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D. C. Norton and S. J. Bolsenga


A new raster-based monthly snowfall climatology was derived from 1951–1980 snowfall station data for the Laurentian Great Lakes. An automated methodology was used to obtain higher spatial resolution than previously obtained. The increase in resolution was attained by using all available monthly snowfall data from over 1230 stations per year combined with a monthly lime step to produce high-resolution grids. These monthly grids were combined to produce snow-year grids. Multiyear average grids were created and compared. This technique minimizes traditional problems associated with missing data and variable length station records.

The three 10-year average distribution maps presented here indicate a period of increasing snowfall. Windowing of the 30 seasonal grids revealed that increasing snowfall was attributable to an increase in lake effect snowfall and not to continental snowfall. The Great Lakes drainage basin was evaluated for trends within and between monthly and seasonal average snowfall through windowing of all 240 monthly grids. The graphical and statistical evaluation of these trends indicates a strong natural variation in the region's snowfall and reveals an increasing trend during the study period.

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R. A. Assel, J. E. Janowiak, D. Boyce, C. O'Connors, F. H. Quinn, and D. C. Norton

Winter 1997/98 occurred during one of the strongest warm El Niño events, and the Great Lakes experienced one of the least extensive ice covers of this century. Seasonal maximum ice cover for the combined area of the Great Lakes was the lowest on record (15%) relative to winters since 1963, a distinction formerly held by winter 1982/83 (25%), which was also an exceptionally strong El Niño winter. Maximum ice covers set new lows in winter 1997/98 for Lakes Erie (5%), Ontario (6%), and Superior (11%), tied the all-time low for Lake Huron (29%), and came close to tying the all-time low on Lake Michigan (15%; all-time low is 13%). Here the authors compare seasonal progression of lake-averaged ice cover for winter 1982/83, winter 1997/98, and a 20-winter normal (1960–79) derived from the NOAA Great Lakes Ice Atlas and discuss the 1997/98 ice cover in detail. Winter air temperatures in the Great Lakes were at or near record high levels, storms were displaced farther to the south over eastern North America, and precipitation was below average in the northern portion of the Great Lakes region. The Northern Hemispheric synoptic flow patterns responsible for this winter weather, the Great Lakes winter severity over the past two centuries, and impacts of this mild winter are briefly discussed.

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L. C. Shaffrey, I. Stevens, W. A. Norton, M. J. Roberts, P. L. Vidale, J. D. Harle, A. Jrrar, D. P. Stevens, M. J. Woodage, M. E. Demory, J. Donners, D. B. Clark, A. Clayton, J. W. Cole, S. S. Wilson, W. M. Connolley, T. M. Davies, A. M. Iwi, T. C. Johns, J. C. King, A. L. New, J. M. Slingo, A. Slingo, L. Steenman-Clark, and G. M. Martin


This article describes the development and evaluation of the U.K.’s new High-Resolution Global Environmental Model (HiGEM), which is based on the latest climate configuration of the Met Office Unified Model, known as the Hadley Centre Global Environmental Model, version 1 (HadGEM1). In HiGEM, the horizontal resolution has been increased to 0.83° latitude × 1.25° longitude for the atmosphere, and 1/3° × 1/3° globally for the ocean. Multidecadal integrations of HiGEM, and the lower-resolution HadGEM, are used to explore the impact of resolution on the fidelity of climate simulations.

Generally, SST errors are reduced in HiGEM. Cold SST errors associated with the path of the North Atlantic drift improve, and warm SST errors are reduced in upwelling stratocumulus regions where the simulation of low-level cloud is better at higher resolution. The ocean model in HiGEM allows ocean eddies to be partially resolved, which dramatically improves the representation of sea surface height variability. In the Southern Ocean, most of the heat transports in HiGEM is achieved by resolved eddy motions, which replaces the parameterized eddy heat transport in the lower-resolution model. HiGEM is also able to more realistically simulate small-scale features in the wind stress curl around islands and oceanic SST fronts, which may have implications for oceanic upwelling and ocean biology.

Higher resolution in both the atmosphere and the ocean allows coupling to occur on small spatial scales. In particular, the small-scale interaction recently seen in satellite imagery between the atmosphere and tropical instability waves in the tropical Pacific Ocean is realistically captured in HiGEM. Tropical instability waves play a role in improving the simulation of the mean state of the tropical Pacific, which has important implications for climate variability. In particular, all aspects of the simulation of ENSO (spatial patterns, the time scales at which ENSO occurs, and global teleconnections) are much improved in HiGEM.

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Jennifer A. MacKinnon, Zhongxiang Zhao, Caitlin B. Whalen, Amy F. Waterhouse, David S. Trossman, Oliver M. Sun, Louis C. St. Laurent, Harper L. Simmons, Kurt Polzin, Robert Pinkel, Andrew Pickering, Nancy J. Norton, Jonathan D. Nash, Ruth Musgrave, Lynne M. Merchant, Angelique V. Melet, Benjamin Mater, Sonya Legg, William G. Large, Eric Kunze, Jody M. Klymak, Markus Jochum, Steven R. Jayne, Robert W. Hallberg, Stephen M. Griffies, Steve Diggs, Gokhan Danabasoglu, Eric P. Chassignet, Maarten C. Buijsman, Frank O. Bryan, Bruce P. Briegleb, Andrew Barna, Brian K. Arbic, Joseph K. Ansong, and Matthew H. Alford


Diapycnal mixing plays a primary role in the thermodynamic balance of the ocean and, consequently, in oceanic heat and carbon uptake and storage. Though observed mixing rates are on average consistent with values required by inverse models, recent attention has focused on the dramatic spatial variability, spanning several orders of magnitude, of mixing rates in both the upper and deep ocean. Away from ocean boundaries, the spatiotemporal patterns of mixing are largely driven by the geography of generation, propagation, and dissipation of internal waves, which supply much of the power for turbulent mixing. Over the last 5 years and under the auspices of U.S. Climate Variability and Predictability Program (CLIVAR), a National Science Foundation (NSF)- and National Oceanic and Atmospheric Administration (NOAA)-supported Climate Process Team has been engaged in developing, implementing, and testing dynamics-based parameterizations for internal wave–driven turbulent mixing in global ocean models. The work has primarily focused on turbulence 1) near sites of internal tide generation, 2) in the upper ocean related to wind-generated near inertial motions, 3) due to internal lee waves generated by low-frequency mesoscale flows over topography, and 4) at ocean margins. Here, we review recent progress, describe the tools developed, and discuss future directions.

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David C. Leon, Jeffrey R. French, Sonia Lasher-Trapp, Alan M. Blyth, Steven J. Abel, Susan Ballard, Andrew Barrett, Lindsay J. Bennett, Keith Bower, Barbara Brooks, Phil Brown, Cristina Charlton-Perez, Thomas Choularton, Peter Clark, Chris Collier, Jonathan Crosier, Zhiqiang Cui, Seonaid Dey, David Dufton, Chloe Eagle, Michael J. Flynn, Martin Gallagher, Carol Halliwell, Kirsty Hanley, Lee Hawkness-Smith, Yahui Huang, Graeme Kelly, Malcolm Kitchen, Alexei Korolev, Humphrey Lean, Zixia Liu, John Marsham, Daniel Moser, John Nicol, Emily G. Norton, David Plummer, Jeremy Price, Hugo Ricketts, Nigel Roberts, Phil D. Rosenberg, David Simonin, Jonathan W. Taylor, Robert Warren, Paul I. Williams, and Gillian Young


The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

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Keith A. Browning, Alan M. Blyth, Peter A. Clark, Ulrich Corsmeier, Cyril J. Morcrette, Judith L. Agnew, Sue P. Ballard, Dave Bamber, Christian Barthlott, Lindsay J. Bennett, Karl M. Beswick, Mark Bitter, Karen E. Bozier, Barbara J. Brooks, Chris G. Collier, Fay Davies, Bernhard Deny, Mark A. Dixon, Thomas Feuerle, Richard M. Forbes, Catherine Gaffard, Malcolm D. Gray, Rolf Hankers, Tim J. Hewison, Norbert Kalthoff, Samiro Khodayar, Martin Kohler, Christoph Kottmeier, Stephan Kraut, Michael Kunz, Darcy N. Ladd, Humphrey W. Lean, Jürgen Lenfant, Zhihong Li, John Marsham, James McGregor, Stephan D. Mobbs, John Nicol, Emily Norton, Douglas J. Parker, Felicity Perry, Markus Ramatschi, Hugo M. A. Ricketts, Nigel M. Roberts, Andrew Russell, Helmut Schulz, Elizabeth C. Slack, Geraint Vaughan, Joe Waight, David P. Wareing, Robert J. Watson, Ann R. Webb, and Andreas Wieser

The Convective Storm Initiation Project (CSIP) is an international project to understand precisely where, when, and how convective clouds form and develop into showers in the mainly maritime environment of southern England. A major aim of CSIP is to compare the results of the very high resolution Met Office weather forecasting model with detailed observations of the early stages of convective clouds and to use the newly gained understanding to improve the predictions of the model.

A large array of ground-based instruments plus two instrumented aircraft, from the U.K. National Centre for Atmospheric Science (NCAS) and the German Institute for Meteorology and Climate Research (IMK), Karlsruhe, were deployed in southern England, over an area centered on the meteorological radars at Chilbolton, during the summers of 2004 and 2005. In addition to a variety of ground-based remote-sensing instruments, numerous rawinsondes were released at one- to two-hourly intervals from six closely spaced sites. The Met Office weather radar network and Meteosat satellite imagery were used to provide context for the observations made by the instruments deployed during CSIP.

This article presents an overview of the CSIP field campaign and examples from CSIP of the types of convective initiation phenomena that are typical in the United Kingdom. It shows the way in which certain kinds of observational data are able to reveal these phenomena and gives an explanation of how the analyses of data from the field campaign will be used in the development of an improved very high resolution NWP model for operational use.

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