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Adam A. Scaife, Elizabeth Good, Ying Sun, Zhongwei Yan, Nick Dunstone, Hong-Li Ren, Chaofan Li, Riyu Lu, Peili Wu, Zongjian Ke, Zhuguo Ma, Kalli Furtado, Tongwen Wu, Tianjun Zhou, Tyrone Dunbar, Chris Hewitt, Nicola Golding, Peiqun Zhang, Rob Allan, Kirstine Dale, Fraser C. Lott, Peter A. Stott, Sean Milton, Lianchun Song, and Stephen Belcher

Abstract

We present results from the first 6 years of this major U.K. government funded project to accelerate and enhance collaborative research and development in climate science, forge a strong strategic partnership between U.K. and Chinese climate scientists, and demonstrate new climate services developed in partnership. The development of novel climate services is described in the context of new modeling and prediction capability, enhanced understanding of climate variability and change, and improved observational datasets. Selected highlights are presented from over 300 peer reviewed studies generated jointly by U.K. and Chinese scientists within this project. We illustrate new observational datasets for Asia and enhanced capability through training workshops on the attribution of climate extremes to anthropogenic forcing. Joint studies on the dynamics and predictability of climate have identified new opportunities for skillful predictions of important aspects of Chinese climate such as East Asian summer monsoon rainfall. In addition, the development of improved modeling capability has led to profound changes in model computer codes and climate model configurations, with demonstrable increases in performance. We also describe the successes and difficulties in bridging the gap between fundamental climate research and the development of novel real-time climate services. Participation of dozens of institutes through subprojects in this program, which is governed by the Met Office Hadley Centre, the China Meteorological Administration, and the Institute of Atmospheric Physics, is creating an important legacy for future collaboration in climate science and services.

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Curtis R. Wood, Samantha J. Arnold, Ahmed A. Balogun, Janet F. Barlow, Stephen E. Belcher, Rex E. Britter, Hong Cheng, Adrian Dobre, Justin J. N. Lingard, Damien Martin, Marina K. Neophytou, Fredrik K. Petersson, Alan G. Robins, Dudley E. Shallcross, Robert J. Smalley, James E. Tate, Alison S. Tomlin, and Iain R. White

In the event of a release of toxic gas in the center of London, emergency services personnel would need to determine quickly the extent of the area contaminated. The transport of pollutants by turbulent flow within the complex streets and building architecture of London, United Kingdom, is not straightforward, and we might wonder whether it is at all possible to make a scientifically reasoned decision. Here, we describe recent progress from a major U.K. project, Dispersion of Air Pollution and its Penetration into the Local Environment (DAPPLE; information online at www.dapple.org.uk). In DAPPLE, we focus on the movement of airborne pollutants in cities by developing a greater understanding of atmospheric flow and dispersion within urban street networks. In particular, we carried out full-scale dispersion experiments in central London from 2003 through 2008 to address the extent of the dispersion of tracers following their release at street level. These measurements complemented previous studies because 1) our focus was on dispersion within the first kilometer from the source, when most of the material was expected to remain within the street network rather than being mixed into the boundary layer aloft; 2) measurements were made under a wide variety of meteorological conditions; and 3) central London represents a European, rather than North American, city geometry. Interpretation of the results from the full-scale experiments was supported by extensive numerical and wind tunnel modeling, which allowed more detailed analysis under idealized and controlled conditions. In this article, we review the full-scale DAPPLE methodologies and show early results from the analysis of the 2007 field campaign data.

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