Analysis of integrated cloud liquid and precipitable water vapor retrievals from microwave radiometers during the Surface Heat Budget of the Arctic Ocean project

Westwater, Ed R.; Han, Yong; Shupe, Matthew D.; Matrosov, Sergey Y.

doi:10.1029/2000jd000055

Cited by 159 publications

(157 citation statements)

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“…Although there are 5-years of data used per month, these distributions should be regarded as rough estimates of the month-to-month differences in LWP. This is because recent research has found that MWR uncertainties exist in the LWP retrievals that limit the attainable accuracy to between 20-30 g m À2 [Liljegren and Lesht, 1996;Westwater et al, 2001;Marchand et al, 2003;Turner et al, 2007], which represents a large uncertainty compared to many of the values shown. Still the shift shown, from smaller to larger LWP from March through June, is consistent with climatological considerations that optically thinner stratiform cloud tend to occur in spring versus summer [e.g., Leontyeva and Stamnes, 1994].…”

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confidence: 99%

Expected magnitude of the aerosol shortwave indirect effect in springtime Arctic liquid water clouds

Lubin

Vogelmann

2007

Geophysical Research Letters

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[1] Radiative transfer simulations are used to assess the expected magnitude of the diurnally-averaged shortwave aerosol first indirect effect in Arctic liquid water clouds, in the context of recently discovered longwave surface heating of order 3 to 8 W m À2 by this same aerosol effect detected at the Barrow, Alaska, ARM Site. We find that during March and April, shortwave surface cooling by the first indirect effect is comparable in magnitude to the longwave surface heating. During May and June, the shortwave surface cooling exceeds the longwave heating. Due to multiple reflection of photons between the snow or sea ice surface and cloud base, the shortwave first indirect effect may be easier to detect in surface radiation measurements than from space. Citation: Lubin, D., and A. M. Vogelmann [2] The search for indirect aerosol effects has taken a unique turn in the Arctic, in emphasizing longwave radiation before shortwave [Garrett et al., 2002]. This is in part due to the greater importance of longwave radiation relative to shortwave at high latitudes [e.g., Intrieri et al., 2002], and also due to the availability of high quality longwave spectral radiation measurements in the Arctic from which the indirect effect can be readily identified [Garrett and Zhao, 2006;Lubin and Vogelmann, 2006]. To date, the deployment in the high Arctic of advanced longwave spectroradiometers [Turner et al., 2003;Knuteson et al., 2004] has not been matched by similar instrumentation that cover the visible through near-IR wavelengths at which clouds both absorb and scatter radiation. We therefore do not yet have a comparable spectral capability in the Arctic to rigorously identify and quantify the shortwave aerosol for all applicable liquid water paths (LWP), as has been done recently for the longwave. The purpose of this study is to demonstrate the expected relative importance of the shortwave and longwave manifestations of the aerosol first indirect effect on the springtime Arctic radiative energy balance.[3] Garrett and Zhao [2006] analyzed Fourier Transform Infrared (FTIR) spectroradiometer data from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program North Slope of Alaska (NSA) site at Barrow, Alaska, and found that the presence of Arctic ''haze'' -an anthropogenic aerosol primarily of Eurasian industrial origin trapped in the Arctic winter and spring troposphere [Barrie, 1986] -reduces the mean effective droplet radius r e of Arctic liquid water clouds by 3 mm. This results in an increase in mean cloud emissivity for all LWP < 80 g m À2 such that downwelling longwave radiation at the Arctic surface increases by 3.3 to 5.5 W m À2 , with all other meteorological variables held constant. Lubin and Vogelmann [2006] performed a similar analysis of NSA FTIR spectroradiometer data, and found a reduction in mean cloud droplet effective radius of 4 mm in Arctic haze relative to background aerosol, and found an increase of 8.2 W m À2 in the downwelling surface longwave flux under liquid water clouds in the ...

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confidence: 99%

Expected magnitude of the aerosol shortwave indirect effect in springtime Arctic liquid water clouds

Lubin

Vogelmann

2007

Geophysical Research Letters

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“…The radiative characteristics of marine stratocumulus are closely tied to cloud fraction, which differs dramatically between regions of continuous unbroken cloud versus regions with mesoscale pockets of open cells (Stevens et al, 2005). Observational and modeling studies have implicated heavy drizzle cells in the transitions between closed-and open-cell mesoscale cloud structures (Stevens et al, 2005;van Zanten and Stevens, 2005;Comstock et al, 2007;Savic-Jovic and Stevens, 2008;Wang and Feingold, 2009;Wood et al, 2011a). These studies suggest that drizzle is a necessary but not sufficient condition for the formation of pockets of open cells.…”

Section: Introductionmentioning

confidence: 90%

“…C-band Z > 0 dBZ is approximately equivalent to LWP ≈ 200 g m −2 (Wood et al, 2011a). This definition corresponds to the subset of drizzle features within marine stratocumulus with sufficiently high drizzle intensity that precipitation usually reaches the surface .…”

Section: Binary Heavy Drizzle Cell Classification Algorithmmentioning

confidence: 99%

“…The variances in observed brightness temperature are primarily a function of the background ocean-surface emission (primarily related to sea surface temperature -SST -and wind speed), gas absorption, water vapor profile, beam filling, and LWP, including cloud and precipitable water (Westwater et al, 2001;Crewell and Lǒhnert, 2003;Greenwald et al, 2007;Horváth and Gentemann, 2007;Greenwald, 2009). Of these sources of emission, we expect that LWP will have the largest variance at spatial scales on the order of individual drizzle cells within marine stratocumulus.…”

Section: Introductionmentioning

confidence: 99%

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Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements

Miller

Yuter

2013

Atmos. Meas. Tech.

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Abstract. This empirical study demonstrates the feasibility of using 89-GHz Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) passive microwave brightness temperature data to detect heavily drizzling cells within subtropical marine stratocumulus. For the purpose of this paper, we define heavily drizzling cells as areas ≥ 6 km × 4 km with C-band Z > 0 dBZ; equivalent to > 0.084 mm h −1 . A binary heavy drizzle product is described that can be used to determine areal and feature statistics of drizzle cells within the major marine stratocumulus regions. Current satellite liquid water path (LWP) and cloud radar products capable of detecting drizzle are either lacking in resolution (AMSR-E LWP), diurnal coverage (MODIS LWP), or spatial coverage (CloudSat). The AMSR-E 89-GHz data set at 6 km × 4 km spatial resolution is sufficient for resolving individual heavily drizzling cells. Radiant emission at 89 GHz by liquid-water cloud and precipitation particles from drizzling cells in marine stratocumulus regions yields local maxima in brightness temperature against an otherwise cloud-free background brightness temperature. The background brightness temperature is primarily constrained by column-integrated water vapor for moderate sea surface temperatures. Clouds containing ice are screened out. Once heavily drizzling pixels are identified, connected pixels are grouped into discrete drizzle cell features. The identified drizzle cells are used in turn to determine several spatial statistics for each satellite scene, including drizzle cell number and size distribution. The identification of heavily drizzling cells within marine stratocumulus regions with satellite data facilitates analysis of seasonal and regional drizzle cell occurrence and the interrelation between drizzle and changes in cloud fraction.

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“…The monthly means of the cloud liquid and ice water paths (IWPs) are calculated from the original data of 1-min resolution provided by M. Shupe. Liquid water paths are derived from the microwave radiometer brightness temperatures at 31.4-GHz frequency (the "liquid" channel insensitive to water vapor or ice), yielding retrievals with 25 g m Ϫ2 accuracy (Han and Westwater 1995;Westwater et al 2001). The data are available from 6 December 1997 until 9 September 1998.…”

Section: Observational Datamentioning

confidence: 99%

The Influence of Cloud and Surface Properties on the Arctic Ocean Shortwave Radiation Budget in Coupled Models*

et al. 2008

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The impact of Arctic sea ice concentrations, surface albedo, cloud fraction, and cloud ice and liquid water paths on the surface shortwave (SW) radiation budget is analyzed in the twentieth-century simulations of three coupled models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The models are the Goddard Institute for Space Studies Model E-R (GISS-ER), the Met Office Third Hadley Centre Coupled Ocean-Atmosphere GCM (UKMO HadCM3), and the National Center for Atmosphere Research Community Climate System Model, version 3 (NCAR CCSM3). In agreement with observations, the models all have high Arctic mean cloud fractions in summer; however, large differences are found in the cloud ice and liquid water contents. The simulated Arctic clouds of CCSM3 have the highest liquid water content, greatly exceeding the values observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. Both GISS-ER and HadCM3 lack liquid water and have excessive ice amounts in Arctic clouds compared to SHEBA observations. In CCSM3, the high surface albedo and strong cloud SW radiative forcing both significantly decrease the amount of SW radiation absorbed by the Arctic Ocean surface during the summer. In the GISS-ER and HadCM3 models, the surface and cloud effects compensate one another: GISS-ER has both a higher summer surface albedo and a larger surface incoming SW flux when compared to HadCM3. Because of the differences in the models' cloud and surface properties, the Arctic Ocean surface gains about 20% and 40% more solar energy during the melt period in the GISS-ER and HadCM3 models, respectively, compared to CCSM3.In twenty-first-century climate runs, discrepancies in the surface net SW flux partly explain the range in the models' sea ice area changes. Substantial decrease in sea ice area simulated during the twenty-first century in CCSM3 is associated with a large drop in surface albedo that is only partly compensated by increased cloud SW forcing. In this model, an initially high cloud liquid water content reduces the effect of the increase in cloud fraction and cloud liquid water on the cloud optical thickness, limiting the ability of clouds to compensate for the large surface albedo decrease. In HadCM3 and GISS-ER, the compensation of the surface albedo and cloud SW forcing results in negligible changes in the net SW flux and is one of the factors explaining moderate future sea ice area trends. Thus, model representations of cloud properties for today's climate determine the ability of clouds to compensate for the effect of surface albedo decrease on the future shortwave radiative budget of the Arctic Ocean and, as a consequence, the sea ice mass balance. * Lamont-Doherty Earth Observatory Contribution Number 7120. ϩ Current affiliation:

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Analysis of integrated cloud liquid and precipitable water vapor retrievals from microwave radiometers during the Surface Heat Budget of the Arctic Ocean project

Cited by 159 publications

References 23 publications

Expected magnitude of the aerosol shortwave indirect effect in springtime Arctic liquid water clouds

Expected magnitude of the aerosol shortwave indirect effect in springtime Arctic liquid water clouds

Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements

The Influence of Cloud and Surface Properties on the Arctic Ocean Shortwave Radiation Budget in Coupled Models*

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