Climate change scenarios: how will it be

American researchers used computer modeling to predict and analyze 100,000 future options in the context of climate change. The results are not comforting.

Image by ELG21 from Pixabay

By running simulations over and over again, adjusting various factors, we can begin to understand the things that can most affect the future of humanity. Given that countries are still far from implementing the Paris Agreement, and carbon dioxide emissions will increase rapidly, finding focus points to curb climate change is now more important than ever.

Most of the climate modeling today is focused on the technical aspects of the problem – the climate itself or technologies mitigating the consequences of its change. Previous studies have shown that we have the technical capabilities to make the necessary changes and that they are economically possible.

But again and again they are hindered by other factors that modeling has so far mostly neglected – social and political systems. The authors of the new work tried to include these factors. To do this, they used data from several different disciplines — in particular, scientists assessed the impact of social, economic and political factors that will affect our emissions figures in order to use them for computer modeling of warming levels by 2100.

They added constraints to their variables using historical data and identified several social factors, including public attitudes to climate change, as key to determining which scenario would be most likely. Modeling shows that it is now extremely unlikely that the increase in the global average temperature will be able to remain at 1.5 °C — with a high probability we will exceed this threshold.

The good news is that the model assumes a high probability of accelerating emissions reduction after all the indicators are taken into account. More than 90 percent of simulations have shown that we are at least on track to reduce the rate of global temperature rise, even if we take into account the higher limits of uncertainty ranges.

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