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A. Gettelman
,
W. J. Randel
,
S. Massie
,
F. Wu
,
W. G. Read
, and
J. M. Russell III

Abstract

The interannual variability of the tropical tropopause region between 14 and 18 km is examined using observations of convection, winds, and tropopause temperatures from reanalyses and water vapor from satellites. This variability is compared to a simulation using the Community Climate Model version 3 (CCM3) general circulation model forced by observed sea surface temperatures. A coherent picture of the effect of the El Niño–Southern Oscillation (ENSO) on the tropopause region is presented in the NCEP–NCAR reanalyses and CCM3. ENSO modifies convection in the Tropics, and the temperature and circulation of the tropical tropopause region, in agreement with idealized models of tropical heating. CCM3 reproduces most details of these changes, but not the zonal mean temperature variations present in the analysis fields, which are not related to ENSO. ENSO also forces significant changes in observed and simulated water vapor fields. In the upper troposphere water vapor is at maximum near convection, while in the tropopause region water vapor is at minimum in the regions of convection and surrounding it. Convection, cirrus clouds, temperatures, and transport are all linked to describe the water vapor distribution and highlight the role of transport in the tropopause region.

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P. R. Field
,
A. Gettelman
,
R. B. Neale
,
R. Wood
,
P. J. Rasch
, and
H. Morrison

Abstract

Identical composite analysis of midlatitude cyclones over oceanic regions has been carried out on both output from the NCAR Community Atmosphere Model, version 3 (CAM3) and multisensor satellite data. By focusing on mean fields associated with a single phenomenon, the ability of the CAM3 to reproduce realistic midlatitude cyclones is critically appraised. A number of perturbations to the control model were tested against observations, including a candidate new microphysics package for the CAM. The new microphysics removes the temperature-dependent phase determination of the old scheme and introduces representations of microphysical processes to convert from one phase to another and from cloud to precipitation species. By subsampling composite cyclones based on systemwide mean strength (mean wind speed) and systemwide mean moisture the authors believe they are able to make meaningful like-with-like comparisons between observations and model output. All variations of the CAM tested overestimate the optical thickness of high-topped clouds in regions of precipitation. Over a system as a whole, the model can both over- and underestimate total high-topped cloud amounts. However, systemwide mean rainfall rates and composite structure appear to be in broad agreement with satellite estimates. When cyclone strength is taken into account, changes in moisture and rainfall rates from both satellite-derived observations and model output as a function of changes in sea surface temperature are in accordance with the Clausius–Clapeyron equation. The authors find that the proposed new microphysics package shows improvement to composite liquid water path fields and cloud amounts.

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Mikael K. Witte
,
Hugh Morrison
,
Jørgen B. Jensen
,
Aaron Bansemer
, and
Andrew Gettelman

Abstract

Microphysics parameterizations in large-scale models often account for subgrid variability in the calculation of process rates by integrating over assumed subgrid distributions of the input variables. The variances and covariances that define distribution width may be specified or diagnosed. The correlation ρ of cloud and rain mass mixing ratio/liquid water content (LWC) is a key input for accurate prediction of the accretion rate and a constant value is typically assumed. In this study, high-frequency aircraft measurements with a spatial resolution of ≈22 cm are used to evaluate the scaling behavior of cloud and rain LWC (q c and q r , respectively) and to demonstrate how and why covariability varies with length scale . It is shown that power spectral densities of both q c and q r exhibit scale invariance across a wide range of scales (2.04–142 m for q c ; 33–1.45 × 104 m for q r ). Because the cloud–rain cospectrum is also scale invariant, ρ is therefore expected to vary with . Direct calculation of ρ shows that it generally increases with , but there is significant variability in the ρ relationship that primarily depends on cloud drop number concentration N and cloud cellular organization, suggesting that ρ may also vary with cloud regime. A parameterization of ρ as a function of and N is developed from aircraft observations and implications for diagnosis of ρ from limited-area model output are also discussed.

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Norman G. Loeb
,
Fred G. Rose
,
Seiji Kato
,
David A. Rutan
,
Wenying Su
,
Hailan Wang
,
David R. Doelling
,
William L. Smith
, and
Andrew Gettelman

Abstract

A new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.

Open access
Adrian A. Hill
,
Zachary J. Lebo
,
Miroslaw Andrejczuk
,
Sylwester Arabas
,
Piotr Dziekan
,
Paul Field
,
Andrew Gettelman
,
Fabian Hoffmann
,
Hanna Pawlowska
,
Ryo Onishi
, and
Benoit Vié

Abstract

The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. In the absence of sedimentation and collision–coalescence, the drop size distributions (DSDs) from the LCMs exhibit similar evolution with expected physical behaviors and good interscheme agreement, with the volume mean diameter (D vol) from the LCMs within 1%–5% of each other. In contrast, the bin schemes exhibit nonphysical broadening with condensational growth. These results further strengthen the case that LCMs are an appropriate numerical benchmark for DSD evolution under condensational growth. When precipitation processes are included, however, the simulated liquid water path, precipitation rates, and response to modified cloud drop/aerosol number concentrations from the LCMs vary substantially, while the bin and bulk schemes are relatively more consistent with each other. The lack of consistency in the LCM results stems from both the collision–coalescence process and the sedimentation process, limiting their application as a numerical benchmark for precipitation processes. Reassuringly, however, precipitation from bulk schemes, which are the basis for cloud microphysics in weather and climate prediction, is within the spread of precipitation from the detailed schemes (LCMs and bin). Overall, this intercomparison identifies the need for focused effort on the comparison of collision–coalescence methods and sedimentation in detailed microphysics schemes, especially LCMs.

Free access
Seok-Woo Son
,
Lorenzo M. Polvani
,
Darryn W. Waugh
,
Thomas Birner
,
Hideharu Akiyoshi
,
Rolando R. Garcia
,
Andrew Gettelman
,
David A. Plummer
, and
Eugene Rozanov

Abstract

The evolution of the tropopause in the past, present, and future climate is examined by analyzing a set of long-term integrations with stratosphere-resolving chemistry climate models (CCMs). These CCMs have high vertical resolution near the tropopause, a model top located in the mesosphere or above, and, most important, fully interactive stratospheric chemistry. Using such CCM integrations, it is found that the tropopause pressure (height) will continue to decrease (increase) in the future, but with a trend weaker than that in the recent past. The reduction in the future tropopause trend is shown to be directly associated with stratospheric ozone recovery. A significant ozone recovery occurs in the Southern Hemisphere lower stratosphere of the CCMs, and this leads to a relative warming there that reduces the tropopause trend in the twenty-first century.

The future tropopause trends predicted by the CCMs are considerably smaller than those predicted by the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) models, especially in the southern high latitudes. This difference persists even when the CCMs are compared with the subset of the AR4 model integrations for which stratospheric ozone recovery was prescribed. These results suggest that a realistic representation of the stratospheric processes might be important for a reliable estimate of tropopause trends. The implications of these finding for the Southern Hemisphere climate change are also discussed.

Full access
Christopher S. Bretherton
,
Isabel L. McCoy
,
Johannes Mohrmann
,
Robert Wood
,
Virendra Ghate
,
Andrew Gettelman
,
Charles G. Bardeen
,
Bruce A. Albrecht
, and
Paquita Zuidema

Abstract

During the Cloud System Evolution in the Trades (CSET) field study, 14 research flights of the National Science Foundation G-V sampled the stratocumulus–cumulus transition between Northern California and Hawaii and its synoptic variability. The G-V made vertically resolved measurements of turbulence, cloud microphysics, aerosol characteristics, and trace gases. It also carried dropsondes and a vertically pointing W-band radar and lidar. This paper summarizes these observations with the goals of fostering novel comparisons with theory, models and reanalyses, and satellite-derived products. A longitude–height binning and compositing strategy mitigates limitations of sparse sampling and spatiotemporal variability. Typically, a 1-km-deep decoupled stratocumulus-capped boundary layer near California evolved into 2-km-deep precipitating cumulus clusters surrounded by patches of thin stratus that dissipated toward Hawaii. Low cloud cover was correlated with estimated inversion strength more than with cloud droplet number, even though the thickest clouds were generally precipitating and ultraclean layers indicative of aerosol–cloud–precipitation interaction were common west of 140°W. Accumulation-mode aerosol concentration correlated well with collocated cloud droplet number concentration and was typically largest near the surface. Aitken mode aerosol concentration was typically larger in the free troposphere. Wildfire smoke produced spikes of aerosol and trace gases on some flights. CSET data are compared with space–time collocated output from MERRA-2 reanalysis and from the CAM6 climate model run with winds and temperature nudged toward this reanalysis. The reanalysis compares better with the observed relative humidity than does nudged CAM6. Both vertically diffuse the stratocumulus cloud layer versus observations. MERRA-2 slightly underestimates in situ carbon monoxide measurements and underestimates ozone depletion within the boundary layer.

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David A. Randall
,
Cecilia M. Bitz
,
Gokhan Danabasoglu
,
A. Scott Denning
,
Peter R. Gent
,
Andrew Gettelman
,
Stephen M. Griffies
,
Peter Lynch
,
Hugh Morrison
,
Robert Pincus
, and
John Thuburn

Abstract

Today’s global Earth system models began as simple regional models of tropospheric weather systems. Over the past century, the physical realism of the models has steadily increased, while the scope of the models has broadened to include the global troposphere and stratosphere, the ocean, the vegetated land surface, and terrestrial ice sheets. This chapter gives an approximately chronological account of the many and profound conceptual and technological advances that made today’s models possible. For brevity, we omit any discussion of the roles of chemistry and biogeochemistry, and terrestrial ice sheets.

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B. H. Kahn
,
J. Teixeira
,
E. J. Fetzer
,
A. Gettelman
,
S. M. Hristova-Veleva
,
X. Huang
,
A. K. Kochanski
,
M. Köhler
,
S. K. Krueger
,
R. Wood
, and
M. Zhao

Abstract

Observations of the scale dependence of height-resolved temperature T and water vapor q variability are valuable for improved subgrid-scale climate model parameterizations and model evaluation. Variance spectral benchmarks for T and q obtained from the Atmospheric Infrared Sounder (AIRS) are compared to those generated by state-of-the-art numerical weather prediction “analyses” and “free-running” climate model simulations with spatial resolution comparable to AIRS. The T and q spectra from both types of models are generally too steep, with small-scale variance up to several factors smaller than AIRS. However, the two model analyses more closely resemble AIRS than the two free-running model simulations. Scaling exponents obtained for AIRS column water vapor (CWV) and height-resolved layers of q are also compared to the superparameterized Community Atmospheric Model (SP-CAM), highlighting large differences in the magnitude of CWV variance and the relative flatness of height-resolved q scaling in SP-CAM. Height-resolved q spectra obtained from aircraft observations during the Variability of the American Monsoon Systems Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-REx) demonstrate changes in scaling exponents that depend on the observations’ proximity to the base of the subsidence inversion with scale breaks that occur at approximately the dominant cloud scale (~10–30 km). This suggests that finer spatial resolution requirements must be considered for future satellite observations of T and q than those currently planned for infrared and microwave satellite sounders.

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J. E. Kay
,
B. R. Hillman
,
S. A. Klein
,
Y. Zhang
,
B. Medeiros
,
R. Pincus
,
A. Gettelman
,
B. Eaton
,
J. Boyle
,
R. Marchand
, and
T. P. Ackerman

Abstract

Satellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are known to explain intermodel differences in cloud feedbacks and climate sensitivity. More generally, this study demonstrates that simulator-facilitated evaluation of cloud properties, such as amount by vertical level and optical depth, can robustly expose large and at times radiatively compensating climate model cloud biases.

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