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Viva F. Banzon, Richard W. Reynolds, Diane Stokes, and Yan Xue

Abstract

A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982–2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.

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Richard W. Reynolds, Nick A. Rayner, Thomas M. Smith, Diane C. Stokes, and Wanqiu Wang

Abstract

A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly −0.03°C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 1°C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales with monthly rms differences exceeding 0.5°C in some regions. These regions are primarily the mid- and high-latitude Southern Oceans and the Arctic where data are sparse, as well as high-gradient areas such as the Gulf Stream and Kuroshio where the gradients cannot be properly resolved on a 1° grid. In addition, globally averaged differences of roughly 0.05°C occur among the products on decadal scales. These differences primarily arise from the same regions where the rms differences are large. However, smaller unexplained differences also occur in other regions of the midlatitude Northern Hemisphere where in situ data should be adequate.

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Thomas M. Smith, Richard W. Reynolds, Robert E. Livezey, and Diane C. Stokes

Abstract

Studies of climate variability often rely on high quality sea surface temperature (SST) anomalies. Although the high-resolution National Centers for Environmental Prediction (formerly the National Meteorological Center) optimum interpolation (OI) SST analysis is satisfactory for these studies, the OI resolution cannot be maintained before November 1931 due to the lack of satellite data. Longer periods of SSTs have come from traditional analyses of in situ (ship and buoy) SST observations alone.

A new interpolation method is developed using spatial patterns from empirical orthogonal functions (E0Fs)—that is, a principal component analysis—to improve analyses of SST anomalies from 1950 to 1981. The method uses the more accurate OI analyses from 1982 to 1993 to produce the spatial EOFs. The dominant EOF modes (which correspond to the largest variance) are used as basis functions and are fit, in a least squares sense, to the in situ data to determine the time dependence of each mode. A complete field of SST anomalies is then reconstructed from these spatial and temporal modes. The use of EOF basis functions produces an improved in situ SST analysis that more realistically represents sparsely sampled, large-scale structures than traditional analyses.

The EOF reconstruction method is developed for the tropical Pacific for the period 1982–92 and compared to the OI. The method is then expanded to the globe and applied to a much longer period, 1950–92. The results show that the reconstructed fields generally have lower rms differences than the traditional in-situ-only analyses relative to the OI. In addition, the reconstructed fields were found to be smoother than the traditional analyses but with enhanced large-scale signals (e.g., ENSO). Regions where traditional analyses are adequate include some parts of the North Atlantic and the North Pacific, where in situ sampling is most dense. Although the shape of SST anomaly patterns can differ greatly between the reconstruction and traditional in situ analysis, area-averaged results from both analyses show similar anomalies.

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Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm, Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

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