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William B. Rossow and Robert A. Schiffer

This progress report on the International Satellite Cloud Climatology Project (ISCCP) describes changes made to produce new cloud data products (D data), examines the evidence that these changes are improvements over the previous version (C data), summarizes some results, and discusses plans for the ISCCP through 2005. By late 1999 all datasets will be available for the period from July 1983 through December 1997. The most significant changes in the new D-series cloud datasets are 1) revised radiance calibrations to remove spurious changes in the long-term record, 2) increased cirrus detection sensitivity over land, 3) increased low-level cloud detection sensitivity in polar regions, 4) reduced biases in cirrus cloud properties using an ice crystal microphysics model in place of a liquid droplet microphysics model, and 5) increased detail about the variations of cloud properties. The ISCCP calibrations are now the most complete and self-consistent set of calibrations available for all the weather satellite imaging radiometers: total relative uncertainties in the radiance calibrations are estimated to be ≲ 5% for visible and ≲ 2% for infrared; absolute uncertainties are < 10% and < 3%, respectively. Biases in (detectable) cloud amounts have been reduced to ≲ 0.05, except in the summertime polar regions where the bias may still be ~ 0.10. Biases in cloud-top temperatures have been reduced to ≲ 2 K for lower-level clouds and ≲ 4 K for optically thin, upper-level clouds, except when they occur over lower-level clouds. Using liquid and ice microphysics models reduces the biases in cloud optical thicknesses to ≲ 10%, except in cases of mistaken phase identification; most of the remaining bias is caused by differences between actual and assumed cloud particle sizes and the small effects of cloud variations at scales < 5 km. Global mean cloud properties averaged over the period July 1983–June 1994 are the following: cloud amount = 0.675 ± 0.012; cloud-top temperature = 261.5 ± 2.8 K; and cloud optical thickness = 3.7 ± 0.3, where the plus–minus values are the rms deviations of global monthly mean values from their long-term average. Long-term, seasonal, synoptic, and diurnal cloud variations are illustrated. The ISCCP dataset quantifies the variations of cloud properties at mesoscale resolution (3 h, 30 km) covering the whole globe for more than a decade, making it possible to study cloud system evolution over whole life cycles, watching interactions with the atmospheric general circulation. Plans for the next decade of the World Climate Research Programme require continuing global observations of clouds and the most practical way to fulfill this requirement is to continue ISCCP until it can be replaced by a more capable system with similar time resolutions and global coverage.

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Lisa Goddard, James W. Hurrell, Benjamin P. Kirtman, James Murphy, Timothy Stockdale, and Carolina Vera

Although differences exist between seasonal- and decadal-scale climate variability, predictability, and prediction, investment in observations, prediction systems, and decision systems for either time scale can benefit both. While some might call Decadal Prediction the new kid on the block, it would be better to consider it the latest addition to the Climate Prediction family. Decadal Prediction is the fascinating baby that all wish to talk about, with such great expectations for what she might

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Andrew D. Gronewold and Craig A. Stow

the Great Lakes follow a strong seasonal pattern closely linked with the timing and magnitude of the major components of the regional water budget, with relatively low water levels in the winter months, rising water levels in the spring, and decreasing water levels in the late summer and early fall. Water-level measurements on Lake Erie during the 2011 and 2012 water years (October 2010 through September 2011, and October 2011 through September 2012, respectively), however, reflect dramatic and

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Andrew W. Robertson, Arun Kumar, Malaquias Peña, and Frederic Vitart

There is growing interest in the scientific, operational, and applications communities in developing forecasts that fill the gap between medium-range weather forecasts (up to 2 weeks) and long-range or seasonal ones (3–6 months). A new World Weather Research Programme/World Climate Research Programme (WWRP/WCRP initiative on subseasonal to seasonal (S2S) prediction has recently been launched to foster collaboration and research in the weather and climate communities, with the goals of improving

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Xiaoyan Wang, Robert E. Dickinson, Liangyuan Su, Chunlüe Zhou, and Kaicun Wang

, possibly from the occurrence of dust storms with strong winds. In the summer, air stagnation effects are small or even reversed, especially over the United States and China (cf. Figs. ES7 and ES8 for details as to reasons). F ig . 7. Seasonal effects of air stagnation events on PM 2.5 mass concentration (%). A day can be defined as an air stagnation day or no-stagnation day based on the wind–BLH thresholds of Fig. 6 . Three continuous stagnation days are defined as an air stagnation event. The

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F. Vitart, C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Déqué, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H.-S. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A. W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D.-J. Won, H. Xiao, R. Zaripov, and L. Zhang

A database containing subseasonal to seasonal forecasts from 11 operational centers is available to the research community and will help advance our understanding of predictability at the subseasonal to seasonal time range. Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather (up to 15 days) and long-range or seasonal (3–6 months) forecasts. Skillful subseasonal to seasonal prediction (forecast range

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Xing Yuan, Joshua K. Roundy, Eric F. Wood, and Justin Sheffield

A multimodel global hydrologic forecasting system that provides hydroclimate prediction services is evaluated to understand the predictability of seasonal hydrologic extremes over global major river basins. Persistent hydrologic extreme events such as droughts and wet spells (rainfall anomalies with long durations) have devastating impacts on the human and natural systems and have caused total economic losses of about hundreds of billions of dollars in the United States ( Smith and Katz 2013

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Z. Q. Li, H. Xu, K. T. Li, D. H. Li, Y. S. Xie, L. Li, Y. Zhang, X. F. Gu, W. Zhao, Q. J. Tian, R. R. Deng, X. L. Su, B. Huang, Y. L. Qiao, W. Y. Cui, Y. Hu, C. L. Gong, Y. Q. Wang, X. F. Wang, J. P. Wang, W. B. Du, Z. Q. Pan, Z. Z. Li, and D. Bu

, while slightly higher at coastal (e.g., Zhoushan and Haikou) and dust sites (e.g., Zhangye and Minqin) than continental sites (e.g., Beijing, Xi’an, Chengdu, and Harbin). Seasonal variations. The multiyear seasonal average AOD (500 nm) and monthly average AE (440–870 nm) are shown in Fig. 4 . These results reveal several seasonal variation patterns: Western and arid regions : Both seasonal trends of AOD and AE at Minqin and Zhangye (about 250-km distance apart) are very similar. Because of frequent

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William Rossow

characterizing the associated properties of the atmosphere and surface that affect cloud processes and, together with cloud properties, also affect Earth's radiation budget. A brief summary of all the research achievements employing ISCCP and other satellite data products highlighted 1) the capability to determine the surface and in-atmosphere radiation budgets as well as the cloud effects on radiative fluxes at the surface, in the atmosphere, and at the top of atmosphere; 2) the beginning of the

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Hunter M. Jones, Amanda V. Quintana, Juli Trtanj, John Balbus, Paul Schramm, Shubhayu Saha, Trisha Castranio, and Tom E. Di Liberto

Climate and Health Monitor and Outlook Workshop What : The U.S. Global Change Research Program convened a workshop to explore the prospect of and process for producing experimental seasonal climate and health outlooks for the United States through consultation with leading experts in vector- and water-borne disease prediction. When : 19 September 2019 Where : Washington, D.C. The U.S. Global Change Research Program (USGCRP) Climate and Health Monitor and Outlook (CHMO) workshop convened 23

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