scholarly journals Contrasting Aerosol Effects on Long‐Wave Cloud Forcing in South East Asia and Amazon Simulated With Community Atmosphere Model Version 5.3

2020 ◽  
Vol 125 (24) ◽  
Author(s):  
Minqi Wang ◽  
Yiran Peng ◽  
Yangang Liu
Keyword(s):  
East Asia ◽  
South East Asia ◽  
Cloud Forcing ◽  
Model Version ◽  
Long Wave ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Aerosol Effects

scholarly journals Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations

2016 ◽  
Vol 121 (8) ◽  
pp. 4162-4176 ◽  
Author(s):  
Jennifer E. Kay ◽  
Line Bourdages ◽  
Nathaniel B. Miller ◽  
Ariel Morrison ◽  
Vineel Yettella ◽  
...  
Keyword(s):  
Model Version ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Lidar Observations

scholarly journals Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

2017 ◽  
Vol 17 (7) ◽  
pp. 4731-4749 ◽  
Author(s):  
Chenglai Wu ◽  
Xiaohong Liu ◽  
Minghui Diao ◽  
Kai Zhang ◽  
Andrew Gettelman ◽  
...  
Keyword(s):  
Nucleation And Growth ◽  
Ice Nucleation ◽  
Ice Crystal ◽  
Ice Clouds ◽  
Model Version ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Ice Water

Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (Ni) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and Ni are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (Dcs), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to Dcs. Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

scholarly journals Modeling dust as component minerals in the Community Atmosphere Model: development of framework and impact on radiative forcing

2014 ◽  
Vol 14 (12) ◽  
pp. 17749-17816 ◽  
Author(s):  
R. A. Scanza ◽  
N. Mahowald ◽  
S. Ghan ◽  
C. S. Zender ◽  
J. F. Kok ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Mineral Soil ◽  
Model Development ◽  
Spatial And Temporal Variability ◽  
Model Version ◽  
Mineral Aerosols ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Aerosol Module

Abstract. The mineralogy of desert dust is important due to its effect on radiation, clouds and biogeochemical cycling of trace nutrients. This study presents the simulation of dust radiative forcing as a function of both mineral composition and size at the global scale using mineral soil maps for estimating emissions. Externally mixed mineral aerosols in the bulk aerosol module in the Community Atmosphere Model version 4 (CAM4) and internally mixed mineral aerosols in the modal aerosol module in the Community Atmosphere Model version 5.1 (CAM5) embedded in the Community Earth System Model version 1.0.5 (CESM) are speciated into common mineral components in place of total dust. The simulations with mineralogy are compared to available observations of mineral atmospheric distribution and deposition along with observations of clear-sky radiative forcing efficiency. Based on these simulations, we estimate the all-sky direct radiative forcing at the top of the atmosphere as +0.05 W m−2 for both CAM4 and CAM5 simulations with mineralogy and compare this both with simulations of dust in release versions of CAM4 and CAM5 (+0.08 and +0.17 W m−2) and of dust with optimized optical properties, wet scavenging and particle size distribution in CAM4 and CAM5, −0.05 and −0.17 W m−2, respectively. The ability to correctly include the mineralogy of dust in climate models is hindered by its spatial and temporal variability as well as insufficient global in-situ observations, incomplete and uncertain source mineralogies and the uncertainties associated with data retrieved from remote sensing methods.

Virtual Field Campaigns on Deep Tropical Convection in Climate Models

2009 ◽  
Vol 22 (2) ◽  
pp. 244-257 ◽  
Author(s):  
Brian Mapes ◽  
Julio Bacmeister ◽  
Marat Khairoutdinov ◽  
Cecile Hannay ◽  
Ming Zhao
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Cloud Model ◽  
Convective Heating ◽  
Cloud Forcing ◽  
Community Atmosphere Model ◽  
Virtual Field ◽  
Atmosphere Model ◽  
Rain Events ◽  
Tropospheric Heating

Abstract High-resolution time–height data over warm tropical oceans are examined, from three global atmosphere models [GFDL’s Atmosphere Model 2 (AM2), NCAR’s Community Atmosphere Model, version 3 (CAM3), and a NASA Global Modeling and Assimilation Office (GMAO) model], field campaign observations, and observation-driven cloud model outputs. The character of rain events is shown in data samples and summarized in lagged regressions versus surface rain rate. The CAM3 humidity and cloud exhibit little vertical coherence among three distinct layers, and its rain events have a short characteristic time, reflecting the convection scheme’s penetrative nature and its closure’s concentrated sensitivity to a thin boundary layer source level. In contrast, AM2 rain variations have much longer time scales as convection scheme plumes whose entrainment gives them tops below 500 hPa interact with humidity variations in that layer. Plumes detraining at model levels above 500 hPa are restricted by cloud work function thresholds, and upper-tropospheric humidity and cloud layers fed by these are detached from the lower levels and are somewhat sporadic. With these discrete entrainment rates and instability thresholds, AM2 also produces some synthetic-looking noise (sharp features in height and time) on top of its slow rain variations. A distinctive feature of the NASA model is a separate anvil scheme, distinct from the main large-scale cloud scheme, fed by relaxed Arakawa–Schubert (RAS) plume ensemble convection (a different implementation than in AM2). Its variability is rich and vertically coherent, and involves a very strong vertical dipole component to its tropospheric heating variations, of both signs (limited-depth convective heating and top-heavy heating in strong deep events with significant nonconvective rain). Grid-scale saturation events occur in all three models, often without nonconvective surface rain, causing relatively rare episodes of large negative top-of-atmosphere cloud forcing. Overall, cloud forcing regressions show a mild net positive forcing by rain-correlated clouds in CAM3 and mild net cooling in the other models, as the residual of large canceling shortwave and longwave contributions.

The role of circulation features on black carbon transport into the Arctic in the Community Atmosphere Model version 5 (CAM5)

2013 ◽  
Vol 118 (10) ◽  
pp. 4657-4669 ◽  
Author(s):  
Po-Lun Ma ◽  
Philip J. Rasch ◽  
Hailong Wang ◽  
Kai Zhang ◽  
Richard C. Easter ◽  
...  
Keyword(s):  
Black Carbon ◽  
The Arctic ◽  
Model Version ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Carbon Transport
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