The Mean Climate of the Community Atmosphere Model (CAM4) in Forced SST and Fully Coupled Experiments

2013 ◽  
Vol 26 (14) ◽  
pp. 5150-5168 ◽  
Author(s):  
Richard B. Neale ◽  
Jadwiga Richter ◽  
Sungsu Park ◽  
Peter H. Lauritzen ◽  
Stephen J. Vavrus ◽  
...  

Abstract The Community Atmosphere Model, version 4 (CAM4), was released as part of the Community Climate System Model, version 4 (CCSM4). The finite volume (FV) dynamical core is now the default because of its superior transport and conservation properties. Deep convection parameterization changes include a dilute plume calculation of convective available potential energy (CAPE) and the introduction of convective momentum transport (CMT). An additional cloud fraction calculation is now performed following macrophysical state updates to provide improved thermodynamic consistency. A freeze-drying modification is further made to the cloud fraction calculation in very dry environments (e.g., the Arctic), where cloud fraction and cloud water values were often inconsistent in CAM3. In CAM4 the FV dynamical core further degrades the excessive trade-wind simulation, but reduces zonal stress errors at higher latitudes. Plume dilution alleviates much of the midtropospheric tropical dry biases and reduces the persistent monsoon precipitation biases over the Arabian Peninsula and the southern Indian Ocean. CMT reduces much of the excessive trade-wind biases in eastern ocean basins. CAM4 shows a global reduction in cloud fraction compared to CAM3, primarily as a result of the freeze-drying and improved cloud fraction equilibrium modifications. Regional climate feature improvements include the propagation of stationary waves from the Pacific into midlatitudes and the seasonal frequency of Northern Hemisphere blocking events. A 1° versus 2° horizontal resolution of the FV dynamical core exhibits superior improvements in regional climate features of precipitation and surface stress. Improvements in the fully coupled mean climate between CAM3 and CAM4 are also more substantial than in forced sea surface temperature (SST) simulations.

2021 ◽  
Author(s):  
Travis O'Brien ◽  
Thomas Burkle ◽  
Michael Krauter ◽  
Thomas Trapp

<p>Midlatitude western coastal regions are recognized as being important for the global energy cycle, marine and terrestrial biodiversity, and regional economies.  These coastal regions exhibit a rich range of weather and climate phenomena, including persistent stratocumulus clouds, sea-breeze circulations, coastally-trapped Kelvin waves, and wind-driven upwelling. During the summer season, when impacts from transient synoptic systems are relatively reduced, the local climate is governed by a complex set of interactions among the atmosphere, land, and ocean.  This complexity has so far inhibited basic understanding of the drivers of western coastal climate, climate variability, and climate change.</p><p>As a way of simplifying the system, we have developed a hierarchical regional climate model experimental framework focused on the western United States. We modify the International Centre for Theoretical Physics RegCM4 to use steady-state initial, lateral, and top-of-model boundary conditions: average July insolation (no diurnal cycle) and average meteorological state (winds, temperature, humidity, surface pressure).  This July <em>Base State</em> simulation rapidly reaches a steady state solution that closely resembles the observed mean climate and the mean climate achieved using RegCM4 in a standard reanalysis-driven configuration.  It is particularly notable that the near-coastal stratocumulus field is spatially similar to the satellite-observed stratocumulus field during arbitrary July days: including gaps in stratocumulus coverage downwind of capes. We run similar <em>Base State</em> simulations for the other calendar months and find that these simulations mimic the annual cycle.  This suggests that the summer coastal stratocumulus field results from the steady-state response of the marine boundary layer to summertime climatological forcing; if true for the real world, this would imply that stratocumulus cloud fraction, within a given month, is temporally modulated by deviations from the summer base state (e.g., transient synoptic disturbances that interrupt the cloud field).  We describe modifications to this simplified experimental framework aimed at understanding the factors that govern stratocumulus cloud fraction and its variability.</p>


2016 ◽  
Vol 121 (8) ◽  
pp. 4162-4176 ◽  
Author(s):  
Jennifer E. Kay ◽  
Line Bourdages ◽  
Nathaniel B. Miller ◽  
Ariel Morrison ◽  
Vineel Yettella ◽  
...  

2014 ◽  
Vol 6 (3) ◽  
pp. 902-922 ◽  
Author(s):  
Katherine J. Evans ◽  
Salil Mahajan ◽  
Marcia Branstetter ◽  
Julie L. McClean ◽  
Julie Caron ◽  
...  

2017 ◽  
Vol 17 (7) ◽  
pp. 4731-4749 ◽  
Author(s):  
Chenglai Wu ◽  
Xiaohong Liu ◽  
Minghui Diao ◽  
Kai Zhang ◽  
Andrew Gettelman ◽  
...  

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.


2017 ◽  
Vol 122 (18) ◽  
pp. 9879-9902 ◽  
Author(s):  
Kai Zhang ◽  
Rong Fu ◽  
Muhammad J. Shaikh ◽  
Steven Ghan ◽  
Minghuai Wang ◽  
...  

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