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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Simulating Mixed‐Phase Open Cellular Clouds Observed During COMBLE: Evaluation of Parameterized Turbulence Closure

Marine cold-air outbreaks, or CAOs, are airmass transformations whereby relatively cold boundary layer (BL) air is transported over relatively warm water. To more deeply understand BL and mixed-phase cloud properties during CAO conditions, the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) took place from late 2019 into early 2020. During COMBLE, the U.S. Department of Energy's first Atmospheric Radiation Measurement Mobile Facility (AMF1) was deployed to Andenes, Norway, far downstream (~1,000 km) from the Arctic pack ice. This study examines the two most intense CAOs sampled at the AMF1 site. The observed BL structures are open cellular with high (~3–5 km) and cold (–30 to –50 °C) cloud tops, and they often have pockets of high liquid water paths (LWPs; up to ~1,000 g m –2 ) associated with strong updrafts and enhanced turbulence. We use a high-resolution mesoscale model to explore how well four turbulence closure methods represent open cellular clouds. After applying a radar simulator to model outputs for direct evaluation, cloud top properties agree well with AMF1 observations (within ~10%), but radar reflectivity and LWP agreement is more variable. Results suggest that the turbulent Prandtl number may play an important role for the simulated BL and cloud properties. All simulations produce enhanced precipitation rates that are well-correlated with a cloud transition. Finally, the eddy-diffusivity/mass-flux approach produces the deepest cloud layer and therefore the largest and most coherent cellular structures. Furthermore, we recommend the use of a non-local turbulence closure approach to better capture turbulent processes in intense CAOs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds

Abstract. Different entrainment–mixing processes can occur in clouds; however, a homogeneous mixing mechanism is often implicitly assumed in most commonly used microphysics schemes. Here, we first present a new entrainment–mixing parameterization that uses the grid mean relative humidity without requiring the relative humidity of the entrained air. Then, the parameterization is implemented in a microphysics scheme in a large eddy simulation model, and sensitivity experiments are conducted to compare the new parameterization with the default homogeneous entrainment–mixing parameterization. The results indicate that the new entrainment–mixing parameterization has a larger impact on the number concentration, volume mean radius, and cloud optical depth in the stratocumulus case than in the cumulus case. This is because inhomogeneous and homogeneous mixing mechanisms dominate in the stratocumulus and cumulus cases, respectively, which is mainly due to the larger turbulence dissipation rate in the cumulus case. Because stratocumulus clouds break up during the dissipation stage to form cumulus clouds, the effects of this new entrainment–mixing parameterization during the stratocumulus dissipation stage are between those during the stratocumulus mature stage and the cumulus case. A large aerosol concentration can enhance the effects of this new entrainment–mixing parameterization by decreasing the cloud droplet size and evaporation timescale. The results of this new entrainment–mixing parameterization with grid mean relative humidity are validated by the use of a different entrainment–mixing parameterization that uses parameterized entrained air properties. This study sheds new light on the improvement of entrainment–mixing parameterizations in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Examining Cloud Feedback Components in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM)

Cloud feedback remains the main source of uncertainty in climate sensitivity estimated by global climate models (GCMs), largely because subgrid cloud responses are parameterized in GCMs due to their coarse resolution. Here, this study examines cloud feedback in the global 3.25-km Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM 3 km) through a pair of 1-yr atmosphere-only simulations with control and +4-K sea surface temperature perturbations. SCREAM 3 km produces a positive cloud feedback that falls within but at the upper end of the range of Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP phase 6 (CMIP6) models and expert judgment. The positive cloud feedback arises from positive contributions from both high- and low-level clouds, with increases in high-cloud altitude and decreases in low-cloud amount and optical depth playing key roles. The stronger-than-CMIP-average feedback is mainly attributable to the high-cloud altitude feedback, owing to cloud tops rising nearly isothermally in SCREAM 3 km. The positive low-cloud amount feedback is weaker in SCREAM than in GCMs because estimated inversion strength (EIS) increases more dramatically with warming. A coarser 12-km resolution version of SCREAM exhibits a weaker positive cloud feedback than SCREAM 3 km, mainly because its low-cloud-radiative flux is more sensitive to EIS, leading to a stronger negative low-cloud amount feedback. With this process-level assessment of cloud feedback, this study reveals where SCREAM aligns with and diverges from conventional GCMs and expert assessment, providing insights to inform further model improvement and future expert assessment.

Cloud radiative effects↗
DOE OSTI2026

Prognostic simulations of mixed-phase clouds with model AC-1D v1.0: the impact of aerosol types and freezing parameterizations on ice crystal budgets

Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (N i ) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and N i , and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and N i over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.

Arctic clouds↗
DOE OSTI2020

Aerosol-Ice Formation Closure Pilot Study (AEROICSTUDY) Field Campaign Report

The aerosol-ice formation closure pilot study (AEROICESTUDY) was a field campaign conducted at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility at the Southern Great Plains (SGP) observatory from October 7 to 28, 2019. The purpose of this campaign was to test a field observational approach for conducting an aerosol-ice formation closure study. In other words, the goal was to predict the number of ice nucleating particles (INPs) from the measurements of physical and chemical aerosol properties and compare those to measured INP number concentrations. This project was motivated by the fact that the aerosol community has widely conducted aerosol radiative closure, aerosol-cloud condensation nuclei (CCN), and CCN-droplet closure studies to test the physical models and parameterizations that cloud-resolving and climate models rely on to perform reliable simulations of the Earth system and energy budget. However, very few closure studies related to INPs have been conducted, and to our knowledge, none using robust, size-resolved, ambient aerosol composition measurements as model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗
DOE OSTI2026

Sensitivity of Anthropogenic Cloud Droplet Number Change to Preindustrial Emission Inventories and Physics Parameterizations

Understanding the preindustrial (PI) to present-day (PD) change in cloud droplet number concentration (ΔNd) is important to constrain the anthropogenic influence on clouds and aid future model projections. Perturbed parameter ensemble simulations of PI and PD conditions in the Energy Exascale Earth System Model (E3SMv3) reveal two locations with consistent negative ΔNd signal across parameter space–the Southeastern US and the United Kingdom–despite a trend of positive ΔNd globally from increased fossil fuel consumption. These negative ΔNd signals are driven by higher anthropogenic biofuel emissions in the PI. By varying the PI aerosol mass and/or number emissions across these data sets' uncertainty ranges, we can change the magnitude and sign of ΔNd and the effective radiative forcing from aerosol-cloud interactions (ERFaci) regionally and globally, with near-zero ΔNd leading to near-zero ERFaci. These results highlight the need to constrain PI emission uncertainty to better understand Earth system responses to aerosol-cloud interactions.

Brown, Hunter Y. [University of Wyoming, Laramie, ↗
DOE OSTI2022

Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm

Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stereo Camera Deployment in Support of TRACER (Field Campaign Report)

An improved understanding of the salient environmental controls on cloud formation, evolution, and eventual dissipation is critical to address ongoing challenges with cloud process and parameterization representations in global climate and Earth system models. One goal for the recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Tracking Aerosol Convection Interactions Experiment (TRACER) campaign was to collect a comprehensive data set that enabled such convective cloud process studies and key demonstrations for those controls that influenced cloud life cycle (i.e., aerosols, thermodynamics) in the Houston, Texas region. To help accomplish this, ARM instrumentation during TRACER was tasked with tracking a large number of individual, isolated convective cells – and to follow the evolution of these cells at high spatiotemporal resolution to characterize changes in cloud dynamic and microphysical properties. Since Houston experiences a range of convective clouds, it was known that the standard ARM Mobile Facility (AMF) instruments may not be sufficient to completely document initiating, transient, or dissipating low- or shallow-cloud behaviors that were also expected during this campaign (in terms of sensitivity, resolution, and/or operational availability). As one partial solution, a supplemental stereo camera deployment (this sub-campaign) was requested to augment the ARM AMF instrumentation to better address shallow and shallow-to-deep transitional types of cloud process drivers during TRACER (ARM stereo cameras for clouds [STEREOCAM]; Romps and Öktem 2018). The primary scientific focus was the relationships between cloud properties and the ambient conditions, which points to several key TRACER science questions including: ‘What is the relationship between cloud size or updraft intensity to the environmental wind shear and/or humidity?’ Overall, the ARM TRACER stereo camera deployment demonstrated unique effectiveness in observing a wide range of critical shallow, congestus, and transitioning or time-evolving cloud characteristics. The data sets from these cameras include information on the clouds' horizontal dimensions, elevations, and depths, while also enabling potential products for cloud initiation and dissipation rates, and vertical velocities. Stereo cameras simultaneously inform on cloud life cycle stage and spatial properties such as cloud fractional coverage, which should provide complementary information for ARM users when combined with TRACER cloud radars, lidar, and/or other profiling sensors. Moreover, camera products offer large-eddy simulation (LES),-scale-appropriate cloud coverage, depth, and spatial variability estimates, while opening additional avenues to challenge difficult process questions on cloud updrafts/entrainment and their covariability with environmental controls such as wind shear and humidity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Predicting the Evolution of Shallow Cumulus Clouds With a Lotka‐Volterra Like Model

Abstract In numerical weather prediction and climate models, boundary‐layer clouds are controlled by a wide range of subgrid‐scale processes. However, understanding the nature of these processes and their role in the evolution of the cloud size distribution as a whole has been elusive. To address this issue, we adopt a novel empirical framework from the field of population dynamics to model the evolution of cloud size statistics by using the shallow cumulus properties obtained from a large‐eddy simulation (LES). Our approach involves representing the cloud size distribution and the total cloud area using a revised Lotka‐Volterra model and ridge linear model, respectively. The physical interpretation of the total cloud area and coefficients obtained from the optimization of the models reveals three stages probably interpreted by dominant processes: the formation of new clouds, the growth of single clouds, and a steady state with organized transitions involving the growth and decay of multiple clouds. Furthermore, we showcase the potential of this framework to serve as a component of scale‐aware parameterizations of shallow‐convective clouds in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Systematic Validation of Ensemble Cloud-Process Simulations using Polarimetric Radar Observations and Simulator over the NASA Wallops Flight Facility

The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long-term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the WFF site via a bilateral approach of storm-scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud-process simulations through mixed-phase, light-rain, and deep-convective precipitation cases. The framework consists of creating a domain-shifted ensemble of large-scale forcing datasets, and configuring and performing cloud-process simulations with three different bulk microphysics schemes. Validation uses NASA S-band dual-POLarimetric radar (NPOL) observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble-based validation framework demonstrates that 1) no particular forcing or microphysics scheme outperforms the rest and 2) the skill scores of coarse- and fine-resolution ensemble simulations with different domain-shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse-resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations (LESs) in support of aircraft and satellite instrument development as well as cloud-precipitation-convection parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding the causes of satellite–model discrepancies in aerosol–cloud interactions using near-LES simulations of marine boundary layer clouds

Aerosol–cloud interactions (ACI) remain the largest source of uncertainty in model estimates of anthropogenic radiative forcing, primarily because of deficiencies in representing aerosol–cloud microphysical processes that lead to inconsistent cloud liquid water path (LWP) responses to aerosol perturbations between observations and models. To investigate this discrepancy, we conducted a series of large-eddy-scale simulations driven by realistic meteorology over the eastern North Atlantic, and evaluated LWP susceptibility, precipitation processes, and boundary layer thermodynamics using satellite and ground-based observations. Simulated LWP responses show a strong dependence on cloud state. Non-precipitating thin clouds exhibit a modest LWP decrease with increasing cloud droplet number concentration (N d ), consistent in sign but weaker in magnitude than satellite estimates, reflecting enhanced turbulent mixing and evaporation. The largest model-observation discrepancy occurs in non-precipitating thick clouds, where simulated LWP susceptibilities are strongly positive (+0.32) while observations indicate large negative values (−0.69). This discrepancy stems from excessive precipitation driven by underestimated entrainment, overly active accretion, and overly broad drop-size distributions in polluted conditions. While our high-resolution setup mitigates the excessive drizzling common in coarser models and captures key regime transitions, these biases persist – highlighting that improved parameterizations of cloud-top processes, precipitation, and aerosol effects are needed beyond simply increasing model resolution. Additionally, misrepresented moisture inversions in reanalysis introduce a moist bias in cloud-top relative humidity, further amplifying positive LWP susceptibility. Our results also suggest that large negative N d –LWP relationships in observations may reflect internal cloud processes rather than true ACI effects.

Aerosol-cloud interaction↗
DOE OSTI2023

The Impact of Circumsolar Diffuse Radiation on the Numerical Computation and Forecasting of DNI

Weather Research and Forecasting model with solar extensions (WRF-Solar) uses the Fast All-sky Radiation Model for Solar applications (FARMS) to predict solar radiation from the forecasted atmospheric gases, aerosols, and clouds. The Beer-Bouguer-Lambert law utilized by FARMS as well as other radiative transfer models assumes Direct Normal Irradiance (DNI) is a narrow beam only covering the sun disk. This assumption is interpreted differently in solar observations by pyrheliometers where the circumsolar diffuse radiation is accounted. This study precisely computes circumsolar diffuse radiation in all-sky conditions using a radiative transfer model based on a parameterization of precomputed cloud transmittance and reflectance. The model is implemented in WRF-Solar to provide day-ahead DNI forecast in the north America. The computation and forecasting of DNI is compared with the Beer-Bouguer-Lambert law to understand the impact of circumsolar diffuse radiation.

aerosols↗
DOE OSTI2020

The role of turbulent fluctuations in aerosol activation and cloud formation

Aerosol indirect effects are one of the leading contributors to cloud radiative properties relevant to climate. Aerosol particles become cloud droplets when the ambient relative humidity (saturation ratio) exceeds a critical value, which depends on the particle size and chemical composition. In the traditional formulation of this problem, only average, uniform saturation ratios are considered. Using experiments and theory, we examine the effects of fluctuations, produced by turbulence. Our measurements, from a multiphase, turbulent cloud chamber, show a clear transition from a regime in which the mean saturation ratio dominates to one in which the fluctuations determine cloud properties. The laboratory measurements demonstrate cloud formation in mean-subsaturated conditions (i.e., relative humidity <100%) in the fluctuation-dominant activation regime. The theoretical framework developed to interpret these measurements predicts a transition from a mean- to a fluctuation-dominated regime, based on the relative values of the mean and standard deviation of the environmental saturation ratio and the critical saturation ratio at which aerosol particles activate or become droplets. The theory is similar to the concept of stochastic condensation and can be used in the context of the atmosphere to explore the conditions under which droplet activation is driven by fluctuations as opposed to mean supersaturation. Overall, it provides a basis for future development of cloud droplet activation parameterizations that go beyond the internally homogeneous parcel calculations that have been used in the past.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Formulation of autoconversion and drop spectra shape in shallow cumulus clouds

Two-moment autoconversion parameterizations as compared to accretion parameterizations exhibit significant errors suggesting that additional moments are needed to increase their accuracy. We develop a three-moment autoconversion parameterization using output from an LES model with size-resolved microphysics. Adding the third moment decreases the errors of parameterization and improves precipitation prediction. However, the errors are still significantly larger than errors of accretion rate. An analysis of the cloud drop size distributions (DSDs) in the simulated tropical convective cloud system reveals that most of DSDs have a significant fraction of cloud liquid water content (qc) in the mid-size droplet range (radii from 20 to 40 microns). Our data indicates that more than 30% of DSDs have over half of qc contained in the mid-size range and about 60% of spectra have, at least, one third of qc in this range. Even when the rain/drizzle mode is small (radar reflectivity Z < -10 dBZ), there is a significant number of spectra in which fraction of qc in the mid-size range is as large as 60%. These DSDs are more complex than the frequently used single-mode Gamma or Log-normal distributions, which usually do not have a considerable tail extending to the mid-size range and can be defined by three microphysical moments. The need to define DSDs by more than three moments explains the large errors in the three-moment autoconversion parameterization. The limitation of three-parameter Gamma or Log-normal distributions should be kept in mind when applying them in precipitating shallow Cu clouds.

Kogan, Yefim↗
DOE OSTI2024

Diabatic Eddy Forcing Increases Persistence and Opposes Propagation of the Southern Annular Mode in MERRA-2

Abstract As a dominant mode of jet variability on subseasonal time scales, the Southern Annular Mode (SAM) provides a window into how the atmosphere can produce internal oscillations on longer-than-synoptic time scales. While SAM’s existence can be explained by dry, purely barotropic theories, the time scale for its persistence and propagation is set by a lagged interaction between barotropic and baroclinic mechanisms, making the exact physical mechanisms challenging to identify and to simulate, even in latest generation models. By partitioning the eddy momentum flux convergence in MERRA-2 using an eddy–mean flow interaction framework, we demonstrate that diabatic processes (condensation and radiative heating) are the main contributors to SAM’s persistence in its stationary regime, as well as the key for preventing propagation in this regime. In SAM’s propagating regime, baroclinic and diabatic feedbacks also dominate the eddy–jet feedback. However, propagation is initiated by barotropic shifts in upper-level wave breaking and then sustained by a baroclinic response, leading to a roughly 60-day oscillation period. This barotropic propagation mechanism has been identified in dry, idealized models, but here we show evidence of this mechanism for the first time in reanalysis. The diabatic feedbacks on SAM are consistent with modulation of the storm-track latitude by SAM, altering the emission temperature and cloud cover over individual waves. Therefore, future attempts to improve the SAM time scale in models should focus on the storm-track location, as well as the roles of the cloud and moisture parameterizations. Significance Statement As they circumnavigate the planet, the tropospheric jet streams slowly drift north and south over about 30 days, longer than the normal limit of weather prediction. Understanding the source of this “memory” could improve our knowledge of how the atmosphere organizes itself and our ability to make long-term forecasts. Current theories have identified several possible internal atmospheric interactions responsible for this memory. Yet most of the theories for understanding the jets’ behavior assume that this behavior is only weakly influenced by atmospheric water vapor. We show that this assumption is not enough to understand jet persistence. Instead, clouds and precipitation are more important contributors in reanalysis data than internal “dry” mechanisms to this memory of the Southern Hemisphere jet.

54 ENVIRONMENTAL SCIENCES↗