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Christopher J. Merchant and Mark A. Saunders

Abstract

The presence of stratospheric aerosol can bias the results of infrared satellite retrievals of sea surface temperature (SST) and total precipitable water (TPW). In the case of linear SST retrieval using the Along Track Scanning Radiometer (ATSR), on the ESA European remote-sensing satellites, constant coefficients can be found that give negligible bias (less than 0.1 K) over a wide range of aerosol amount (11-μm optical thickness from 0.0 to 0.022). For TPW retrieval, in contrast, the biases associated with stratospheric aerosol are less satisfactory (2 kg m−2 or greater across a range of 11-μm optical thickness of 0.0–0.01). However, the authors show how to find optimal aerosol-dependent retrieval coefficients for any stratospheric aerosol distribution from knowledge of the mean and variance of that aerosol distribution. Examples of SST and TPW retrieval using simulated ATSR brightness temperature data are given.

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Christopher J. Merchant and Pierre Le Borgne

Abstract

The retrieval (estimation) of sea surface temperatures (SSTs) from space-based infrared observations is increasingly performed using retrieval coefficients derived from radiative transfer simulations of top-of-atmosphere brightness temperatures (BTs). Typically, an estimate of SST is formed from a weighted combination of BTs at a few wavelengths, plus an offset. This paper addresses two questions about the radiative transfer modeling approach to deriving these weighting and offset coefficients. How precisely specified do the coefficients need to be in order to obtain the required SST accuracy (e.g., scatter <0.3 K in week-average SST, bias <0.1 K)? And how precisely is it actually possible to specify them using current forward models? The conclusions are that weighting coefficients can be obtained with adequate precision, while the offset coefficient will often require an empirical adjustment of the order of a few tenths of a kelvin against validation data. Thus, a rational approach to defining retrieval coefficients is one of radiative transfer modeling followed by offset adjustment. The need for this approach is illustrated from experience in defining SST retrieval schemes for operational meteorological satellites. A strategy is described for obtaining the required offset adjustment, and the paper highlights some of the subtler aspects involved with reference to the example of SST retrievals from the imager on the geostationary satellite GOES-8.

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Emma M. A. Dodd, Christopher J. Merchant, Nick A. Rayner, and Colin P. Morice

Abstract

Time series of global and regional mean surface air temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice was investigated using reanalysis data as a test bed. Techniques that interpolated anomalies were found to result in smaller errors than noninterpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies, and simple kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, root-mean-square errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Noninterpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979–2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

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Richard W. Reynolds, Dudley B. Chelton, Jonah Roberts-Jones, Matthew J. Martin, Dimitris Menemenlis, and Christopher John Merchant

Abstract

Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis.

The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.

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Chunxue Yang, Francesca Elisa Leonelli, Salvatore Marullo, Vincenzo Artale, Helen Beggs, Bruno Buongiorno Nardelli, Toshio M. Chin, Vincenzo De Toma, Simon Good, Boyin Huang, Christopher J. Merchant, Toshiyuki Sakurai, Rosalia Santoleri, Jorge Vazquez-Cuervo, Huai-Min Zhang, and Andrea Pisano

Abstract

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users’ convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño).

Open access
Elizabeth C. Kent, John J. Kennedy, Thomas M. Smith, Shoji Hirahara, Boyin Huang, Alexey Kaplan, David E. Parker, Christopher P. Atkinson, David I. Berry, Giulia Carella, Yoshikazu Fukuda, Masayoshi Ishii, Philip D. Jones, Finn Lindgren, Christopher J. Merchant, Simone Morak-Bozzo, Nick A. Rayner, Victor Venema, Souichiro Yasui, and Huai-Min Zhang

Abstract

Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.

Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method.

New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.

Open access
Thomas Popp, Michaela I. Hegglin, Rainer Hollmann, Fabrice Ardhuin, Annett Bartsch, Ana Bastos, Victoria Bennett, Jacqueline Boutin, Carsten Brockmann, Michael Buchwitz, Emilio Chuvieco, Philippe Ciais, Wouter Dorigo, Darren Ghent, Richard Jones, Thomas Lavergne, Christopher J. Merchant, Benoit Meyssignac, Frank Paul, Shaun Quegan, Shubha Sathyendranath, Tracy Scanlon, Marc Schröder, Stefan G. H. Simis, and Ulrika Willén

Abstract

Climate data records (CDRs) of essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS) derived from satellite instruments help to characterize the main components of the Earth system, to identify the state and evolution of its processes, and to constrain the budgets of key cycles of water, carbon, and energy. The Climate Change Initiative (CCI) of the European Space Agency (ESA) coordinates the derivation of CDRs for 21 GCOS ECVs. The combined use of multiple ECVs for Earth system science applications requires consistency between and across their respective CDRs. As a comprehensive definition for multi-ECV consistency is missing so far, this study proposes defining consistency on three levels: 1) consistency in format and metadata to facilitate their synergetic use (technical level); 2) consistency in assumptions and auxiliary datasets to minimize incompatibilities among datasets (retrieval level); and 3) consistency between combined or multiple CDRs within their estimated uncertainties or physical constraints (scientific level). Analyzing consistency between CDRs of multiple quantities is a challenging task and requires coordination between different observational communities, which is facilitated by the CCI program. The interdependencies of the satellite-based CDRs derived within the CCI program are analyzed to identify where consistency considerations are most important. The study also summarizes measures taken in CCI to ensure consistency on the technical level, and develops a concept for assessing consistency on the retrieval and scientific levels in the light of underlying physical knowledge. Finally, this study presents the current status of consistency between the CCI CDRs and future efforts needed to further improve it.

Free access
Thomas Popp, Michaela I. Hegglin, Rainer Hollmann, Fabrice Ardhuin, Annett Bartsch, Ana Bastos, Victoria Bennett, Jacqueline Boutin, Carsten Brockmann, Michael Buchwitz, Emilio Chuvieco, Philippe Ciais, Wouter Dorigo, Darren Ghent, Richard Jones, Thomas Lavergne, Christopher J. Merchant, Benoit Meyssignac, Frank Paul, Shaun Quegan, Shubha Sathyendranath, Tracy Scanlon, Marc Schröder, Stefan G. H. Simis, and Ulrika Willén

Capsule

In this study, the ESA Climate Change Initiative (CCI) introduces a three-level definition of consistency between multiple satellite-based Climate Data Records (CDRs) of Essential Climate Variables (ECVs), discusses consistency status and requirements and develops a concept for assessing inter and across ECV consistency.

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Thomas Popp, Michaela I. Hegglin, Rainer Hollmann, Fabrice Ardhuin, Annett Bartsch, Ana Bastos, Victoria Bennett, Jacqueline Boutin, Carsten Brockmann, Michael Buchwitz, Emilio Chuvieco, Philippe Ciais, Wouter Dorigo, Darren Ghent, Richard Jones, Thomas Lavergne, Christopher J. Merchant, Benoit Meyssignac, Frank Paul, Shaun Quegan, Shubha Sathyendranath, Tracy Scanlon, Marc Schröder, Stefan G. H. Simis, and Ulrika Willén
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Nick A. Rayner, Renate Auchmann, Janette Bessembinder, Stefan Brönnimann, Yuri Brugnara, Francesco Capponi, Laura Carrea, Emma M. A. Dodd, Darren Ghent, Elizabeth Good, Jacob L Høyer, John J Kennedy, Elizabeth C. Kent, Rachel E Killick, Paul van der Linden, Finn Lindgren, Kristine S. Madsen, Christopher J. Merchant, Joel R Mitchelson, Colin P Morice, Pia Nielsen-Englyst, Patricio F. Ortiz, John J Remedios, Gerard van der Schrier, Antonello A. Squintu, Ag Stephens, Peter W. Thorne, Rasmus T. Tonboe, Tim Trent, Karen L Veal, Alison M Waterfall, Kate Winfield, Jonathan Winn, and R. Iestyn Woolway

Capsule

The main goals and activities of the EUSTACE project are discussed along with some key results, including a global, multi-decadal daily air temperature record from satellite and in situ measurements.

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