The radiative cooling rate in the tropical upper troposphere is expected to increase as climate warms. Since the tropics are approximately in radiative-convective equilibrium (RCE), this implies an increase in the convective heating rate, which is the sum of the latent heating rate and the eddy heat flux convergence. We examine the impact of these changes on the vertical profile of cloud ice amount in cloud-resolving simulations of RCE. Three simulations are conducted: a control run, a warming run, and an experimental run in which there is no warming but a temperature forcing is imposed to mimic the warming-induced increase in radiative cooling. Surface warming causes a reduction in cloud fraction at all upper tropospheric temperature levels but an increase in the ice mixing ratio within deep convective cores. The experimental run has more cloud ice than the warming run at fixed temperature despite the fact that their latent heating rates are equal, which suggests that the efficiency of latent heating by cloud ice increases with warming. An analytic expression relating the ice-related latent heating rate to a number of other factors is derived and used to understand the model results. This reveals that the increase in latent heating efficiency is driven mostly by 1) the migration of isotherms to lower pressure and 2) a slight warming of the top of the convective layer. These physically robust changes act to reduce the residence time of ice along at any particular temperature level, which tempers the response of the mean cloud ice profile to warming.

The prediction of the weather at subseasonal-to-seasonal (S2S) timescales is dependent on both initial and boundary conditions. An open question is how to best initialize a relatively small-sized ensemble of numerical model integrations to produce reliable forecasts at these timescales. Reliability in this case means that the statistical properties of the ensemble forecast are consistent with the actual uncertainties about the future state of the geophysical system under investigation. In the present work, a method is introduced to construct initial conditions that produce reliable ensemble forecasts by projecting onto the eigenfunctions of the Koopman or the Perron-Frobenius operators, which describe the time-evolution of observables and probability distributions of the system dynamics, respectively. These eigenfunctions can be approximated from data by using the Dynamic Mode Decomposition (DMD) algorithm. The effectiveness of this approach is illustrated in the framework of a low-order ocean-atmosphere model exhibiting multiple characteristic timescales, and is compared to other ensemble initialization methods based on the Empirical Orthogonal Functions (EOFs) of the model trajectory and on the backward and covariant Lyapunov vectors of the model dynamics. Projecting initial conditions onto a subset of the Koopman or Perron-Frobenius eigenfunctions that are characterized by time scales with fast-decaying oscillations is found to produce highly reliable forecasts at all lead times investigated, ranging from one week to two months. Reliable forecasts are also obtained with the adjoint covariant Lyapunov vectors, which are the eigenfunctions of the Koopman operator in the tangent space. The advantages of these different methods are discussed.

The overall level of solar activity, and space weather response at earth, varies within and between successive solar cycles and can be characterized by the statistics of bursts, that is, time-series excursions above a threshold. We consider non-overlapping 1 year samples of the auroral electrojet index (AE) and the SuperMAG-based ring current index (SMR), across the last four solar cycles. These indices respectively characterize high latitude and equatorial geomagnetic disturbances. We suggest that average burst duration τ̅ and burst return period R̅ form an activity parameter, τ̅/R̅ which characterizes the fraction of time the magnetosphere spends, on average, in an active state for a given burst threshold. If the burst threshold takes a fixed value, τ̅/R̅ for SMR tracks sunspot number, while τ̅/R̅ for AE peaks in the solar cycle declining phase. Crossing theory directly relates τ̅/R̅ to the observed index value cumulative distribution function (cdf). For burst thresholds at fixed quantiles, we find that the probability density functions of τ̅ and R each collapse onto single empirical curves for AE at solar cycle minimum, maximum, and declining phase and for (-)SMR at solar maximum. Moreover, underlying empirical cdf tails of observed index values collapse onto common functional forms specific to each index and cycle phase when normalized to their first two moments. Together, these results offer operational support to quantifying space weather risk which requires understanding how return periods of events of a given size vary with solar cycle strength.

In this study, we use measurements from over 4,735 globally distributed Global Navigation Satellite System (GNSS) receivers to track the progression of travelling ionospheric disturbances (TIDs) associated with the 15 January 2022 Hunga Tonga-Hunga Ha’apai submarine volcanic eruption. We identify two distinct Large Scale TIDs (LSTIDs) and several subsequent Medium Scale TIDs (MSTIDs) that propagate radially outward from the eruption site. Within 3000 km of epicenter, LSTIDs of >1600 km and ~1350 km wavelengths are initially observed propagating at speeds of ~950 ms-1 and ~555 ms-1, before substantial slowing to ~600 ms-1 and ~390 ms-1, respectively. MSTIDs with speeds of 200-400 ms-1 are observed for six hours following eruption, the first of which comprises the dominant global ionospheric response and coincides with the atmospheric surface pressure disturbance associated with the eruption. These are the first results demonstrating the global impact of the Tonga eruption on the ionospheric state.

Volcanic aerosol forcing has been reported in the literature to be less effective in changing the earth’s surface temperature than CO2 forcing. This implies a different feedback strength, and therefore different contributions from individual feedback mechanisms. We employ the CMIP6 version of MPI-ESM to understand the reasons for these apparent differences in the ability to change the surface air temperature. Using a highly idealized eruption scenario and comparing it to a doubling and a halving of CO2 concentration, we identify key reasons for changes in the magnitude of the feedback parameter. We show that the “efficacy” [Hansen et al. 2005] of volcanic aerosol forcing depends strongly on the method and the time scale used to calculate it. We argue that the seemingly established result of a lower-than-unity efficacy of volcanic aerosol forcing might only hold under the specific methodological choices other authors have made, but not in general. Furthermore, we find qualitative differences between the cooling and warming simulations, but strong similarities between the 0.5xCO2 and the idealized eruption cases. This hints towards processes, which are not forcing agent-specific, but specific to the sign of the forcing. A pronounced curvature in the N(T) plot (“Gregory plot”) for the cooling scenarios makes the computation of feedback through regression even more sensitive to subjective choices than in the 2xCO2 case. We disentangle the role of ocean heat uptake efficacy and atmospheric feedback processes in the framework of the pattern effect.

Accurate predictions of the properties of interplanetary coronal mass ejection (ICME)-driven disturbances are a key objective for space weather forecasts. The ICME’s time of arrival (ToA) at Earth is an important parameter and one that is amenable to a variety of modeling approaches. Previous studies suggest that the best models can predict the arrival time to within an absolute error of 10-15 hours. Here, we investigate the main sources of error in predicting a CME’s ToA at Earth. These can be broken into two main categories: (1) the initial properties of the ejecta, including its speed, mass, and direction of propagation; and (2) the properties of the ambient solar wind into which it propagates. To estimate the relative contribution to ToA errors, we construct a set of numerical experiments of cone-model CMEs, where we vary the initial speed, mass, and direction at the inner radial boundary. Additionally, we build an ensemble of 12 ambient solar wind solutions using realizations from the ADAPT model. We find that each component in the chain contributes between ±2.5 and ±7 hours of uncertainty to the estimate of the CME’s ToA. Importantly, different realizations of the synoptic produce the largest errors. This suggests that estimates of ToA will continue to be plagued with intrinsic errors of ±10 hours until tighter constraints can be found for these boundary conditions. Our results suggest that there are clear benefits to focused investigations aimed at reducing the uncertainties in CME speed, mass, direction, and input boundary magnetic fields.

Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. In this study, we operate a wind turbine array collectively to maximize total array production through wake steering. The selection of the farm control strategy relies on the optimization of computationally efficient flow models. We develop a physics-based, data-assisted flow control model to predict the optimal control strategy. In contrast to previous studies, we first design and implement a multi-month field experiment at a utility-scale wind farm to validate the model over a range of control strategies, most of which are suboptimal. The flow control model is able to predict the optimal yaw misalignment angles for the array within +/-5 degrees for most wind directions (11-32% power gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 2.7% and 1.0%, for the wind directions of interest and for wind speeds between 6 and 8 m/s and all wind speeds, respectively. The developed and validated predictive model can enable a wider adoption of collective wind farm operation.

Warming experiments with a uniformly insolated, non-rotating climate model with a slab ocean are conducted by increasing the solar irradiance. As the global mean surface temperature warms from the current global mean surface temperature of 289K, the surface temperature contrast between the warm-rising and cool-subsiding regions decreases to a small value at around 298K, then increases with further warming. The growing surface temperature contrast is associated with reduced climate sensitivity, mostly due to reduced strength of the greenhouse effect in the subsiding region. The clouds in the convective region are always more reflective than those in the subsiding region and this difference increases as the climate warms, acting to reduce the surface temperature contrast. At lower temperatures between 289K and 298K the shortwave suppression of SST contrast increases faster than the longwave enhancement of SST contrast. At warmer temperatures between 298K and 309K the longwave enhancement of SST contrast with warming is stronger than the shortwave suppression of SST contrast, so that the SST contrast increases. Above 309K the greenhouse effect in the subsiding region begins to grow, the SST contrast declines and the climate sensitivity increases. The transitions at 298K and 309K can be related to the increasing vapor pressure path with warming. The mass circulation rate between warm and cool regions consists of shallow and deep cells. Both cells increase in strength with SST contrast. The lower cell remains connected to the surface, while the upper cell rises to maintain a roughly constant temperature.

Terrestrial Gamma-ray Flashes exhibit slopes of ionizing radiation associated with bremsstrahlung. Bremsstrahlung has a continuous spectrum of radiation from radio waves to ionizing radiation. The Poynting vector of the emitted radiation, i.e., the radiation pattern around a single particle under the external lightning electric field during interaction with other particles or atoms, is not quite well known. The overall radiation pattern arises from the combination of radiation of parallel and perpendicular motions of a particle caused by the acceleration from the lightning electric field and the bremsstrahlung. The calculations and displays of radiation patterns are generally limited to a low-frequency approximation for radio waves and separate parallel and perpendicular motions. Here we report the radiation patterns of combined parallel and perpendicular motions from accelerated relativistic particles at low and high frequencies of the bremsstrahlung process with an external lightning electric field. The primary outcome is that radiation patterns have four relative maxima with two forward peaking and two backward peaking lobes. The asymmetry of the radiation pattern, i.e., the different intensities of forward and backward peaking lobes, are caused by the Doppler effect. A novel outcome is that bremsstrahlung has an asymmetry of the four maxima around the velocity vector caused by the curvature of the particle's trajectory as it emits radiation. In addition, change in kinetic energy of bremsstrahlung electron and shrinking radiation lobe due to bremsstrahlung asymmetry were found to increase electron's energy concentration towards the outer regions of the curved trajectory. This mathematical modeling helps to better understand the physical processes of a single particle's radiation pattern, which might assist the interpretation of observations with networks of radio receivers and arrays of gamma-ray detectors.

The Greenland ice sheet (GrIS) has been losing mass in the last several decades, and is currently contributing around 0.7 mm sea level equivalent (SLE) yr-1 to global mean sea level rise (SLR). As ice sheets are integral parts of the Earth system, it is important to gain process-level understanding of GrIS mass loss. This paper presents an idealized high-forcing simulation of 350 years with the Community Earth System Model version 2.1 (CESM2.1) including interactively coupled, dynamic GrIS with the Community Ice Sheet Model v2.1 (CISM2.1). From pre-industrial levels (287 ppmv), the CO2 concentration is increased by 1% yr-1 till quadrupling (1140 ppmv) is reached in year 140. After this, the forcing is kept constant. Global mean temperature anomaly of 5.2 K and 8.5 K is simulated by years 131–150 and 331-150, respectively. The North Atlantic Meridional Overturning Circulation strongly declines, starting before GrIS runoff substantially increases. The projected GrIS contribution to global mean SLR is 107 mm SLE by year 150, and 1140 mm SLE by year 350. The accelerated mass loss is driven by the SMB. Increased long-wave radiation from the warmer atmosphere induces an initial slow SMB decline. An acceleration in SMB decline occurs after the ablation areas have expanded enough to trigger the ice-albedo feedback. Thereafter, short-wave radiation becomes an increasingly important contributor to the melt energy. The turbulent heat fluxes further enhance melt and the refreezing capacity becomes saturated. The global mean temperature anomaly at the start of the accelerated SMB decline is 4.2 K.

RF energy harvesting is a new area of interest as a research topic during last decade. This paper presents the development of RF energy harvester circuits using a broadband Wilkinson power combiner technique. A modified broadband Wilkinson power combiner is introduced and studied. Authors have proposed a modified voltage multiplier circuit which is the combination of conventional voltage multiplier circuits like Greinacher and Villard voltage multipliers. Author has also proposed and studied a simple MOS based RF energy harvester circuit. The performance of a Schottky diode based RF energy harvester circuit and a MOS based RF energy harvesting circuit have been studied. Author also has carried out Monte Carlo simulation and the simulation results show very small deviation from its nominal output value. Author has developed a prototype of a single stage Greinacher voltage multiplier.

The winter Arctic Oscillation (AO) is important for understanding the Northern Hemisphere climate variability and predictability. However, ENSEMBLES models produce inconsistent predictions when applied to the interannual variability of the 1962–2006 winter AO. In this study, the interannual increment of the winter AO index (DY_AOI) during 1962–2006 is first improved by a dynamical‐statistical model with two predictors: the preceding autumn Arctic sea ice and the concurrent winter ENSEMBLES‐predicted sea surface temperature over the North Pacific. Next, the improved final AOI is obtained by adding the improved DY_AOI to the preceding observed AOI. Because the interannual increment approach can amplify prediction signals and takes advantage from the previous observed AOI, this method shows promise for significantly improving the interannual variability prediction capabilities of the winter AO during 1962–2006 in the ENSEMBLES models. Therefore, this study offers important insights for AO predictions, even other climate variables predictions.

Recent studies have documented that the Madden-Julian Oscillation (MJO) has impacts in extreme dry/wet conditions over tropical regions and in atmospheric state. They are based, however, in correlation analysis, and therefore do not consider non-linear interactions nor they establish cause-effect relationships.In this study we introduce a generalization of the non-linear Granger causality (GC) test to identify causal relations between MJO and hydrological extremes. The method is able to identify causal relations under noisy, nonlinear and non-stationary scenarios. A probabilistic extension is also introduced where the causal test operates directly on the marginal likelihood (also called evidence) of the observations, which is analytic. We apply our proposed method to MJO and satellite-based soil moisture (SM) data, and revisit the global teleconnection patterns induced by MJO events. Since El Ni\~no Southern Oscillation (ENSO) is a modulating factor that can result in abnormal SM global distributions, we also include it in the analysis as a potential driver of SM variability. Including ENSO allows us to differentiate the effect of the MJO and ENSO on the global SM anomalies and to learn the causal graph of their cause-effect relationships, and also the mutual relation between MJO and ENSO extreme events.

A new methodology for satellite retrieval of cloud condensation nuclei (CCN) in shallow marine boundary layer clouds is developed and validated in this study. The methodology is based on retrieving cloud base drop concentrations (Nd) and updrafts (Wb), and calculating the supersaturation (S) based on that. Then Nd is the CCN at S. 50 The accuracy of the satellite retrievals was validated against ship borne measurements of CCN done in recent campaigns in the Southern Oceans (ACE-SPACE, MARCUS & PEGASO [2015-2018]) and in the subtropics (MAGIC [2012-2013]). The satellite retrieve Nd and S at cloud base was related to the actually measured CCN(S) at sea surface. The main findings show that: (a) coupled clouds have good agreement 55 between satellite retrievals and ship measurements of CCN(S); (b) the best agreement is achieved when using the brightest 10% of the clouds, and accounting for their adiabatic fraction, as measured by aircrafts; (c) most of the decoupled clouds had much lower CCN(S) than at the underlying surface. This means that most CCN originate from the surface and not from the free troposphere. This validates the satellite retrievals and 60 allows us to further quantify the relationships between CCN(S) and cloud microphysical properties.

Observations of radio waves in the Extremely Low Frequency and Very Low Frequency band (ELF/VLF, 0.3-30 kHz) have a host of geophysical uses, including lightning detection and characterization, D-region ionosphere remote sensing, detection of solar flares and geomagnetic storms, gravity waves, gamma-ray burst detection, observations of whistlers, chorus and hiss, to infer wave-particle interactions in the magnetosphere, plasmaspheric state. It’s been looked at for earthquake forecasting and also has commercial uses like submarine communications and subterranean prospecting. For many years ELF/VLF data have been collected at various locations and by various groups around the world for a variety of scientific purposes, but most of this data is not available publicly. We introduce the World Archive of Low-frequency Data and Observations (WALDO), a repository of ELF/VLF data from recordings taken over the past two decades by Stanford University and subsequently by Georgia Tech and University of Colorado Denver. The locations of the recordings are all around the world, including Alaska, Antarctica, Australia, and many low and mid latitude stations. Some sites were more consistent than others but there’s a lot of untapped value in this dataset. Funding for these recordings came from many years of funding from NSF, NASA, DoD, and others, on various basic science projects, and we feel a responsibility to make sure the datasets are now preserved. We are in the process of transferring many 100s of TBs of data and sharing every raw bit for anyone to download and analyze. This includes both “broadband” data that includes the entire spectrum from 500 Hz – 50 kHz, and “narrowband” data corresponding to amplitudes and phases of specific transmitting beacons. We are also including automatically generated summary plots, and a host of basic analysis tools to allow anyone to download and analyze the data. We will announce and present WALDO, update its status and timeline for full deployment, and detail some of the uses of ELF/VLF data, with the goal of enabling its use by anyone interested. We will not be finished by the Fall meeting (ripping 80,000 DVDs can take a while) but whatever we finished will be public and hopefully we will be far along by then. Finally, we will have the answer to the age-old question…”Where’s WALDO?”

In the TRACMIP ensemble of aquaplanet climate model experiments, CO2-induced warming is amplified in the poles in 10 out of 12 models, despite the lack of sea ice. We attribute causes of this amplification by perturbing individual radiative forcing and feedback components in a moist energy balance model. We find a strikingly linear pattern of tropical versus polar warming contributions across models and processes, implying that polar amplification is an inherent consequence of diffusion of moist static energy by the atmosphere. The largest contributor to polar amplification is the instantaneous CO2 forcing, followed by the water vapor feedback and, for some models, cloud feedbacks. Extratropical feedbacks affect polar amplification more strongly, but even feedbacks confined to the tropics can cause polar amplification. Our results contradict studies inferring warming contributions directly from the meridional gradient of radiative perturbations, highlighting the importance of interactions between feedbacks and moisture transport for polar amplification.

Experimental measurements of collision-induced absorption (CIA) cross-sections for CO-H2 and CO2-CH4 complexes were performed using Fourier transform spectroscopy over a spectral range of 100-500 cm and a temperature range of 200-300 K. These experimentally derived CIA cross-sections agree with the spectral range and temperature dependence of the calculation by Wordsworth (2017), however the amplitude is half of what was predicted. Furthermore, the CIA cross-sections reported here agree with those measured by Turbet (2019). The CIA cross-sections can be applied to planetary systems with CO2-rich atmospheres, such as Mars and Venus, and will be useful to terrestrial spectroscopists. Additionally, radiative transfer calculations of the early Mars atmosphere were performed and showed that CO2-CH4 CIA would require surface pressure greater than 3 bar for a 10% methane atmosphere to achieve 273 K at the surface. CO2-H2, however, liquid water is possible with 5\% hydrogen and less than 2 bar of surface pressure.

The Greenland Ice Sheet (GrIS) mass balance is examined with an Earth system/ice sheet model that interactively couples the GrIS to the land and atmosphere. The simulation runs from 1850 to 2100, with historical and SSP5-8.5 forcing. By mid-21st century, the cumulative contribution to global mean sea level rise (SLR) is 23 mm. Over the second half of the 21st century, the surface mass balance becomes negative in all drainage basins, and an additional 86 mm of SLR is contributed. The annual mean GrIS mass loss in the last two decades is 2.7 mm sea level equivalent (SLE) yr-1. Strong decrease in SMB (3.1 mm SLE yr-1) is counteracted by a reduction in ice discharge from thinning and retreat of outlet glaciers. The southern GrIS drainage basins contribute 73% of the mass loss by mid-century. This decreases to 55% by 2100, as surface runoff in the northern basins strongly increases.

The ability of an observationally-constrained cloud-system resolving model (Weather Research and Forecasting; WRF, 4-km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1-degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The month-long period is divided into days with and without the presence of observed propagating mesoscale convective systems (MCSs) over the central Amazon. The MCSs are strongly associated with rain amounts over the basin and also control the observed spatial variability of the diurnal rain rate. WRF model coupled with a 3-D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea-breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations with and without nudging the large-scale winds towards global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. Both models produce contrast in easterly wind profiles between days with and without MCS that are similar to data collected by U.S. DOE Atmospheric Radiation Measurement (ARM) facility during the Green Ocean Amazon (GoAmazon2014/5) campaign and other operational radiosondes. A multivariate perturbation analysis indicates the dryness of low-level air transported from ocean to inland has higher impact on the formation and maintenance of MCS in the Amazon than other processes.

In this work, we develop a data-driven subgrid-scale (SGS) model using a fully convolutional neural network (CNN) for large eddy simulation of forced 2D turbulence. Forced 2D turbulence is a fitting prototype for many large-scale geophysical and environmental flows (where rotation and/or stratification dominate) and has been widely used as a testbed for novel techniques, including machine-learning-based SGS modeling. We first conduct direct numerical simulation (DNS) and obtain training, validation, and testing data sets by applying a Gaussian spatial filter to the DNS solution. With the filtered DNS (FDNS) data in hand, we train the CNN with the filtered state variables. A priori analysis shows that the CNN-predicted SGS term accurately captures the inter-scale energy transfer. A posteriori analysis indicates that the LES-CNN outperforms the physics-based models in both short-term prediction and long-term statistics. Although the CNN-based model is promising in predicting the SGS term, it requires big data to perform satisfactorily. In the small-data limit, the LES-CNN generates artificial instabilities and thus leads to unphysical results. We propose three remedies for the CNN to work in the small-data limit, i.e., data augmentation and group convolution neural network (GCNN), leveraging the rotational equivariance of the SGS term and incorporating a physical constraint on the SGS enstrophy transfer. The SGS term is both translational and rotational equivariant in a square periodic flow field. While primitive CNN can capture the translational equivariance, the rotational equivariance can be accounted for by either including rotated snapshots in the training data set or by a GCNN that enforces rotational equivariance as a hard constraint. Additionally, The SGS enstrophy transfer constraint can be implemented in the loss function of the CNN. A priori and a posteriori analyses show that the CNN/GCNN with knowledge/constraints of rotational equivariance and SGS enstrophy transfer enhances the SGS model and allows the data-driven model to work stably and accurately in a small-data limit. These findings can potentially help the ongoing efforts in using machine-learning for SGS modeling in weather/climate models, where high-quality training data are scarce and instabilities have been reported in many past studies.

The polar summer mesosphere is the Earth’s coldest region, allowing the formation of mesospheric ice clouds. These clouds produce strong polar mesospheric summer echoes (PMSE) that are used as tracers of mesospheric dynamics. Here we report the first observations of extreme vertical drafts (±50~m/s) in the mesosphere obtained from PMSE, characterized by velocities more than five standard deviations larger than the observed vertical wind variability. Using aperture synthesis radar imaging, the observed PMSE morphology resembles mesospheric bores, i.e., narrow along propagation (3–4~km) and elongated (>10~km) transverse to propagation direction. Additionally, our event presents a large vertical extent (± 3–4~km), resembling a “super bore”. Powerful vertical drafts, intermittent in space and time, emerge also in direct numerical simulations of stratified flows, predicting non-Gaussian statistics of vertical velocities. This evidence suggests that our event, and perhaps previous bores, might result from the interplay of gravity waves and turbulent motions.

Tropical islands are simultaneously some of the most biodiverse and vulnerable places on Earth. Water resources help maintain the delicate balance on which the ecosystems and the population of tropical islands rely. Hydrogen and oxygen isotope analyses are a powerful tool in the study of the water cycle on tropical islands, although the scarcity of long-term and high-frequency data makes interpretation challenging. Here, a new dataset is presented based on weekly collection of rainfall H and O isotopic composition on the island of O‘ahu, Hawai‘i, beginning from July 2019 and still ongoing. Throughout this time, a variety of weather conditions have affected the island, each producing rainfall with different isotopic ratios: precipitation from Kona lows was found to have the lowest isotopic ratios, whereas trade-wind showers had the highest. These data also show some differences between the windward and the leeward side of the island, the latter being associated with higher rainfall isotope ratios due to increased rain evaporation. At all sites, the measured deuterium excess shows a marked seasonal cycle which is attributed to different origins of the air masses that are responsible for rainfall in the winter and summer months. The local meteoric water line is then determined and compared with similar lines for O‘ahu and other Hawaiian islands. Finally, a comparison is made with data collected on Hawai‘i Island for a longer period of time, and it is shown that the isotopic composition of rainfall exhibits significant interannual variability.

Tropical modes of variability, including the Madden‐Julian Oscillation (MJO) and the El Niño‐Southern Oscillation (ENSO), are challenging to represent in climate models. Previous studies suggest their fundamental dependence on zonal asymmetry, but such dependence is rarely addressed with fully coupled ocean dynamics. This study fills the gap by using fully coupled, idealized Community Earth System Model (CESM) and comparing two nominally ocean-covered configurations with and without a meridional boundary. For the MJO-like intraseasonal mode, its separation from equatorial Kelvin waves and the eastward propagation of its convective and dynamic signals depend on the zonal gradient of the mean state. For the ENSO-like interannual mode, in the absence of the ocean’s meridional boundary, a circum-equatorial dominant mode emerges with distinct ocean dynamics. The interpretation of the dependence of these modes on zonal asymmetry is relevant to their representation in realistic climate models.

Twenty-six balloon-borne ozonesondes were launched near the north shore of central Long Island in the summers of 2018 and 2019 as part of the Long Island Sound Tropospheric Ozone Study (LISTOS). While surface concentrations of ozone are routinely monitored, ozone aloft is infrequently measured, but critical for a full understanding of ozone production and transport. Special attention is given to the lower troposphere from the surface to about 2 km altitude. The observed vertical ozone profiles are presented and analyzed with additional data sources and modeling tools, including LiDAR wind profiles from the New York State Mesonet, back trajectories based on 3 km resolution High-Resolution Rapid Refresh (HRRR) model data, and surface data, aircraft observations, sonde, and ozone LiDAR measurements from other LISTOS participants. The cases analyzed in detail illustrate events with high observed ozone, often with pronounced vertical structure in the profile. Specifically, easily discernable layers are identified with ozone excursions of up to 40 ppbv over short vertical distances. The analysis indicates that meteorological processes can combine to generate the observed vertical profiles. Hot, sunny days with high pressure systems are accompanied by high precursor emissions due to increased power demands, plentiful radiation for photochemistry, and stagnation of synoptic winds. These in turn allow shearing due to meso- and smaller scale flows like low level jets and sea-breeze/shore-breeze circulation to become dominant and produce the complex vertical layered structure observed. The five cases presented illustrate these processes.