Search Results

You are looking at 1 - 10 of 4,506 items for :

  • Radiative fluxes x
  • Journal of Climate x
  • All content x
Clear All
John E. Walsh, William L. Chapman, and Diane H. Portis

time-specific reference frame common to the reanalyses and the ARM measurements. Specifically, we will address three central questions: Do the reanalysis models show any systematic errors in cloud cover and radiative fluxes? If so, how do these errors vary seasonally and across the reanalyses? Are biases in reanalysis radiation variables independent of the cloud fraction biases? Are the characteristics of the reanalyses’ clouds at Barrow, Alaska, representative of reanalyses’ clouds over the Arctic

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

1. Introduction Cloud radiative effect (CRE), defined as the difference between the radiative flux over a region in the presence of clouds and that under cloud-free conditions, is one of the highest priority quantities used in climate model evaluation efforts ( Burrows et al. 2018 ). Its use in climate model evaluation was first proposed in Ramanathan (1987) and Cess and Potter (1987) , and first demonstrated in Ramanathan et al. (1989) . Since then, many studies have used satellite

Open access
Jonathan M. Winter and Elfatih A. B. Eltahir

available energy. The empirical responses of latent heat flux to changes in available energy are generally greater than the theoretical sensitivities of latent heat flux to available energy derived from the Penman–Monteith framework. Both models underestimate the response of latent heat flux to changes in available energy when compared to FLUXNET using the empirical method. This damped response of latent heat flux to an increase in the radiative forcing will have a substantial effect on the energy

Full access
Jason Cole, Howard W. Barker, Norman G. Loeb, and Knut von Salzen

Ramanathan 1985 ) in GCMs is an ongoing line of research that ultimately addresses both the structural properties of clouds, ranging from particle size distributions to cloud fraction parameterizations, and radiative transport solvers that use these properties to compute fluxes and heating rate profiles ( Barker et al. 2003 ). Given the global nature of the problem, it is essential that GCM CREs be compared to global observations such as those provided by satellite-based instruments. Typically, GCM top

Full access
C. J. Stubenrauch, F. Eddounia, J. M. Edwards, and A. Macke

section 3 . Here, p cld corresponds to a radiatively active altitude that is about the midlevel pressure of the clouds, as has been demonstrated in a study with quasi-simultaneous data from the Lidar In-Space Technology Experiment (LITE) onboard the space shuttle Discovery ( Stubenrauch et al. 2005 ). The radiative flux computations are performed for large-scale semitransparent cirrus: clouds with a horizontal extent of at least 1° latitude × 1° longitude, p cld < 440 hPa and 0.50 < ε cld < 0

Full access
Jason M. English, Andrew Gettelman, and Gina R. Henderson

occurred in recent decades and projecting future Arctic climate change is challenging because of the complexity of representing the processes associated with Arctic amplification and the sensitivity of radiative fluxes to differences in cloud, snow-extent, and sea-ice coverage and albedo. Hence, in order to accurately represent Arctic climate, models must accurately represent numerous components including surface type and albedo, cloud amount, and cloud phase. Additionally, the Arctic region suffers

Full access
Behnjamin J. Zib, Xiquan Dong, Baike Xi, and Aaron Kennedy

must first be addressed. Several studies have investigated the performance of reanalyses over the Arctic for a variety of fields including atmospheric moisture budgets ( Bromwich et al. 2000 , 2002 ), upper-level winds ( Francis 2002 ), precipitation ( Serreze and Hurst 2000 ), cloud fraction (CF) and radiative fluxes ( Walsh et al. 2009 ), and general tropospheric assessments ( Bromwich and Wang 2005 ; Bromwich et al. 2007 ). These studies, however, were based on the earlier generations of

Full access
Virendra P. Ghate, Bruce A. Albrecht, Christopher W. Fairall, and Robert A. Weller

Climate Processes in the Coupled Ocean–Atmosphere System (EPIC) was conducted in 2001 ( Bretherton et al. 2004 ). Under this study the Woods Hole Oceanography Institute’s (WHOI) Upper Ocean Process (UOP) group deployed an Ocean Reference Station (Stratus ORS) near the annual maximum of stratus cloud cover in October 2000. The Stratus ORS is located at 20°S, 85°W and has collected observations of broadband radiative fluxes and surface meteorological parameters continuously since it was launched

Full access
Brian J. Soden, Isaac M. Held, Robert Colman, Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

and correlations between different feedback variables. There are two widely used, but very different, approaches for quantifying climate feedbacks in GCMs. The first method, introduced by Wetherald and Manabe (1988) , uses offline calculations to compute the change in radiative fluxes that results from substituting one variable at a time from the perturbed climate state into the control climate. This procedure can be computationally expensive, and there are complexities in its implementation that

Full access
Karen M. Shell, Jeffrey T. Kiehl, and Christine A. Shields

, the climate changes in response to restore the energy balance. In a steady state, imposed top-of-the-atmosphere (TOA) radiative flux changes, G , must be balanced by changes in outgoing longwave radiation, F , and absorbed solar radiation, Q : The climate sensitivity determines how much the climate, represented by the surface temperature, T s , needs to change in order for the TOA fluxes to return to equilibrium: where γ is the feedback parameter, the inverse of the climate sensitivity. From

Full access