Climate Change

Climate change has been in the news a lot, what with extreme weather, wildfires, and the recent international negotiations in Scotland. What I thought I could do here is go into the science behind what’s happening to the climate system and to leave the policy implications to my fellow citizens and their representatives; kind of like we had to do when I worked at NASA’s Jet Propulsion Lab where we were enjoined by law from advocating policy. 

The basic science behind climate change, sometimes called imprecisely “global warming,” is pretty straightforward, but the steps from observations and measurements to using climate models to predict the future are less so. Science is a repetitive process of making observations, coming up with an idea that explains those observations, and then testing that idea with more observations. If the idea fails to explain the new observations, then it’s back to the drawing board with a new idea to test. Non-scientists can get a little frustrated as they see scientists changing their ideas as new data become available. The response to the pandemic is a good example. 

Our understanding of climate change is also subject to updating as new observations and ideas emerge, but the basic mechanism is clear: certain atmospheric gases, which are transparent at the optical wavelengths the sun delivers to us, absorb thermal infrared wavelengths that are emitted by the warm Earth. As I discussed in my very first column, any body above absolute zero radiates electromagnetic waves at a wavelength determined by its temperature. Earth at “room temperature” radiates most strongly in the thermal infrared, at about 12 micrometers wavelength, far beyond our vision, although we can certainly feel the warm ground radiating its heat. The molecules of some of those optically transparent gases absorb electromagnetic waves in the thermal infrared range and that causes them to warm up. Those gases, the so-called ‘greenhouse gases,’ are carbon dioxide, water vapor, methane, ozone, and some others. 

This effect was noted as far back as 1824 by Joseph Fourier, but it took until 1896 before Svante Arrhenius realized that we could have a problem if we increased atmospheric CO₂ too much. Incidentally, the term “greenhouse effect” as applied to the warming of the atmosphere by the above gases is a misnomer – a greenhouse operates by blocking the movement of air, thereby minimizing convective heat loss, whereas the warming of the atmosphere by these gases is completely different. But the term, coined in 1901, has stuck. It’s also useful to note that having some CO₂ in Earth’s atmosphere has kept our planet at a more comfortable temperature through most of its history; if we had no CO₂, the Earth would be frozen. At the opposite extreme is Venus, where a thick CO₂ atmosphere has caused a runaway greenhouse effect making it the hottest planet in the solar system. 

So what do we know from observations and measurements about the “greenhouse effect” in Earth’s atmosphere? We know that water vapor, CO₂, methane, and ozone absorb thermal infrared waves and we can calculate the amount. We know water vapor varies a lot as water evaporates and rains, and that methane breaks down over a period of years from exposure to sunlight. But CO₂ can last centuries, partly because it’s ‘well-mixed’ as climate scientists say: as soon as it’s released, it spreads out evenly in the atmosphere and it doesn’t break down. 

Another key observation is that the CO₂ concentration in our atmosphere is increasing. That’s thanks to a forward-thinking scientist named C.D. Keeling, who set up a monitoring station high on the flanks of Mauna Loa volcano in Hawaii. Since 1958, the CO₂ concentration at Mauna Loa has risen about 25%. Methane has risen about 15% since 1985. 

The above observations and measurements have very little uncertainty, but the next set of observations have a little more. And here I use the term “uncertainty” in its scientific/engineering sense, where it doesn’t mean climate scientists don’t know the answer, just that they don’t know the exact number. The observations with a little more uncertainty are factors like exactly how much of the extra atmospheric heat the oceans absorb as well as how much of the excess CO₂ dissolves in them. We also don’t have an accurate estimate of the CO₂ budget: for example, we know that the source of most of the extra CO₂ is fossil-fuel burning, but exactly how much? And how about the sinks like growing vegetation? We can make good estimates, but we’d like to have a better handle on the entire carbon budget. NASA has been flying a series of instruments called the Orbiting Carbon Observatory since 2014 to help measure CO₂ globally. 

Can the Future be Predicted?

So, we come to the crux of the matter: how to predict the future. A lot of money has gone into this effort and a lot of progress has been made. One fallout that we all benefit from is improved weather forecasts. While they’re short-term and not particularly helpful for long-term climate prediction, weather forecasts use similar computer models. You may not have noticed because it took a number of years, but weather forecasts have gone from a few days to commonly 10 days, with a fair amount of confidence, or “skill” as the forecasters say. Better observations of weather patterns have also helped. 

There are a couple dozen main climate models that have been developed by various national meteorological services, academic institutions, and other research organizations. In the U.S., the National Center for Atmospheric Research (NCAR), the Geophysical Fluid Dynamics Laboratory (GFDL), and Los Alamos National Laboratory are examples. The models themselves are incredibly complex as they try to solve for mass and energy movements throughout the three-dimensional atmosphere as well as their interactions with the ocean and land, including ecosystems. They generally break up these 3-D volumes into millions of cubes ranging both horizontally and vertically. The driving energy input is solar illumination and thermal radiation from the warm Earth. Obviously, these kinds of models must be run on supercomputers, which still require long run times to simulate even short periods into the future. 

Not every climate-related process is known with enough certainty to solve accurately. And that’s where many of the models diverge. For example, changes in soil moisture over time are hard to measure globally and hard to predict, so some models treat the soil like a simple bucket. A relatively new NASA satellite called SMAP is helping with measurements of soil moisture and a new experimental instrument mounted on the Space Station called EcoStress is trying to measure evaporation from soil and vegetation. Clouds are another major source of uncertainty in the models: high clouds tend to have a cooling effect as they reflect incoming solar illumination away, but the water vapor in lower-level clouds absorbs thermal infrared emitted by the warm Earth, thus warming the atmosphere. There are also feedbacks that could affect the simulations strongly, like the amount of snow-cover, which reflects solar illumination, and the possibility that methane could be released from arctic tundra as permafrost thaws out. 

A program to compare all these models, called the Coupled Model Intercomparison Project (CMIP) has been running for several years now by the World Climate Research Program. To try to evaluate the models, historical simulations are run from 1850 to the present. In that way, we’ve found that most of the models predict trends in global temperature pretty well, but are pretty bad at predicting how precipitation will change. Obviously, some models work better than others for some regions and for some scenarios and much of the work now is in trying to combine the best parts of each model to create better models. 

The various climate models are now being used to predict global and regional changes in temperature and precipitation patterns, given certain CO₂ and methane emission scenarios – things like “business as usual” or elimination of all excess CO₂ emissions are two examples. These can then be used by policy makers and the public to evaluate, with their inherent uncertainties, how the world may change over time. 

The primary source of science-based information on climate change can be found at the Intergovernmental Panel on Climate Change (https://www.ipcc.ch), to which thousands of scientists worldwide are contributing coordinated studies on different aspects of climate change ranging from the technical sides of the models to social and economic effects. Their 6th report was published last year and includes a Synthesis Report, an Executive Summary, and other products for non-specialists. An interactive atlas can be found at https://interactive-atlas.ipcc.ch and regional fact sheets can be downloaded at https://www.ipcc.ch/report/ar6/wg1/#Regional.  

In terms of regional North American changes the models predict, it looks like we’re headed for somewhat warmer temperatures, with the strongest effect in Canada and Alaska. Precipitation is expected to remain mostly unchanged except for the western coast of the U.S., which will become drier with more extreme events, while Canada and Alaska will become somewhat wetter. The ocean is predicted to endure more intense and longer heat waves and acidification will increase, especially along the coasts. 

The state of California has run its own Climate Change Assessments; the 4th Assessment was published in 2018 (https://www.climateassessment.ca.gov). The Key Findings and Statewide Summary Report are good summaries, but many other technical reports are also available. As with the IPCC, the reports cover technical aspects as well as the effects on people and communities, infrastructure, and natural systems including agriculture and the ocean. Adaptation is also a significant part of the assessment. 

Anyone interested in knowing more should have a look at these reports, which not only tell us what’s happening now, but point the way toward policies and practices that can take us into the future.  

Tom Farr joined NASA’s Jet Propulsion Laboratory in 1975 and has helped develop the first geologic applications of imaging radar using aircraft, satellites, and the Space Shuttle. He has taught a class on planetary exploration at Santa Barbara City College for more than 10 years. He currently resides in Montecito