A new coupled global NCEP Reanalysis for the period 1979-present is now available, at much higher temporal and spatial resolution, for climate studies. T he first reanalysis at NCEP (all acronyms are defined in the appendix), conducted in the 1990s, resulted in the NCEP-NCAR reanalysis (Kalnay et al. 1996), or R1 for brevity, and ultimately covered many years, from 1948 to the present (Kistler et al. 2001). It is still being executed at NCEP, to the benefit of countless users for monthly, and even daily, updates of the current state of the atmosphere. At the same time, other reanalyses were being conducted, namely, ERA-15 (Gibson et al. 1997) was executed for a more limited period (1979-93) at the ECMWF, COLA conducted a short reanalysis covering the May 1982-November 1983 period (Paolino et al. 1995), and NASA GSFC conducted a reanalysis covering the 1980-94 period (Schubert et al. 1997). The general purpose of conducting reanalyses is to produce multiyear global state-of-the-art gridded representations of atmospheric states, generated by a constant model and a constant data assimilation system. To use the same model and data assimilation over a very long period was the great advance during the 1990s, because gridded datasets available before 1995 had been created in real time by ever-changing models and analysis methods, even by hand analyses prior to about 1965. The hope was that a reanalysis,

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period , which provided the initial conditions to carry out a comprehensive reforecast over 29 years . This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

Abstract. Snow on Antarctic sea ice plays a complex and highly variable role in air-sea-ice interaction processes and the Earth's climate system. Using data collected mostly during the past 10 years, this paper reviews the following topics: snow thickness and snow type and their geographical and seasonal variations; snow grain size, density, and salinity; frequency of occurrence of slush; thermal conductivity, snow surface temperature, and temperature gradients within snow; and the effect of snow thickness on albedo. Major findings include large regional and seasonal differences in snow properties and thicknesses; the consequences of thicker snow and thinner ice in the Antarctic relative to the Arctic (e.g., the importance of flooding and snow-ice formation); the potential impact of increasing snowfall resulting from global climate change; lower observed values of snow thermal conductivity than those typically used in models; periodic large-scale melt in winter; and the contrast in summer melt processes between the Arctic and the Antarctic. Both climate modeling and remote sensing would benefit by taking account of the differences between the two polar regions. INTRODUCTIONAt maximum extent each year (September-October), sea ice covers a vast area of the Southern Ocean (---19 million km2), attaining latitudes as far north as ---55øS [Gloersen et al., 1992]. In so doing, it profoundly alters the exchange of energy and mass between ocean and atmosphere and forms an integral part of the global climate system. These effects are significantly amplified by the presence of an insulative snow cover which is itself highly variable in thickness and properties. Persistently strong winds redistribute the snow, and its properties [Gordon and Huber, 1990] on snow distribution and properties have only been conducted in the past 5-10 years. These studies are beginning to establish the full significance of snow on Antarctic sea ice as a key component of the global climate system. In this paper we review the major findings. Section 2 is a summary of snow data from five Antarctic sectors (designated by Gloersen et al. [1992]), namely, the Weddell Sea (20øE-60øW), the Indian Ocean (20øE-90øE), the western Pacific Ocean (90øE-160øE), the Ross Sea (160øE-140øW), and the Bellingshausen and Amundsen Seas (140øW-60øW), as shown in Figure 1. The Indian and western Pacific Ocean sectors are collectively referred to as the East Antarctic sector. Section 3 assesses the significance of snow in the air-sea-ice interaction system. New findings have significant implications for modeling (both physical and biological) and remotesensing studies of Antarctic sea ice. Gaps in our current knowledge are identified. Finally, the possible enhanced role of snow under global warming conditions is examined. Throughout, snow is described using the combined morphological and process-oriented classification of snow types of Colbeck et al. [1990] As a result, thickness may not be directly related to either the frequency or duration of snowfall.Mean snow thi...

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Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.