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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model

Instantaneous, coincident, footprint-level satellite observations of cloud properties and radiation taken during austral summer over the Southern Ocean are used to study relationships between clouds and large-scale meteorology. Cloud properties are very sensitive to the strength of vertical motion in the middle-troposphere, and low-cloud properties are sensitive to estimated inversion strength, low-level temperature advection, and sea surface temperature. These relationships are quantified. An index for the meteorological anomalies associated with midlatitude cyclones is presented, and it is used to reveal the sensitivity of clouds to the meteorology within the warm- and cold-sector of cyclones. The observed relationships between clouds and meteorology are compared to those in the Community Atmosphere Model version 5 (CAM5) using satellite simulators. Low-clouds simulated by CAM5 are too few, too bright, and contain too much ice, and low-clouds located in the cold-sector of cyclones are too sensitive to variations in the meteorology. The latter two biases are dramatically reduced when CAM5 is coupled with an updated boundary layer parameterization know as Cloud Layers Unified by Binormals (CLUBB). More generally, this study demonstrates that examining the instantaneous timescale is a powerful approach to understanding the physical processes that control clouds and how they are represented in climate models. Such an evaluation goes beyond the cloud climatology and exposes model bias under various meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Assessing the Sensitivity of the Tropical Cyclone Boundary Layer to the Parameterization of Momentum Flux in the Community Earth System Model

Recent studies have demonstrated that high-resolution (~25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. Importantly, we demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The HTAP_v3.2 emission mosaic: merging regional and global monthly emissions (2000–2020) to support air quality modelling and policies

This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the need of the global and regional atmospheric modelling community of having a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of a high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3.2, an ad-hoc global mosaic of anthropogenic inventories, is an update to the HTAP_v3 global mosaic inventory and has been developed by integrating official inventories over specific areas (North America, Europe, Asia including China, Japan and Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO 2 , NO x , CO, NMVOC, NH 3 , PM 10 , PM 2.5 , Black Carbon (BC), and Organic Carbon (OC), with a spatial resolution of 0.1 × 0.1° and time intervals of months and years covering the period 2000–2020 (https://doi.org/10.5281/zenodo.17086684, Crippa, 2025, https://edgar.jrc.ec.europa.eu/dataset_htap_v32, last access: 27 October 2025). The emissions are further disaggregated to 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3.2 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past two decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policy making, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

Guizzardi, Diego [European Commission, Ispra (Ital↗
DOE OSTI2014

Influence of surface morphology on the immersion mode ice nucleation efficiency of hematite particles

In this paper, the effect of the morphological modification of aerosol particles with respect to heterogeneous ice nucleation is comprehensively investigated for laboratory-generated hematite particles as a model substrate for atmospheric dust particles. The surface-area-scaled ice nucleation efficiencies of monodisperse cubic hematite particles and milled hematite particles were measured with a series of expansion cooling experiments using the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud simulation chamber. Complementary offline characterization of physico-chemical properties of both hematite subsets were also carried out with scanning electron microscopy (SEM), energy dispersive X-ray (EDX) spectroscopy, dynamic light scattering (DLS), and an electro-kinetic particle charge detector to further constrain droplet-freezing measurements of hematite particles. Additionally, an empirical parameterization derived from our laboratory measurements was implemented in the single-column version of the Community Atmospheric Model version 5 (CAM5) to investigate the model sensitivity in simulated ice crystal number concentration on different ice nucleation efficiencies. From an experimental perspective, our results show that the immersion mode ice nucleation efficiency of milled hematite particles is almost an order of magnitude higher at -35.2 °C < T < -33.5 °C than that of the cubic hematite particles, indicating a substantial effect of morphological irregularities on immersion mode freezing. Our modeling results similarly show that the increased droplet-freezing rates of milled hematite particles lead to about one order magnitude higher ice crystal number in the upper troposphere than cubic hematite particles. Finally and overall, our results suggest that the surface irregularities and associated active sites lead to greater ice activation through droplet freezing.

Hiranuma, N.↗
DOE OSTI2022

Modeling Isoprene Emission Response to Drought and Heatwaves Within MEGAN Using Evapotranspiration Data and by Coupling With the Community Land Model

We introduce two new drought stress algorithms designed to simulate isoprene emission with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model. The two approaches include the representation of the impact of drought on isoprene emission with a simple empirical approach for offline MEGAN applications and a more process-based approach for online MEGAN in Community Land Model (CLM) simulations. The two versions differ in their implementation of leaf-temperature impacts of mild drought. For the online version of MEGAN that is coupled to CLM, the impact of drought on leaf temperature is simulated directly and the calculated leaf temperature is considered for the estimation of isoprene emission. For the offline version, we apply an empirical algorithm derived from whole-canopy flux measurements for simulating the impact of drought ranging from mild to severe stage. In addition, the offline approach adopts the ratio ($f$ PET ) of actual evapotranspiration to potential evapotranspiration to quantify the severity of drought instead of using soil moisture. We applied the two algorithms in the CLM-CAM-chem (the Community Atmosphere Model with Chemistry) model to simulate the impact of drought on isoprene emission and found that drought can decrease isoprene emission globally by 11% in 2012. We further compared the formaldehyde (HCHO) vertical column density simulated by CAM-chem to satellite HCHO observations. We found that the proposed drought algorithm can improve the match with the HCHO observations during droughts, but the performance of the drought algorithm is limited by the capacity of the model to capture the severity of drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

On the use of finite difference matrix-vector products in Newton-Krylov solvers for implicit climate dynamics with spectral elements

Efficient solutions of global climate models require effectively handling disparate length and time scales. Implicit solution approaches allow time integration of the physical system with a step size governed by accuracy of the processes of interest rather than by stability of the fastest time scales present. Implicit approaches, however, require the solution of nonlinear systems within each time step. Usually, a Newton's method is applied to solve these systems. Each iteration of the Newton's method, in turn, requires the solution of a linear model of the nonlinear system. This model employs the Jacobian of the problem-defining nonlinear residual, but this Jacobian can be costly to form. If a Krylov linear solver is used for the solution of the linear system, the action of the Jacobian matrix on a given vector is required. In the case of spectral element methods, the Jacobian is not calculated but only implemented through matrix-vector products. The matrix-vector multiply can also be approximated by a finite difference approximation which may introduce inaccuracy in the overall nonlinear solver. In this paper, we review the advantages and disadvantages of finite difference approximations of these matrix-vector products for climate dynamics within the spectral element shallow water dynamical core of the Community Atmosphere Model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Final scientific report for DOE award title: Improving the Representation of Ice Sedimentation Rates in Global Climate Models

It is well known that cirrus clouds play a major role in regulating the earth’s climate, but the details of how this works are just beginning to be understood. This project targeted the main property of cirrus clouds that influence climate processes; the ice fall speed. That is, this project improves the representation of the mass-weighted ice particle fall velocity, V m , in climate models, used to predict future climate on global and regional scales. Prior to 2007, the dominant sizes of ice particles in cirrus clouds were poorly understood, making it virtually impossible to predict how cirrus clouds interact with sunlight and thermal radiation. Due to several studies investigating the performance of optical probes used to measure the ice particle size distribution (PSD), as well as the remote sensing results from our last ARM project, it is now well established that the anomalously high concentrations of small ice crystals often reported prior to 2007 were measurement artifacts. Advances in the design and data processing of optical probes have greatly reduced these ice artifacts that resulted from the shattering of ice particles on the probe tips and/or inlet tube, and PSD measurements from one of these improved probes (the 2-dimensional Stereo or 2D-S probe) are utilized in this project to parameterize V m for climate models. Our original plan in the proposal was to parameterize the ice PSD (in terms of temperature and ice water content) and ice particle mass and projected area (in terms of mass- and area-dimensional power laws or m-D/A-D expressions) since these are the microphysical properties that determine V m , and then proceed to calculate V m from these parameterized properties. But the 2D-S probe directly measures ice particle projected area and indirectly estimates ice particle mass for each size bin. It soon became apparent that the original plan would introduce more uncertainty in the V m calculations than simply using the 2D-S measurements to directly calculate V m . By calculating V m directly from the measured PSD, ice particle projected area and estimated mass, more accurate estimates of V m are obtained. These V m values were then parameterized for climate models by relating them to (1) sampling temperature and ice water content (IWC) and (2) the effective diameter (D e ) of the ice PSD. Parameterization (1) is appropriate for climate models having single-moment microphysical schemes whereas (2) is appropriate for double-moment microphysical schemes and yields more accurate V m estimates. These parameterizations were developed for tropical cirrus clouds, Arctic cirrus, mid-latitude synoptic cirrus and mid-latitude anvil cirrus clouds based on field campaigns in these regions. An important but unexpected result of this research was the discovery of microphysical evidence indicating the mechanisms by which ice crystals are produced in cirrus clouds. This evidence, derived from PSD measurements, indicates that homogeneous freezing ice nucleation dominates in mid-latitude synoptic cirrus clouds, whereas heterogeneous ice nucleation processes dominate in mid-latitude anvil cirrus. Based on these findings, D e was parameterized in terms of temperature (T) for conditions dominated by (1) homo- and (2) heterogeneous ice nucleation. From this, an experiment was designed for global climate models (GCMs). The net radiative forcing from cirrus clouds may be affected by the means ice is produced (homo- or heterogeneously), and this net forcing contributes to climate sensitivity (i.e. the change in mean global surface temperature resulting from a doubling of CO 2 ). The objective of this GCM experiment was to determine how a change in ice nucleation mode affects the predicted global radiation balance. In the first simulation (Run 1), the D e -T relationship for homogeneous nucleation is used at all latitudes, while in the second simulation (Run 2), the D e -T relationship for heterogeneous nucleation is used at all latitudes. For both runs, V m is calculated from D e . Two GCMs were used; the Community Atmosphere Model version 5 (CAM5) and a European GCM known as ECHAM5 (thanks to our European colleagues who collaborated with us). Similar results were obtained from both GCMs in the Northern Hemisphere mid-latitudes, with a net cooling of ~ 1.0 W m -2 due to heterogeneous nucleation, relative to Run 1. The mean global net cooling was 2.4 W m -2 for the ECHAM5 GCM while CAM5 produced a mean global net cooling of about 0.8 W m -2 . This dependence of the radiation balance on nucleation mode is substantial when one considers the direct radiative forcing from a CO 2 doubling is 4 W m -2 . The differences between GCMs in mean global net cooling estimates may demonstrate a need for improving the representation of cirrus clouds in GCMs, including the coupling between microphysical and radiative properties. Unfortunately, after completing this GCM experiment, we learned from the company that provided the 2D-S microphysical data that the data was corrupted due to a computer program coding problem. Therefore the microphysical data had to be reprocessed and reanalyzed, and the GCM experiments were redone under our current ASR project but using an improved experimental design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

On the Use of Finite Difference Matrix-Vector Products in Newton-Krylov Solvers for Implicit Climate Dynamics with Spectral Elements

Efficient solution of global climate models requires effectively handling disparate length and time scales. Implicit solution approaches allow time integration of the physical system with a time step dictated by accuracy of the processes of interest rather than by stability governed by the fastest of the time scales present. Implicit approaches, however, require the solution of nonlinear systems within each time step. Usually, a Newton s method is applied for these systems. Each iteration of the Newton s method, in turn, requires the solution of a linear model of the nonlinear system. This model employs the Jacobian of the problem-defining nonlinear residual, but this Jacobian can be costly to form. If a Krylov linear solver is used for the solution of the linear system, the action of the Jacobian matrix on a given vector is required. In the case of spectral element methods, the Jacobian is not calculated but only implemented through matrix-vector products. The matrix-vector multiply can also be approximated by a finite-difference which may show a loss of accuracy in the overall nonlinear solver. In this paper, we review the advantages and disadvantages of finite-difference approximations of these matrix-vector products for climate dynamics within the spectral-element based shallow-water dynamical-core of the Community Atmosphere Model (CAM).

climate modeling↗
DOE OSTI2015

Quantifying sensitivities of ice crystal number and sources of ice crystal number variability in CAM 5.1 using the adjoint of a physically based cirrus formation parameterization

Abstract We present the adjoint of a cirrus formation parameterization that computes the sensitivity of ice crystal number concentration to updraft velocity, aerosol, and ice deposition coefficient. The adjoint is driven by simulations from the National Center for Atmospheric Research Community Atmosphere Model version 5.1 CAM 5.1 to understand the sensitivity of formed ice crystal number concentration to 13 variables and quantify which contribute to its variability. Sensitivities of formed ice crystal number concentration to updraft velocity, sulfate number, and is sufficient but sulfate number concentration is low, indicating a sulfate‐limited regime. Outside of the tropics, competition between homogeneous and heterogeneous nucleation may shift annually averaged sensitivities to higher magnitudes, when infrequent strong updrafts shift crystal production away from purely heterogeneous nucleation. Outside the tropics, updraft velocity is responsible for approximately 52.70% of the ice crystal number variability. In the tropics, sulfate number concentration and updraft jointly control variability in formed crystal number concentration. Insoluble aerosol species play a secondary, but still important, role in influencing the variability in crystal concentrations, with coarse‐mode dust being the largest contributor at nearly 50% in certain regions. On a global scale, more than 95% of the temporal variability in crystal number concentration can be described by temperature, updraft velocity, sulfate number, and coarse‐mode dust number concentration.

Sheyko, B. A.↗
DOE OSTI2015

The parametric sensitivity of CAM5's MJO

Abstract We systematically explore the ability of the Community Atmospheric Model version 5 (CAM5) to simulate the Madden‐Julian Oscillation (MJO), through an analysis of MJO metrics calculated from a 1100‐member perturbed parameter ensemble of 5 year simulations with observed sea surface temperatures. Parameters from the deep convection scheme make the greatest contribution to the variance in MJO simulation quality with a much smaller contribution from parameters in the large‐scale cloud, shallow convection, and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller autoconversion of cloud to rainwater and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low‐level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5's parameterization suite are needed in order to simultaneously well simulate the MJO and mean‐state climate.

Boyle, J. S.↗
DOE OSTI2014

Precipitation characteristics of CAM5 physics at mesoscale resolution during MC3E and the impact of convective timescale choice

Abstract The physics suite of the Community Atmosphere Model version 5 (CAM5) has recently been implemented in the Weather Research and Forecasting (WRF) model to explore the behavior of the parameterization suite at high resolution and within the controlled setting of a limited area model. The initial paper documenting this capability characterized the behavior for a northern high‐latitude region. This paper characterizes the precipitation characteristics for continental, midlatitude, springtime conditions during the Midlatitude Continental Convective Clouds Experiment (MC3E) over the central United States. This period exhibited a range of convective conditions from those driven strongly by large‐scale synoptic regimes to more locally driven convection. The study focuses on the precipitation behavior at 32 km grid spacing to better anticipate how the physics will behave in a global model when used at similar grid spacing in the coming years. Importantly, one change to the Zhang‐McFarlane deep convective parameterization when implemented in WRF was to make the convective timescale parameter an explicit function of grid spacing. This study examines the sensitivity of the precipitation to the default value of the convective timescale in WRF, which is 600 s for 32 km grid spacing, to the value of 3600 s used for 2° grid spacing in CAM5. For comparison, a 1200 s and an infinite convective timescale are also used. The results show that the 600 s timescale gives the most accurate precipitation amount over the central United States. However, this setting has the worst precipitation diurnal cycle, with the convection too tightly linked to the daytime surface heating. Longer timescales greatly improve the diurnal cycle but result in less precipitation and produce a low bias. An analysis of rain rates shows the accurate precipitation amount with the shorter timescale is assembled from an over abundance of drizzle combined with too few heavy rain events. With longer timescales, one can improve the frequency distribution, particularly for the extreme rain rates. Ultimately, without changing other aspects of the physics, one must decide between accurate diurnal timing and rain amount when choosing an appropriate convective timescale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Parametric behaviors of CLUBB in simulations of low clouds in the C ommunity A tmosphere M odel ( CAM )

Abstract In this study, we investigate the sensitivity of simulated low clouds to 14 selected tunable parameters of Cloud Layers Unified By Binormals (CLUBB), a higher‐order closure (HOC) scheme, and four parameters of the Zhang‐McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 (CAM5). A Quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is applied to study the responses of simulated cloud fields to tunable parameters. Our results show that the variance in simulated low‐cloud properties (cloud fraction and liquid water path) can be explained by the selected tunable parameters in two different ways: macrophysics itself and its interaction with microphysics. First, the parameters related to dynamic and thermodynamic turbulent structure and double Gaussian closure are found to be the most influential parameters for simulating low clouds. The spatial distributions of the parameter contributions show clear cloud‐regime dependence. Second, because of the coupling between cloud macrophysics and cloud microphysics, the coefficient of the dissipation term in the total water variance equation is influential. This parameter affects the variance of in‐cloud cloud water, which further influences microphysical process rates, such as autoconversion, and eventually low‐cloud fraction. This study improves understanding of HOC behavior associated with parameter uncertainties and provides valuable insights for the interaction of macrophysics and microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Low-cloud characteristics over the tropical western Pacific from ARM observations and CAM5 simulations

Here, this study evaluates the ability of the Community Atmospheric Model version 5 (CAM5) to reproduce low clouds observed by the Atmospheric Radiation Measurement (ARM) cloud radar at Manus Island of the tropical western Pacific during the Years of Tropical Convection. Here low clouds are defined as clouds with their tops below the freezing level and bases within the boundary layer. Low-cloud statistics in CAM5 simulations and ARM observations are compared in terms of their general occurrence, mean vertical profiles, fraction of precipitating versus nonprecipitating events, diurnal cycle, and monthly time series. Other types of clouds are included to put the comparison in a broader context. The comparison shows that the model overproduces total clouds and their precipitation fraction but underestimates low clouds in general. The model, however, produces excessive low clouds in a thin layer between 954 and 930 hPa, which coincides with excessive humidity near the top of the mixed layer. This suggests that the erroneously excessive low clouds stem from parameterization of both cloud and turbulence mixing. The model also fails to produce the observed diurnal cycle in low clouds, not exclusively due to the model coarse grid spacing that does not resolve Manus Island. Lastly, this study demonstrates the utility of ARM long-term cloud observations in the tropical western Pacific in verifying low clouds simulated by global climate models, illustrates issues of using ARM observations in model validation, and provides an example of severe model biases in producing observed low clouds in the tropical western Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Distinct effects of anthropogenic aerosols on the East Asian summer monsoon between multidecadal strong and weak monsoon stages

Industrial emissions of anthropogenic aerosols over East Asia have greatly increased in recent decades, and so the interactions between atmospheric aerosols and the East Asian summer monsoon (EASM) have attracted enormous attention. In order to further understand the aerosol-EASM interaction, we investigate the impacts of anthropogenic aerosols on the EASM during the multidecadal strong (1950–1977) and weak (1978–2000) EASM stages using the Community Atmospheric Model 5.1. Numerical experiments are conducted for the whole period, including the two different EASM stages, with present day (PD, year 2000) and preindustrial (PI, year 1850) aerosol emissions, as well as the observed time-varying aerosol emissions. A comparison of the results from PD and PI shows that, with the increase in anthropogenic aerosols, the large-scale EASM intensity is weakened to a greater degree (-9.8%) during the weak EASM stage compared with the strong EASM stage (-4.4%). The increased anthropogenic aerosols also result in a significant reduction in precipitation over North China during the weak EASM stage, as opposed to a statistically insignificant change during the strong EASM stage. Because of greater aerosol loading and the larger sensitivity of the climate system during weak EASM stages, the aerosol effects are more significant during these EASM stages. Moreover, these results suggest that anthropogenic aerosols from the same aerosol emissions have distinct effects on the EASM and the associated precipitation between the multidecadal weak and strong EASM stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A mechanism-denial study on the Madden-Julian Oscillation with reduced interference from mean state changes

In this paper, mechanism-denial experiments using Superparameterized Community Atmosphere Model are conducted to investigate the importance of extratropical and circumnavigating waves, wind-evaporation feedback, and radiative-convective feedback to the Madden-Julian Oscillation (MJO). A common issue with mechanism-denial studies is the interference from mean state changes when processes are turned off in the model. Here time-invariant forcing and nudging on effective timescales longer than the intraseasonal timescale are implemented to maintain the mean state. The MJO activity remains largely unchanged with suppressed extratropical and circumnavigating waves when the mean state is maintained to be close to that of the control run, suggesting that excitation of MJO by extratropical and circumnavigating waves is not necessary for the existence of MJO in this model. Finally, it is also shown that the wind-evaporation feedback slows down eastward propagation of the MJO, and the radiative-convective feedback amplifies the MJO.

58 GEOSCIENCES↗
DOE OSTI2017

Observational evidence against strongly stabilizing tropical cloud feedbacks

In this paper, we present a method to attribute cloud radiative feedbacks to convective processes, using subcloud layer buoyancy as a diagnostic of stable and deep convective regimes. Applying this approach to tropical remote sensing measurements over years 2000–2016 shows that an inferred negative short-term cloud feedback from deep convection was nearly offset by a positive cloud feedback from stable regimes. The net cloud feedback was within statistical uncertainty of the National Center for Atmospheric Research Community Atmosphere Model (CAM5) with historical forcings, with discrepancies in the partitioning of the cloud feedback into convective regimes. Compensation between high-cloud responses to tropics-wide warming in stable and unstable regimes resulted in smaller net changes in high-cloud fraction with warming. In addition, deep convection and associated high clouds set in at warmer temperatures in response to warming, as a consequence of nearly invariant subcloud buoyancy. Lastly, this invariance further constrained the magnitude of cloud radiative feedbacks and is consistent with climate model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Assessment of marine boundary layer cloud simulations in the CAM with CLUBB and updated microphysics scheme based on ARM observations from the Azores

To assess marine boundary layer (MBL) cloud simulations in three versions of the Community Atmosphere Model (CAM), three sets of short-term global hindcasts are performed and compared to Atmospheric Radiation Measurement Program (ARM) observations on Graciosa Island in the Azores from June 2009 to December 2010. Here, the three versions consist of CAM5.3 with default schemes (CAM5.3), CAM5.3 with Cloud Layers Unified By Binormals (CLUBB-MG1), and CAM5.3 with CLUBB and updated microphysics scheme (CLUBB-MG2). Our results show that relative to CAM5.3 default schemes, simulations with CLUBB better represent MBL cloud base height, the height of the major cloud layer, and the daily cloud cover variability. CLUBB also better simulates the relationship of cloud fraction to cloud liquid water path (LWP) most likely due to CLUBB's consistent treatment of these variables through a probability distribution function (PDF) approach. Subcloud evaporation of precipitation is substantially enhanced in simulations with CLUBB-MG2 and is more realistic based on the limited observational estimate. Despite these improvements, all model versions underestimate MBL cloud cover. CLUBB-MG2 reduces biases in in-cloud LWP (clouds are not too bright) but there are still too few of MBL clouds due to an underestimate in the frequency of overcast scenes. Thus, combining CLUBB with MG2 scheme better simulates MBL cloud processes, but because biases remain in MBL cloud cover CLUBB-MG2 does not improve the simulation of the surface shortwave cloud radiative effect (CRE SW ).

54 ENVIRONMENTAL SCIENCES↗