Novel Method to Predict Global Warming with Lower Uncertainties

According to a new study, the threshold for hazardous global warming will probably be encountered between 2027 and 2042, which is a considerably narrower window compared to the predictions of the Intergovernmental Panel on Climate Change for the period between now and 2052.

Novel Method to Predict Global Warming with Lower Uncertainties

Image Credit: McGill University.

Published in the Climate Dynamics journal, the study reports a new and more accurate method developed by scientists from McGill University to predict the temperature of the Earth. By using historical data, the method significantly decreases uncertainties compared to earlier methods.

For decades, climate models have been used by researchers to make predictions of future global warming. Such models tend to play a significant role in gaining better insights into the Earth’s climate and how it will possibly change. However, it is essential to determine the accuracy of the model.

Dealing with Uncertainty

Climate models are mathematical simulations of various factors that interact to influence Earth’s climate, like the ice, ocean, land surface, atmosphere and the sun. Although these models are developed using the best understanding of the Earth’s systems available, uncertainties still tend to persist when it comes to predicting the future.

Climate skeptics have argued that global warming projections are unreliable because they depend on faulty supercomputer models. While these criticisms are unwarranted, they underscore the need for independent and different approaches to predicting future warming.

Bruno Tremblay, Study Co-Author and Professor, Department of Atmospheric and Oceanic Sciences, McGill University

To date, outcomes in various mitigation scenarios have been hard to pinpoint due to wide ranges in overall temperature projections. For example, upon doubling atmospheric CO2 concentrations, the General Circulation Models (GCMs) utilized by the Intergovernmental Panel on Climate Change (IPCC) estimate a highly possible increase in the global average temperature between 1.9 °C and 4.5 °C—a massive range that covers moderate climate changes on the lower end and disastrous ones on the other.

A New Approach

Our new approach to projecting the Earth’s temperature is based on historical climate data, rather than the theoretical relationships that are imperfectly captured by the GCMs. Our approach allows climate sensitivity and its uncertainty to be estimated from direct observations with few assumptions.

Raphaël Hébert, Study Co-Author and Former Graduate Researcher, McGill University

Hébert is currently working at the Alfred-Wegener-Institute in Potsdam, Germany.

In the study, the team describes the new Scaling Climate Response Function (SCRF) model to predict the temperature of the Earth to 2100. Based on historical data, it decreases forecast uncertainties by around half, compared to the method used by the IPCC at present.

Upon examining the outcomes, the team discovered that the threshold for hazardous warming (+1.5 °C) will probably be crossed between 2027 and 2042. This is a considerably narrower window compared to GCMs prediction of between now and 2052.

The team also discovered that, on average, predicted warming was a little lower, by around 10% to 15%. However, they have also found that the 'very likely warming ranges' of the SCRF were less than those of the GCMs, supporting the predictions of the latter.

Now that governments have finally decided to act on climate change, we must avoid situations where leaders can claim that even the weakest policies can avert dangerous consequences. With our new climate model and its next generation improvements, there’s less wiggle room.

Shaun Lovejoy, Study Co-Author and Professor, Physics Department, McGill University

Journal Reference:

Hébert, R., et al. (2020) An observation-based scaling model for climate sensitivity estimates and global projections to 2100. Climate Dynamics. doi.org/10.1007/s00382-020-05521-x.

Source: https://www.mcgill.ca/

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