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NASA NTRS1995

Simulated interannual variability of the Greenland Sea deep water formation and its connection to surface forcing

A fully prognostic Arctic ice-ocean model is used to study the interannual variability of deepwater formation in the Greenland Sea Gyre based on the simulations for the Arctic ice-ocean system for the period 1955 and 1960 - 1985. The model uses monthly climatology for thermodynamic forcing components (such as air temperature and cloudiness), together with constant annual net precipitation and river runoff. The daily wind forcing is derived from analyzed sea level air pressures from the National Center for Atmospheric Research (NCAR). In summary, the model shows that the occurence of deep convection in the Greenland Sea Gyre is controlled by the extensive Fram Strait ice export and/or local wind conditions in the Greenland Sea. In the latter case the weakening of the local wind curl allows the Polar Front to move eastward. The movement of the Polar Front causes adverse ice conditions, often together with much larger than normal ice export from the Arctic, such as in 1968, which can block convection in the gyre. The density difference between upper and lower layers is investigated as an indication of water mass formation through convection, occurring as strong diffusion in the model. The model-simulated density difference between the average top 100 m and deep levels reveals that the period 1960 - 1985 had only a few distinct years with weak stratification, and, especially, the model predicts no deep convection since the nid-1970s. The common factor for the years of the weakest decrease of the model-predicted heat content of the upper 2000 m which can, to a high degree, be explained by local heat loss.

Haekkinen, Sirpa↗

NASA NTRS2005

Retrieve Optically Thick Ice Cloud Microphysical Properties by Using Airborne Dual-Wavelength Radar Measurements

An algorithm to retrieve optically thick ice cloud microphysical property profiles is developed by using the GSFC 9.6 GHz ER-2 Doppler Radar (EDOP) and the 94 GHz Cloud Radar System (CRS) measurements aboard the high-altitude ER-2 aircraft. In situ size distribution and total water content data from the CRYSTAL-FACE field campaign are used for the algorithm development. To reduce uncertainty in calculated radar reflectivity factors (Ze) at these wavelengths, coincident radar measurements and size distribution data are used to guide the selection of mass-length relationships and to deal with the density and non-spherical effects of ice crystals on the Ze calculations. The algorithm is able to retrieve microphysical property profiles of optically thick ice clouds, such as, deep convective and anvil clouds, which are very challenging for single frequency radar and lidar. Examples of retrieved microphysical properties for a deep convective clouds are presented, which show that EDOP and CRS measurements provide rich information to study cloud structure and evolution. Good agreement between IWPs derived from an independent submillimeter-wave radiometer, CoSSIR, and dual-wavelength radar measurements indicates accuracy of the IWC retrieved from the two-frequency radar algorithm.

Wang, Zhien↗

DOE OSTI2025

Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE)

Convective clouds play an important role in Earth’s climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. To improve predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled “Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE).” ESCAPE took place between 30 May and 30 September 2022 in the vicinity of Houston, Texas, because this area frequently experiences isolated deep convection that interacts with the region’s mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling and developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars and one mobile Doppler lidar truck equipped with soundings were deployed from 30 May to 28 June. From 1 August to 30 September 2022, a dual-polarization C-band radar was deployed and operated using a novel, multisensor agile adaptive sampling strategy to track the entire life cycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Analysis of a Simulated Cloud Climatology

Cloudiness simulations with the UCLA/Goddard general circulation model were analyzed by comparison of simulated and radiation statistics with corresponding observations. A description of the model formulation is given by Suarez et al. (1983), and some results are discussed by Randall et al. (1985). The global mean cloudiness is near 50%, in agreement with observations. However, comparison with the satellite observations of Susskind et al. (1983) shows that the model produces too many low level clouds at high latitudes and too few high clouds in the tropics. The simulated outgoing longwave radiation at the top of the atmosphere is too low in middle and high latitudes, and much too high in regions of deep convection. The most serious deficiency of the current model's cloudiness simulation is its gross underprediction of the cirrus cloudiness associated with deep convection.

Randall, D.↗

DOE OSTI2023

QBO deepens MJO convection

Abstract The underlying mechanism that couples the Quasi-Biennial Oscillation (QBO) and the Madden-Julian oscillation (MJO) has remained elusive, challenging our understanding of both phenomena. A popular hypothesis about the QBO-MJO connection is that the vertical extent of MJO convection is strongly modulated by the QBO. However, this hypothesis has not been verified observationally. Here we show that the cloud-top pressure and brightness temperature of deep convection and anvil clouds are systematically lower in the easterly QBO (EQBO) winters than in the westerly QBO (WQBO) winters, indicating that the vertical growth of deep convective systems within MJO envelopes is facilitated by the EQBO mean state. Moreover, the deeper clouds during EQBO winters are more effective at reducing longwave radiation escaping to space and thereby enhancing longwave cloud-radiative feedback within MJO envelopes. Our results provide robust observational evidence of the enhanced MJO activity during EQBO winters by mean state changes induced by the QBO.

Jin, Daeho (ORCID:0000000343894393)↗

NASA NTRS2023

QBO Deepens MJO Convection

The underlying mechanism that couples the Quasi-Biennial Oscillation (QBO) and the Madden-Julian oscillation (MJO) has remained elusive, challenging our understanding of both phenomena. A popular hypothesis about the QBO-MJO connection is that the vertical extent of MJO convection is strongly modulated by the QBO. However, this hypothesis has not been verified observationally. Here we show that the cloud-top pressure and brightness temperature of deep convection and anvil clouds are systematically lower in the easterly QBO (EQBO) winters than in the westerly QBO (WQBO) winters, indicating that the vertical growth of deep convective systems within MJO envelopes is facilitated by the EQBO mean state. Moreover, the deeper clouds during EQBO winters are more effective at reducing longwave radiation escaping to space and thereby enhancing longwave cloud-radiative feedback within MJO envelopes. Our results provide robust observational evidence of the enhanced MJO activity during EQBO winters by mean state changes induced by the QBO.

quasi-biennial oscillation↗

NASA NTRS2012

Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched aboard the Terra spacecraft on December 18, 1999 and Aqua spacecraft on May 4, 2002. A comprehensive set of remote sensing algorithms for the retrieval of cloud physical and optical properties have enabled over twelve years of continuous observations of cloud properties from Terra and over nine years from Aqua. The archived products from these algorithms include 1 km pixel-level (Level-2) and global gridded Level-3 products. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. Results include the latitudinal distribution of cloud optical and radiative properties for both liquid water and ice clouds, as well as latitudinal distributions of cloud top pressure and cloud top temperature. MODIS finds the cloud fraction, as derived by the cloud mask, is nearly identical during the day and night, with only modest diurnal variation. Globally, the cloud fraction derived by the MODIS cloud mask is approx.67%, with somewhat more clouds over land during the afternoon and less clouds over ocean in the afternoon, with very little difference in global cloud cover between Terra and Aqua. Overall, cloud fraction over land is approx.55%, with a distinctive seasonal cycle, whereas the ocean cloudiness is much higher, around 72%, with much reduced seasonal variation. Cloud top pressure and temperature have distinct spatial and temporal patterns, and clearly reflect our understanding of the global cloud distribution. High clouds are especially prevalent over the northern hemisphere continents between 30 and 50 . Aqua and Terra have comparable zonal cloud top pressures, with Aqua having somewhat higher clouds (cloud top pressures lower by 100 hPa) over land due to afternoon deep convection. The coldest cloud tops (colder than 230 K) generally occur over Antarctica and the high clouds in the tropics (ITCZ and the deep convective clouds over the western tropical Pacific and Indian sub-continent).

King, Michael D.↗

NASA NTRS2020

Cascading toward a Kilometer‐scale GCM: Impacts of a Scale‐aware Convection Parameterization in the Goddard Earth Observing System GCM.

The NASA Goddard Earth Observing System global model is evaluated through a cascade of simulations with increasing horizontal resolution. This model employs a non-hydrostatic dynamical core and includes a scale-aware, deep convection parameterization (DPCP). 40-daysimulations at six resolutions (100km to 3km) with unvarying model formulation were produced. At the highest resolution, extreme experiments were carried out; one with no DPCP, and one with its scale-awareness eliminated. Simulated precipitation, radiative balance, and atmospheric thermodynamic and dynamical variables are well reproduced with respect to both observational and reanalysis data. As model resolution increases, the convective precipitation smoothly transitions from being mostly produced by the convection parameterization to the cloud microphysics parameterization. However, contrary to current thought, these extreme cases argue for maintaining, to some extent, the scale-aware deep convection parameterization even at 3kmscale, as the run relying solely on explicit grid-scale production of rainfall performs more poorly at this resolution.

Saulo Freitas↗

NASA NTRS2000

TRMM Observations of Polarization Difference in 85 GHz: Information About Hydrometeors and Rain Rate

Observations made by the Precipitation Radar (PR) and the Microwave Imager (TMI) radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite help us to show the significance of the 85 GHz polarization difference, PD85, measured by TMI. Rain type, convective or stratiform, deduced from the PR allows us to infer that PD85 is generally positive in stratiform rain clouds, while PD85 can be markedly negative in deep convective rain clouds. Furthermore, PD85 increases in a gross manner as stratiform rain rate increases. On the contrary, in a crude fashion PD85 decreases as convective rain rate increases. From the observations of TMI and PR, we find that PD85 is a weak indicator of rain rate. Utilizing information from existing polarimetric radar studies, we infer that negative values of PD85 are likely associated with vertically-oriented small oblate or wet hail that are found in deep convective updrafts.

Prabhakara, C.↗

DOE OSTI2024

A thermal-driven graupel generation process to explain dry-season convective vigor over the Amazon

Large-eddy simulations (LESs) are conducted for each day of the intensive observation periods (IOPs) of the Green Ocean Amazon (GoAmazon) field campaign to characterize the updrafts and microphysics within deep convective cores while contrasting those properties between Amazon wet and dry seasons. Mean Doppler velocity (V dop ) values simulated using LESs are compared with 2-year measurements from a radar wind profiler (RWP) as viewed by statistical composites separated according to wet- and dry-season conditions. In the observed RWP and simulated LES V dop composites, we find more intense low-level updraft velocity, vigorous graupel generation, and intense surface rain during the dry periods compared with the wet periods. To investigate coupled updraft–microphysical processes further, single-day golden cases are selected from the wet and dry periods to conduct detailed cumulus thermal tracking analysis. Tracking analysis reveals that simulated dry-season environments generate more droplet-loaded low-level thermals than wet-season environments. This tendency correlates with seasonal contrasts in buoyancy and vertical moisture advection profiles in large-scale forcing. Employing a normalized time series of mean thermal microphysics, the simulated cumulus thermals appear to be the primary generator of cloud droplets. When subsequent thermals penetrate the ice crystal layer, droplets within the thermals interact with entrained ice crystals, which enhances riming in the thermals. This appears to be a production pathway of graupel/hail particles within simulated deep convective cores. In addition, less-diluted dry-case thermals tend to be elevated higher, and graupel grows further during sedimentation after spilling out from thermals. Therefore, greater concentrations of low-level moist thermals likely result in more graupel/hail production and associated dry-season convective vigor.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Causally‐Informed Deep Learning to Improve Climate Models and Projections

Abstract Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines.

Meteorology & Atmospheric Sciences↗

NASA NTRS1990

Sea breeze: Induced mesoscale systems and severe weather

Sea-breeze-deep convective interactions over the Florida peninsula were investigated using a cloud/mesoscale numerical model. The objective was to gain a better understanding of sea-breeze and deep convective interactions over the Florida peninsula using a high resolution convectively explicit model and to use these results to evaluate convective parameterization schemes. A 3-D numerical investigation of Florida convection was completed. The Kuo and Fritsch-Chappell parameterization schemes are summarized and evaluated.

Nicholls, M. E.↗

NASA NTRS2005

Comparison of Cloud Resolving Model Simulations to Remote Sensing Data

The purpose of this research was to evaluate the ability of a cloud-resolving model (CRM) to simulate the dynamical, radiative, and microphysical properties of deep convective cloud objects identified using CERES (Clouds and the Earth s Radiant Energy System) on board the Tropical Rainfall Measuring Mission (TRMM) satellite platform, for many cases. A deep convective cloud object is a contiguous region that is composed of satellite footprints that fulfill the following selection criteria: 100% cloud fraction, cloud optical depth > 10, and a cloud top height of at least 10 km. Selection criteria have also been formed for different types of boundary-layer clouds, as described in Xu et al. (2005). The purpose of the cloud object approach is to identify specific areas of where the cloud properties simulated by the CRM systematically differ from the observed cloud properties. Where these systematic differences exist, concrete steps can be made to improve the CRM s simulation of an entire class of clouds, rather than by tuning the model to correctly simulate a single case study, as is often done. Additional information regarding detailed approaches and findings are presented.

Randall, David A.↗

NASA NTRS2010

Mechanisms of Diurnal Precipitation over the United States Great Plains: A Cloud-Resolving Model Simulation

The mechanisms of summertime diurnal precipitation in the US Great Plains were examined with the two-dimensional (2D) Goddard Cumulus Ensemble (GCE) cloud-resolving model (CRM). The model was constrained by the observed large-scale background state and surface flux derived from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program s Intensive Observing Period (IOP) data at the Southern Great Plains (SGP). The model, when continuously-forced by realistic surface flux and large-scale advection, simulates reasonably well the temporal evolution of the observed rainfall episodes, particularly for the strongly forced precipitation events. However, the model exhibits a deficiency for the weakly forced events driven by diurnal convection. Additional tests were run with the GCE model in order to discriminate between the mechanisms that determine daytime and nighttime convection. In these tests, the model was constrained with the same repeating diurnal variation in the large-scale advection and/or surface flux. The results indicate that it is primarily the surface heat and moisture flux that is responsible for the development of deep convection in the afternoon, whereas the large-scale upward motion and associated moisture advection play an important role in preconditioning nocturnal convection. In the nighttime, high clouds are continuously built up through their interaction and feedback with long-wave radiation, eventually initiating deep convection from the boundary layer. Without these upper-level destabilization processes, the model tends to produce only daytime convection in response to boundary layer heating. This study suggests that the correct simulation of the diurnal variation in precipitation requires that the free-atmospheric destabilization mechanisms resolved in the CRM simulation must be adequately parameterized in current general circulation models (GCMs) many of which are overly sensitive to the parameterized boundary layer heating.

Lee, M.-I.↗

DOE OSTI2024

Causes of Reduced Climate Sensitivity in E3SM From Version 1 to Version 2

The effective climate sensitivity in the Department of Energy's Energy Exascale Earth System Model (E3SM) has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly due to a weaker positive cloud feedback that leads to a stronger negative radiative feedback. Present-day atmosphere-only experiments with uniform 4 K sea surface temperature warming are used to separate the contributions of individual model modifications to the reduced cloud feedback. We find that the reduced cloud feedback is mostly driven by changes over the tropical marine low cloud regime, mainly related to a new trigger function for the deep convection scheme and modifications in the cloud microphysics scheme. The new trigger function helps weaken the low cloud reduction by increasing the cloud water detrainment at low levels from deep convection under warming. Changes to the formula of autoconversion rate from liquid to rain and an introduced minimum cloud droplet number concentration threshold in cloud microphysical calculations help sustain clouds against dissipation by suppressing precipitation generation with warming. In the midlatitudes, the increased Wegener-Bergeron-Findeisen (WBF) efficiency strongly reduces present-day liquid water and leads to a stronger negative cloud optical depth feedback. The reduced trade cumulus cloud feedback in v2 is closer to estimates from recent observational and large-eddy modeling studies but might not be due to the right physical reasons. The reduced mid-latitude cloud feedback may be more plausible because more realistic present-day mixed-phase clouds are produced through the change in the WBF efficiency.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2008

A Sample of What We Have Learned from A-Train Cloud Measurements

The A-train active sensors CloudSat and CALIPSO provide detailed information about cloud vertical structure. Coarse vertical information can also be obtained from a combination of passive sensors (e.g. cloud liquid water content from AMSR-E, cloud ice properties from MLS and HIRDLS, cloud-top pressure from MODIS and AIRS, and UVNISINear IR absorption and scattering from OMI, MODIS, and POLDER). In addition, the wide swaths of instruments such as MODIS, AIRS, OMI, POLDER, and AMSR-E can be exploited to create estimates of the three-dimensional cloud extent. We will show how data fusion from A-train sensors can be used, e.g., to detect and map the presence of multiple layer/phase clouds. Ultimately, combined cloud information from Atrain instruments will allow for estimates of heating and radiative flux at the surface as well as UV/VIS/Near IR trace-gas absorption at the overpass time on a near-global daily basis. CloudSat has also dramatically improved our interpretation of visible and UV passive measurements in complex cloudy situations such as deep convection and multiple cloud layers. This has led to new approaches for unique and accurate constituent retrievals from A-train instruments. For example, ozone mixing ratios inside tropical deep convective clouds have recently been estimated using the Aura Ozone Monitoring Instrument (OMI). Field campaign data from TC4 provide additional information about the spatial variability and origin of trace-gases inside convective clouds. We will highlight some of the new applications of remote sensing in cloudy conditions that have been enabled by the synergy between the A-train active and passive sensors.

Joiner, Joanna↗

NASA NTRS2023

Lifetimes of Overshooting Convective Events using High-Frequency Gridded Radar Composites

Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-min intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to form tracks. This process is performed for 12 four-day sample windows in the months May–August of 2017–19. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2–3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 1600–1700 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.

Atmosphere↗

DOE OSTI2025

Rapid SACR Observations of Convection at Bankhead National Forest (RAPID) Field Campaign Report

Improving our representation of convective cell processes requires better quantification of convective clouds throughout their entire life cycle. This includes gaining a clearer understanding of the controls on key convective cloud properties, such as updraft intensity, particle size distributions, rainfall rates, and hydrometeor species. Our inability to improve convective cloud process modeling stems, in part, from a limited understanding of convective cell properties, particularly given how rapidly these storms evolve. This lack of detailed observations in the most intense and organized convective storms is especially significant, as large errors remain in representing these clouds, which are critical for severe weather prediction and Earth system model performance. Cloud and precipitation radars are essential tools for studying cloud microphysics and dynamics, particularly in deeper convective clouds. The recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s third Mobile Facility (AMF3) Bankhead National Forest (BNF) deployment provides a unique opportunity to investigate important land–atmosphere interactions, as well as the environmental controls on deep convective cloud processes, in a location favorable for frequent convection. We operate the X/Ka-band Scanning ARM Cloud Radar (X/Ka SACR) using a scan strategy optimized to capture these rapidly evolving clouds and their properties, thereby improving studies of deep convective cloud processes. This effort is strengthened by a complementary and coordinated partnership with ongoing university and multi-agency radar activities collocated in north Alabama—a unique opportunity to examine clouds and precipitation from a lifetime-centric perspective.

54 ENVIRONMENTAL SCIENCES↗