Forecasting hydroclimatic extremes holds significant importance considering the increasing trends in natural cascading climate-induced hazards such as wildfires, floods, and droughts. This study evaluates the performance of five Copernicus Climate Change Service (C3S) seasonal forecast models (i.e., CMCC, DWD, ECCC, UK-Met, and Météo-France) in predicting extreme precipitation events from 1993 to 2016 using 28 extreme precipitation indices reflecting timing and intensity of precipitation in a seasonal timescale. We design indices using various precipitation thresholds to reflect model skill in capturing the distribution and intensity of precipitation over a season. We use percentage bias, the Kendall Tau rank correlation, and ROC scores for skill evaluation. We introduce an impact-based framework to evaluate model skill in capturing extreme events over regions prone to natural disasters such as floods and wildfires. The performance of models varies across regions and seasons. The model skill is highlighted primarily in the tropical and inter-tropical regions, while skill in extra-tropical regions is markedly lower. Elevated precipitation thresholds correlate with heightened model bias, revealing deficiencies in modelling severe precipitation events. The impact-based framework analysis highlights the superior predictive capabilities of the UK-Met and Météo-France models for extreme event forecasting across many regions and seasons. In contrast, other models exhibit strong performance in specific regions and seasons. These results advance our understanding of an impact-based framework in capturing a broad spectrum of extreme climatic events through the strategic amalgamation of diverse models across different regions and seasons, offering valuable insights for disaster management and risk analysis.

Cloud responses to surface-based sources of aerosol perturbation depend in part on the characteristics of the aerosol transport to cloud base and the resulting spatial and temporal distribution of aerosol. However, interactions among aerosol, cloud, and turbulence processes complicate the prediction of this aerosol transport and can obscure diagnosis of the aerosols' effects on cloud and turbulence properties. Here, scenarios of plume injection below a marine stratocumulus cloud are modeled using large eddy simulations coupled to a prognostic bulk aerosol and cloud microphysics scheme. Both passive plumes, consisting of an inert tracer, and active plumes are investigated, where the latter are representative of saltwater droplet plumes such as have been proposed for marine cloud brightening. Passive plume scenarios show a spurious in-plume cloud brightening due solely to the connections between updrafts, cloud condensation, and scalar transport. Numerical sensitivities are first assessed to establish a suitable model configuration. Then sensitivity to particle injection rate is investigated. Trade-offs are identified between the number of injected particles and the suppressive effect of droplet evaporation on plume loft and spread. Furthermore, as the in-plume brightening effect does not depend significantly on injection rate given a suitable definition of perturbed versus unperturbed regions of the flow, plume area is a key controlling factor on the overall cloud brightening effect of an aerosol perturbation.

This study evaluates the performance of a global storm-resolving model (GSRM), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). We analyze marine boundary layer clouds in a cold air outbreak over the Norwegian Sea in a 40-day simulation, and compare them to observations from satellite and a field campaign of the Atmospheric Radiation Measurement program (ARM). SCREAM qualitatively captures the cold air outbreak cloud transition in terms of the boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also correctly locates the greatest ice and liquid in the mesoscale updraft. However, the study finds that SCREAM might underestimate cloud supercooled liquid water in the cumulus cloud regime. This study showcases the promise of employing high-resolution and high-frequency observations under similar large-scale conditions for evaluating GSRMs. This approach can help identify model features for future process-level studies before allocating extra resources for a time-matched model intercomparison of a specific case.

Arctic aerosols affect cloud properties and climate. However, the magnitudes and mechanisms are uncertain, as are how aerosol-cloud relationships might change in a rapidly warming environment. We assessed some of the complex relationships between aerosols, surface, and meteorology in the relatively pristine summertime Arctic and quantified resulting impacts on clouds using CloudSat/CALIPSO data, AIRS relative humidity and temperature, plus MERRA-2 aerosol and meteorological reanalysis products. In line with previous studies, dust aerosol layers over the summertime Arctic sea ice are associated with icier clouds. However, summertime dust-associated cloud glaciation is uncommon at temperatures >-10 ºC and not likely at lower altitudes in the summer. We use DMS concentrations as a proxy for marine new particle formation. When DMS is elevated, open ocean clouds near the surface (0.6-1.5 km) are up to 12% more prevalent. These findings allow us to make some educated guesses about where key processes are occurring, such as ice nucleation from dust, and to more effectively prioritize aircraft targeting in future field campaigns, such as ARCSIX.

(Revised) The streamer to leader transition defines much of the physics of long sparks near atmospheric pressures. Streamer length is an important parameter in understanding lightning protection because of its link to step length and striking distance. While streamers are routinely observed in the lab, there have been only a few observations in the field. Fewer still are of natural flashes, and almost none have been observed much above sea-level.

One of the outstanding questions in lightning research is how dart leaders (also called recoil leaders or K-leaders) initiate and develop during a lightning flash. Dart leaders travel quickly (106-107 m/s) along previously ionized channels and occur intermittently in the later stage of a flash. We have recently reported some insights into dart leader initiation and development based on our BIMAP-3D observations. In this presentation we will expand on that work by combining observations and modeling to try to understand the observed dart leader behaviors. BIMAP-3D consists of two broadband interferometric mapping and polarization (BIMAP) systems that are separated by 11.5km at Los Alamos National Laboratory. Each station maps the lightning VHF sources in a 2D space, and the combination of the 2-station measurements provides a detailed 3D source map. A fast antenna is also included at each station for electric field change measurements. Our previously reported observations suggest dart leaders commonly exhibit an initial acceleration, followed by a more gradual deceleration to a stop. We also modeled the dart leader electric field change with a simple configuration of two point-charges, finding that the modeled tip charge increased in magnitude during the initial acceleration in some simple cases. We now employ a more sophisticated model to better understand the distribution of charge along the dart leader channel, and the background electric field in which the dart leader develops.Presented at the AGU 2023 Fall Meeting

Spontaneous aggregation of deep convection is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative-Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid-scale processes. This study focuses on the role played by mixing and entrainment, either provided by the turbulence model or the implicit numerical dissipation. We have analyzed the results of two different models, WRF and SAM, and compared different configurations by varying the turbulence models, the initial conditions and the horizontal spatial resolution. At coarse grid resolution (3 km), the removal of turbulent mixing prevents the occurrence of Convective Self-Aggregation (CSA) in models with low numerical diffusivity, while it is preserved in models with high numerical diffusivity. When the horizontal grid resolution is refined to 1 km (thus reducing the implicit numerical dissipation), CSA is only achieved by increasing the explicit turbulent mixing. In this case, CSA was found to occur even with a small amount of shallow clouds. Therefore, this study suggests that the sensitivity of CSA to horizontal grid resolution is not primarily due to the corresponding decrease in shallow clouds. Instead, it is found that turbulent mixing and dissipation at small scales regulate the amplitude of initial humidity perturbations introduced by convection in the free troposphere: the greater the dissipation at small scales, the greater the size and the strength of humidity perturbations in the free troposphere that can destabilize the RCE state.

Accurately predicting and modeling the total electron content (TEC) of the ionosphere can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study tested the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data from a single station in Addis Ababa, Ethiopia. The models used were gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial six months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of $\sim$0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the GBDT-based algorithms and their stacked integration demonstrated promising potential for predicting VTEC over Addis Ababa, Ethiopia.

Understanding how entrainment and mixing shape the cloud droplet size distribution (DSD) is crucial for understanding the optical properties and precipitation efficiency of clouds. Different mixing scenarios, mainly homogeneous and inhomogeneous, shape the DSD in a distinct way and alter the cloud’s impact on climate. However, the prevalence of these mixing scenarios and how they vary in space and time is still uncertain, as underlying processes are commonly unresolved by conventional numerical models. To overcome this challenge, we employ the $L^3$ model, which considers supersaturation fluctuations and turbulent mixing down to the finest relevant lengthscales, making it possible to represent different mixing scenarios realistically. We investigate the spatial and temporal evolution of mixing scenarios over the life cycle of shallow cumulus clouds for varying boundary layer humidities and aerosol concentrations. Our findings suggest homogeneous mixing is generally predominant in cumulus clouds, while different mixing scenarios occur concurrently in the same cloud. Notably, inhomogeneous mixing increases over the cloud life cycle across all analyzed cases. The mean and standard deviation of supersaturation are found to be the most capable indicators of this evolution, providing a comprehensive insight into the characteristics of mixing scenarios. Finally, we show inhomogeneous mixing is more prevalent in drier boundary layers and for higher aerosol concentrations, underscoring the need for a more comprehensive investigation of how these mixing dynamics evolve in a changing climate.

A document by Haoming Chen. Click on the document to view its contents.

Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6-hour and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by strong water vapor transport from the Gulf of Mexico—the key flood-generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large-scale atmospheric fields from a climate model, reflecting both spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine-scale precipitation arising from a specific large-scale atmospheric environment. Model parameters were fitted for each month from December-May, based on storms identified from 1979-2021 ERA5 reanalysis data and AORC precipitation. Validation showed good consistency in key storm characteristics between StormLab simulations and AORC data. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agreed well with AORC data and displayed bounded tail behavior. To our knowledge, this is the first SRG simulating nonstationary, anisotropic high-resolution precipitation over continental-scale river basins, demonstrating the value of conditioning such stochastic models on large-scale atmospheric variables. The simulated events provide a wide range of extreme precipitation scenarios that can be further used for design floods in the MRB.

Ionospheric plasma blobs have long been studied since it was first reported in 1986. Blobs are localized regions of enhanced plasma with a factor of 2 or 3 above ambient plasma. In this paper, we studied the occurrence of blobs over Nigeria (9.08⁰N, 8.67⁰E geographic coordinates) using the SWARM constellation satellites – ionospheric plasma density dataset specifically. We considered only the nighttime pass of the satellites over Nigeria with time frame 18:00 to 04:59 LT. The satellites passed over Nigeria 126 times in 2019 with 41 cases of plasma blobs. The results show that 58% of the cases were found without bubbles nearby, 29% of the cases were found in the presence of small-scale fluctuations in ionospheric plasma density (henceforth “SSFiI”). From the spectral analysis, the average wavelength, period and the propagating speed of SSFiI are 11 km, 2-4 seconds, and 2.75 – 5.5 km/s, respectively. The rate of change of the electron density inside the blobs associated with SSFiI was ~50% above that of the blobs in the absence of SSFiI. This suggests that bubbles may not be the only prerequisite for the development and dynamics of blobs; and SSFiI may play a significant role in the morphology and dynamics of blobs.

A document by Raj Pandya. Click on the document to view its contents.

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Often, the greatest source of uncertainty in predicting a satellite's trajectory in low Earth orbit originates from atmospheric neutral mass density variability caused by enhanced geomagnetic activity and solar EUV absorption. This work investigates the impacts of solar and geomagnetic index forecasting uncertainty on satellite drag and satellite maneuver decision-making. During an averaged point in the solar cycle, accurate index forecasts with reduced uncertainty are shown to provide significantly improved advance notice for dangerous conjunction events above 500 km. Below 500 km, forecast improvements are less impactful. This boundary of utility from forecast improvements shifts upward and downward during solar maximum and solar minimum, respectively. Improved index forecasts are shown to have little impact on making maneuver decisions 12-24 hours from a potential conjunction event, but are demonstrated to be very useful when trying to make maneuver decisions with more lead time. These improved forecasts of the space weather indices help in making actionable, durable conjunction predictions sooner than is currently possible.

The use of large ensembles of model simulations is growing due to the need to minimize the influence of internal variability in evaluation of climate models and the detection of climate change induced trends. Yet, exactly how many ensemble members are required to effectively separate internal variability from climate change varies from model to model and metric to metric. Here we analyze the first three statistical moments (i.e., mean, variance and skewness) of detrended precipitation and sea surface temperature (interannual anomalies for variance and skewness) in the eastern equatorial Pacific from observations and ensembles of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate simulations. We then develop/assess the equations, based around established statistical theory, for estimating the required ensemble size for a user defined uncertainty range. Our results show that — as predicted by statistical theory — the uncertainties in ensemble means of these statistics decreases with the square root of the time series length and/or ensemble size. Further to this, as the uncertainties of these ensemble-mean statistics are generally similar when computed using pre-Industrial control runs versus historical runs, the pre-industrial runs can sometimes be used to estimate: i) the number of realizations and years needed for a historical ensemble to adequately characterize a given statistic; or ii) the expected uncertainty of statistics computed from an existing historical simulation or ensemble, if a large ensemble is not available.

We introduce an innovative method to distinguish soil nitrogen oxides (NOx=NO+NO2) emissions from satellite-based total NOx emissions using its seasonal characteristics. To evaluate the approach, we compare the deviation between the tropospheric NO2 concentration observed by satellite and two atmospheric composition model simulations driven by the newly estimated soil NOx emissions and the Copernicus Atmosphere Monitoring Service (CAMS) inventory. The estimated average soil NOx emissions in Europe are 2.5 kg N ha-1 yr-1 in 2019, and the annual soil NOx emissions is approximately 2.5 times larger than that of the CAMS inventory. Our method can easily be extended to other regions at middle or high latitudes with similar seasonal characteristics of soil emissions. The soil emissions are subtracted from the total NOx emissions yielding realistic anthropogenic NOx emissions. We further show this also yields realistic anthropogenic CO2 emissions using known CO2/NOx factors from bottom-up inventories.

A document by William Keely. Click on the document to view its contents.

Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are 1) data imbalance related to learning rare (often large-amplitude) samples; 2) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and 3) generalization to other climates, e.g., those with higher radiative forcing. Here, we examine the performance of methods for addressing these challenges using NN-based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics-based gravity wave (GW) parameterizations as the test case. WACCM has complex, state-of-the-art parameterizations for orography-, convection- and frontal-driven GWs. Convection- and orography-driven GWs have significant data imbalance due to the absence of convection or orography in many grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria on when a NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4XCO2). However, the generalization accuracy is significantly improved by applying transfer learning, e.g., re-training only one layer using ~1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data-driven parameterizations for various processes, including (but not limited) to GWs.

A document by Meinrat Andreae. Click on the document to view its contents.

The variability of the shortwave radiative fluxes at the surface and top of atmosphere (TOA) is examined in a pre-industrial modelling setup using the Pacific Decadal Oscillation (PDO) as a possible pacemaker of atmospheric decadal-scale variability. Within models from the Coupled Model Intercomparison Project – Phase 6, downwelling shortwave radiation at the surface, the net shortwave fluxes at the surface and TOA, as well as cloud radiative effects show remarkably similar patterns associated with the PDO. Through ensemble simulations designed with a pure PDO pattern in the North Pacific only, we show that the PDO relates to about 20-40% of the unforced year-to-year variability of these shortwave fluxes over the Northern Hemispheric continents. The SST imprint on shortwave-flux variability over land is larger for spatially aggregated time series as compared to smaller areas, due to the blurring effect of small-scale atmospheric noise. The surface and TOA radiative flux anomalies associated with the PDO index range of [-1.64; 1.64] are estimated to reach up to ±6Wm-2 for North America, ∓ 3Wm-2 for India and±2Wm-2 for Europe. We hypothesise that the redistribution of clouds in response to a North Pacific PDO anomaly can impact the South Pacific and North Atlantic SSTs.

This study examines 40 years of monthly precipitation data in Senegal (1979-2018) using CRU observations and ERA5 reanalyses, aiming to understand the influence of oceanic and atmospheric factors on Senegal’s precipitation in July, August and September (JAS). Comparing Senegal’s precipitation variability with the broader Sahel region, it emerges that Senegal’s precipitation is more closely associated with the Northeastern Tropical Atlantic (NETA) Sea Surface Temperature (SST). The increased Senegal’s precipitation is linked to the northward shift of the InterTropical Convergence Zone (ITCZ), consistent with numerous previous studies. Over the continent, this shift corresponds to a northward shift of the African Easterly Jet (AEJ) and, consequently, the Mesoscale Convective Systems responsible for most precipitation. It seems primarily driven by the northward shift of the Heat Low.Over the ocean west of Senegal, there is a comparable shift of the AEJ, accompanied by increased low-level moisture transport convergence within the West African Westerly Jet (WAWJ). This phenomenon is triggered by a negative pressure anomaly in the NETA, located above a positive SST anomaly: we suggest that the latter is the origin of the former, forming a feedback mechanism that potentially significantly influences Senegal’s precipitation. The mechanism involves a geostrophic adjustment of the WAWJ to the southern gradients of the SST anomaly. To validate the NETA SST feedback’s role in Senegal’s precipitation, further investigations using daily data or regional atmospheric models are recommended. The findings hold potential for enhancing seasonal forecasting capabilities.

Shifts in Southern Ocean (SO, $40 - 85^{o}S$) shortwave cloud feedback ($SW_{FB}$) toward more positive values are the dominant contributor to higher effective climate sensitivity (ECS) in Coupled Model Intercomparison Project phase 6 (CMIP6) models. To provide an observational constraint on the SO $SW_{FB}$, we use a simplified physical model to connect SO $SW_{FB}$ with the response of column-integrated liquid water mass (LWP) to warming and the susceptibility of albedo to LWP in 50 CMIP5 and CMIP6 GCMs. In turn, we predict the responses of SO LWP using a cloud-controlling factor (CCF) model. The combination of the CCF model and radiative susceptibility explains about $50$\% of the variance in the GCM-simulated $SW_{FB}$ in the SO. Observations of SW radiation fluxes, LWP, and CCFs from reanalysis are used to constrain the SO $SW_{FB}$. The response of SO LWP to warming is constrained to $2.76\ -\ 4.19$ $g\ m^{-2}\ K^{-1}$, relative to a GCM prior of $0.64\ -\ 9.33$ $g\ m^{-2}\ K^{-1}$. The susceptibility of albedo to LWP is constrained to be $0.43\ -\ 0.90$ $ (kg\ m^{-2})^{-1}$, relative to $0.30\ -\ 3.91$ $(kg\ m^{-2})^{-1}$. The overall constraint on the contribution of SO to global mean $SW_{FB}$ is $-0.168\ -\ 0.051$ $W\ m^{-2}\ K^{-1}$, relative to $-0.277\ -\ 0.270$ $ W m^{-2} K^{-1}$. In summary, observations suggest SO $SW_{FB}$ is less likely to be as extremely positive as predicted by some CMIP6 GCMs, but more likely to range from moderate negative to weakly positive.

A document by Antoine Hermant. Click on the document to view its contents.

Recent advances in regional climate modelling include finer-scale simulations that can partially resolve deep convective processes. These convection-permitting models can help provide insight on how regional distributions of hazardous convection could evolve in possible future climates. However, the use of these models for representing climatologies of severe wind gusts related to convection has not been explored in detail, including for Australian regional climate. As a result, future projections of this hazard have mostly been estimated using changes in the large-scale environment from global climate models, with significant uncertainties related to this method. Here, we present findings on the ability of a regional, convection-permitting climate model in representing severe wind gusts associated with convection in southeastern Australia. We also examine future changes in the frequency and intensity of severe convective wind events as represented by this convection-permitting model, and compare these with changes in the large-scale environment from the global climate model that forces these simulations.