scholarly journals The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3

2011 ◽  
Vol 24 (13) ◽  
pp. 3484-3519 ◽  
Author(s):  
Leo J. Donner ◽  
Bruce L. Wyman ◽  
Richard S. Hemler ◽  
Larry W. Horowitz ◽  
Yi Ming ◽  
...  

Abstract The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.

2019 ◽  
Vol 46 (16) ◽  
pp. 9920-9929 ◽  
Author(s):  
Ran Feng ◽  
Bette L. Otto‐Bliesner ◽  
Yangyang Xu ◽  
Esther Brady ◽  
Tamara Fletcher ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Fangqun Yu ◽  
Johannes Quaas

AbstractSatellite-based estimates of radiative forcing by aerosol–cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from −0.38 to −0.59 W m−2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to −1.09 W m−2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.


2019 ◽  
Vol 59 ◽  
pp. 11.1-11.72 ◽  
Author(s):  
Sonia M. Kreidenweis ◽  
Markus Petters ◽  
Ulrike Lohmann

Abstract This chapter reviews the history of the discovery of cloud nuclei and their impacts on cloud microphysics and the climate system. Pioneers including John Aitken, Sir John Mason, Hilding Köhler, Christian Junge, Sean Twomey, and Kenneth Whitby laid the foundations of the field. Through their contributions and those of many others, rapid progress has been made in the last 100 years in understanding the sources, evolution, and composition of the atmospheric aerosol, the interactions of particles with atmospheric water vapor, and cloud microphysical processes. Major breakthroughs in measurement capabilities and in theoretical understanding have elucidated the characteristics of cloud condensation nuclei and ice nucleating particles and the role these play in shaping cloud microphysical properties and the formation of precipitation. Despite these advances, not all their impacts on cloud formation and evolution have been resolved. The resulting radiative forcing on the climate system due to aerosol–cloud interactions remains an unacceptably large uncertainty in future climate projections. Process-level understanding of aerosol–cloud interactions remains insufficient to support technological mitigation strategies such as intentional weather modification or geoengineering to accelerating Earth-system-wide changes in temperature and weather patterns.


2019 ◽  
Vol 116 (39) ◽  
pp. 19311-19317 ◽  
Author(s):  
Martí Galí ◽  
Emmanuel Devred ◽  
Marcel Babin ◽  
Maurice Levasseur

Dimethylsulfide (DMS), a gas produced by marine microbial food webs, promotes aerosol formation in pristine atmospheres, altering cloud radiative forcing and precipitation. Recent studies suggest that DMS controls aerosol formation in the summertime Arctic atmosphere and call for an assessment of pan-Arctic DMS emission (EDMS) in a context of dramatic ecosystem changes. Using a remote sensing algorithm, we show that summertime EDMS from ice-free waters increased at a mean rate of 13.3 ± 6.7 Gg S decade−1 (∼33% decade−1) north of 70°N between 1998 and 2016. This trend, mostly explained by the reduction in sea-ice extent, is consistent with independent atmospheric measurements showing an increasing trend of methane sulfonic acid, a DMS oxidation product. Extrapolation to an ice-free Arctic summer could imply a 2.4-fold (±1.2) increase in EDMS compared to present emission. However, unexpected regime shifts in Arctic geo- and ecosystems could result in future EDMS departure from the predicted range. Superimposed on the positive trend, EDMS shows substantial interannual changes and nonmonotonic multiyear trends, reflecting the interplay between physical forcing, ice retreat patterns, and phytoplankton productivity. Our results provide key constraints to determine whether increasing marine sulfur emissions, and resulting aerosol–cloud interactions, will moderate or accelerate Arctic warming in the context of sea-ice retreat and increasing low-level cloud cover.


2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Armin Sorooshian ◽  
Hanh T. Duong

Two case studies are discussed that evaluate the effect of ocean emissions on aerosol-cloud interactions. A review of the first case study from the eastern Pacific Ocean shows that simultaneous aircraft and space-borne observations are valuable in detecting links between ocean biota emissions and marine aerosols, but that the effect of the former on cloud microphysics is less clear owing to interference from background anthropogenic pollution and the difficulty with field experiments in obtaining a wide range of aerosol conditions to robustly quantify ocean effects on aerosol-cloud interactions. To address these limitations, a second case was investigated using remote sensing data over the less polluted Southern Ocean region. The results indicate that cloud drop size is reduced more for a fixed increase in aerosol particles during periods of higher ocean chlorophyll A. Potential biases in the results owing to statistical issues in the data analysis are discussed.


2020 ◽  
Author(s):  
Calvin Howes ◽  
Pablo Saide ◽  
Paquita Zuidema ◽  
Jianhao Zhang ◽  
Michael Diamond ◽  
...  

2009 ◽  
Vol 137 (8) ◽  
pp. 2547-2558 ◽  
Author(s):  
Hailong Wang ◽  
William C. Skamarock ◽  
Graham Feingold

Abstract In the Advanced Research Weather Research and Forecasting Model (ARW), versions 3.0 and earlier, advection of scalars was performed using the Runge–Kutta time-integration scheme with an option of using a positive-definite (PD) flux limiter. Large-eddy simulations of aerosol–cloud interactions using the ARW model are performed to evaluate the advection schemes. The basic Runge–Kutta scheme alone produces spurious oscillations and negative values in scalar mixing ratios because of numerical dispersion errors. The PD flux limiter assures positive definiteness but retains the oscillations with an amplification of local maxima by up to 20% in the tests. These numerical dispersion errors contaminate active scalars directly through the advection process and indirectly through physical and dynamical feedbacks, leading to a misrepresentation of cloud physical and dynamical processes. A monotonic flux limiter is introduced to correct the generally accurate but dispersive solutions given by high-order Runge–Kutta scheme. The monotonic limiter effectively minimizes the dispersion errors with little significant enhancement of numerical diffusion errors. The improvement in scalar advection using the monotonic limiter is discussed in the context of how the different advection schemes impact the quantification of aerosol–cloud interactions. The PD limiter results in 20% (10%) fewer cloud droplets and 22% (5%) smaller cloud albedo than the monotonic limiter under clean (polluted) conditions. Underprediction of cloud droplet number concentration by the PD limiter tends to trigger the early formation of precipitation in the clean case, leading to a potentially large impact on cloud albedo change.


2021 ◽  
Vol 13 (15) ◽  
pp. 2982
Author(s):  
Richard Dworak ◽  
Yinghui Liu ◽  
Jeffrey Key ◽  
Walter N. Meier

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.


2012 ◽  
Vol 25 (5) ◽  
pp. 1431-1452 ◽  
Author(s):  
Alexandra Jahn ◽  
Kara Sterling ◽  
Marika M. Holland ◽  
Jennifer E. Kay ◽  
James A. Maslanik ◽  
...  

To establish how well the new Community Climate System Model, version 4 (CCSM4) simulates the properties of the Arctic sea ice and ocean, results from six CCSM4 twentieth-century ensemble simulations are compared here with the available data. It is found that the CCSM4 simulations capture most of the important climatological features of the Arctic sea ice and ocean state well, among them the sea ice thickness distribution, fraction of multiyear sea ice, and sea ice edge. The strongest bias exists in the simulated spring-to-fall sea ice motion field, the location of the Beaufort Gyre, and the temperature of the deep Arctic Ocean (below 250 m), which are caused by deficiencies in the simulation of the Arctic sea level pressure field and the lack of deep-water formation on the Arctic shelves. The observed decrease in the sea ice extent and the multiyear ice cover is well captured by the CCSM4. It is important to note, however, that the temporal evolution of the simulated Arctic sea ice cover over the satellite era is strongly influenced by internal variability. For example, while one ensemble member shows an even larger decrease in the sea ice extent over 1981–2005 than that observed, two ensemble members show no statistically significant trend over the same period. It is therefore important to compare the observed sea ice extent trend not just with the ensemble mean or a multimodel ensemble mean, but also with individual ensemble members, because of the strong imprint of internal variability on these relatively short trends.


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