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I. D. Stewart and T. R. Oke

View of LCZ 1 in Seattle, Washington. Photo: I. D. Stewart The new “local climate zone” (LCZ) classification system provides a research framework for urban heat island studies and standardizes the worldwide exchange of urban temperature observations. The study of urban heat islands (UHIs) implicates two of the most serious environmental issues of the twentieth century: population growth and climate change. This partly explains why the worldwide stock of heat island studies has grown so

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Brent M. Lofgren and Andrew D. Gronewold

projection and hydrologic projection? How do we serve the data needs of the hydrologic and meteorological communities in a mutually consistent way? What is the role of empirical and process-based models in a nonstationary regime? How do we educate researchers and the general public about relevant caveats in simulations of hydrologic impacts of climate change? BRIDGING CLIMATE AND HYDROLOGICAL SCIENCE. Efforts to converge on a consistent answer to the question of how to bridge the gap between projections

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Stephan Rasp, Hauke Schulz, Sandrine Bony, and Bjorn Stevens

regimes. This coherence supports the hypothesis that the subjective patterns are associated with meaningful and distinct physical processes. Though the combination of crowdsourced labels and deep learning helped answering many of the questions raised at the outset of this study it also raises some new ones, for instance whether important cloud regimes are missing from our classification. Unsupervised classification algorithms like the one deployed by Denby (2020) can be a good starting point to

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John P. Dawson, Bryan J. Bloomer, Darrell A. Winner, and Christopher P. Weaver

climate change may exacerbate high PM episodes, but the meteorological variables to which these studies point as driving changes in pollution episodes require more attention in modeling studies of future climate. Potentially important aspects of this issue include stagnation, synoptic-scale meteorology, weather-type classification, and precipitation frequency. In this paper we have summarized the current understanding in these areas to suggest a set of high-priority foci for future climate and PM

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R. Hollmann, C. J. Merchant, R. Saunders, C. Downy, M. Buchwitz, A. Cazenave, E. Chuvieco, P. Defourny, G. de Leeuw, R. Forsberg, T. Holzer-Popp, F. Paul, S. Sandven, S. Sathyendranath, M. van Roozendael, and W. Wagner

been produced from single satellite sensors for a given epoch but are not fully consistent ( Jung et al. 2006 ). Furthermore, land-cover classifications are not consistent with plant functional types—a parameterization concept used in climate models. Long time series of high-resolution land surface reflectance are yet to be processed in a coherent way to deliver consistent land-cover information for decadal time scales. The results of the land-cover requirements analysis therefore focused on issues

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Linda O. Mearns, Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, and Mark Snyder

models reproduce climatically distinct subregions of the domain. Southern California has a Mediterranean climate ( Köppen classifications Csa and Csb; Köppen 1900 ) with a pronounced summer dry season and mild, damp winters. There is a strong ENSO signal in this region. The Great Plains has a midlatitude continental climate (mostly Köppen classifications Dfa and Dfb) with a large annual temperature range, having warm summers and cold winters. The region has a precipitation maximum in late spring and

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Vasubandhu Misra, Tracy Irani, Lisette Staal, Kevin Morris, Tirusew Asefa, Chris Martinez, and Wendy Graham

in coproduction met several of these classifications and they will become apparent from the discussions presented in the following subsections. The facilitation process. The purpose of establishing this partnership was to embark in coproduction of user-relevant data, tools, and information on climate variability and change through collaborative learning and shared dialogue. The facilitation process was informed by two theoretical foundations: experiential learning ( Kolb 1984 ) and communities of

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Amy H. Butler, Dian J. Seidel, Steven C. Hardiman, Neal Butchart, Thomas Birner, and Aaron Match

Six decades after the discovery of sudden stratospheric warmings, their multiple, and somewhat ambiguous, definitions merit scrutiny in light of contemporary research and forecasting challenges and opportunities. Sudden stratospheric warmings 1 (SSWs) are among the most impressive dynamical events in the physical climate system. Driven by the breaking of planetary waves propagating up from the troposphere, these events involve a large and rapid temperature increase (>30–40 K in a matter of

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Agus Santoso, Harry Hendon, Andrew Watkins, Scott Power, Dietmar Dommenget, Matthew H. England, Leela Frankcombe, Neil J. Holbrook, Ryan Holmes, Pandora Hope, Eun-Pa Lim, Jing-Jia Luo, Shayne McGregor, Sonja Neske, Hanh Nguyen, Acacia Pepler, Harun Rashid, Alex Sen Gupta, Andréa S. Taschetto, Guomin Wang, Esteban Abellán, Arnold Sullivan, Maurice F. Huguenin, Felicity Gamble, and Francois Delage

. Climate Dyn . , 41 , 443 – 453 , https://doi.org/10.1007/s00382-012-1593-8 . 10.1007/s00382-012-1593-8 Sullivan , A. , J.-J. Luo , A. C. Hirst , D. Bi , W. Cai , and J. He , 2016 : Robust contribution of decadal anomalies to the frequency of central-Pacific El Niño . Sci. Rep. , 6 , 38540 , https://doi.org/10.1038/srep38540 . 10.1038/srep38540 Takahashi , K. , and B. Dewitte , 2016 : Strong and moderate nonlinear El Niño regimes . Climate Dyn . , 46 , 1627 – 1645

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Luiz A. T. Machado, Maria A. F. Silva Dias, Carlos Morales, Gilberto Fisch, Daniel Vila, Rachel Albrecht, Steven J. Goodman, Alan J. P. Calheiros, Thiago Biscaro, Christian Kummerow, Julia Cohen, David Fitzjarrald, Ernani L. Nascimento, Meiry S. Sakamoto, Christopher Cunningham, Jean-Pierre Chaboureau, Walter A. Petersen, David K. Adams, Luca Baldini, Carlos F. Angelis, Luiz F. Sapucci, Paola Salio, Henrique M. J. Barbosa, Eduardo Landulfo, Rodrigo A. F. Souza, Richard J. Blakeslee, Jeffrey Bailey, Saulo Freitas, Wagner F. A. Lima, and Ali Tokay

modeling, is expected to create a solid basis for the development of improved database on cloud process over the continental tropics. This dataset contains hydrometeor classifications, thermodynamics profiles, rainfall drop size distributions, and several remote sensing (both active and passive) cloud property measurements. Realistic parameterizations of cloud processes are a prerequisite for reliable current and future climate simulations. Meteorological models, at very high resolution, explicitly

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