Search Results

You are looking at 1 - 10 of 10 items for :

  • Author or Editor: Lazaros Oreopoulos x
  • Journal of Climate x
  • Refine by Access: All Content x
Clear All Modify Search
Lazaros Oreopoulos
and
Roger Davies

Abstract

The relationship between sea surface temperature (SST) and albedo or cloud cover is examined for two tropical regions with high values of cloud radiative forcing and persistent marine stratocumulus (mSc)–one off the west coast of Peru, the other off the west cost of Angola. The data span five years, from December 1984 to November 1989. Albedos are from the Earth Radiation Budget Experiment, cloud covers are from the International Satellite Cloud Climatology Project, and SSTs are from the Climate Analysis Center.

Negative correlation coefficients between albedo and SST are found to be about −0.8 when the seasonal variation of the entire dataset is analyzed. The interannual variation and the spatial variation of individual months also yields correlation coefficients that are negative. The correlation between cloud cover and SST is found to be similar to but weaker than the correlation between albedo and SST, suggesting a decrease in cloud amount and a decrease in cloud albedo with increasing SST for these regions. The corresponding albedo sensitivity averages −0.018 K−1 with local values reaching −0.04 K −1. These findings are valid from 19°C to 25°*C for the Peru mSc and 22°C to 27°C for the Angola mSc. These temperatures approximately bound the domains over which mSc is the prevalent cloud type within each region.

These results imply a potential positive feedback to global warming by marine stratocumulus that ranges from ∼0.14 W m−2 K−1 to ∼1 W m−2 K−1, depending on whether or not our results apply to all marine stratocumulus. While these values are uncertain to at least ±50%, the sensitivity of albedo to see surface temperature in the present climate may serve as a useful diagnostic tool in monitoring the performance of global climate models.

Full access
Jackson Tan
and
Lazaros Oreopoulos

Abstract

The distribution of mesoscale precipitation exhibits diverse patterns: precipitation can be intense but sporadic, or it can be light but widespread. This range of behaviors is a reflection of the different weather systems in the global atmosphere. Using MODIS global cloud regimes as proxies for different atmospheric systems, this study investigates the subgrid precipitation properties within these systems. Taking advantage of the high resolution of Integrated Multisatellite Retrievals for GPM (IMERG; GPM is the Global Precipitation Measurement mission), precipitation values at 0.1° are composited with each cloud regime at 1° grid cells to characterize the regime’s spatial subgrid precipitation properties. The results reveal the diversity of the subgrid precipitation behavior of the atmospheric systems. Organized convection is associated with the highest grid-mean precipitation rates and precipitating fraction, although on average only half the grid is precipitating and there is substantial variability between different occurrences. Summer extratropical storms have the next highest precipitation, driven mainly by moderate precipitation rates over large areas. These systems produce more precipitation than isolated convective systems, for which the lower precipitating fractions balance out the high intensities. Most systems produce heavier precipitation in the afternoon than in the morning. The grid-mean precipitation rate is also found to scale with the fraction of precipitation within the grid in a faster-than-linear relationship for most systems. This study elucidates the precipitation properties within cloud regimes, thus advancing our understanding of the precipitation structures of these atmospheric systems.

Full access
Lazaros Oreopoulos
and
Roger Davies

Abstract

Due to cloud heterogeneity and the nonlinear dependence of albedo on cloud water content, the average albedo of a cloudy scene found by calculating the albedo of independent pixels within the scene tends to be different from the albedo calculated using the average cloud water in the scene. This difference, termed the plane parallel albedo bias (PPH bias), which has previously been estimated from limited case studies, is evaluated here for the first time using an extensive set of Advanced Very High Resolution Radiometer data over oceanic scenes. This dataset yields visible PPH biases that range from 0.02 to 0.30, depending in part on the size of the scene, the viewing–illumination directions, and the assumptions made retrieving cloud optical depths.

The PPH biases increase when atmospheric effects are accounted for but are relatively insensitive to assumptions about cloud microphysics. Due to the limitations of a one-dimensional retrieval, they tend to increase with solar zenith angle and to be larger in the backscattering than the forward scattering direction. Placed in the context of those general circulation models that do not provide subgrid-scale information on cloud amount, these biases are even larger. PPH biases in the broadband-reflected shortwave flux from general circulation models are estimated to exceed 30 W m−2, typically requiring the introduction of a compensatory bias in the model’s treatment of cloud water content.

The resolution of the satellite sensor and the averaging/sampling of the satellite substantially influences the calculated PPH bias. The authors find a significant drop in albedo bias (∼0.02–0.05) when averaging/sampling original local area coverage (LAC) data to global area coverage (GAC) resolution or when Landsat data were averaged to LAC resolution. These results, along with stochastic simulations of internal LAC pixel variability indicate that the bias discrepancies among variable resolution satellite data are mostly due to the neglect of subpixel cloud fraction, which makes clouds appear thinner than they actually are.

Full access
Lazaros Oreopoulos
and
Roger Davies

Abstract

Using the same satellite observations as in Part I of this paper, the authors explore ways to remove the cloud albedo bias (or plane parallel albedo bias), the difference between the plane parallel homogeneous albedo and the average albedo of independent pixels, in regions similar in size to climate model grid boxes.

Scaling regional mean optical depths with the reduction factor of R. F. Cahalan et al. provides albedos close to the independent pixel values. Computed albedos approach the independent pixel values within 0.01 for ∼40% of the regions tested and give standard deviations ∼0.02–0.04. Fitting lognormal distributions to the observed optical depth distributions gives albedos within 0.01 of the independent pixel values more than 70% of the time, with standard deviations ∼0.02–0.06. Gamma distributions are less successful than lognormal distributions, giving acceptable results (average bias ∼0.01–0.02, standard deviation ∼0.05–0.08) only when their parameters are estimated from the maximum likelihood estimates method. The poor performance of the gamma distribution when the method of moments is used for parameter estimation (as H. W. Barker et al. did) is attributed to the presence of high optical depth values in our retrieved fields.

To apply any of the above corrections in GCMs, quantities that are not presently provided by these models are required. The reduction factor and “gamma IP” method require the mean logarithm of optical depth, whereas the lognormal method also requires the variance. The authors suggest a parameterization of these quantities in terms of mean optical depth and cloud fraction, variables available in most GCMs. The albedos resulting from the parameterized versions of the correction methods are still much closer to the independent pixel values than the albedos of the plane parallel homogeneous assumption. Although the “lognormal IP” gives the best overall performance, it requires knowledge of two logarithmic moments and numerical integration. It may therefore prove more appealing for observational than modeling applications.

Full access
Lazaros Oreopoulos
and
Robert F. Cahalan

Abstract

Two full months (July 2003 and January 2004) of Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level-3 data from the Terra and Aqua satellites are analyzed in order to characterize the horizontal variability of vertically integrated cloud optical thickness (“cloud inhomogeneity”) at global scales. The monthly climatology of cloud inhomogeneity is expressed in terms of standard parameters, initially calculated for each day of the month at spatial scales of 1° × 1° and subsequently averaged at monthly, zonal, and global scales. Geographical, diurnal, and seasonal changes of inhomogeneity parameters are examined separately for liquid and ice phases and separately over land and ocean. It is found that cloud inhomogeneity is overall weaker in summer than in winter. For liquid clouds, it is also consistently weaker for local morning than local afternoon and over land than ocean. Cloud inhomogeneity is comparable for liquid and ice clouds on a global scale, but with stronger spatial and temporal variations for the ice phase, and exhibits an average tendency to be weaker for near-overcast or overcast grid points of both phases. Depending on cloud phase, hemisphere, surface type, season, and time of day, hemispheric means of the inhomogeneity parameter ν (roughly the square of the ratio of optical thickness mean to standard deviation) have a wide range of ∼1.7 to 4, while for the inhomogeneity parameter χ (the ratio of the logarithmic to linear mean) the range is from ∼0.65 to 0.8. The results demonstrate that the MODIS Level-3 dataset is suitable for studying various aspects of cloud inhomogeneity and may prove invaluable for validating future cloud schemes in large-scale models capable of predicting subgrid variability.

Full access
Lazaros Oreopoulos
,
Robert F. Cahalan
, and
Steven Platnick

Abstract

The authors present the global plane-parallel shortwave albedo bias of liquid clouds for two months, July 2003 and January 2004. The cloud optical properties necessary to perform the bias calculations come from the operational Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and MODIS Aqua level-3 datasets. These data, along with ancillary surface albedo and atmospheric information consistent with the MODIS retrievals, are inserted into a broadband shortwave radiative transfer model to calculate the fluxes at the atmospheric column boundaries. The plane-parallel homogeneous (PPH) calculations are based on the mean cloud properties, while independent column approximation (ICA) calculations are based either on 1D histograms of optical thickness or joint 2D histograms of optical thickness and effective radius. The (positive) PPH albedo bias is simply the difference between PPH and ICA albedo calculations. Two types of biases are therefore examined: 1) the bias due to the horizontal inhomogeneity of optical thickness alone (the effective radius is set to the grid mean value) and 2) the bias due to simultaneous variations of optical thickness and effective radius as derived from their joint histograms. The authors find that the global bias of albedo (liquid cloud portion of the grid boxes only) is ∼+0.03, which corresponds to roughly 8% of the global liquid cloud albedo and is only modestly sensitive to the inclusion of horizontal effective radius variability and time of day, but depends strongly on season and latitude. This albedo bias translates to ∼3–3.5 W m−2 of bias (stronger negative values) in the diurnally averaged global shortwave cloud radiative forcing, assuming homogeneous conditions for the fraction of the grid box not covered by liquid clouds; zonal values can be as high as 8 W m−2. Finally, the (positive) broadband atmospheric absorptance bias is about an order of magnitude smaller than the albedo bias. The substantial magnitude of the PPH bias underlines the importance of predicting subgrid variability in GCMs and accounting for its effects on cloud–radiation interactions.

Full access
Daeho Jin
,
Ryan J. Kramer
,
Lazaros Oreopoulos
, and
Dongmin Lee

Abstract

Twenty years of satellite-based cloud and radiation observations allow us to examine the observed cloud radiative effect (CRE) feedback (i.e., CRE change per unit change in global mean surface temperature). By employing a decomposition method to separate the contribution of “internal changes” and “relative-frequency-of-occurrence (RFO) changes” of distinct cloud regime (CR) groups, notable seasonal contrasts of CRE feedback characteristics emerge. Boreal winter CRE feedback is dominated by the positive shortwave CRE (SWCRE) feedback of oceanic low-thick clouds, due to their decreasing RFO as temperature rises. This signal is most likely due to El Niño–Southern Oscillation (ENSO) activity. When ENSO signals are excluded, boreal winter CRE feedback becomes qualitatively similar to the boreal summer feedback, where several CR groups contribute to the total CRE feedback more evenly. Most CR groups’ CRE feedbacks largely come from changing RFO (e.g., the predominant transition from oceanic cumulus to broken clouds and more occurrences of higher convective clouds with warming temperature). At the same time, low-thick and broken clouds experience optical thinning and decreasing cloud fraction, and these features are more prominent in boreal summer than winter. Overall, the seasonally asymmetric patterns of CRE feedback, primarily due to ENSO, introduce complexity in assessments of CRE feedback.

Restricted access
Dongmin Lee
,
Lazaros Oreopoulos
,
George J. Huffman
,
William B. Rossow
, and
In-Sik Kang

Abstract

The authors examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35°S–35°N) for a 10-yr period. The main precipitation dataset used is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis operational product 3B42 dataset, but Global Precipitation Climatology Project daily data are also used for comparison. It is found that the most convectively active ISCCP weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropics; yet, even this weather state appears to not precipitate about half the time, although this may be to some extent an artifact of detection and spatiotemporal matching limitations of the precipitation dataset. WS1 exhibits a modest annual cycle of the domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states appear to be stronger when occurring before or after WS1. The precipitation rates of the various weather states are different between ocean and land, with WS1 producing higher daytime rates on average over ocean than land, likely because of the larger size and more persistent nature of oceanic WS1s. The results of this study, in addition to advancing the understanding of tropical hydrology, can serve as higher-order diagnostics for evaluating the realism of tropical precipitation distributions in large-scale models.

Full access
Xianglei Huang
,
Xiuhong Chen
,
Gerald L. Potter
,
Lazaros Oreopoulos
,
Jason N. S. Cole
,
Dongmin Lee
, and
Norman G. Loeb

Abstract

Longwave (LW) spectral flux and cloud radiative effect (CRE) are important for understanding the earth’s radiation budget and cloud–radiation interaction. Here, the authors extend their previous algorithms to collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) observations over the entire globe and show that the algorithms yield consistently good performances for measurements over both land and ocean. As a result, the authors are able to derive spectral flux and CRE at 10-cm−1 intervals over the entire LW spectrum from all currently available collocated AIRS and CERES observations. Using this multiyear dataset, they delineate the climatology of spectral CRE, including the far IR, over the entire globe as well as in different climate zones. Furthermore, the authors define two quantities, IR-effective cloud-top height (CTHeff) and cloud amount (CAeff), based on the monthly-mean spectral (or band by band) CRE. Comparisons with cloud fields retrieved by the CERES–Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm indicate that, under many circumstances, the CTHeff and CAeff can be related to the physical retrievals of CTH and CA and thus can enhance understandings of model deficiencies in LW radiation budgets and cloud fields. Using simulations from the GFDL global atmosphere model, version 2 (AM2); NASA’s Goddard Earth Observing System, version 5 (GEOS-5); and Environment Canada’s Canadian Centre for Climate Modelling and Analysis (CCCma) Fourth Generation Canadian Atmospheric General Circulation Model (CanAM4) as case studies, the authors further demonstrate the merits of the CTHeff and CAeff concepts in providing insights on global climate model evaluations that cannot be obtained solely from broadband LW flux and CRE comparisons.

Full access
Xianglei Huang
,
Jason N. S. Cole
,
Fei He
,
Gerald L. Potter
,
Lazaros Oreopoulos
,
Dongmin Lee
,
Max Suarez
, and
Norman G. Loeb

Abstract

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM’s radiation code is uniquely valuable for GCM evaluation because 1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and 2) the fractional contribution of each band to the LW broadband CRE (f CRE) is sensitive to cloud-top height but largely insensitive to cloud fraction, thereby presenting a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band-by-band quantities from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations. A study is presented here that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs—the GFDL Atmospheric Model version 2 (GFDL AM2), NASA Goddard Earth Observing System version 5 (GEOS-5), and the fourth-generation AGCM of the Canadian Centre for Climate Modelling and Analysis (CCCma CanAM4)—forced by observed SST. The models agree with observation on the annual-mean LW broadband CRE over the tropical oceans within ±1 W m−2. However, the differences among these three GCMs in some bands can be as large as or even larger than ±1 W m−2. Observed seasonal cycles of f CRE in major bands are shown to be consistent with the seasonal cycle of cloud-top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of f CRE agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of f CRE highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

Full access