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Atmospheric Radiation Working Group (ARWG)

This document is a summary of the major problems in atmospheric radiation, together with recommendations for appropriate action, as evaluated by the U.S. Atmospheric Radiation Working Group (ARWG). 1 It is intended for information and possible use by atmospheric scientists, scientific committees, agencies engaged in the support of atmospheric research, and those who have the responsibility for planning future scientific programs.

The report summarizes the present status and outlines the major unsolved problems of the following five aspects of atmospheric radiation: 1) radiative transfer in realistic atmospheres, 2) radiative energy budgets, 3) radiative properties of atmosphere and surface, 4) radiative instruments and measurements, and 5) radiative interactions in dynamical systems. The final, and probably most important, section consists of recommendations for action that can be taken now to start filling the gaps in our knowledge of atmospheric radiation which are considered by the ARWG to be of highest priority. A list of members of the ARWG steering committee is included in the Appendix.

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Bingyi Wu

are generally used to characterize Arctic warming and AA. However, SATs are strongly influenced by the external forcing, such as sea ice concentrations (SICs) and surface sea temperatures (SSTs). Thus differences in SATs between the Arctic and the midlatitudes may exaggerate the thermal contrast of atmospheric circulation in the middle and lower troposphere. The updated study also indicated that warming in the lower troposphere associated with AA is not a direct driver of anomalous midlatitude

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Philip A. Feiner
,
William H. Brune
,
David O. Miller
,
Li Zhang
,
Ronald C. Cohen
,
Paul S. Romer
,
Allen H. Goldstein
,
Frank N. Keutsch
,
Kate M. Skog
,
Paul O. Wennberg
,
Tran B. Nguyen
,
Alex P. Teng
,
Joost DeGouw
,
Abigail Koss
,
Robert J. Wild
,
Steven S. Brown
,
Alex Guenther
,
Eric Edgerton
,
Karsten Baumann
, and
Juliane L. Fry

1. Introduction Copious emissions of biogenic volatile organic compounds (BVOCs) dictate the atmospheric chemical composition and chemistry in forests. During the day, these BVOCs are oxidized primarily through reactions with the hydroxyl radical (OH) and ozone (O 3 ), which leads to the production of many oxygen-containing volatile, semivolatile, and low-volatility compounds and secondary organic aerosol. Because forests blanket almost a third of the global land, understanding forest oxidation

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Zhenchen Liu
,
Wen Zhou
,
Ruhua Zhang
,
Yue Zhang
, and
Ya Wang

investigated based on the direct or remote influences of four large-scale triggers: Atmospheric circulations and patterns . For example, propagating Rossby waves originating in the west Pacific lead to upper-level enhanced anticyclones during the 1988 Great Plains drought ( Chen and Newman 1998 ), while the descending branch of the Hadley circulation is responsible for amplified subsidence and associated long-term precipitation deficits during the 2011 eastern China spring–summer drought ( Jin et al

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R. D. Sharman
,
L. B. Cornman
,
G. Meymaris
,
J. Pearson
, and
T. Farrar

,” “moderate,” “severe,” or “extreme” ( Federal Aviation Administration 2012 , their Table 7-1-9]. Although formal definitions of these severity categories are provided in terms of normal accelerations or airspeed fluctuations, in practice they are both subjective (based on aircrew interpretation) and aircraft dependent, making them ill suited for providing reliable and consistent maps of atmospheric turbulence levels. To address these deficiencies, an in situ turbulence-reporting algorithm ( Cornman et al

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Hai Lin
,
Gilbert Brunet
, and
Jacques Derome

1. Introduction The Madden–Julian oscillation (MJO) is the dominant mode of intraseasonal variability in the Tropics. It organizes convection and precipitation, thus has a great impact on the weather in the Tropics. It has a significant influence on the extratropical atmospheric variability, possibly through Rossby wave propagation (e.g., Ferranti et al. 1990 ; Hsu 1996 ), and thus could provide an important signal source for the extratropical weather forecasts on intraseasonal time scales

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Clara Deser
and
Adam S. Phillips

1. Introduction The atmospheric circulation plays a central role in climate, influencing the global distributions of precipitation and air temperature. Since the middle of the twentieth century, the large-scale circulation has exhibited numerous changes, including an intensification of the midlatitude westerlies over the North Pacific, North Atlantic, and Southern Oceans, and a weakening of the trade winds over the tropical Pacific ( Trenberth and Hurrell 1994 ; Hurrell 1995 ; Clarke and

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Maxime Perron
and
Philip Sura

1. Introduction In meteorology, it is often assumed that the probability distribution function (PDF) of many atmospheric variables is Gaussian, or normal, except for midlatitude regions exhibiting potential regime behavior. In a Gaussian PDF, the entire distribution can be described by knowing the mean and variance of the data. Approximately 68%, 95%, and 99% of the data can be found between ±1, 2, and 3 standard deviations, respectively. Indeed, the central limit theorem states that as the sum

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Jing Huang
,
Yang Zhang
,
Xiu-Qun Yang
,
Xuejuan Ren
, and
Haibo Hu

wintertime North Pacific. Relative to the impacts of the SAFZ on the low-level storm track, the SAFZ’s impacts on the upper-level storm track and atmospheric circulations, however, show much less agreement. The case study of Taguchi et al. (2009) showed that the enhancement of storm track with the SAFZ is confined in the lower troposphere and much weaker in the upper troposphere. O’Reilly and Czaja (2015) found that the response of wintertime atmospheric circulation to the SST front exhibits dipole

Open access
Philip Sura
and
Abdel Hannachi

scales are the result of complex nonlinear interactions among many subsystems, degrees of freedom, or modes (e.g., Ghil and Childress 1987 ; Ghil and Robertson 2002 ). Because this paper (with a few exceptions) focuses on synoptic and large-scale atmospheric variability, we predominantly distinguish between synoptic time scales of up to a few days and low-frequency variability, defined as being above the synoptic time scale. Whereas the synoptic band is populated by dynamically relatively well

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