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Qiuhong Tang and Guoyong Leng

temperature change can be affected either through a change in surface solar heating or through a change of surface evaporative cooling given that the reported change trend in downward longwave radiation is relatively small ( Wild et al. 2004 ). The cool temperature in the east-central United States, which is teleconnected with warm tropical Pacific SSTs, is accompanied by diminished solar radiation and increased cloud cover ( Robinson et al. 2002 ). The lack of warming is also associated with regional

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Hideaki Kawai, Tsuyoshi Koshiro, and Mark J. Webb

moist static energy (MSE) to explain low cloud changes, while Webb et al. (2015) showed that models with few midlevel clouds or low MSE near the top of the boundary layer (BL) tend to have a large positive tropical cloud feedback. Wood and Bretherton (2006) showed that estimated inversion strength (EIS), which is a modification of lower-tropospheric stability (LTS; Klein and Hartmann 1993 ), is a physically more plausible and useful index than LTS for determining low cloud cover (LCC) in the

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Stephen Smith and Ralf Toumi

1. Introduction Clouds are crucial regulators of both weather and climate. Properties such as cloud cover, cloud radiative temperature, and clear sky radiative temperature have an impact on the earth’s radiative balance ( Harrison et al. 1990 ), the hydrological cycle ( Groisman et al. 2004 ), and are important constraints on climate models ( Wielicki et al. 1995 ). In recent years efforts have been made to develop reliable automated systems to provide continuous sets of cloud data. This is

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Melissa Free and Bomin Sun

cover record at NWS stations that did not continue to make human cloud observations ( Warren et al. 1991 ; Sun 2003 ; Sun and Groisman 2004 ; Dai et al. 2006 ). At a limited number of large airport stations, cloud observations are now augmented with human observations that may allow us to extend total cloud cover records beyond the time when ASOS was introduced. Many military weather station observers also continued to make cloud cover observations. The goal of our current work is to use data

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Qiuhong Tang and Taikan Oki

, fractional vegetation cover, vegetation condition, and biomass ( Wiegand et al. 1979 ; Zhang and Williams 1997 ; Carlson and Ripley 1997 ). Previous studies have also related the NDVI to components of the water balance equation, such as soil moisture, precipitation, and evaporation ( Choudhury and Golus 1988 ; Grist et al. 1997 ; Szilagyi et al. 1998 ). However, clouds can block satellite observations, and numerous studies have explored methods to yield a “cloud free” NDVI, as would be measured at

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J. A. Griggs and J. L. Bamber

comparable to the uncertainty in radiative balance associated with a ±1 K uncertainty in temperature. The complexity of feedbacks associated with clouds, however, means that, while a symmetric change in energy balance is associated with a ±1 K change in temperature, the same is not true for a ±5% change in cloud cover. Unlike most other climate variables, suitable models fully resolving cloud characteristics are not widely available. In global and regional atmospheric models, which are now being used to

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Marina Aleksandrova, Sergey K. Gulev, and Konstantin Belyaev

1. Introduction Accurate estimation of cloud cover over the ocean is critically important for many applications. Clouds largely determine shortwave and longwave radiation fluxes over the ocean, and the cloud fraction is a key parameter of many parameterizations of radiative fluxes ( Zillman 1972 ; Reed 1977 ; Dobson and Smith 1988 ; Malevsky et al. 1992 ; Gulev 1995 ; Josey et al. 2003 ; Fairall et al. 2008 ; Dupont and Haeffelin 2008 ; Kalisch and Macke 2008 , 2012 ; Hanschmann et al

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Stephen G. Warren, Ryan M. Eastman, and Carole J. Hahn

atlases of cloud cover and cloud types ( Warren et al. 1986 , 1988 ), extending the period of record by 15 yr and applying a moonlight criterion to the nighttime observations ( Hahn et al. 1995 ) so as to obtain more reliable diurnal cycles. As part of that project we undertook the analysis of interannual variations and trends reported here, both for their own interest and also to help improve the cloud climatology by identifying stations with questionable observations. In this paper our purpose is

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Catherine M. Naud, James F. Booth, and Anthony D. Del Genio

1. Introduction In the past few years, a renewed interest in modeled representation of cloud cover in the midlatitudes has occurred, in part motivated by the work of Trenberth and Fasullo (2010) , who found a systematic negative cloud-cover bias in most general circulation models (GCMs) over the southern oceans and suggested that the resulting excess absorption of shortwave radiation has implications for global climate sensitivity. Hwang and Frierson (2013) suggested that this shortwave bias

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Nathalie Söhne, Jean-Pierre Chaboureau, and Françoise Guichard

offers the advantage that the satellite data are used in an objective way, without being combined with any ancillary data. It is especially powerful in identifying discrepancies of cloud cover forecasts ( Chaboureau et al. 2000 ; Chevallier and Kelly 2002 ) and tuning critical parameters in cloud schemes ( Chaboureau et al. 2002 ; Keil et al. 2006 ). Previous works have used the model-to-satellite approach on a long-term series of forecasts made by the Méso-NH model ( Lafore et al. 1998 ) to test

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