climate modeling

Asian American and Pacific Islander Heritage Month Spotlight: Dr. Syukuro “Suki” Manabe

By Anjuli S. Bamzai, AMS President

My graduate advisor at George Mason University, Dr. Jagadish Shukla, displayed the photos of four meteorologists in his office: Drs. Norman A. Phillips, Jule Charney, Edward Lorenz, and Syukuro “Suki” Manabe. All giants in their field, they had been his PhD advisers at Massachusetts Institute of Technology (MIT). In the 1990s, as I pursued my graduate degree at Dr. Shukla’s Center for Ocean-Land-Atmosphere Studies (COLA), the scientific family tree remained strongly connected, and so I in turn had the chance to cross paths with luminaries like Manabe in person.

Suki Manabe photo

Circa 1994, I had the privilege of hearing Manabe–or, as I came to refer to him, Suki-san–give the inaugural talk at the newly established COLA. He spoke about the use of dynamical general circulation models to study the atmosphere and its coupling to land, using a simple ‘bucket’ model to discover emergent properties of this complex, chaotic system. He was an animated speaker; it was apparent that he was driven by curiosity and sheer love of the science that he was pursuing.

I was inspired by his ability to explain the properties of such a complex system as the Earth in such elegant terms. Suki-san’s clarity and scientific passion resulted in contributions to our understanding of climate the importance of which cannot be overstated. As I began my own foray into Earth system science, those initial interactions were a formative experience.

Left: Suki-san enjoying his work. Photo courtesy of Dr. V. Ramaswamy.

The models he used were relatively simple compared to the complex Earth system models of today. Yet Manabe and Wetherald (1967), published in the AMS’s Journal of the Atmospheric Sciences, is arguably one of the most influential papers in climate science. It demonstrated a key feature of the atmosphere with an increase in carbon dioxide: rising temperatures closer to the ground while the upper atmosphere got colder. If the variation in solar radiation was primarily responsible for the temperature increase, the entire atmosphere would have gotten warmer.

Graphic from Phys.org, based on Manabe and Wetherald (1967), Figure 16, “Vertical distributions of temperature in radiative convective equilibrium for various values of CO2 content.”

The work that Suki-san and his team conducted comprised a major component of the 1979 report, “Carbon dioxide and climate: A scientific assessment.” Led by Jule Charney from MIT, it is now commonly referred to as the Charney Report. The main result of the succinct 22-page report was that “the most probable global warming for a doubling of [atmospheric] CO2 [is] near 3°C with a probable error of ± 1.5°C.” Perhaps most importantly, the report ruled out the possibility that increasing CO2 would have negligible effects. This estimate of climate sensitivity has pretty much withstood the test of time; in the past forty years, annual average CO2 concentrations increased by ~ 21% and the global average surface temperature increased by ~0.66°C. How prescient!

Suki-san was one of the panelists who shared their insights at a session that the National Academy of Sciences’ Board on Atmospheric Sciences and Climate (BASC) convened during its November 2019 meeting to commemorate the 40th anniversary of the Charney Report. Suki-san’s concluding slide pretty much summed up his philosophy: make your model just as complicated as it needs to be, no more. (See photo below.)

Panelists photo and concluding slide. Slide text says, "Concluding Remarks: [Bullet point one] Satellite observation of outgoing radiation over annual and inter-decadal time scale should provides macroscopic constraint that is likely to be useful for reducing large uncertainty in climate sensitivity. [Bullet point two] It is desirable to make parameterization of subgrid-scale process 'as simple as possible', because simpler parameterization is more testable."
Left: Panelists at the November 21, 2019 session on The Charney Report: Reflections after 40 years at the BASC meeting. (Left to right) Drs. Jagadish Shukla, former student of Jule Charney; D. James Baker, member of the original authoring committee; Jim Hansen and Syukuro Manabe, major contributors to the original report; and John Perry, staff lead for the report. Right: Dr. Manabe’s final slide at the Charney Report session at BASC. Photos courtesy of Anjuli Bamzai.

October 5, 2021, was such an exciting day to wake up to! The Nobel Prize in Physics was shared by Drs. Syukuro Manabe, Klaus Hasselman, and Giorgio Parisi. The citation reads: “The Nobel Prize in Physics 2021 was awarded for groundbreaking contributions to our understanding of complex physical systems” with one half jointly to Syukuro Manabe and Klaus Hasselmann “for the physical modelling of Earth’s climate, quantifying variability and reliably predicting global warming,” and the other half to Giorgio Parisi “for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales.”

As he eloquently stated on the momentous day that he received the Nobel Prize, “I did these experiments out of pure scientific curiosity. I never realized that it would become a problem of such wide-ranging concern for all of human society.”

The accompanying press release on the Nobel Prize particularly cites Suki-san’s work at NOAA in the 1960s, noting that “he led the development of physical models of the Earth’s climate and was the first person to explore the interaction between radiation balance and the vertical transport of air masses. His work laid the foundation for the development of current climate models.”

Suki Manabe with Jagadish Shukla and Marcia McNutt.
Suki Manabe with V. Ramaswamy and Whit Anderson.

Left: Event to honor Nobel Laureate Dr. Suki Manabe at National Academy of Sciences. (Left to right) Drs. Jagadish Shukla, Suki Manabe and Marcia McNutt, President National Academy of Sciences. Photo courtesy of Dr. J. Shukla. Right: (Left to right) Drs. V. Ramaswamy, Director, NOAA GFDL, Suki Manabe, and Whit Anderson, Deputy Director, NOAA GFDL, celebrating the big news of Suki-san’s Nobel Prize, October 2021. Photo courtesy of Dr. V. Ramaswamy.

It is no exaggeration to state that the modeling findings by Suki Manabe and, about a decade later, Klaus Hasselman, opened not only an era of climate modeling but also an entirely new subfield of climate science, viz., detection and attribution (D&A) through fingerprinting and other techniques. Observations have provided an important reality check to model simulations through these D&A efforts.

The current torchbearers of the D&A tradition are Drs. Ben Santer, Tim DelSole, Reto Knutti, Francis Zwiers, Xuebin Zhang, Gabi Hegerl, Claudia Tebaldi, Jerry Meehl, Phil Jones, David Karoly, Peter Stott MBE, Tom Knutson, and Michael Wehner, among others. Over the years several of them have also gone on to receive AMS awards—including, in Meehl’s case, the Jule G. Charney Medal. Speaking of awards, Jonathan Gregory is the most recent recipient of AMS’s Syukuro Manabe Climate Research Award, which has also been bestowed on Drs. Joyce Penner and Cecilia Bitz. Next year, consider nominating someone for the Manabe Award, the Charney Medal, or the new Jagadish Shukla Earth System Predictability Prize!

Equations developed by Dr. Suki Manabe, displayed in the Paris subway during the 2015 UN Climate Change Conference (COP21) in Paris. Photos courtesy of Dr. V. Ramaswamy.

Those of us in the atmospheric and related sciences benefit directly from Suki Manabe’s scientific legacy and intellectual passion, and all of human society owes Suki-san a great debt for helping us to understand climate change, one of the greatest challenges humankind has ever faced.

Anjuli is grateful to Katherine ‘Katie’ Pflaumer for providing useful edits.

A Good Climate for Looking at Clouds

How much do we know about clouds and the effects they have on climate change? It’s a lingering source of uncertainty, with as many questions as answers. No wonder the National Science Foundation calls them “The Wild Card of Climate Change” on its new website about the effect of clouds in climate.
The site is good place to start thinking about this complicated issue. The NSF page features videos of cloud experts like David Randall of Colorado State University and AMS President Peggy LeMone of NCAR, as well as a slide show, animations, articles, and other educational material that address some of most salient cloud/climate questions, such as: Will clouds help speed or slow climate change? Why is cloud behavior so difficult to predict? And how are scientists learning to project the behavior of clouds?
The impression one gets from the website about the progress of the science in this area may vary depending on your point of view, but Randall, for one, sounds about as optimistic as you can get. In his video, he admits that optimism is a job requirement for climate modelers, but in his assessment, “We’re not in the infant stages of understanding [clouds] any more; we’re in first or second grade, and on the way to adolescence.” His hope for solving their role in climate and representing cloud effects in climate modeling rests in part on better computers and in part on the numerous bright people entering the field now, ready to overshadow the work of their mentors.
The AMS Annual Meeting in Seattle will be a good occasion to dig deeper at the roots of Randall’s optimism and sample some of the emerging solutions to the cloud/climate relationship. For example, Andrei Sokolov and Erwan Monier of MIT will discuss the influence that adjusting cloud feedback has on climate sensitivity  (Wednesday, 26 January, 11:30 a.m. in Climate Variability and Change). Basically, they’re using small adjustments to the cloud cover used to calculate surface radiation in a model to create a suite of results–an ensemble. The range of results better reflects the sensitivity of climate observed in the 20th century better than some other methods of creating ensembles, such as adjusting the model physics.
Randall says in his video that early predictions about climate change are already coming to pass and this leads to optimism that more predictions will verify well in the coming years as we scrutinize climate more and more closely. This of course presupposes sustained efforts to observe and verify. Laying the groundwork for this task–and for thus better climate models–are Stuart Evans (University of Washington) and colleagues in a study they are presenting in Seattle. According to their abstract, “Improving cloud parameterizations in large scale models hinges on understanding the statistical connection between large scale dynamics and the cloud fields they produce.” Their study focuses on the relationship between synoptic-scale dynamic patterns and cloud properties (Monday, 24 January, 11 a.m. in Climate Variability and Change). Evans et al. dig through 13 years of cloud vertical radar profiles from the US Southern Plains site of the DOE ARM program and relate it to atmospheric “states”, thus providing a metric for evaluating how well climate models relate cloudiness to radiation and other surface properties.
While Evans and colleagues use upward looking remote sensing, Joao Teixeira (JPL/Cal Tech) and coauthors look down at boundary layer cloudiness from above–using satellites. They expect to show how new methodologies with satellite data can improve the way low level clouds are parameterized in climate models (Thursday, 27 January, 9:30 a.m., in Climate Variability and Change). A recent workshop at Cal Tech on space-based studies of this problem stated:

Clouds in the boundary layer, the lowermost region of the atmosphere adjacent to the Earth’s surface, are known to play the key role in climate feedbacks that lead to these large uncertainties. Yet current climate models remain far from realistically representing the cloudy boundary layer, as they are limited by the inability to adequately represent the small-scale physical processes associated with turbulence, convection and clouds.

The lack of realism of the models at this low level is compounded by the lack of global observing of what goes on underneath the critical low-level cloud cover–hence the effort of Teixeira et al. (and others) to “leverage” satellite observing, with its global reach, to improve understanding of low level thermodynamics in the name of improving climate simulations.

From the new NSF web page on clouds and climate, this picture shows a series of mature thunderstorms in southern Brazil. Photo credit: Image Science & Analysis Laboratory, NASA Johnson Space Center

Climatology: Inverting the Infrastructure

Atmospheric science may not seem like a particularly subversive job, but from an information science perspective, it involves continually dismantling the infrastructure that it requires to survive. At least that’s the way Paul Edwards, Associate Professor of Information at the University of Michigan described climatology, and one other sister science, in an interesting hour-long interview on the radio show, “Against the Grain” last week. (Full audio is also available  for download.)
In the interview Edwards describes how the weather observing and forecasting infrastructure works (skip to about the 29 minute mark if that’s familiar), then notes that climatology is the art of undoing all that:

To know anything about the climate of the world as a whole we have to look back at all those old [weather] records. …But then you need to know about how reliable those are. [Climate scientists] unpack all those old records and study them, scrutinize them and find out how they were made and what might be wrong with them–how they compare with each other, how they need to be adjusted, and all kinds of other things–in order to try to get a more precise and definitive record of the history of weather since records have been kept. That’s what I call infrastructural inversion. They take the weather infrastructure and they flip it on its head. They look at its guts.

In his book, The Vast Machine: Computer, Models, Climate Data, and the Politics of Global Warming, Edwards points out that people don’t realize how much of this unpacking—and with it multiple layers of numerical modeling–is necessary to turn observations into usable, consistent data for analysis and (ultimately) numerical weather and climate predictions. The relationship between data and models is complicated:

In all data there are modeled aspects, and in all models, there are lots of data. Now that sounds like it might be something specific to [climate] science, but …in any study of anything large in scope, you’ll find the same thing.

In part because of this “complicated relationship” between observations and models, there’s a lot of misunderstanding about what scientists mean when they talk about “uncertainty” in technical terms rather than in the colloquial sense of “not knowing”. Says Edwards,

We will always keep on finding out more about how people learned about the weather in the past and will always find ways to fix it a little bit. It doesn’t mean [the climate record] will change randomly or in some really unexpected way. That’s very unlikely at this point. It means that it will bounce around within a range…and that range gets narrower and narrower. Our knowledge is getting better. It’s just that we’ll never fix on a single exact set of numbers that describes the history of weather.

Climatology is not alone in this perpetual unpacking of infrastructure. Economists seem like they know all about what’s going on today with their indexes, Gross Domestic Products, inflation rates, and money supply numbers. That’s like meteorology. But to put together an accurate history of the economy, they have to do a huge amount of modeling and historical research to piece together incongruous sources from different countries.

There is a thing called national income accounting that has been standardized by the United Nations. It wasn’t really applied very universally until after the Cold War….Just to find out the GDP of nations you have to compare apples and oranges and find out what the differences are.

And to go back as recently as the 1930s?

You would have to do the same things the climate scientists have to do…invert the infrastructure.