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S. Indira Rani, T. Arulalan, John P. George, E. N. Rajagopal, Richard Renshaw, Adam Maycock, Dale M. Barker, and M. Rajeevan

1. Introduction Meteorological reanalyses produce the best estimate of the state of the atmosphere using the latest NWP systems with past observational data for any period and place ( Thorne and Vose 2010 ). Observations alone can provide only a limited understanding of the past weather and climate, specific to observed variables at few locations. In contrast, the reanalysis provides comprehensive snapshots of conditions at regular intervals over long periods, often years or decades, from

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Ryan O’Donnell, Nicholas Lewis, Steve McIntyre, and Jeff Condon

noninstrumented points. In Chapman and Walsh (2007) , interpolation was guided by correlation length scales calculated using the International Comprehensive Ocean–Atmosphere Dataset (ICOADS) for ocean and coastal areas and station-to-station pairs for the Antarctic interior. In Monaghan et al. (2008) , the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data were utilized to provide the kriging field. In contrast, S09 perform multiple linear regression of satellite

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Guyu Cao and Guang J. Zhang

1. Introduction The Madden–Julian oscillation (MJO; Madden and Julian 1971 , 1972 ) is a fundamental tropical process contributing to intraseasonal variability. It has been widely studied because of its significant influence on tropical and global climate and weather systems ( Zhang 2005 ). However, it is very difficult to predict MJO accurately by global weather prediction models and to simulate it realistically by global climate models (GCMs). Several possible reasons have been suggested

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Hervé Le Treut, Michèle Forichon, Olivier Boucher, and Zhao-Xin Li

response to given modifications of its boundary conditions. The objective of the present study is to determine to what extent they also constrain the geographical response of the climate system. For that purpose, we compare the equilibrium response of the model to different prescribed forcings. The nature and amplitude of these forcings refer to the anthropogenic influence over the past century. Two main factors are considered, because they are probably the dominant terms of this past evolution and

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Jonathan Gula and W. Richard Peltier

inhabitants living within their watershed, are vital to the economies of both the United States and Canada. How this region will be impacted by the ongoing process of global warming is clearly of significant interest. Large freshwater systems such as the Great Lakes play a key role in determining the climate of their basins and adjacent regions by airmass modification through the exchange of heat and moisture with the atmosphere. They significantly affect the structure of the atmospheric boundary layer

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Prince K. Xavier, Jean-Philippe Duvel, Pascale Braconnot, and Francisco J. Doblas-Reyes

–ocean fluxes to represent realistic ISV properties. The ISV evaluation metric bears a significant relationship with the representation of both HF variance and seasonal mean climate. In a seamless modeling context, it is postulated that synoptic-scale weather systems could possibly impact the ISV through the modification of ocean heat content and the associated air–sea interaction processes. An important link that bridges the gap between weather and climate over the Asian monsoon region is the ISV, and its

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Menglin Jin, Robert E. Dickinson, and Da Zhang

effect in New York City. J. Appl. Meteor. , 7 , 575 – 582 . Brest , C. L. , 1987 : Seasonal albedo of an urban/rural landscape from satellite observations. J. Climate Appl. Meteor. , 26 , 1169 – 1187 . Changnon Jr. , S. A. , 1978 : Urban effects on severe local storms at St. Louis. J. Appl. Meteor. , 17 , 578 – 592 . Changnon Jr. , S. A. , 1992 : Inadvertent weather modification in urban areas: Lessons for global climate change. Bull. Amer. Meteor. Soc. , 73 , 619 – 627

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David L. Williamson and Jerry G. Olson

closely related to its predecessor CAM2 ( Collins et al. 2003 ; Kiehl and Gent 2004 ), with a few of its component parameterizations essentially unchanged. Nevertheless, extensive modifications have been introduced into the cloud and precipitation processes and are described in Boville et al. (2006) , Zhang et al. (2003) , and Collins et al. (2006b) . Many of the formulation changes were introduced to eliminate significant biases in the climate simulated by CAM2, which limited its utility for

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Klaus Fraedrich, Axel Kleidon, and Frank Lunkeit

performance, in particular its hydrological cycle, in very good agreement with observations ( Lau et al. 1996 ). Therefore, it is an ideal tool to determine the atmospheric general circulation at its extreme vegetation bounds. a. Vegetation setting An integral part of the model is the land parameterization scheme. It describes the interaction between the GCM’s weather and soil by the surface fluxes of heat, water, and momentum. Soil hydrology is incorporated by a budget equation. It includes surface

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S. Vannitsem and F. Chomé

propose transient simulation strategies in order to compensate for these deficiencies. Chomé et al. (1999 , 2002 ) have shown, in the context of a one-dimensional convection model and in an operational regional weather prediction model, the decisive and complex role played by the domain size on the quality of the solution. One spectacular result is that depending on the domain size, a regional operational weather prediction model can generate qualitatively different solutions (periodic, chaotic, etc

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