Our longest, stable record of cloud-top pressure (CTP) and cloud-top height (CTH) are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) on Terra. Because of single cloud-layer assumptions in their standard algorithms, they provide only single CTP/CTH retrievals in multi-layered situations. In the predominant multi-layered regime of thin cirrus over low clouds, MODIS significantly overestimates cirrus CTP and emissivity, while MISR accurately retrieves low-cloud CTH. Utilizing these complementary capabilities, we develop a retrieval algorithm for accurately determining both-layer CTP and cirrus emissivity for such 2-layered clouds, by applying the MISR low-cloud CTH as a boundary condition to a modified MODIS CO2-slicing retrieval. We evaluate our 2-layered retrievals against collocated Cloud-Aerosol Transport System (CATS) lidar observations. Relative to CATS, the mean bias of the upper cloud CTP and emissivity are reduced by ~90% and ~75% respectively in the new technique, compared to standard MODIS products. We develop an error model for the 2-layered retrieval accounting for systematic and random errors. We find up to 88% of all residuals lie within modeled 95% confidence intervals, indicating a near-closure of error budget. This reduction in error leads to a reduction in modeled atmospheric longwave radiative flux biases ranging between 5-40 Wm-2, depending on the position and optical properties of the layers. Given this large radiative impact, we recommend that the pixel-level 2-layered MODIS+MISR fusion algorithm be applied over the entire MISR swath for the 22-year Terra record, leading to a first-of-its-kind 2-layered cloud climatology from Terra’s morning orbit.

Many chemical processes depend non-linearly on temperature. Gravity-wave-induced temperature perturbations have been previously shown to affect atmospheric chemistry, but accounting for this process in chemistry-climate models has been a challenge because many gravity waves have scales smaller than the typical model resolution. Here, we present a method to account for subgrid-scale orographic gravity-wave-induced temperature perturbations on the global scale for the Whole Atmosphere Community Climate Model (WACCM). The method consists of deriving the temperature perturbation amplitude $\hat{T}$ consistent with the model’s subgrid-scale gravity wave parametrization, and imposing $\hat{T}$ as a sinusiodal temperature perturbation in the model’s chemistry solver. Because of limitations in the gravity wave parameterization, scaling factors may be necessary to maintain a realistic wave amplitude. We explore scaling factors between 0.6 and 1 based on comparisons to altitude-dependent $\hat{T}$ distributions in two observational datasets. We probe the impact on the chemistry from the grid-point to global scales, and show that the parametrization is able to represent mountain wave events as reported by previous literature. The gravity waves for example lead to increased surface area densities of stratospheric aerosols. This in turn increases chlorine activation, with impacts on the associated chemical composition. We obtain large local changes in some chemical species (e.g., active chlorine, NOx, N2O5) which are likely to be important for comparisons to airborne or satellite observations, but find that the changes to ozone loss are more modest. This approach enables the chemistry-climate modeling community to account for subgrid-scale gravity wave temperature perturbations in a consistent way.

In-situ measurements from the Magnetospheric Multiscale (MMS) mission are used to estimate electron density from spacecraft potential and investigate compressive turbulence in the Earth’s magnetosheath. During the MMS Solar Wind Turbulence Campaign in February 2019, the four MMS spacecraft were arranged in a logarithmic line constellation enabling the study of measurements from multiple spacecraft at varying distances. We estimate the electron density from spacecraft potential for a time interval in which the ion emitters actively control the potential. The derived electron density data product has a higher temporal resolution than the plasma instruments, enabling the examination of fluctuation for scales down to the sub-ion range. The inter-spacecraft separations range from 132 km to 916 km; this corresponds to scales of 3.5 to 24.1 ion inertial lengths. The derived density and magnetic field data are used to study fluctuations in the magnetosheath through time lags on a single spacecraft and spatial lags between pairs of spacecraft over almost one decade in scale. The results show an increase in anisotropy as the scale decreases.

Deforestation alters the exchange of heat, moisture, and momentum between the Earth's surface and the atmosphere, which can significantly affect the surface energy balance and water budget. However, changes in surface heat fluxes in response to deforestation are diverse among multi-model simulations. Changes in surface heat fluxes may lead to further energy partitioning and different land-atmosphere interactions. This study explores factors that might cause different changes in surface fluxes under tropical deforestation. The mediating effect of the Bowen ratio on changes in turbulent surface fluxes in response to the removal of tropical rainforests is examined with the Community Earth System Model of the National Center for Atmospheric Research. Different flux partitioning in the mean state of the Bowen ratio is associated with various flux changes under deforestation. When the mean Bowen ratio is smaller, deforestation tends to increase sensible heat fluxes and reduce latent heat fluxes. Our research further indicates that the simulated mean-state Bowen ratios in the Land Use Model Intercomparison Project model archive might modulate changes in surface heat fluxes that provide some clues for the land surface model developments.

Large-scale convection associated with the Madden-Julian Oscillation (MJO) initiates over the Indian Ocean and propagates eastward across the Maritime Continent (MC). Over the MC, MJO events are generally weakened due to complex interactions between the large-scale MJO and the MC landmass. The MC barrier effect is responsible for the dissipation of 40-50\% of observed MJO events and is often exaggerated in weather and climate models. We examine how MJO propagation over the MC is affected by two aspects of the MC - its land-sea contrast and its terrain. To isolate the effects of mountains and land-sea contrast on MJO propagation, we conduct three high-resolution coupled atmosphere-ocean model experiments: 1) control simulation (CTRL) of the 2011 November-December MJO event, 2) flattened terrain without MC mountains (FLAT), and 3) no-land simulation (WATER) in which the MC islands are replaced with 50 m deep ocean. CTRL captures the general properties of the diurnal cycle of precipitation and MJO propagation across the MC. The WATER simulation produces a more intense and smoother-propagating MJO compared with that of CTRL. In contrast, the FLAT simulation produces much more convection and precipitation over land (without mountains) than CTRL, which results in a stronger barrier effect on MJO propagation. The land-sea contrast induced land-locked convection weakens the MJO’s convective organization. The land-locked convective systems over land in FLAT are more intense, grow larger, and last longer, which is more detrimental to MJO propagation over the MC, than the mountains that are present in CTRL.

The results of a study aimed at assessing the utility of transionospheric 35 MHz scintillation measurements toward cosmic radio sources for estimating the level of spatial coherence in high frequency (HF) skywave systems are presented. This was done using an array of four antennas in southern Maryland called the Deployable Low-band Ionosphere and Transient Experiment (DLITE). Two of the antennas within a ~350-m north/south baseline were used to monitor 35-MHz intensity variations of two bright cosmic sources, Cynus A and Cassiopeia A. The other two antennas, which were within a ~420-m east/west baseline, recorded the 7.85 MHz skywave from the CHU radio station near Ottawa, Ontario. These HF measurements were used to quantify the level of spatial coherence by measuring the amplitudes of the cross correlation of the two antennas’ recorded voltages relative to the received power, which were typically ~0.5-0.9, but occasionally near zero. A method was developed to estimate the expected cross-correlation amplitude based on the 35-MHz scintillations. This method assumes the case of weak scattering, which is generally appropriate for mid-latitudes, and that the irregularity distribution follows that of the background electron density. These calculations typically captured the day-to-day variations in spatial coherence quite well (correlation coefficient r≈0.6) while only marginally reproducing hour-to-hour variations (r≈0.2). Thus, this method holds promise as an economical and passive means to assess the spatial coherence expected for skywave propagation within a given mid-latitude region.

El Niño-Southern Oscillation (ENSO) is the most prominent interannual climate variability in the tropics and exhibits diverse features in spatiotemporal patterns. This paper develops a simple multiscale intermediate coupled stochastic model to capture the ENSO diversity and complexity. The model starts with a deterministic and linear coupled interannual atmosphere, ocean, and sea surface temperature (SST) system. It can generate two dominant linear solutions representing the eastern Pacific (EP) and the central Pacific (CP) El Niños, respectively. In addition to adopting a stochastic model for characterizing the intraseasonal wind bursts, another simple stochastic process is developed to describe the decadal variation of the background Walker circulation. The latter links the two dominant modes in a simple nonlinear fashion and advances the modulation of the strength and occurrence frequency of the EP and the CP events. Finally, cubic nonlinear damping is adopted to parameterize the relationship between subsurface temperatures and thermocline depth. The model succeeds in reproducing the spatiotemporal dynamical evolution of different types of ENSO events. It also accurately recovers the strongly non-Gaussian probability density function, the seasonal phase locking, the power spectrum, and the temporal autocorrelation function of the SST anomalies in all the three Niño regions (3, 3.4 and 4) across the equatorial Pacific. Furthermore, both the composites of the SST anomalies for various ENSO events and the strength-location bivariate distribution of equatorial Pacific SST maxima for the El Niño events from the model simulation highly resemble those from the observations.

Jet streams are important sources of non-orographic internal gravity waves and clear air turbulence (CAT). We analyze non-orographic gravity waves and CAT during a merging of the polar front jet stream (PFJ) with the subtropical jet stream (STJ) above the southern Atlantic. Thereby, we use a novel combination of airborne observations covering the meso-scale and turbulent scale in combination with high-resolution deterministic short-term forecasts. Coherent phase fronts stretching along a highly sheared tropopause fold are found in the ECMWF IFS (integrated forecast system) forecasts. During the merging event, the PFJ reverses its direction from antiparallel to parallel with respect to the STJ, going along with strong wind shear and horizontal deformation. Temperature perturbations in limb-imaging and lidar observations onboard the research aircraft HALO in the framework of the SouthTRAC campaign show remarkable agreement with the IFS data. Ten hours earlier, the IFS data show a new “X-shaped” phase line pattern emanating from the sheared tropopause fold. The analysis of tendencies in the IFS wind components shows that these gravity waves are excited by a local body force as the PFJ impinges the STJ. In situ observations of temperature and wind components at 100 Hz confirm upward propagation of the probed portion of the gravity waves. They furthermore reveal embedded episodes of light-to-moderate CAT, Kelvin Helmholtz waves, and indications for partial wave reflection. Patches of low gradient Richardson numbers in the IFS data coincide with episodes where CAT was observed, suggesting that this event was well accessible to turbulence forecasting.

We investigate a set of Energy Exascale Earth System Model Multi-scale Modeling Framework (E3SM-MMF) simulations that vary the dimensionality and momentum transport configurations of the embedded cloud-resolving models (CRMs), including unusually ambitious 3D configurations. Issues endemic to all MMF simulations include too much ITCZ rainfall and too little over the Amazon. Systematic MMF improvements include more on-equatorial rainfall across the Warm Pool. Interesting sensitivities to CRM domain are found in the regional time-mean precipitation pattern over the tropics. The 2D E3SM-MMF produces an unrealistically rainy region over the northwestern tropical Pacific; this is reduced in computationally ambitious 3D configurations that use 1024 embedded CRM grid columns per host cell. Trajectory analysis indicates that these regional improvements are associated with desirably fewer tropical cyclones and less extreme precipitation rates. To understand why and how the representation of precipitation improved in 3D, we propose a framework that dilution is stronger in 3D. This viewpoint is supported by multiple indirect lines of evidence, including a delayed moisture-precipitation pickup, smaller precipitation efficiency, and amplified convective mass flux profiles and more high clouds. We also demonstrate that the effects of varying embedded CRM dimensionality and momentum transport on precipitation can be identified during the first few simulated days, providing an opportunity for rapid model tuning without high computational cost. Meanwhile the results imply that other less computationally intensive ways to enhance dilution within MMF CRMs may also be strategic tuning targets.

Magnetic field draping occurs when the magnetic field lines frozen in a plasma flow wrap around a body or plasma environment. The draping of the interplanetary magnetic field (IMF) around the Earth’s magnetosphere has been confirmed in the early days of space exploration. However, its global and three-dimensional structure is known from modeling only, mostly numerical. Here, this structure in the dayside of the Earth’s magnetosheath is determined as a function of the upstream IMF orientation purely from in-situ spacecraft observations. We show the draping structure can be organized in three regimes depending on how radial the upstream IMF is. Quantitative analysis demonstrates how the draping pattern results from the magnetic field being frozen in the magnetosheath flow, deflected around the magnetopause. The role of the flow is emphasized by a comparison of the draping structure to that predicted to a magnetostatic draping.

The thermal balance of forests regulates land-atmosphere feedbacks. Forests dominated by different plant functional types have contrasting energy balances, but little is known about the influence of forest structure and functional traits. By combining spaceborne measurements of land surface temperature from ECOSTRESS with ground-based meteorological data, we estimate the thermal balance at the surface (∆Tcan-air) during four summers in a region located at the Mediterranean-temperate ecotone in the NE Iberian Peninsula. We then analyze the spatiotemporal drivers of ∆Tcan-air by quantifying the effects of meteorology, forest structure (e.g. basal area, tree height) and ecophysiology (hydraulic traits, water use efficiency), during normal days and hot spells. Canopy temperatures fluctuate according to changes in air temperature but are on average 3.2˚K warmer than the near-surface air. During hot spells, ∆Tcan-air is smaller than normal periods because the advection of hot and dry air masses from the Sahara region results in a sudden increase in air temperature relative to the canopy temperature. Vapor pressure deficit (VPD) is negatively correlated to ∆Tcan-air, since the highest VPD values coincide with peaks in heat advection. Still canopy temperatures increase with VPD due to decreased transpiration and stomatal conductance and transpiration. Meanwhile, soil water availability is shown to enhance evaporative cooling. Importantly, we demonstrate that plot-scale forest structural and hydraulic traits are key determinants for the forest thermal balance. The integration of functional traits and forest structure over relevant spatial scales could improve our ability to understand and model land-atmosphere feedbacks in forested regions.

Dynamical cores used to study the circulation of the atmosphere employ various numerical methods ranging from finite-volume, spectral element, global spectral, and hybrid methods. In this work, we explore the use of Flux-Differencing Discontinuous Galerkin (FDDG) methods to simulate a fully compressible dry atmosphere at various resolutions. We show that the method offers a judicious compromise between high-order accuracy and stability for large-eddy simulations and simulations of the atmospheric general circulation. In particular, filters, divergence damping, diffusion, hyperdiffusion, or sponge-layers are not required to ensure stability; only the numerical dissipation naturally afforded by FDDG is necessary. We apply the method to the simulation of dry convection in an atmospheric boundary layer and in a global atmospheric dynamical core in the standard benchmark of Held and Suarez (1994).

The Solar Irradiance Science Team #2 (SIST-2) program is a competitively solicited National Aeronautics and Space Administration (NASA) Earth Science Division (ESD) science research program providing three-year awards beginning in July 2018 to quantify and understand the solar irradiance and its variability. A key motivation for the SIST-2 program is to understand the solar radiation variability and implications for Earth’s climate and atmospheric composition. The purpose for the SIST-2 program is limited to the accurate specification of the incoming solar irradiance into the Earth system considering the 43-year satellite data record as well as proxies to which the satellite record can be tied. The SIST-2 program funded eight research grants to study the variability of the total solar irradiance (TSI) and solar spectral irradiance (SSI) and to develop improved space-based data sets, solar proxies, and variability models of the solar irradiance. The SIST-2 projects are briefly introduced.

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

Evaluating historical simulations from global climate models (GCMs) remains an important exercise for better understanding future projections of climate change and variability in rapidly warming regions, such as the Arctic. As an alternative approach for comparing climate models and observations, we set up a machine learning classification task using a shallow artificial neural network (ANN). Specifically, we train an ANN on maps of annual mean near-surface temperature in the Arctic from a multi-model large ensemble archive in order to classify which GCM produced each temperature map. After training our ANN on data from the large ensembles, we input annual mean maps of Arctic temperature from observational reanalysis and sort the prediction output according to increasing values of the ANN’s confidence for each GCM class. To attempt to understand how the ANN is classifying each temperature map with a GCM, we leverage a feature attribution method from explainable artificial intelligence. By comparing composites from the attribution method for every GCM classification, we find that the ANN is learning regional temperature patterns in the Arctic that are unique to each GCM relative to the multi-model mean ensemble. In agreement with recent studies, we show that ANNs can be useful tools for extracting regional climate signals in GCMs and observations.

Leveraging aerosol data from multiple airborne and surface-based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate-sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154 – 0.0296; R = 0.76; N = 2948). Ground-based measurements of the oxalate-sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio towards higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing.

Four-dimensional variational (4D-Var) data assimilation is an effective method for obtaining physically consistent time-varying states. In this study, a method using a neural network surrogate model obtained by machine learning is proposed to solve one of the most serious challenges in 4D-Var: to construct an adjoint model. The feasibility of the proposed method was demonstrated by a 4D-Var experiment using a surrogate model for the Lorenz 96 model. In the method, several effective procedures have been proposed to obtain an accurate surrogate model and the assimilated initial conditions, including two-stage learning (i.e., single- and multi-step learning) of neural networks, limiting the target states of the surrogate model to a small subspace of the state phase space, and updating the surrogate model during 4D-Var iterations.

With the utilization of a novel synergistic approach, we constrain the vertical distribution of water vapor on Mars with measurements from nadir-pointing instruments. Water vapor column abundances were retrieved simultaneously with PFS (sensing the thermal infrared range) and SPICAM (sensing the near-infrared range) on Mars Express, yielding distinct yet complementary sensitivity to different parts of the atmospheric column. We show that by exploiting a spectral synergy retrieval approach, we obtain more accurate water vapor column abundances compared to when only one instrument is used, providing a new and highly robust reference climatology from Mars Express. We present a composite global dataset covering all seasons and latitudes, assembled from co-located observations sampled from seven Martian years. The synergy also offers a way to study the vertical partitioning of water, which has remained out of the scope of nadir observations made by single instruments covering a single spectral interval. Special attention is given to the north polar region, with extra focus on the sublimation of the seasonal polar cap during the late spring and summer seasons. Column abundances from the Mars Climate Database were found to be significantly higher than synergistically retrieved values, especially in the summer Northern Hemisphere. Deviances between synergy and model in both magnitude and meridional variation of the vertical confinement were also discovered, suggesting that certain aspects of the transport and dynamics of water vapor are not fully captured by current models.

The diurnal-eastward propagating tide with zonal wavenumber 3 (DE3) has gained significant attention due to its ability to preferentially propagate to the ionosphere and thermosphere (IT) from the tropical troposphere, thus effectively coupling these atmospheric regions. In this work, we demonstrate the existence of a pronounced zonal wavenumber 4 (WN4) structure in the low-latitude ionosphere during May 27 - June 5, 2020 using concurrent in-situ total ion number density measurements from the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) and the Ionospheric Connection Explorer (ICON) satellites. Temperature observations from the Thermosphere Ionosphere Mesosphere Energetics Dynamics Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER) instrument near 105 km and output from the Specified Dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD/WACCM-X) demonstrate that this global-scale ionospheric WN4 structure is due to DE3 propagating through the lower thermosphere.

A new automated method to retrieve charge layer polarity from flashes, named Chargepol, is presented in this paper. Using data from the NASA Lightning Mapping Array (LMA) deployed during the RELAMPAGO field campaign in Cordoba, Argentina, from November 2018 to April 2019, this method estimates the polarity of vertical charge distributions and their altitudes and thicknesses (or vertical depth) using the very-high frequency (VHF) source emissions detected by LMAs. When this method is applied to LMA data for extended periods of time, it is capable of inferring a storm’s bulk electrical charge structure throughout its life cycle. This method reliably predicted the polarity of charge within which lightning flashes propagated and was validated in comparison to methods that require manual assignment of polarities via visual inspection of VHF lightning sources. Examples of normal and anomalous charge structures retrieved using Chargepol for storms in Central Argentina during RELAMPAGO are presented for the first time. Application of Chargepol to five months of LMA data in Central Argentina and several locations in the United States allowed for the characterization of the charge structure in these regions and for a reliable comparison using the same methodology. About 13.3% of Cordoba thunderstorms were defined by an anomalous charge structure, slightly higher than in Oklahoma (12.5%) and West Texas (11.1%), higher than Alabama (7.3%), and considerably lower than in Colorado (82.6%). Some of the Cordoba anomalous thunderstorms presented enhanced low-level positive charge, a feature rarely if ever observed in Colorado thunderstorms.

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

Current inference techniques for processing multi-needle Langmuir Probe (m-NLP) data are often based on adaptations of the Orbital Motion-Limited (OML) theory which relies on several simplifying assumptions. Some of these assumptions, however, are typically not well satisfied in actual experimental conditions, thus leading to uncontrolled uncertainties in inferred plasma parameters. In order to remedy this difficulty, three-dimensional kinetic particle in cell simulations are used to construct a synthetic data set, which is used to compare and assess different m-NLP inference techniques. Using a synthetic data set, regression-based models capable of inferring electron density and satellite potentials from 4-tuples of currents collected with fixed-bias needle probes similar to those on the NorSat-1 satellite, are trained and validated. The regression techniques presented show promising results for plasma density inferences with RMS relative errors less than 20 %, and satellite potential inferences with RMS errors less than 0.2 V for potentials ranging from -6 V to -1 V. The new inference approaches presented are applied to NorSat-1 data, and compared with existing state-of-the-art inference techniques.

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

For decades, the Arctic has been warming at least twice as fast as the rest of the globe. As a first step towards quantifying parametric uncertainty in Arctic feedbacks, we perform a variance-based global sensitivity analysis (GSA) using a fully-coupled, ultra-low resolution (ULR) configuration of version 1 of the Department of Energy’s Energy Exascale Earth System Model (E3SMv1). The study randomly draws 139 realizations of ten model parameters spanning three E3SMv1 components (sea ice, atmosphere and ocean), which are used to generate 75 year long projections of future climate using a fixed pre-industrial forcing. We quantify the sensitivity of six Arctic-focused quantities of interest (QOIs) to these parameters using main effect, total effect and Sobol sensitivity indices computed with a Gaussian process emulator. A sensitivity index-based ranking of model parameters shows that the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) scheme have significant impact on sea ice status and the larger Arctic climate. We also use the Gaussian process emulator to predict the response of varying each variable when the impact of other parameters are averaged out. These results allow one to assess the non-linearity of a parameter’s impact on a QOI and investigate the presence of local minima encountered during the spin-up tuning process. Our study confirms the necessity of performing global analyses involving fully-coupled climate models, and motivates follow-on investigations in which the ULR model is compared rigorously to higher resolution configurations to confirm its viability as a lower-cost surrogate in fully-coupled climate uncertainty analyses.