Climate models have been the basis for concern about climate change for more than 35 years. The US government, the United Nations, and organizations across the world have used model projections to warn about global warming and to demand a shift to renewable energy. But Trump administration budget cuts at NASA, NOAA, and other federal agencies threaten to shut down the models, the heart of climate change alarmism.

In June of 1988, Senator Tim Wirth, then chair of the Committee on Energy and Natural Resources, held the first-ever hearing on the science of climate change. Dr. James Hansen, head of a computer-modeling team at NASA, testified that he was “ … 99 percent confident that the world really was getting warmer and that there was a high degree of probability that it was due to human-made greenhouse gases.”

Since Dr. Syukuro Manabe of the Geophysical Fluid Dynamics Laboratory in Washington D.C. developed one of the first climate models in the 1960s, modelers have been warning that humans are causing dangerous climate change. Global surface temperatures have risen only a little more than one degree Celsius over the last 140 years, but models project a faster additional rise of 0.5ꟷ3.5oC by the year 2100.

Climate models have been used by scientists, researchers, and governmental policy makers to estimate possible future climate impacts. Global organizations, such as The Intergovernmental Panel on Climate Change (IPCC) of the United Nations and the World Bank use model projections to urge climate action. Non-governmental organizations such as Greenpeace use model projections to raise funds. But the Trump administration appears to be about to shut down the US climate models.

There are more than 40 climate models operating across the world, with 13 of the leading models located in the US. The US models are operated by National Aeronautics and Space Administration (NASA) in New York City, the National Oceanic and Atmospheric Administration (NOAA) in Princeton, New Jersey, and the Department of Energy (DOE) in Boulder, Colorado. Each of these organizations has been ordered to reduce staff as part of Trump administration budget cuts.

The White House may soon tell NASA to focus work on space programs, not climate change. In February, the administration denied NASA officials permission to travel to an international climate meeting in China. At the same time, NASA management cut off funding for a support contract for the 7th Assessment Report of the IPCC. NASA has been a primary contributor to previous IPCC Assessment Reports. Preliminary government spending plans for fiscal year 2026 would cut NASA’s science budget by almost half, to $3.9 billion.

The administration also wants to end climate change programs at NOAA. Plans call for a 27% cut to NOAA’s budget, down to $4.5 billion. Final budget totals for NASA and NOAA will need to be approved by Congress, with members concerned about the climate sure to put up a fight.

Climate models run on supercomputers and are expensive. Supercomputers cost about $50 million up front and $20 million per year to support each climate-modeling team. The NASA, NOAA, and DOE modeling teams may not be able to survive large projected cuts.

Beyond climate models, budgets of other climate projects will also be cut. The Sea Level Research Group at the University of Colorado has been studying sea level rise for about two decades. This group gets much of its funding from NASA and other federal agencies. The Mauna Loa Laboratory in Hawaii has been measuring the rise in atmospheric CO2 concentration since the 1950s, but it may be closed due to NOAA funding cuts. Three NASA satellites used to collect climate data also need to be replaced, but there are no plans to do so.

The Trump administration is cutting funding for climate research across all federal departments, with major impacts on US and world efforts to force action on climate change. Maybe it’s time for NASA to stick to space exploration, NOAA to stick to weather forecasting, and for the climate models to be shut down.

This article was originally published in MasterResource and is used here by the author’s permission.