rain belts in East Asia frequently pose threats to human societies and natural systems. Advances in a skillful forecast on heavy precipitation require a deeper understanding of the preconditioned environments and the hydrologic cycle. Here, we disentangle 15 dominant moisture channels along four corridors reaching the Somali Jet, South Asia, Bay of Bengal and Pacific basin for the warm-season rain belts. Among them, the Somali and South Asian channels were underappreciated in the literature. The results also highlight the importance of terrestrial moisture sources and the close relationship between the moisture pathways and rain belts' characteristics. Back-tracing the weather within a 2-week lead time reveals the pre-existing weather systems and circumglobal wave trains that govern the moisture channels. Findings from this work develop a better understanding of East Asian rain belts' water cycle and may offer insights into model evaluation and heavy rainfall prediction at a longer lead time.

A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, non-overlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, though clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two non-trivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well-suited for isolating rare or anomalous members of a dataset. The method is inductive, in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.

This article introduces factors contributing significantly to climate change that have been largely neglected in both the scientific and popular press. These factors have immediate implications for public policy directed at slowing, halting and even reversing climate change and its effects. This article argues that in addition to the known contributions made by greenhouse gasses, climate change is also driven by shifts in the patterns of global atmospheric circulation which are influenced by persistent, large-scale vortices caused by the wake turbulence left by commercial air traffic. Because this traffic is highly concentrated along the most frequently traveled routes, the vortices aircraft create have transformed into semi-permanent atmospheric circulation which have widespread effects on how the atmosphere traps and releases heat. It is also possible that these changes alter the loss of water from the atmosphere. This would endanger all life on earth, not just the human population.

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations; the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve – plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.

Current lightning predictions are uncertain because they either rely on empirical diagnostic relationships based on the present climate or use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection-permitting resolutions (km-scale) improve lightning predictions compared to coarser-grid simulations using convection parameterization for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a 955x540 km2 domain including the Great Slave Lake in Canada for six lightning seasons. The simulations are performed at convection-parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics (MP) schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network (CLDN), in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. Concerning the model configuration, regardless of the spatial resolution, the Thompson scheme is superior to the Goddard 4ICE scheme in predicting the daily climatology but worse in predicting the spatial patterns of lightning occurrence. Several evaluation metrics indicate the benefit of working at a convection-permitting resolution. The relative performance of the different lightning indices depends on the evaluation criteria. Finally, this study demonstrates issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity in the study area.

Reproducibility and replicability in analyzing data is one of the main requirements for the advancement of scientific fields that rely heavily on computational data analysis, such as atmospheric science. However, there are very few research activities that field in Indonesia that emphasize the principle of transparency of codes and data in the dissemination of the results. This issue is a major challenge for the Indonesian scientific community to verify the output of research activities from their peers. One common obstacle to the reproducibility of data-driven research is the portability issue of the computing environment used to reproduce the results. Therefore, in this article, we would like to offer a solution through Debian-based dockerized Jupyter Notebook that have been installed with several Python libraries that are often used in atmospheric science research. Through this containerized computing environment, we expect to overcome the portability and dependency constraints that often faced by atmospheric scientists and also to encourage the growth of research ecosystem in Indonesia through an open and replicable environment.

The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.

Water storage plays an important role in mitigating heat and flooding in urban areas. Assessment of the water storage capacity of cities remains challenging due to the inherent heterogeneity of the urban surface. Traditionally, effective storage has been estimated from runoff. Here, we present a novel approach to estimate effective water storage capacity from recession rates of observed evaporation during precipitation-free periods. We test this approach for cities at neighborhood scale with eddy-covariance based latent heat flux observations from fourteen contrasting sites with different local climate zones, vegetation cover and characteristics, and climates. Based on analysis of 583 drydowns, we find storage capacities to vary between 1.3-28.4 mm, corresponding to e-folding timescales of 1.8-20.1 days. This makes the storage capacity at least one order of magnitude smaller than the observed values for natural ecosystems, reflecting an evaporation regime characterised by extreme water limitation.

The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.

Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

Soft x-ray and EUV radiation from the Sun is absorbed by and ionizes the atmosphere, creating both the ionosphere and thermosphere. Temporal changes in irradiance energy and spectral distribution can have drastic impacts on the ionosphere, impacting technologies such as satellite drag and radio communication. Because of this, it is necessary to estimate and predict changes in Solar EUV spectral irradiance. Ideally, this would be done by direct measurement but the high cost of solar EUV spectrographs makes this prohibitively expensive. Instead, scientists must use data driven models to predict the solar spectrum for a given irradiance measurement. In this study, we further develop the Synthetic Reference Spectral Irradiance Model (SynRef). The SynRef model, which uses broadband EUV irradiance data from EUVM at Mars, was created to mirror the SORCE XPS model which uses data from the TIMED SEE instrument and the SORCE XPS instrument at Earth. Both models superpose theoretical Active Region and Quiet Sun spectra generated by CHIANTI to match daily measured irradiance data, and output a modeled solar EUV spectrum for that day. By adjusting the weighting of Active Region and Quiet Sun spectra, we update the SynRef model to better agree with the FISM model and with spectral data collected from sounding rocket flights. We also use the broadband EUVM measurements to estimate AR temperature. This will allow us to select from a library of AR reference spectra with different temperatures. We present this updated SynRef model to more accurately characterize the Solar EUV and soft x-ray spectra.

Aura Microwave Limb Sounder (MLS) measurements show that chemical processing was critical to the observed record-low Arctic stratospheric ozone in spring 2020. The 16-year MLS record indicates more denitrification and dehydration in 2019/2020 than in any Arctic winter except 2015/2016. Chlorine activation and ozone depletion began earlier than in any previously observed winter, with evidence of chemical ozone loss starting in November. Active chlorine then persisted as late into spring as it did in 2011. Empirical estimates suggest maximum chemical ozone losses near 2.8 ppmv by late March in both 2011 and 2020. However, peak chlorine activation, and thus peak ozone loss, occurred at lower altitudes in 2020 than in 2011, leading to the lowest Arctic ozone values ever observed at potential temperature levels from ~400–480 K, with similar ozone values to those in 2011 at higher levels.

An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely-structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2–10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.

More recurrent heatwaves and extreme ozone episodes are likely to occur during the next decades and a key question is about the concurrence of those hazards, the atmospheric patterns behind their appearance and their joint effect on human health. In this work, we use surface maximum temperature and O3 observations during extended summers in two cities from Morocco: Casablanca and Marrakech, between 2010 and 2019. We assess the connection between these data and climate indexes (North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO) and Saharan Oscillation (SaOI)). We then identify concurrent heatwaves and ozone episodes, the weather type behind this concurrence and the combined health risks. Our findings show that the concurrence of heatwaves and O3 episodes depends both on the specific city and the large-scale atmospheric circulation. The likely identified synoptic pattern is when the country is under the combined influence of an anticyclonic area in the north and the Saharan trough extending the depression centered in the south. This pattern generates a warm flow and may foster photochemical pollution. Our study is the first step towards the establishment of an alert system. It will help to provide recommendations for coping with concurrent heatwaves and air pollution episodes.