The Absurdity Of Global Mean Temperature And Mean Sea Level Metrics

Global mean temperature (GMT) and mean sea level (MSL) are frequently used as key indicators of climate change.

However, a critical examination reveals that these metrics, while convenient for broad discussions, may lack scientific significance due to the immense variability and complexity of the Earth’s climate and geophysical systems. [emphasis, links added]

GMT is a widely utilized metric, serving as a generalized gauge of the Earth’s climatic health. It amalgamates temperature data from across the globe into a singular numerical representation.

However, this approach belies the inherent diversity and complexity of the Earth’s climate.

GMT is estimated using a combination of direct temperature measurements and statistical techniques. The primary sources of direct temperature data include:

  1. Surface Stations: A network of weather stations worldwide continuously records temperature data, providing in-situ observations of air temperature at the surface level.
  2. Buoys and Ships: Ocean-based observations are collected from buoys and ships that measure sea surface temperature, providing data from vast oceanic regions.
  3. Satellites: Satellite-based sensors measure the radiance emitted by the Earth’s surface and atmosphere, which is then converted into temperature estimates.

Due to the uneven distribution of these direct measurements, particularly over sparsely populated areas like oceans and deserts, statistical techniques are employed to fill in the gaps and create a comprehensive global temperature dataset.

These techniques include:

  1. Interpolation: Interpolation involves filling in missing data points by estimating their values based on surrounding observations. This assumes that temperature changes gradually over space, allowing for a reasonable estimation of missing values.
  2. Extrapolation: Extrapolation involves estimating temperature values beyond the range of available observations. This is often used to extend temperature records back in time or to estimate temperatures in regions with limited data.

The proportion of actual measurements in GMT datasets varies depending on the specific data source and processing methods.

In general, surface station data provides the most direct and reliable measurements, with a higher proportion of in-situ observations.

Satellite data, while covering a broader area, has a lower proportion of actual measurements due to the need to convert radiance into temperature estimates.

The IPCC estimates that around 60% of the data used to calculate GMT comes from direct measurements, while the remaining 40% is derived from interpolation and extrapolation.

This indicates that a significant portion of GMT is based on statistical estimates, rather than direct observations.

However, this assumes that a large area around each station is of constant temperature, an assumption known to be false.

In actuality, if we assume about a one-km area around each station, approximately 0.01% of Earth’s surface is directly measured for temperature.

Thus, approximately 99.99% is estimated through statistical methods.

The relatively small proportion of direct temperature measurements raises questions about the accuracy of GMT as an indicator of global climate change.

The use of interpolation and extrapolation can introduce uncertainties into GMT estimates. It is important to note that statistical techniques have been refined over time, improving the accuracy of interpolated and extrapolated data.

However, recent claims of knowing the GMT to the nearest hundredths [of a degree] have been challenged by leading climate scientists.

Read rest at Irrational Fear

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