May 6, 2025 06:02 PM
https://rogerpielkejr.substack.com/p/the...ortance-of
EXCERPTS: . . . Quantifying internal variability with respect to any climate metric is challenging, typically with multiple valid interpretations possible. Superimposed upon the challenge is the fact that internal variability itself has been influenced by human factors, notably the emission of greenhouse gases.
A common simplifying assumption underlying the belief that a particular trend indicates change is that of stationarity in the statistics of variable of interest — meaning an expectation that the various statistical characteristics of a time series (such as the mean, standard deviation, skew, etc.) would not change over time, but for a human influence.
Such an assumption obviously makes the tasks of detection and attribution of change much easier. In addition, under an assumption of underlying stationarity, a longer observational time series for a climate metric might be useful, but would not be necessary for the detection of change. For instance, if an observational record of tropical cyclones is 50 years, then an assumption of stationarity in the underlying statistics would lead to an expectation that the statistics of tropical cyclones in the 50 years before that would be the same. Similarly, any trends found in tropical cyclone metrics over the most recent 50 years would indicate a detected change.
Of course, we know that 50 years of observations is not sufficient to fully characterize internal variability associated with tropical cyclones. However, studies are routinely published suggesting the detection of trends indicating a change in climate using 50 years of data, and often much less.
The stationarity assumption is implicit in the figure below, from the U.S. Environmental Protection Agency. The figure is often used by professional climate communicators to explain how climate change increases the probability of extreme events.
[...] This does not mean that humans are not influencing the climate system or extreme events, or that such influences are not important. It certainly does not mean that we should forget about mitigation and adaptation policies.
What it does mean is that the climate is more variable than many appreciate... (MORE - missing details)
EXCERPTS: . . . Quantifying internal variability with respect to any climate metric is challenging, typically with multiple valid interpretations possible. Superimposed upon the challenge is the fact that internal variability itself has been influenced by human factors, notably the emission of greenhouse gases.
A common simplifying assumption underlying the belief that a particular trend indicates change is that of stationarity in the statistics of variable of interest — meaning an expectation that the various statistical characteristics of a time series (such as the mean, standard deviation, skew, etc.) would not change over time, but for a human influence.
Such an assumption obviously makes the tasks of detection and attribution of change much easier. In addition, under an assumption of underlying stationarity, a longer observational time series for a climate metric might be useful, but would not be necessary for the detection of change. For instance, if an observational record of tropical cyclones is 50 years, then an assumption of stationarity in the underlying statistics would lead to an expectation that the statistics of tropical cyclones in the 50 years before that would be the same. Similarly, any trends found in tropical cyclone metrics over the most recent 50 years would indicate a detected change.
Of course, we know that 50 years of observations is not sufficient to fully characterize internal variability associated with tropical cyclones. However, studies are routinely published suggesting the detection of trends indicating a change in climate using 50 years of data, and often much less.
The stationarity assumption is implicit in the figure below, from the U.S. Environmental Protection Agency. The figure is often used by professional climate communicators to explain how climate change increases the probability of extreme events.
[...] This does not mean that humans are not influencing the climate system or extreme events, or that such influences are not important. It certainly does not mean that we should forget about mitigation and adaptation policies.
What it does mean is that the climate is more variable than many appreciate... (MORE - missing details)