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Katie Lean and Roger W. Saunders

Abstract

The Along-Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) project aims to create an independent climate data record of sea surface temperatures (SSTs) covering recent decades that can be used for climate change analysis. Here, the ARC SSTs are assessed using comparisons with collocated drifting buoy observations and a three-way error analysis that also includes Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data. The SSTs using the three-channel nighttime retrievals in the ARC data at 1-m depth are found to have a warm bias of 0.054 K (standard deviation 0.151 K) with respect to the drifting buoy data for the 1995–2009 time period using ATSR-2 and Advanced Along-Track Scanning Radiometer (AATSR) instrument data. However, when studying the two-channel retrievals, the ATSR-1 data are found to be less stable and with more extreme values than in later years. Some dependence on latitude, season, and fields such as total column water vapor is found in the ATSR-2 and AATSR period. An assessment of the ARC SST uncertainty shows a stable bias for low uncertainty values but more deviation above 0.6 and 0.35 K for the two- and three-channel nighttime retrievals, respectively. The three-way error analysis reveals a standard deviation of error of 0.14 K for the ARC 1-m depth SSTs using the three-channel nighttime retrieval. Estimates of the standard deviation of error for the drifting buoys are also produced and show evidence of improvement in the buoy network in the years 2003–09 from 0.19 to 0.15 K.

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Michael Rohn, Graeme Kelly, and Roger W. Saunders

Abstract

Enhanced wind datasets of the European satellite Meteosat are now provided every 90 mins together with the quality indicator (QI) derived by the quality control of the Meteorological Product Extraction Facility (MPEF) at the European Organisation for the Exploitation of Meteorological Satellites. All three channel cloud motion winds and clear sky water vapor motion winds have been passively monitored by comparison with the European Centre for Medium-Range Weather Forecasts model background field. The evaluation of the relationship between the MPEF QI and the observation − background differences indicate possible benefits to be gained from the use of the QI within the observation screening of the assimilation system. The MPEF quality indicator is used as a selection criterion within the screening. The applied thresholds are restricted in the Tropics compared to the extratropical regions where the threshold for high-level winds has been relaxed below the automatic quality control at MPEF. The wind data derived from imagery of both Meteosat platforms at 0° and 118°E are used in this study. The overall effect is an increase of active Meteosat winds by a factor of 2. This means a considerably increased impact of Meteosat winds on the tropospheric analyses. The assessment of mean wind increments indicates that the increased temporal sampling together with the use of the quality indicator within the observation screening leads to an improvement of the consistency of the atmospheric motion wind data actively used within the four-dimensional variational assimilation system. The averaged impact on the short- and medium-range forecasts is found to be neutral in the Northern Hemisphere and positive in the Southern Hemisphere. In a selected synoptic case study the use of the new Meteosat wind product indicates a considerable improvement of the medium-range forecasts for the North Atlantic and European areas.

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Anne G. O’Carroll, Roger W. Saunders, and James G. Watts

Abstract

A near-continuous series of global retrievals of sea surface temperature (SST) has been made from the Along-Track Scanning Radiometer (ATSR) series of instruments from 1991 to 2005. To analyze possible long-term trends in the global or regional SST throughout the period daily anomalies are computed using a 1961–90 daily climatology, averaged into global monthly means, and plotted as a global time series. To evaluate any biases in these anomalies they are compared with other satellite SST datasets that have been computed and compared over the same time period. Global infrared satellite SST data have been received from the Advanced Very High Resolution Radiometer (AVHRR) series, microwave SST data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and global microwave SST data from the Advanced Microwave Sounding Radiometer (AMSR)-E on Aqua. Additionally, the anomalies have also been compared with the Hadley Centre Global Sea Ice Coverage and Sea Surface Temperature (HadISST1) anomalies. HadISST1 is a globally complete 1° SST analysis compiled from in situ and bias-corrected AVHRR SSTs at the Met Office (UK).

The results of the study show the high accuracy of the Advanced Along Track Scanning Radiometer (AATSR) SSTs, but there are concerns with the NOAA-14 AVHRR data (1996–2000) being biased cold, especially in the Northern Hemisphere, and the AMSR-E SSTs (version 4), which show unexplained biases. Since 1999 TMI SSTs appear to have a consistently warm (∼0.2 K) bias relative to the infrared sensors and HadISST1.

The time series in (A)ATSR SSTs indicate the possibility of warming trends between 0.1 and 0.2 K decade−1, but the remaining ATSR-1 data are required to confirm this.

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Anne G. O’Carroll, John R. Eyre, and Roger W. Saunders

Abstract

Using collocations of three different observation types of sea surface temperatures (SSTs) gives enough information to enable the standard deviation of error on each observation type to be derived. SSTs derived from the Advanced Along-Track Scanning Radiometer (AATSR) and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instruments are used, along with SST observations from buoys. Various assumptions are made within the error theory, including that the errors are not correlated, which should be the case for three independent data sources. An attempt is made to show that this assumption is valid and that the covariances between the different observations because of representativity error are negligible. Overall, the spatially averaged nighttime AATSR dual-view three-channel bulk SST observations for 2003 are shown to have a very small standard deviation of error of 0.16 K, whereas the buoy SSTs have an error of 0.23 K and the AMSR-E SST observations have an error of 0.42 K.

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Sarah J. Doherty, Stephan Bojinski, Ann Henderson-Sellers, Kevin Noone, David Goodrich, Nathaniel L. Bindoff, John A. Church, Kathy A. Hibbard, Thomas R. Karl, Lucka Kajfez-Bogataj, Amanda H. Lynch, David E. Parker, I. Colin Prentice, Venkatachalam Ramaswamy, Roger W. Saunders, Mark Stafford Smith, Konrad Steffen, Thomas F. Stocker, Peter W. Thorne, Kevin E. Trenberth, Michel M. Verstraete, and Francis W. Zwiers

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is “unequivocal” and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.

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