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Peter W. Henderson and Robert Pincus

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This work uses long-term lidar and radar retrievals of the vertical structure of cloud at the Atmospheric Radiation Measurement (ARM) program’s Southern Great Plains site to evaluate cloud occurrence in multiyear runs of a cloud system–resolving model in three configurations of varying resolution and sophistication. The model is nudged to remain near the observed thermodynamic state and model fields are processed to mimic the operation of the observing system. The model’s skill in predicting cloud occurrence is evaluated using both traditional performance measures that assume ergodicity and probabilistic measures that do not require temporal averaging of the observations.

The model shows considerable skill in predicting cloud occurrence when its thermodynamic state is close to that observed. The overall bias in modeled cloud occurrence is relatively small in all model runs, suggesting that this field is relatively well calibrated. The Brier scores attained by all configurations also suggest considerable model skill. Greater differences in performance are found between seasons than between model configurations during the same season, despite substantial differences between the computational costs of the configurations. Several significant seasonal dependencies are identified, most notably greater conditional bias, but better timing, of boundary layer cloud in winter, and substantially less conditional bias in high cloud during summer.

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Christopher S. Bretherton and Robert Pincus

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A goal of the Atlantic Stratocumulus Transition Experiment (ASTEX) southeast of the Azores Islands in the east-central Atlantic Ocean during June 1992 was to examine the coupled evolution of cloud, dynamical, and thermodynamical vertical structure in a marine boundary layer (MBL) air mass as it advected from cold to warm water in the trade winds. In two “Lagrangian” observation periods during ASTEX, an unprecedentedly complete view of MBL and cloud evolution was achieved by nearly continuous aircraft coverage of such an air mass for 36–48 hours using three boundary layer aircraft, supplemented by satellite, ship, and balloon observations.

During the first Lagrangian period, an accelerated stratocumulus to trade cumulus transition occurred in a clean marine air mass. In the second Lagrangian period, a 200-hPa-deep decoupled modified continental MBL persisted with almost no change in structure. Cumulus rising into intermittent stratocumulus were observed throughout the period. The two contrasting ASTEX Lagrangians will allow both direct comparison with MBL models and budget studies with essentially all uncertainty from poorly measured advective tendencies removed.

The authors present the synoptic setting and the evolution of cloudiness as seen from satellite for both Lagrangians, and vertical sections of wind, temperature mixing ratio, liquid water, droplet concentration, and ozone formed from time series of 17 aircraft soundings during each Lagrangian. In Part II, an analyses of sea surface temperature and surface fluxes, cloudiness, drizzle, and entrainment rate during the Lagrangians are presented.

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Robert Pincus and K. Franklin Evans

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This paper examines the tradeoffs between computational cost and accuracy for two new state-of-the-art codes for computing three-dimensional radiative transfer: a community Monte Carlo model and a parallel implementation of the Spherical Harmonics Discrete Ordinate Method (SHDOM). Both codes are described and algorithmic choices are elaborated. Two prototype problems are considered: a domain filled with stratocumulus clouds and another containing scattered shallow cumulus, absorbing aerosols, and molecular scatterers. Calculations are performed for a range of resolutions and the relationships between accuracy and computational cost, measured by memory use and time to solution, are compared.

Monte Carlo accuracy depends primarily on the number of trajectories used in the integration. Monte Carlo estimates of intensity are computationally expensive and may be subject to large sampling noise from highly peaked phase functions. This noise can be decreased using a range of variance reduction techniques, but these techniques can compromise the excellent agreement between the true error and estimates obtained from unbiased calculations. SHDOM accuracy is controlled by both spatial and angular resolution; different output fields are sensitive to different aspects of this resolution, so the optimum accuracy parameters depend on which quantities are desired as well as on the characteristics of the problem being solved. The accuracy of SHDOM must be assessed through convergence tests and all results from unconverged solutions may be biased.

SHDOM is more efficient (i.e., has lower error for a given computational cost) than Monte Carlo when computing pixel-by-pixel upwelling fluxes in the cumulus scene, whereas Monte Carlo is more efficient in computing flux divergence and downwelling flux in the stratocumulus scene, especially at higher accuracies. The two models are comparable for downwelling flux and flux divergence in cumulus and upwelling flux in stratocumulus. SHDOM is substantially more efficient when computing pixel-by-pixel intensity in multiple directions; the models are comparable when computing domain-average intensities. In some cases memory use, rather than computation time, may limit the resolution of SHDOM calculations.

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Robert Pincus, Richard Hemler, and Stephen A. Klein

Abstract

A new method for representing subgrid-scale cloud structure in which each model column is decomposed into a set of subcolumns has been introduced into the Geophysical Fluid Dynamics Laboratory’s global atmospheric model AM2. Each subcolumn in the decomposition is homogeneous, but the ensemble reproduces the initial profiles of cloud properties including cloud fraction, internal variability (if any) in cloud condensate, and arbitrary overlap assumptions that describe vertical correlations. These subcolumns are used in radiation and diagnostic calculations and have allowed the introduction of more realistic overlap assumptions. This paper describes the impact of these new methods for representing cloud structure in instantaneous calculations and long-term integrations. Shortwave radiation computed using subcolumns and the random overlap assumption differs in the global annual average by more than 4 W m−2 from the operational radiation scheme in instantaneous calculations; much of this difference is counteracted by a change in the overlap assumption to one in which overlap varies continuously with the separation distance between layers. Internal variability in cloud condensate, diagnosed from the mean condensate amount and cloud fraction, has about the same effect on radiative fluxes as does the ad hoc tuning accounting for this effect in the operational radiation scheme. Long simulations with the new model configuration show little difference from the operational model configuration, while statistical tests indicate that the model does not respond systematically to the sampling noise introduced by the approximate radiative transfer techniques introduced to work with the subcolumns.

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Daniel Klocke, Robert Pincus, and Johannes Quaas

Abstract

The distribution of model-based estimates of equilibrium climate sensitivity has not changed substantially in more than 30 years. Efforts to narrow this distribution by weighting projections according to measures of model fidelity have so far failed, largely because climate sensitivity is independent of current measures of skill in current ensembles of models. This work presents a cautionary example showing that measures of model fidelity that are effective at narrowing the distribution of future projections (because they are systematically related to climate sensitivity in an ensemble of models) may be poor measures of the likelihood that a model will provide an accurate estimate of climate sensitivity (and thus degrade distributions of projections if they are used as weights). Furthermore, it appears unlikely that statistical tests alone can identify robust measures of likelihood. The conclusions are drawn from two ensembles: one obtained by perturbing parameters in a single climate model and a second containing the majority of the world’s climate models. The simple ensemble reproduces many aspects of the multimodel ensemble, including the distributions of skill in reproducing the present-day climatology of clouds and radiation, the distribution of climate sensitivity, and the dependence of climate sensitivity on certain cloud regimes. Weighting by error measures targeted on those regimes permits the development of tighter relationships between climate sensitivity and model error and, hence, narrower distributions of climate sensitivity in the simple ensemble. These relationships, however, do not carry into the multimodel ensemble. This suggests that model weighting based on statistical relationships alone is unfounded and perhaps that climate model errors are still large enough that model weighting is not sensible.

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Robert Pincus, Cécile Hannay, and K. Franklin Evans

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Three-dimensional radiative transfer calculations are accurate, though computationally expensive, if the spatial distribution of cloud properties is known. The difference between these calculations and those using the much less expensive independent column approximation is called the 3D radiative transfer effect. Assessing the magnitude of this effect in the real atmosphere requires that many realistic cloud fields be obtained, and profiling instruments such as ground-based radars may provide the best long-term observations of cloud structure. Cloud morphology can be inferred from a time series of vertical profiles obtained from profilers by converting time to horizontal distance with an advection velocity, although this restricts variability to two dimensions. This paper assesses the accuracy of estimates of the 3D effect in shallow cumulus clouds when cloud structure is inferred in this way. Large-eddy simulations provide full three-dimensional, time-evolving cloud fields, which are sampled every 10 s to provide a “radar’s eye view” of the same cloud fields. The 3D effect for shortwave surface fluxes is computed for both sets of fields using a broadband Monte Carlo radiative transfer model, and intermediate calculations are made to identify reasons why estimates of the 3D effect differ in these fields. The magnitude of the 3D effect is systematically underestimated in the two-dimensional cloud fields because there are fewer cloud edges that cause the effect, while the random error in hourly estimates is driven by the limited sample observed by the profiling instrument.

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Robert Pincus, Malgorzata Szczodrak, Jiujing Gu, and Philip Austin

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The uncertainty in optical depths retrieved from satellite measurements of visible wavelength radiance at the top of the atmosphere is quantified. Techniques are briefly reviewed for the estimation of optical depth from measurements of radiance, and it is noted that these estimates are always more uncertain at greater optical depths and larger solar zenith angles. The lack of radiometric calibration for visible wavelength imagers on operational satellites dominates the uncertainty retrievals of optical depth. This is true for both single-pixel retrievals and for statistics calculated from a population of individual retrievals. For individual estimates or small samples, sensor discretization (especially for the VAS instrument) can also be significant, but the sensitivity of the retrieval to the specification of the model atmosphere is less important. The relative uncertainty in calibration affects the accuracy with which optical depth distributions measured by different sensors may be quantitatively compared, while the absolute calibration uncertainty, acting through the nonlinear mapping of radiance to optical depth, limits the degree to which distributions measured by the same sensor may be distinguished.

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Robert Pincus, Marcia B. Baker, and Christopher S. Bretherton

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Marine stratocumulus clouds have a large impact on the earth’s radiation budget. Their optical properties vary on two distinct timescales, one associated with the diurnal cycle of solar insolation and another with the downstream transition to trade cumulus. Hypotheses regarding the control of cloud radiative properties fall broadly into two groups: those focused on the effects of precipitation, and those concerned with the environment in which the clouds evolve. Reconciling model results and observations in an effort to develop parameterizations of cloud optical properties is difficult because marine boundary layer clouds are not in equilibrium with their local environment.

The authors describe a new technique for the observation of boundary layer cloud evolution in a moving or Lagrangian frame of reference. Blending satellite imagery and gridded environmental information, the method provides a time series of the environmental conditions to which the boundary layer is subject and the properties of clouds as they respond to external forcings. The technique is combined with in situ observations of precipitation off the coast of California and compared with the downstream evolution of cloud fraction in five cases that were observed to be precipitating with three cases that were not. In this small dataset cloud fraction remains almost uniformly high, and there is no relationship between the presence of precipitation and the evolution of cloud fraction on 1- and 2-day timescales.

Analysis of a large number of examples shows that clouds in this region have a typical pattern of diurnal evolution such that clouds that are optically thicker than about 10 during the morning are unlikely to break up over the course of the day but will instead show a large diurnal cycle in optical depth. Morning cloud optical thickness and the resultant susceptibility to breakup have a much larger impact on diurnally averaged cloud radiative forcing than do diurnal variations in cloud properties. Cloud response is significantly correlated with lower tropospheric temperature stratification at all times, though the best correlation exists when cloud response lags stability by at least 16 h. Sea surface temperature is also correlated with cloud properties during the period in which cloud response is measured and the 12 h prior. The authors suggest that sea surface temperature plays two competing roles in determining boundary layer cloudiness, with rapid changes in SST promoting cloudiness on short timescales but tending to lead to a more rapid transition to the trade cumulus regime.

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Philip Austin, Yinong Wang, Vincent Kujala, and Robert Pincus

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The spatial and temporal variability of precipitating stratocumulus layers is examined using aircraft observations, satellite retrievals of cloud optical depth, and one-dimensional models that include coalescence and a simple representation of layer turbulence. The aircraft observations show large horizontal variations in cloud thickness and precipitation, with local rain rates 4–5 times larger than the replacement moisture flux, and evidence for precipitation scavenging of small cloud droplets. The satellite observations show that, despite this local water loss, the distribution of cloud optical thickness remains nearly constant over the course of a day, indicating that on larger scales precipitation removal and cloud-top entrainment are in approximate balance with the vapor flux. The authors apply analytic and numerical models of steady-state precipitation to the observed microphysical conditions, and find that the models can match the drop size distributions observed during both heavy and light stratocumulus rainfall, but are especially sensitive to the processes governing the growth rate of the smallest drizzle drops.

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Robert Pincus, Steven Platnick, Steven A. Ackerman, Richard S. Hemler, and Robert J. Patrick Hofmann

Abstract

The properties of clouds that may be observed by satellite instruments, such as optical thickness and cloud-top pressure, are only loosely related to the way clouds are represented in models of the atmosphere. One way to bridge this gap is through “instrument simulators,” diagnostic tools that map the model representation to synthetic observations so that differences can be interpreted as model error. But simulators may themselves be restricted by limited information or by internal assumptions. This paper considers the extent to which instrument simulators are able to capture essential differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Satellite Cloud Climatology Project (ISCCP), two similar but independent estimates of cloud properties. The authors review the measurements and algorithms underlying these two cloud climatologies, introduce a MODIS simulator, and detail datasets developed for comparison with global models using ISCCP and MODIS simulators. In nature MODIS observes less midlevel cloudiness than ISCCP, consistent with the different methods used to determine cloud-top pressure; aspects of this difference are reproduced by the simulators. Differences in observed distributions of optical thickness, however, are not captured. The largest differences can be traced to different approaches to partly cloudy pixels, which MODIS excludes and ISCCP treats as homogeneous. These cover roughly 15% of the planet and account for most of the optically thinnest clouds. Instrument simulators cannot reproduce these differences because there is no way to synthesize partly cloudy pixels. Nonetheless, MODIS and ISCCP observations are consistent for all but the optically thinnest clouds, and models can be robustly evaluated using instrument simulators by integrating over the robust subset of observations.

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