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Rebecca J. Ross
,
William P. Elliott
,
Dian J. Seidel
, and
Participating AMIP-II Modeling Groups

water vapor (e.g., Hall and Manabe 1999 ; Garratt et al. 1999 ). Surface and tropospheric warming lead to enhanced tropospheric specific humidity, which, by virtue of the greenhouse effect of water vapor, further warms the surface. Here, model simulations from the second Atmospheric Model Intercomparison Project (AMIP II) are examined to determine how well the models simulate the observed joint distributions of temperature and humidity. This comparison provides a second-order test of the models

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Bryan C. Weare
and
Amip Modeling Groups

DECEMBER 1996 W E A R E 3419Evaluation of the Vertical Structure of Zonally Averaged Cloudiness and Its Variability in the Atmospheric Model Intercomparison Project BRYAN C. WEAREAtmospheric Science Program, University of California at Davis, Davis, California AMIP MODELING GROUPS,*(Manuscript received 14 June 1995, in final form 13 May 1996

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Miodrag Rančić
,
R. James Purser
,
Dušan Jović
,
Ratko Vasic
, and
Thomas Black

1. Introduction This paper describes a cubed-sphere version of a global, nonhydrostatic model of the atmosphere, the Nonhydrostatic Multiscale Model on the B grid (NMMB) (e.g., Janjić et al. 2001 ; Janjić 2003 ; Janjić and Gall 2012 ), developed at the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP). The NMMB was developed in both regional and global modes, the latter using the standard longitude–latitude (or geographical) grid, with polar

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Randal D. Koster
,
Qing Liu
,
Sarith P. P. Mahanama
, and
Rolf H. Reichle

errors from the radio frequency interference that can plague such datasets ( Piepmeier et al. 2014 , 2017 ; Kerr et al. 2016 ). Overall, SMAP soil moisture retrieval accuracy has been shown to be quite high ( Chan et al. 2016a , 2018 ). A unique feature of the core SMAP mission is the publication of an enhanced Level-4 Soil Moisture (L4_SM) product through the assimilation of the observed brightness temperatures into a land surface model (LSM). Through the assimilation process ( Reichle et al

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Stanley G. Benjamin
,
Stephen S. Weygandt
,
John M. Brown
,
Ming Hu
,
Curtis R. Alexander
,
Tatiana G. Smirnova
,
Joseph B. Olson
,
Eric P. James
,
David C. Dowell
,
Georg A. Grell
,
Haidao Lin
,
Steven E. Peckham
,
Tracy Lorraine Smith
,
William R. Moninger
,
Jaymes S. Kenyon
, and
Geoffrey S. Manikin

1. Introduction In this paper, we describe the latest version of the Rapid Refresh assimilation/model system, which provides a critical component of NOAA weather guidance focusing on situational awareness and short-range forecasting. Environmental situational awareness can be defined [e.g., Jeannot (2000) for air-traffic management] as having a synthesis of all available observations describing current environmental conditions to allow improved decision-making. Weather information is a key

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Hyun-Sook Kim
,
Carlos Lozano
,
Vijay Tallapragada
,
Dan Iredell
,
Dmitry Sheinin
,
Hendrik L. Tolman
,
Vera M. Gerald
, and
Jamese Sims

and Chen (2012) have demonstrated in their numerical studies that two-way interaction must be included, in order to improve atmosphere and ocean forecast skill. Based on the analysis of the 11-yr storm guidance performance of the operational Geophysical Fluid Dynamics Laboratory (GFDL) hurricane–ocean model, Bender et al. (2007) have concluded that skill improvement in hurricane intensity forecast can be also be improved by accurate initialization of ocean mesoscale features. Toward having a

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J. F. Scinocca
,
V. V. Kharin
,
Y. Jiao
,
M. W. Qian
,
M. Lazare
,
L. Solheim
,
G. M. Flato
,
S. Biner
,
M. Desgagne
, and
B. Dugas

1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of

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Shuyi S. Chen
,
Wei Zhao
,
Mark A. Donelan
, and
Hendrik L. Tolman

1. Introduction The intensity of a hurricane is affected by two competing physical processes at the air–sea interface: the heat and moisture fluxes that fuel the storm and the surface friction (or momentum flux into the ocean) that dissipates the storm. Using an idealized axisymmetric tropical cyclone (TC) model Emanuel (1995) has demonstrated the sensitivity of TC intensity to the surface-exchange coefficients of the enthalpy (heat and moisture) and momentum fluxes. However, the enthalpy and

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Fang Dong
,
Yangchun Li
,
Bin Wang
,
Wenyu Huang
,
Yanyan Shi
, and
Wenhao Dong

1. Introduction The ocean plays key roles in regulating atmospheric CO 2 concentration and affecting climate change ( Sabine et al. 2004 ; Le Quéré et al. 2009 , 2010 ; Doney et al. 2014 ). The ocean had taken 48% of the total anthropogenic CO 2 emissions during 1800–1994 based on direct measurements of inorganic carbon ( Sabine et al. 2004 ) and had taken about 33% of the total anthropogenic CO 2 emissions during 1960–2007 based on models forced by observed atmospheric CO 2 ( Sarmiento

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Xudong Yin
,
Juanjuan Liu
, and
Bin Wang

1. Introduction There are various empirical parameters in climate models, and these parameters have great uncertainties, which may lead to significant uncertainties in climate simulations. There are many studies that quantify uncertainties in climate simulations due to uncertainties in model parameters ( Webster and Sokolov 1998 ; Murphy et al. 2004 ; Stainforth et al. 2005 ; Tebaldi et al. 2005 ; Jackson et al. 2008 ; Meinshausen et al. 2009 ; Rogelj et al. 2012 ). Moolenaar and Selten

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