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Jörg Schulz, Jens Meywerk, Stefan Ewald, and Peter Schlüssel

Abstract

A method of determining ocean–atmosphere latent heat flux using the Special Sensor Microwave/Imager (SSM/I) and the Advanced Very High Resolution Radiometer (AVHRR) is presented and evaluated. While sea surface temperatures are retrieved from AVHRR data with an accuracy of 0.5–1.0 K, the near-surface wind speed and the surface air humidity are retrieved from measurements of the SSM/I with accuracies of 1.4 m s−1 and 1.1 g kg−1, respectively. The latent heat flux is then computed with a stability-dependent bulk parameterization model. The derived fluxes are compared to globally distributed instantaneous shipboard and buoy measurements and to monthly averages of 2° × 2° longitude and latitude bins. The standard error for instantaneous flux estimates is approximately 30 W m−2, and that for monthly averages decreases to 15 W m−2. Additionally, a 1-yr time series of latent heat flux at the weathership M in the North Atlantic and two shorter time series during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) and the Central Equatorial Pacific Experiment (CEPEX) in the tropical Pacific are compared to satellite measurements. The SSM/I-derived parameters, as well as the latent heat flux, are represented very well on the weathership M. During TOGA COARE and CEPEX, the near-surface humidity is sometimes systematically overestimated in the warm pool region, which results in an underestimation of the latent heat flux. Nevertheless, the representation of the latent heat flux is always in the range of the in situ measurements.

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Sebastian Mieruch, Stefan Noël, Maximilian Reuter, Heinrich Bovensmann, John P. Burrows, Marc Schröder, and Jörg Schulz

Abstract

Global total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite data and from globally distributed radiosonde measurements, archived and quality controlled by the Deutscher Wetterdienst (DWD).

The control of atmospheric water vapor amount by the hydrological cycle plays an important role in determining surface temperature and its response to the increase in man-made greenhouse effect. As a result of its strong infrared absorption, water vapor is the most important naturally occurring greenhouse gas. Without water vapor, the earth surface temperature would be about 20 K lower, making the evolution of life, as we know it, impossible. The monitoring of water vapor and its evolution in time is therefore of utmost importance for our understanding of global climate change. Comparisons of trends derived from independent water vapor measurements from satellite and radiosondes facilitate the assessment of the significance of the observed changes in water vapor.

In this manuscript, the authors have compared observed water vapor change and trends, derived from independent instruments, and assessed the statistical significance of their differences. This study deals with an example of the Behrens–Fisher problem, namely, the comparison of samples with different means and different standard deviations, applied to trends from time series.

Initially the Behrens–Fisher problem for the derivation of the consolidated change and trends is solved using standard (frequentist) hypothesis testing by performing the Welch test. Second, a Bayesian model selection is applied to solve the Behrens–Fisher problem by integrating the posterior probabilities numerically by using the algorithm Differential Evolution Markov Chain (DEMC). Additionally, an analytical approximative solution of the Bayesian posterior probabilities is derived by means of a quadratic Taylor series expansion applied in a computationally efficient manner to large datasets. The two statistical methods used in the study yield similar results for the comparison of the water vapor changes and trends from the different measurements, yielding a consolidated and consistent behavior.

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Axel Andersson, Christian Klepp, Karsten Fennig, Stephan Bakan, Hartmut Grassl, and Jörg Schulz

Abstract

Today, latent heat flux and precipitation over the global ocean surface can be determined from microwave satellite data as a basis for estimating the related fields of the ocean surface freshwater flux. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) is the only generally available satellite-based dataset with consistently derived global fields of both evaporation and precipitation and hence of freshwater flux for the period 1987–2005. This paper presents a comparison of the evaporation E, precipitation P, and the resulting freshwater flux EP in HOAPS with recently available reference datasets from reanalysis and other satellite observation projects as well as in situ ship measurements. In addition, the humidity and wind speed input parameters for the evaporation are examined to identify sources for differences between the datasets. Results show that the general climatological patterns are reproduced by all datasets. Global mean time series often agree within about 10% of the individual products, while locally larger deviations may be found for all parameters. HOAPS often agrees better with the other satellite-derived datasets than with the in situ or the reanalysis data. The agreement usually improves in regions of good in situ sampling statistics. The biggest deviations of the evaporation parameter result from differences in the near-surface humidity estimates. The precipitation datasets exhibit large differences in highly variable regimes with the largest absolute differences in the ITCZ and the largest relative biases in the extratropical storm-track regions. The resulting freshwater flux estimates exhibit distinct differences in terms of global averages as well as regional biases. In comparison with long-term mean global river runoff data, the ocean surface freshwater balance is not closed by any of the compared fields. The datasets exhibit a positive bias in EP of 0.2–0.5 mm day−1, which is on the order of 10% of the evaporation and precipitation estimates.

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Alessio Lattanzio, Jörg Schulz, Jessica Matthews, Arata Okuyama, Bertrand Theodore, John J. Bates, Kenneth R. Knapp, Yuki Kosaka, and Lothar Schüller

Climate has been recognized to have direct and indirect impact on society and economy, both in the long term and daily life. The challenge of understanding the climate system, with its variability and changes, is enormous and requires a joint long-term international commitment from research and governmental institutions. An important international body to coordinate worldwide climate monitoring efforts is the World Meteorological Organization (WMO). The Global Climate Observing System (GCOS) has the mission to provide coordination and the requirements for global observations and essential climate variables (ECVs) to monitor climate changes. The WMO-led activity on Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) is responding to these requirements by ensuring a continuous and sustained generation of climate data records (CDRs) from satellite data in compliance with the principles and guidelines of GCOS. SCOPE-CM represents a new partnership between operational space agencies to coordinate the generation of CDRs. To this end, pilot projects for different ECVs, such as surface albedo, cloud properties, water vapor, atmospheric motion winds, and upper-tropospheric humidity, have been initiated. The coordinated activity on land surface albedo involves the operational meteorological satellite agencies in Europe [European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], in Japan [the Japan Meteorological Agency (JMA)], and in the United States [National Oceanic and Atmospheric Administration (NOAA)]. This paper presents the first results toward the generation of a unique land surface albedo CDR, involving five different geostationary satellite positions and approximately three decades of data starting in the 1980s, and combining close to 30 different satellite instruments.

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Juliane Otto, Calum Brown, Carlo Buontempo, Francisco Doblas-Reyes, Daniela Jacob, Martin Juckes, Elke Keup-Thiel, Blaz Kurnik, Jörg Schulz, Andrea Taylor, Tijl Verhoelst, and Peter Walton
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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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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