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Climate change is a physics problem


Klaus Hasselmann. Photo credit: Julia Knop, MPG.

This year, the Nobel Prize in Physics was awarded to two founding fathers of climate research and climate modeling. They steered early climate research in the direction of both basic and societally relevant research, which is still as important now as it was then.

This year’s Nobel Prize in Physics was given “for the scientific modeling of Earth’s climate, characterizing variability, and correctly projecting global warming,” according to the Nobel Committee. Klaus Hasselmann and Syukuro Manabe, two of the three laureates, pioneered climate modeling, from elegant conceptual explanations of known climatic features to the complete modeling based on numerical fluid dynamics that is utilized today. Climate modeling currently moves (nearly) effortlessly from weather forecasting to decadal climate prediction and projection, and will ultimately include digital Earths ( Modeling is the finest way for us to see into the future. As a result, it is critical to comprehending and controlling climate change.

The Nobel Prize also emphasizes the fact that climate modeling is physics. This makes the question “do you believe in global warming” meaningless: the physics of energy balance determines whether the world heats in response to greenhouse gas increases. It is not influenced by religious beliefs. Both Hasselmann and Manabe created the scientific basis for worry about growing greenhouse gas concentrations, notably the rise in CO2 in the atmosphere resulting from the combustion of fossil fuels, via their scientific work. The route of scientific inquiry they blazed leads directly to a series of United Nations Conferences of the Parties on Climate Change, which aim to minimize global climate change. In November 2021, the 26th conference in the series, COP26, came together global leaders in Glasgow.

There are two champions and two hubs

Syukuro (“Suki”) Manabe worked at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton as a senior researcher. He was a key figure in the development of the first climate model, which began in the 1960s. His early atmospheric modeling work examined convection and the fluid dynamics of the atmosphere, as well as the vertical temperature structure of the atmosphere as a reaction to radiatively active trace gases1. Many of Manabe’s works are now considered classics by climate experts (including me). On all timeframes, from ice ages to the reaction to human-caused greenhouse gas increases since the Industrial Revolution2,3, his modeling work gave key insights into the physics of the linked climate system.

The Max–Planck Institute for Meteorology (MPI-M) in Hamburg was founded by Klaus Hasselmann (shown in Fig. 1). He began by concentrating on theoretical work: Klaus’s article on’stochastic climate models,’ which is one of my favorites and his most cited paper4, is one of my favorites. The theory is complicated, since it is based on Brownian motion statistics, but the basic concept is straightforward: short-term weather variability leads to long-term climatic variability. This happens because the sluggish components of the climate system, notably the ocean, integrate daily to monthly weather variability into climate variability on annual and multi-decadal timescales. His study on stochastic climate models is lovely, because it explains the origins of unforced long-term climate variability, including its spectrum form, which can be seen in recorded sea surface temperatures5 as well as the variability of today’s climate models. The principle extends to even longer durations: massive ice sheets, for example, combine variability on ocean timeframes with variability on thousands-of-year timescales. Our knowledge of observed climate has evolved as a result of this acknowledgment of the stochastic character of climate variability.

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Klaus’ group initiated a comprehensive climate modeling method based on fluid dynamics equations for the atmosphere and ocean a few years after his groundbreaking 1976 article. Climate models were used by the GFDL and MPI-M research groups to simulate the effects of rising greenhouse gas concentrations. Both sets of researchers have cautioned that significant long-term warming is predicted as a result of the response3,6,7. Due to a lack of computer capability, these early models were very rudimentary, but they already included many elements of future climate change that are still projected today. They simulated a sensitivity to sustained CO2 doubling that is consistent with current models and the Intergovernmental Panel on Climate Change (IPCC)8’s most recent assessment report. The projected pattern of change in surface temperature has been constant as well: the Arctic heats faster than the tropics, and the land warms faster than the ocean. We forecast warming in the lower atmosphere (troposphere), but cooling in the stratosphere due to increased absorption of greenhouse gases in the troposphere and human-caused ozone depletion in the lower stratosphere.

Work as a detective

However, identifying a projected shift in observational data is a major test of the models’ accuracy in projecting human effect on climate. Klaus next considered what it would take to identify warming in response to rising greenhouse gas levels against the backdrop of high climatic variability across all timeframes, as predicted by his statistical model. Rather than looking for a needle in a haystack of multi-dimensional climate variability, he advocated looking for the pattern of climate model-simulated change in response to greenhouse gases that has been convincingly predicted by climate models in observational data. The key to success is to identify and then concentrate on the features of the pattern that are most unique from natural climate variability, which is referred to as the ‘optimal fingerprint’ of climate change9. This is done using a noise-reducing measure based on climatic variability’s inverse covariance matrix.

Following Klaus’ thesis that one of the characteristic aspects predicted in the reaction to greenhouse gas concentrations is a fast warming trend10, I utilized the approach to examine trends in global surface temperature data under Klaus’ and Hans von Storch’s direction. Ben Santer used a similar method to radiosonde and satellite data of the vertical profile of atmospheric temperature change at the same time. The vertical profile was predicted to exhibit a significant signal of coupled tropospheric warming and stratospheric cooling based on Suki’s early modeling work and signal-to-noise analyses from Ben’s cooperation with Klaus11.

By the 1990s, both surface temperature and atmospheric temperature profile data revealed that climate was changing statistically significant: they demonstrated a shift that surpassed estimates of unforced or ‘internal’ climate variability known at the time. These findings have been verified and enhanced since then: the observed human-induced shift has become stronger, and both models and observations have improved. The difference between reported climate changes and internal climate variability has now surpassed the statistical threshold for detecting elementary particles in physics12. The evidence for anthropogenic warming is now “unambiguous”8. Klaus also offered a technique for clearly distinguishing between various probable sources of climate change13, which we used to demonstrate that the observed warming is compatible with the combined impact of greenhouse gases and aerosols, but not with solar variability or greenhouse gas effects alone14.

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Leaders who inspire

Klaus and Suki both employed climate models to answer basic issues about how the climate system works, how it changes, how it evolved through time, and how increases in greenhouse gases may influence it, in addition to directing research groups that built climate models. There were many intriguing new findings concerning processes of climate variability and reasons of observed change during this period of early climate modeling. Models were employed to explain the past in palaeoclimatic research, and previous data was increasingly utilized to test the models. The intellectual environment at MPI-M was one of exciting, pioneering research, with new discoveries emerging from the use of new modeling methods to better understand climate variability and change (see, for example, Fig. 2). We looked at all timeframes, from the long-term implications of huge changes in ocean topologies that occurred thousands to millions of years ago to the long-term ramifications of greenhouse gas increases in the next decades and centuries.

The founding director of MPI-M guided the science and posed intriguing questions. The group then sought to answer these questions, guided and pushed by Klaus, in a golden period of discovery that the participants vividly recall (see this account15). I got the impression from talking to folks who worked at GFDL that the scientific environment there was comparable. Klaus and Suki were regarded by their colleagues and coworkers as very great scientists, as well as amazing human beings and fascinating people to be around: passionate, cheerful, and optimistic.

Klaus remained upbeat about scientific research as well as humanity’s capacity to cope with climate change. I really hope that his optimism is well-founded, and that the transition away from fossil fuels will open up new doors. Klaus has always encouraged people to attempt new things, even in science.

Toward the future

The study of climate change continues. All mechanisms involved in the interplay of the atmosphere, ocean, land surface, and biosphere are yet unknown to us. In terms of the carbon cycle, these interactions are very essential. It’s considerably difficult to consider social connections with the Earth system, but it’s crucial. Human activities have a significant impact on many climatic issues, such as water supply. Climate change is influenced by our decisions on water management, irrigation, and the growth or removal of plants. As climate change progresses, increased evaporation may exacerbate droughts, increase severe heat, and increase fire activity, affecting our capacity to reduce emissions. Melting glaciers and ice sheets have an impact on the climate surrounding them, as well as on sea level rise and water availability during the summer. We need to be aware of nonlinearities and tipping points so that we can avoid them (see We also want accurate data in order to make informed judgments about how to adapt to climate change.

The two new Nobel laureates guided us successfully through the early days of climate science and opened us a large area of scientific inquiry. Climate research will continue to deliver both interesting discoveries about how the globe works and useful knowledge about the human impact on the planet.

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