UAHv6.0 tlt corroborated

Spencer and Christy’s new UAHv6.0 gl TLT dataset is impressively and conspicuously corroborated by:

# HadCRUt3 gl with Jan’98 –0.064K adjustment.

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HadCRUt3 vs. ERA Interim

Climate (or global atmospheric) reanalyses are an alternative way to assess how the global climate evolves over time, a blend of model and observation. They tend to include a multitude of variables, but I would like to focus on the one specifically pertaining to our recent discussion about GISTEMP vs. HadCRUt3: global temperatures.

There’s a host of different climate reanalyses around; among the most reputable ones, though, are those conducted by the American agencies NCEP (NOAA) and NCAR, the Japanese JMA, and the European ECMWF.

So, what is a climate reanalysis?

ECMWF explains:

“A climate reanalysis gives a numerical description of the recent climate, produced by combining models with observations. It contains estimates of atmospheric parameters such as air temperature, pressure and wind at different altitudes, and surface parameters such as rainfall, soil moisture content, and sea-surface temperature. (…)

ECMWF periodically uses its forecast models and data assimilation systems to ‘reanalyse’ archived observations, creating global data sets describing the recent history of the atmosphere, land surface, and oceans. Reanalysis data are used for monitoring climate change, for research and education, and for commercial applications.

Current research in reanalysis at ECMWF focuses on the development of consistent reanalyses of the coupled climate system, including atmosphere, land surface, ocean, sea ice, and the carbon cycle, extending back as far as a century or more. The work involves collection, preparation and assessment of climate observations, ranging from early in-situ surface observations made by meteorological observers to modern high-resolution satellite data sets. Special developments in data assimilation are needed to ensure the best possible temporal consistency of the reanalyses, which can be adversely affected by biases in models and observations, and by the ever-changing observing system.”

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