A particular strength of lightning remote sensing is the variety of lightning types observed, each with a unique occurrence context and characteristically different emission. Distinct energetic intra-cloud (EIC) lightning discharges – compact intra-cloud lightning discharges (CIDs) and energetic intra-cloud pulses (EIPs) – produce intense RF radiation, suggesting large currents inside the cloud, and they also have different production mechanisms and occurrence contexts. A Low-Frequency (LF) lightning remote sensing instrument array was deployed during the RELAMPAGO field campaign in west central Argentina, designed to investigate convective storms that produce high-impact weather. LF data from the campaign can provide a valuable dataset for researching the lightning context of EICs in a variety of sub-tropical convective storms. This paper describes the production of an LF-CID dataset in RELAMPAGO, and includes a preliminary analysis of CID prevalence. Geolocated lightning events and their corresponding observed waveforms from the RELAMPAGO LF dataset are used in the classification of EICs. Height estimates based on skywave reflections are computed, where pre-fit residual data editing is used to improve robustness against outliers. Even if EIPs occurred within the network, given the low number of very high peak current events and receiver saturation, automatic classification of EIPs may not be feasible using this dataset. The classification of CIDs, on the other hand, is straightforward and their properties, for both positive and negative polarity, are investigated. A few RELAMPAGO case studies are also presented, where high variability of CID prevalence in ordinary storms and high-altitude positive CIDs, possibly in overshooting tops, are observed.

Underestimation of the transfer of energy between the magnetosphere and ionosphere, the Poynting flux, is a persistent issue in space weather studies and the high-latitude ionospheric models. Thought to be due to the inability to resolve small-scale fluctuations of the ionospheric electric field, this underestimation could lead to significant further underestimations in parameters such as the thermospheric mass density and consequential satellite drag. Utilising 16Hz ion velocity and magnetic field measurements from the Swarm satellite mission, we examine the observed Poynting flux due to electric field fluctuations on very small spatial scales (~1km), and then artificially smooth the data to increase the observed scale. We quantify the decrease of integrated Poynting flux, poleward of 60/-60 degrees geomagnetic latitude, with increasing spatial scale. The decrease can be underestimated by as much as 15% by increasing scale from 1km to only 8.6km, or 16Hz to 2Hz equivalent, with upward Poynting flux decreasing significantly faster. Our results thus point to a significant Alfvén wave driven component of the Poynting flux on kilometre scales. Additionally, we observe a northern hemisphere preference for increased Poynting flux, of which we examine its dependence on scale size and interplanetary magnetic field.

Blue electric streamer discharges in the upper reaches of thunderclouds are observed as flashes of 337.0 nm (blue) with faint or no emissions of 777.4 nm (red). Analyzing 3 years of measurements by the Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS), we find that their distribution in rise time falls into two categories. One with fast rise times of 30 μs or less that are relatively unaffected by cloud scattering and emanate from within ∼2 km of the cloud tops, and another with longer rise times from deeper within the clouds. 46% of cells generating shallow events are associated with overshooting tops compared to 31% of cells generating deeper events. The median Convective Available Potential Energy (CAPE) of the cells is ∼50% higher for the shallow events and ∼30% higher for the deeper events than for lightning cells, suggesting the discharges are favoured by strongly convective environments.

We analyzed the variations in the mean wind field, turbulence, and turbulent flux at the landfall sites of three typhoons using observational data obtained from an offshore monitoring platform. These variations were different for onshore and offshore winds. The turbulent fluctuation intensity and friction velocity increased with wind speed both before and after landfall. However, the turbulent flow decreased with increasing wind speed during landfall. The relationships between the friction velocity and drag coefficient and the wind speed were affected by whether the typhoon makes landfall, and the relative position of the landfall site of the typhoon and the observation site.

The radiation bias over the Southern Ocean was terribly bad in MRI-CGCM3 that was used for CMIP5 simulations. However, the bias is significantly reduced in MRI-ESM2 (Yukimoto et al. 2019) that is used for CMIP6 simulations by various modifications related to clouds (Kawai et al. 2019). On the other hand, the double-ITCZ problem is also alleviated in the MRI-ESM2 (Tian and Dong, 2020). Is the reduction in the Southern Ocean radiation bias the cause of alleviation of the double-ITCZ problem? Each modification that contributed to the reduction of the Southern Ocean radiation bias in the MRI-ESM2 was progressively reverted to the corresponding older treatment in order to examine their individual impacts on the ITCZ problem. Results show the double-ITCZ problem worsens almost monotonically when the excessive shortwave insolation over the Southern Ocean increases (Kawai et al. 2020, 2021).

Long-term temperature changes drive coastal Marine Heat Waves (MHW) trends globally. Here, we provide a more comprehensive global analysis of cross-shore gradients of MHW and SST changes using an ensemble of three satellite SST products during recent decades. Our analysis reveals depressed onshore SST trends in more than 2/3 of coastal pixels, including both eastern and western boundary current systems. These were well correlated with depressed trends of MHW exposure and severity, ranging from a -2 to -10 decrease in MHW days per decade and a –2.5 to –15°C.days per decade decrease in cumulative intensity. Results were consistent across all satellite products, indicating that these cross-shore gradients are a robust feature of observations. ERA reanalysis data shows that neither air-sea heat fluxes nor wind driven upwelling were found to be consistent drivers. Global ocean circulation models (OFAM3 and ACCESS-OM2) have limited ability to simulate the depressed onshore trends. A heat budget analysis performed in the Chilean coast region, where models agree with observations, showed that the gradient of temperature change was controlled by an onshore increase of longwave radiative cooling, despite an increase in upwelling. This highlights the complexity of small-scale coastal ocean-atmosphere feedbacks, which coarser resolution climate models do not resolve. Here, we show that global coastal regions may act as thermal refugia for marine ecosystems from aspects of climate change and pulsative (MHW) changes. Contrary to the literature, our results suggest that driving mechanisms are region dependant, stressing the necessity to improve climate models resolution.

We present a neural network (NN) based algorithm for the retrieval of cloud and aerosol properties from above cloud aerosol (ACA) scenes. The large state space explored in ACA scenes causes traditional retrieval approaches slow and complicated. This is especially true for optimal inversion retrieval approaches, where a growth in the number of dependent variables can drastically complicate and slow the retrieval search. Our NN retrieval is applied to data from the airborne Research Scanning Polarimeter (RSP), which measures both polarized and total reflectance in the spectral range of 410 to 2260 nm, scanning along the flight track at ~150 viewing zenith angles spanning the angular range between -60˚ to 60˚. We apply this algorithm to field campaign data from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) 2016 and 2017 campaigns and compare to results obtained from other algorithms.

Single-column models (SCMs) are often used to evaluate model physics and aid parameterization development. However, few studies have systematically compared the results obtained using 1D setups with those of their corresponding 3D models, and examined what factors potentially impact their comparability. This paper addresses these questions. We focus on the application of SCMs under idealized RCE conditions and use a multi-column model (MCM) setup as stepping stone for a 3D model. We find that convective organization in the MCM depends at least as much on the convection scheme used as on other mechanisms known to organize convection (e.g., radiative feedback). Moreover, convective organization emerges as a robust factor affecting SCM-MCM comparability, with more aggregated states in 3D associated with larger behavior deviations from the 1D counterpart. This is found across five convection schemes and applies to simulated mean states, linear responses to small tendency perturbations, and adjustments to doubled-CO2 forcing. Applying a “model-as-truth” approach, we find that even when convection is organized, behavior differences between pairs of schemes in the SCM are largely preserved in the MCM. This indicates that when model physics produces accurate behavior in a 1D setup, it will be more likely to do so in a 3D setup. We also demonstrate the practical value of linear responses by showing that they can accurately predict an SCM’s tropospheric adjustment to doubled-CO2 forcing.

Airborne allergic pollen is a well known trigger for several cases of public health issues affecting millions of people. The effect is highly prevalent in the temperature region, especially in the North American region and Europe. For example, about 50 million Americans are affected by pollen caused allergy and similarly, studies show quite a significant population of Europe is affected. Contrary to the higher abundance of pollen in rural areas, pollen allergy is severe in urban areas than rural environments. Of all sources, it is the Ambrosia pollen that affects most due to its abundant production, strong allergic potency and its high prevalence near urban areas. Hence estimating the concentration of allergic pollen in the ambient atmosphere and notifying the public is crucial for people with allergies and health professionals who care for them. In this workshop, we present estimation of allergic pollen (particularly Ambrosia pollen) using advanced machine learning methods and input parameters from a suite of sources ranging from land surface to global reanalysis models and NEXRAD weather radar measurements at location of Tulsa, Oklahoma. We will present results of the machine learning model tested using an independent dataset and characterization of each atmospheric and land surface parameters’ importance for the machine learning estimation.

Atmospheric Rivers (ARs) are filamentary channels of strong poleward water vapour transport in the midlatitudes. Recent studies have found that ARs are northwesterly and northeasterly orientated and can make landfall in all directions over New Zealand. In this study, we further investigate the characteristics, in particular orientation and landfall direction, of detected landfalling ARs based on two atmospheric reanalysis datasets over 35 years. Daily rainfall records from 655 rain gauges between 1979 to 2018 were used to investigate the spatial variability of the AR contribution to annual rainfall and extreme rainfall linked with AR events with different orientations and mean landfall directions. A modified AR impact ranking scale was then evaluated regarding AR-event orientation and mean landfall direction, different “AR impact” sectors, and peak daily AR-event rainfall. We found that landfalling ARs (events) with a northwesterly orientation and northwesterly landfall direction (NW-NW ARs) are the most frequent and relatively stronger, more coherent and concentrated over the country. As a result, NW-NW ARs are major contributors to annual rainfall and extreme rainfall for the country’s West Coast. Generally, the windward side experiences anomalously high rainfall as ARs reach the country from different directions, and the spatial distribution of AR-event heavy rainfall is shown to vary with an AR’s orientation and landfall direction. Moreover, the AR impact ranking scale performs well for NW-NW ARs over the West Coast. However, more factors need to be considered to improve the applicability of the scale on the East Coast.

Meteorological elements have different lag periods for solar activities (SA), climatic oscillation (CO) and other influencing factors at different spatiotemporal scales. To further understand the “solar-climate-water resource” system, this study considers China as the study area and investigates the monthly data of temperature (T) and precipitation (P) during 1900–2020 that were obtained from 3836 grid stations. The strong interaction and lag distribution between T or P with SA and CO were studied and influence weights of SA, CO, and geographical factors (GF) of each grid station were calculated. A multivariate hysteretic decomposition model was established to simulate and quantitatively decompose the periodic lag considering the factors of the earth’s revolution. The results indicate the existence of two dividing lines in the distribution of T and P lag periods. Additionally, the underlying surface conditions and urbanisation were observed to have significant effects on the periodic lag of meteorological elements.

Sensitivities of the backscattering properties to microphysical properties (in particular, size and shape) of mineral dust aerosols are examined based on TAMUdust2020, a comprehensive single-scattering property database of irregular aerosol particles. We develop the bulk mineral dust particle models based on size-resolved particle ensembles with randomly distorted shapes and spectrally resolved complex refractive indices, which are constrained with in-situ observations reported in the literature. The lidar ratio is more sensitive to particle shape than particle size, while the depolarization ratio is sensitive to particle size. The simulated bulk backscattering properties (i.e., the lidar ratio and the depolarization ratio) of typical mineral dust particles with effective radii of 0.5–3 µm are reasonably consistent with lidar observations made during several field campaigns. The dust bulk optical property models are applicable to lidar-based remote sensing of dust aerosol properties.

Different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non-orographic events. NCEP Stage-IV product used as a reference. All of the evaluated products show more significant errors for the orographic than the non-orographic events. The Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), Microwave Humidity sounder (MHS), and GPM Microwave Imager (GMI) severely underestimate the precipitation rates, especially for heavy precipitation (> 4 mm/day), whereas infrared precipitation of the Integrated Multi-satellite Retrievals for GPM (IMERG-IR) and a reanalysis product (ERA5) show relatively better estimation. Satellite products tend to show a lower fraction of precipitation occurrence and amount for orographic than no-orographic classes. It was found that rate BIAS varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm-season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (< 10 0C) and lower TPW (<15mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non-orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20 0C), where satellite products tend to overestimate precipitation amounts.

Lightning is observed to incept in thundercloud electric fields below the threshold value $E_k$ for discharge initiation. To explain this, the local enhancement of the electric field by hydrometeors is considered. The conditions for the onset of positive corona discharges are studied in air for ellipsoidal geometries. A hydrometeor is simulated as an individual charged conductor in zero ambient field; there is only a field generated by the charge on the hydrometeor surface. By doing so, the feasibility of corona inception from ellipsoidal hydrometeors can be formulated based on the self-sustaining condition of electron avalanches. For representative hydrometeor volumes and typical thundercloud pressure, values between $1.2\,E_k$ and $37\,E_k$ were found for the onset electric field at the tip of the ellipsoid. From simulations the required ambient electric field for corona onset from an uncharged hydrometeor can then be derived. This results in values between $0.07\,E_k$ and $0.8\,E_k$ for semi-axes aspect ratios between 0.01 and 1. The charge required on the hydrometeor surface for corona onset is minimum for semi-axes aspect ratios between 0.04 and 0.07 depending on the considered hydrometeor volume. For the simulated hydrometeors, the values of this onset charge for typical pressures are between 1500\,pC and 3200\,pC. Including a size-correction for comparison to in situ measurement shows agreement with measured precipitation charges. From the results it is concluded that corona onset from ellipsoidal hydrometeors of a realistic volume can be achieved in thundercloud conditions for certain aspect ratios.

This article focuses on the new generation of Very Long Baseline Interferometry (VLBI), the VLBI Global Observing System (VGOS), and measurements carried out during CONT17 campaign. It uses broadband technology that increases both the number and precision of observations. These characteristics make VGOS a suitable tool for studying the atmosphere. This study focuses on the effects of the ionosphere on VGOS signals using a model that incorporates and extends ideas originally published in Hobiger et al. (2006). Our investigation revealed that the differential Total Electron Content (dTEC) data product calculated with the VGOS post-processing software revealed a sign error in the inferred dTEC values. Fortunately, this error does not change the final values of the phase and group delay. Therefore, this study was a way to identify this problem within the VGOS data. After diagnosing and solving this problem, the Hobiger et al. (2006) model was modified such that instead of considering a single unknown for the latitude gradient of the ionosphere, a time series of latitude gradients were taken into account. For comparison purposes, time series of Vertical Total Electron Content (VTEC) at each station during CONT17 campaign were constructed from Global Ionosphere Map (GIM) data. In this way, it is shown that the final accuracy of estimating VTEC at each station using VGOS data was between 1.0 and 5.8 TEC Units (TECU).

Quantifying Effective Radiative Forcing due to aerosol-cloud interactions (ERFACI) remains a largely uncertain process, and the magnitude remains unconstrained in general circulation models. Previous studies focus on the magnitude of ERFACI arising from all cloud types, or examine it in the framework of dynamical regimes. Aerosol forcing due to aerosol-cloud interactions in the HadGEM3-GA7.1 global climate model is decomposed into several global observational cloud regimes. Regimes are allocated to model gridboxes and forcing due to aerosol-cloud interactions is calculated on a regime-by-regime basis with a 20-year meaning period. Patterns of regime occurrence are in good agreement with satellite observations. ERFACI is then further decomposed into three terms, representing radiative changes within a given regime, transitions between different cloud regimes, and nonlinear effects. The total global mean ERFACI is -1.8 Wm-2. When decomposed, simulated ERFACI is greatest in the stratocumulus regime (-0.75 Wm-2).

Recent studies have found that terrestrial dryness indices like the Palmer Drought Severity Index, Standardized Precipitation Evapotranspiration Index, and Aridity Index calculated from climate model projections are mostly negative, implying a drier land surface with future warming. Yet, the same models’ prognostic runoff and bulk soil moisture projections instead feature regional signals of varying sign, suggesting that the dryness indices could overstate climate change’s direct impacts. Observed trends also show this “index-impact gap.” Most studies have attributed this gap to the indices’ omission of CO2-driven stomatal closure. However, here we show that the index-impact gap is still wide even in model experiments that switch off CO2 effects on plants. In these simulations, mean PDSI, Aridity Index, and SPEI still decline broadly with warming, while mean runoff and bulk soil moisture still respond more equivocally. This implies that CO2-plant effects are not the dominant or sole reason for the index-impact gap.

Stringent mitigation measures have reduced wintertime PM2.5 concentrations by 42.2% from 2013 to 2018 in the BTH. The observed nitrate aerosols have not exhibited a significant decreasing trend and constituted a major fraction (about 20%) of the total PM2.5, although the surface-measured NO2 level has decreased by over 20%. It still remains elusive about contributions of nitrogen oxides (NOx) emissions mitigation to the nitrate and PM2.5 level. The WRF-Chem model simulations of a persistent haze episode in January 2019 in the BTH reveal that NOx emissions mitigation does not help lower wintertime nitrate and PM2.5 concentrations under current conditions in the BTH, because the substantial O3 increase induced by NOx mitigation offsets the HNO3 loss and enhances sulfate and secondary organic aerosols formation. Our results are further consolidated by occurrence of the severe PM pollution in the BTH during the COVID-19 outbreak with a significant reduction of NO2.

Since 2016, the Juno-UVS instrument has been taking spectral images of Jupiter’s auroras during its polar fly-bys. These observations provide a great opportunity to study Jupiter’s auroras in their full extent, including the nightside, which is inaccessible from Earth. We present a systematic analysis of features in Jupiter’s polar auroras called auroral bright spots observed during the first 25 Juno orbits. Bright spots were identified in 16 perijoves (PJ) out of 24 (there was no available data for perijove 2), in both the northern and southern hemispheres. The emitted power of the bright spots is time variable with peak power ranging from a few tens to a hundred of gigawatts. Moreover, we found that, for some perijoves, bright spots exhibit quasiperiodic behavior. The spots, within PJ4 and PJ16, each reappeared at almost the same system III position of their first appearance with periods of 28 and 22 minutes, respectively. This period is similar to that of quasiperiodic emissions previously identified in X-rays and various other observations. The bright spot position is in a specific region in the northern hemisphere in system III, but are scattered around the magnetic pole in the southern hemisphere, near the edge of the swirl region. Furthermore, our analysis shows that the bright spots can be seen at any local time, rather than being confined to the noon sector as previously thought based on biased observations. This suggests that the bright spots might not be firmly connected to the noon facing magnetospheric cusp processes.

In 2020, the long-persisting Meiyu season around the Yangtze River (YR) started in early-June and ended in mid­–late-July. Its accumulated precipitation amount broke the record since 1961. We showed that the sequential warm and cold Meiyu front regulated by the North Atlantic Oscillation was responsible for this record-breaking Meiyu rainfall. From 11 to 25 June with the positive NAO, the interaction between South Asian High (SAH) and western Pacific subtropical high maintained a warm front to strengthen the rainband north of YR. Afterward, the coupling between SAH and mid-latitude Mongolian Cyclone induced a cold front, which retreated the rainband to the south of YR from 30 June to 13 July with the negative NAO. Although the ECMWF S2S successfully predicted the warm-front-related Meiyu rainband, it failed to forecast the Meiyu rainband in the cold-front period, suggesting a great challenge of the S2S forecast on Meiyu rainfall.

The air-water exchange of trace gases such as CO2 is usually parameterized in terms of a gas transfer velocity, which can be derived from direct measurements of the air-sea gas flux. The transfer velocity of poorly soluble gases is driven by near-surface ocean turbulence, which may be enhanced or suppressed by the presence of sea ice. A lack of measurements means that air-sea fluxes in polar regions, where the oceanic sink of CO2 is not well known, are generally estimated using open-ocean transfer velocities scaled by ice fraction. Here, we describe direct determinations of the CO2 gas transfer velocity from eddy covariance flux measurements at a sea-ice lead during the summer-autumn transition in the central Arctic Ocean. CO2 uptake by the lead water is determined using flux footprint analysis of water-atmosphere and ice-atmosphere flux measurements made under conditions (low humidity and high CO2 signal) that minimise errors due to humidity cross-talk. The mean gas transfer velocity over the lead is found to have a quadratic dependence on wind speed: k660 = 0.189 U10^2 which is 25 to 30% lower than commonly used open-ocean parameterizations. As such, current estimates of polar ocean carbon uptake are likely to overestimate gas exchange rates in typical summertime conditions of weak convective turbulence. The gas transfer velocities also exhibit a dependence on the dimension of the lead, via its impact on fetch length and hence sea state. Scaling transfer velocity parameterizations for regional gas exchange estimates will therefore require incorporating lead width data.

Wheat production plays an important role in Morocco with the country typically producing more than half of Northwest African grain production. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, Morocco's wheat production is mostly rainfed and thus strongly tied to fluctuations in rainfall, which in turn depend on slowly evolving climate dynamics. This offers a source of predictability at longer timescales. Using physically-guided causal discovery algorithms we extract climate precursors for wheat yield variabilityfrom gridded fields of geopotential height and sea surface temperatures which show potential for accurate yield forecasts already in December. The detected interactions are physically meaningful and consistent with documented ocean-atmosphere feedbacks. Reliable yield forecasts at such long lead times could provide farmers and policy-makers with necessary information for early action and strategic adaptation measurements to support food security.

The equivalent source method of Spherical Elementary Current Systems (SECS) has contributed valuable results for spatial magnetic interpolation purposes where no observations are available, as well as for modeling equivalent currents both in the ionosphere and in the subsurface, thus providing a separation between external and internal sources. It has been successfully applied to numerous Space Weather (SW) events, whereas some advantages have been reported over other techniques such as Fourier or Spherical (Cap) Harmonic Analysis. Although different modalities of SECS exist (either 1-D, 2-D or 3-D) depending on the number of space dimensions involved, the method provides a sequence of instantaneous pictures of the source current. We present an extension of SECS consisting in the introduction of a temporal dependence in the formulation based on a cubic B-splines expansion. The technique thus adds one dimension, becoming 4-D in general (e.g., 3D + t), and its application is envisaged for, though not restricted to, the analysis of past events including heterogeneous geomagnetic datasets, such as those containing gaps, different sampling rates or diverse data sources. A synthetic model based on the Space Weather Modeling Framework (SWMF) is used to show the efficacy of the extended scheme. We apply this method to characterize the current systems of past and significant SW events producing geomagnetically induced currents (GIC), which we exemplify with an outstanding geomagnetic sudden commencement (SC) occurred on March 24, 1991.

Measurements of dust size usually obtain the optical or the projected area-equivalent diameters, whereas model calculations of dust impacts use the geometric and the aerodynamic diameters. As such, accurate conversions between the four types of diameters are critical. However, most current conversions assume dust is spherical, which is problematic as numerous studies show that dust is highly aspherical. Here, we obtain conversions between different diameter types that account for dust asphericity. Our conversions indicate that optical particle counters using optical diameter to determine dust size underestimate dust geometric diameter at coarse sizes. We further use the diameter conversions to obtain a consistent observational constraint of size distributions of emitted dust in terms of geometric and aerodynamic diameters. The resulting size distributions are coarser than accounted for by parameterizations used in climate models, which which underestimate the mass of emitted dust within 10≤D_geo≤20 μm by a factor of ~2 and do not account for dust emission with D_geo≥20 μm. This finding suggests that current models substantially underestimate coarse dust emission.