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<\/div>Bubbles trapped in Antarctic ice were used to estimate past temperatures<\/p>\n
aldiami\/Andreas Alexander\/Alamy<\/p>\n<\/div>\n<\/figcaption><\/figure>\n<\/p>\n
Humans have already caused approximately 1.5\u00b0C of warming since the start of the industrial revolution, according to new estimates based on temperature data gleaned from bubbles of air trapped in ice.<\/p>\n
Measurements of human-caused global warming generally use the period from 1850 to 1900 as the pre-industrial baseline, since this is when temperature records began. 2024 is almost certain to be the first year where average temperatures rose more than 1.5\u00b0C<\/a> above this baseline. This data for a single year is influenced by naturally occurring factors such as a strong El Ni\u00f1o event<\/a>, which pushed up global temperatures.<\/p>\n Once this natural variability is removed, scientists think humanity alone has caused 1.31\u00b0C of warming since the industrial revolution. But by 1850, the industrial revolution was already well under way, with fossil fuel-powered engines in use around the world.<\/p>\n <\/p>\n Andrew Jarvis<\/a> at Lancaster University and Piers Forster<\/a> at the University of Leeds, both in the UK, set out to establish a new pre-industrial baseline using data from Antarctic ice core samples. The duo analysed the composition of air bubbles trapped in ice cores to establish the carbon dioxide concentration in the atmosphere during the period from AD 13 to 1700, before humans had any meaningful impact on atmospheric temperatures. They then used this CO2 data to establish global mean temperatures during the same period, assuming a linear relationship between CO2 and temperature increase.<\/p>\n Using this new pre-1700 baseline, humanity had caused 1.49\u00b0C of warming by 2023, meaning the 1.5\u00b0C level \u201chas now in effect been reached\u201d, the team write in a paper reporting the findings. \u201cWe have provided a new, scientifically defensible way of coming up with a pre-industrial baseline against which we are measuring the warming,\u201d Jarvis told reporters in a press briefing.<\/p>\n Jarvis says the new method can also help reduce uncertainty around temperature estimates based on the current 1850-1900 baseline, which is used by the Intergovernmental Panel on Climate Change. Using ice core data to establish the 1850-1900 baseline, the team says humans have caused 1.31\u00b0C of warming. That is in line with existing central estimates, but with a vastly reduced uncertainty range, the team points out.<\/p>\n \u201cThe problem with just looking at surface temperature observations is that the further back in time you go, they become more uncertain,\u201d says Forster. \u201cWe can be far more certain than before that we are currently at about 1.3\u00b0C.\u201d<\/p>\n Jarvis and Forster hope their new method will be adopted by scientists and policy-makers as the main way of judging humanity\u2019s progress against global climate goals. \u201cI do think there is still scope for the policy community and the science community to rethink the pre-industrial baseline,\u201d said Jarvis. \u201cWe know that there is warming baked into the 1850-1900 estimate, simply because that is not the beginning of the industrial revolution. We are offering a way out there, to a much more scientifically secure baseline to operate from.\u201d<\/p>\n However, the new method may not be future-proof. The linear relationship between CO2 concentrations and global temperatures may falter as climate change advances, for example if we trigger so-called tipping points in Earth systems<\/a>\u00a0that cause a cascade of warming events.<\/p>\n The new method also doesn\u2019t change the climate change effects being felt on the ground, says Forster. \u201cThe impacts today we are experiencing \u2013 of people being killed in Spain and by these hurricanes \u2013 the impacts are exactly the same if you call that 1.3\u00b0C above pre-industrial levels or if you call that 1.5\u00b0C above pre-industrial levels. The impacts are the impacts.\u201d<\/p>\n