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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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Ahreum Lee, Byung-Ju Sohn, Ed Pavelin, Yoonjae Kim, Hyun-Suk Kang, Roger Saunders, and Young-Chan Noh

Abstract

The Unified Model (UM) data assimilation system incorporates a 1D-Var analysis of cloud variables for assimilating hyperspectral infrared radiances. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation, a first guess of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiance difference between observation and model simulation. In this study, we examined how those MR-based cloud retrievals behave, using “optimum” CTP and CF that yield the best 1D-Var analysis results. It is noted that the MR method tends to overestimate cloud top height while underestimating cloud fraction, compared to the optimum results, necessitating an improved cloud retrieval. An artificial neural network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases shown in the MR method are much reduced, giving better 1D-Var analysis results. Furthermore, the computational time can be substantially reduced by the ANN method, compared to the MR method. The evaluation of the ANN method in a global weather forecasting system demonstrated that it helps to use more temperature channels in the assimilation, although its impact on UM forecasts was found to be near neutral. It is suggested that the neutral impact may be improved when error covariances for the cloudy sky are employed in the UM assimilation system.

Open access
Ghassem Asrar, Sandrine Bony, Olivier Boucher, Antonio Busalacchi, Anny Cazenave, Mark Dowell, Greg Flato, Gabi Hegerl, Erland Källén, Teruyuki Nakajima, Alain Ratier, Roger Saunders, Julia Slingo, Byung-Ju Sohn, Johannes Schmetz, Bjorn Stevens, Peiqun Zhang, and Francis Zwiers
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Stefan Brönnimann, Rob Allan, Christopher Atkinson, Roberto Buizza, Olga Bulygina, Per Dahlgren, Dick Dee, Robert Dunn, Pedro Gomes, Viju O. John, Sylvie Jourdain, Leopold Haimberger, Hans Hersbach, John Kennedy, Paul Poli, Jouni Pulliainen, Nick Rayner, Roger Saunders, Jörg Schulz, Alexander Sterin, Alexander Stickler, Holly Titchner, Maria Antonia Valente, Clara Ventura, and Clive Wilkinson

Abstract

Global dynamical reanalyses of the atmosphere and ocean fundamentally rely on observations, not just for the assimilation (i.e., for the definition of the state of the Earth system components) but also in many other steps along the production chain. Observations are used to constrain the model boundary conditions, for the calibration or uncertainty determination of other observations, and for the evaluation of data products. This requires major efforts, including data rescue (for historical observations), data management (including metadatabases), compilation and quality control, and error estimation. The work on observations ideally occurs one cycle ahead of the generation cycle of reanalyses, allowing the reanalyses to make full use of it. In this paper we describe the activities within ERA-CLIM2, which range from surface, upper-air, and Southern Ocean data rescue to satellite data recalibration and from the generation of snow-cover products to the development of a global station data metadatabase. The project has not produced new data collections. Rather, the data generated has fed into global repositories and will serve future reanalysis projects. The continuation of this effort is first contingent upon the organization of data rescue and also upon a series of targeted research activities to address newly identified in situ and satellite records.

Open access
Paul Poli, Dick P. Dee, Roger Saunders, Viju O. John, Peter Rayer, Jörg Schulz, Kenneth Holmlund, Dorothee Coppens, Dieter Klaes, James E. Johnson, Asghar E. Esfandiari, Irina V. Gerasimov, Emily B. Zamkoff, Atheer F. Al-Jazrawi, David Santek, Mirko Albani, Pascal Brunel, Karsten Fennig, Marc Schröder, Shinya Kobayashi, Dieter Oertel, Wolfgang Döhler, Dietrich Spänkuch, and Stephan Bojinski

Abstract

To better understand the impacts of climate change, environmental monitoring capabilities must be enhanced by deploying additional and more accurate satellite- and ground-based (including in situ) sensors. In addition, reanalysis of observations collected decades ago but long forgotten can unlock precious information about the recent past. Historical, in situ observations mainly cover densely inhabited areas and frequently traveled routes. In contrast, large selections of early meteorological satellite data, waiting to be exploited today, provide information about remote areas unavailable from any other source. When initially collected, these satellite data posed great challenges to transmission and archiving facilities. As a result, data access was limited to the main teams of scientific investigators associated with the instruments. As archive media have aged, so have the mission scientists and other pioneers of satellite meteorology, who sometimes retired in possession of unique and unpublished information.

This paper presents examples of recently recovered satellite data records, including satellite imagery, early infrared hyperspectral soundings, and early microwave humidity soundings. Their value for climate applications today can be realized using methods and techniques that were not yet available when the data were first collected, including efficient and accurate observation simulators and data assimilation into reanalyses. Modern technical infrastructure allows serving entire mission datasets online, enabling easy access and exploration by a broad range of users, including new and old generations of climate scientists.

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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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Roberto Buizza, Stefan Brönnimann, Leopold Haimberger, Patrick Laloyaux, Matthew J. Martin, Manuel Fuentes, Magdalena Alonso-Balmaseda, Andreas Becker, Michael Blaschek, Per Dahlgren, Eric de Boisseson, Dick Dee, Marie Doutriaux-Boucher, Xiangbo Feng, Viju O. John, Keith Haines, Sylvie Jourdain, Yuki Kosaka, Daniel Lea, Florian Lemarié, Michael Mayer, Palmira Messina, Coralie Perruche, Philippe Peylin, Jounie Pullainen, Nick Rayner, Elke Rustemeier, Dinand Schepers, Roger Saunders, Jörg Schulz, Alexander Sterin, Sebastian Stichelberger, Andrea Storto, Charles-Emmanuel Testut, Maria-Antóonia Valente, Arthur Vidard, Nicolas Vuichard, Anthony Weaver, James While, and Markus Ziese

Abstract

The European Reanalysis of Global Climate Observations 2 (ERA-CLIM2) is a European Union Seventh Framework Project started in January 2014 and due to be completed in December 2017. It aims to produce coupled reanalyses, which are physically consistent datasets describing the evolution of the global atmosphere, ocean, land surface, cryosphere, and the carbon cycle. ERA-CLIM2 has contributed to advancing the capacity for producing state-of-the-art climate reanalyses that extend back to the early twentieth century. ERA-CLIM2 has led to the generation of the first European ensemble of coupled ocean, sea ice, land, and atmosphere reanalyses of the twentieth century. The project has funded work to rescue and prepare observations and to advance the data-assimilation systems required to generate operational reanalyses, such as the ones planned by the European Union Copernicus Climate Change Service. This paper summarizes the main goals of the project, discusses some of its main areas of activities, and presents some of its key results.

Open access