Climate Change and everyday weather

It is perhaps well-recognised that Climate and our Daily Weather are not the same thing. When we talk about “Climate Change” what is generally being referenced is a change in the distribution of weather that slowly emerges from large variability over decades.

A new paper that has just been published by a team of Swiss researchers in Nature that changes this.

Climate change now detectable from any single day of weather at global scale

Published on 2nd January 2020, the Swiss research team explain their research as follows …

Here we show that on the basis of a single day of globally observed temperature and moisture, we detect the fingerprint of externally driven climate change, and conclude that Earth as a whole is warming.

How did they do this?

Our detection approach invokes statistical learning and climate model simulations to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are projected onto this relationship to detect climate change. The fingerprint of climate change is detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data.

How good do they claim this to be?

Detection is robust even when ignoring the long-term global warming trend. This complements traditional climate change detection, but also opens broader perspectives for the communication of regional weather events, modifying the climate change narrative: while changes in weather locally are emerging over decades, global climate change is now detected instantaneously.

The weather-​is-not-climate paradigm is no longer applicable

If you look out your window and observe a below average cold snap, then what does this new study imply?

It means that while your location may be below average, there will be other places on the globe that are above average. Taken together on a global scale on any single day any below average deviation is in effect eliminated.

Uncovering the climate change signal in daily weather conditions calls for a global perspective, not a regional one,

Sebastian Sippel, a postdoc working in Knutti’s research group and lead author of this new study

Paper Content

They sum it all up within the paper as follows …

We have shown, using fingerprints of key climate change metrics, that observations, reanalyses and model simulations agree that climate change is detected now from global weather in a single year, month or even a single day. This result is robust also if detection is based only on the spatial pattern of temperature and land humidity with any global mean signal removed. The afore- mentioned is a remarkable physical consistency check between models and observations, and allows daily detection even if, for example, the global mean long-term trend is judged to be unreliable due to changes in observing systems. Daily global weather has therefore emerged from the background of natural variability, and the total energy content of the planet keeps increasing. As in other detection studies, this result relies on the assumption that climate models do not severely underestimate low-frequency variability, but our results are robust to various combinations of CMIP5 models used to construct the reference distribution (Supplementary Text 3).

Broader implications for climate science include, first, that weather on global scales carries important climate information that may be exploited for short-term D&A. This may complement the study of long-term climate trends, which may be masked locally by internal climate variability. Second, statistical learning offers opportunities to optimally combine multivariate spatiotemporal information to maximize a desired climate signal against internal variability or other factors. Third, our approach opens a broader perspective and context for the communication of regional weather events against the backdrop of a warming climate. This allows one to overcome a disconnect between formal fingerprinting studies that rely on observed long-term trends, and event attribution (that is, the assessment of externally forced changes in the magnitude or frequency of short-term (extreme) weather events that interpret model simulations probabilistically). Short-term detection may help to formally assess the role of external forcing on global or sub- global ‘weather’ up to instantaneous timescales.

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