Sunday, February 05, 2023

Data-driven predictions of the time remaining until critical global warming thresholds are reached (10-15 years)

Don't you love it when a prestigious journal like the Proceedings of the National Academy of Sciences spreads alarmism and hysteria! That is not exactly scientific! This actually comes close to medieval superstition!

These pseudoscientists used "machine learning methods to make "truly out-of-sample predictions"! This by itself is a joke! Machine learning is not even ready for such an application!

The great pretension of keeping global warming to below "2 °C and pursue 1.5 °C" is the greatest hoax of at least one generation! Such precision is pseudoscience! Nobody can measure global warming with such precision!

"A new study has found that emission goals designed to achieve the world’s most ambitious climate target – 1.5 degrees Celsius above pre-industrial levels – may in fact be required to avoid more extreme climate change of 2 degrees Celsius. ..."

From the significance and abstract:
"Significance
The United Nations Paris Agreement aims to hold global warming well below 2 °C and pursue 1.5 °C. Given the clear evidence for accelerating climate impacts, the time remaining until these global thresholds are reached is a topic of considerable interest. We use machine learning methods to make truly out-of-sample predictions of that timing, based on the spatial pattern of historical temperature observations. Our results confirm that global warming is already on the verge of crossing the 1.5 °C threshold, even if the climate forcing pathway is substantially reduced in the near-term. Our predictions also suggest that even with substantial greenhouse gas mitigation, there is still a possibility of failing to hold global warming below the 2 °C threshold.
Abstract
Leveraging artificial neural networks (ANNs) trained on climate model output, we use the spatial pattern of historical temperature observations to predict the time until critical global warming thresholds are reached. Although no observations are used during the training, validation, or testing, the ANNs accurately predict the timing of historical global warming from maps of historical annual temperature. The central estimate for the 1.5 °C global warming threshold is between 2033 and 2035, including a ±1σ range of 2028 to 2039 in the Intermediate (SSP2-4.5) climate forcing scenario, consistent with previous assessments. However, our data-driven approach also suggests a substantial probability of exceeding the 2 °C threshold even in the Low (SSP1-2.6) climate forcing scenario. While there are limitations to our approach, our results suggest a higher likelihood of reaching 2 °C in the Low scenario than indicated in some previous assessments—though the possibility that 2 °C could be avoided is not ruled out. Explainable AI methods reveal that the ANNs focus on particular geographic regions to predict the time until the global threshold is reached. Our framework provides a unique, data-driven approach for quantifying the signal of climate change in historical observations and for constraining the uncertainty in climate model projections. Given the substantial existing evidence of accelerating risks to natural and human systems at 1.5 °C and 2 °C, our results provide further evidence for high-impact climate change over the next three decades."

Data-driven predictions of the time remaining until critical global warming thresholds are reached | PNAS (open access)

Earth likely to cross critical climate thresholds even if emissions decline, Stanford study finds Artificial intelligence provides new evidence our planet will cross the global warming threshold of 1.5 degrees Celsius within 10 to 15 years. Even with low emissions, we could see 2 C of warming. But a future with less warming remains within reach.


Fig. 1. Time to global warming thresholds in global climate model ensembles.


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