In 1969 scientists from the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey, published results from the world’s first climate model. Though the model gave scientists their first look at how the ocean and atmosphere interact to influence climate, it covered only one-sixth of the surface of the Earth and did not take into account any human-made, or anthropogenic, causes of climate change.
Forty years later, using an ancestor of this pioneering model, a team led by GFDL climate scientist Venkatramani Balaji is using the Cray XT5 known as Jaguar—housed in the OLCF—to simulate and assess both natural and anthropogenic causes of climate change at exceptional resolutions.
“Resolving our models to 50 kilometers, you can see states and counties. That’s what people are really asking for [from climate science],” said Balaji, head of the Modeling Systems Group at GFDL, a branch of NOAA devoted to developing and using mathematical models and computer simulations to improve understanding of the Earth’s atmosphere, ocean, and climate. The group’s models are higher in resolution than required by the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4).
Climate models from which data was gleaned for the IPCC AR4, released in 2007, were in the 100-kilometer range for both ocean and atmosphere. Accurate regional climate forecasting is in-
valuable to areas such as the American Southwest, which experiences persistent droughts that are
rapidly depleting water reservoirs. If climate models can prove that anthropogenic causes are mostly
to blame, stakeholders can amend policies and practices to make the most of scarce resources.
Causes of climate change
Natural variations in global climate arise from phenomena including changes in solar activity, periodic alterations in the Earth’s orbit, and sulfate aerosols from volcanic eruptions. However, the AR4 claims, “It is extremely unlikely (less than 5 percent) that the global pattern of warming during the past half century can be explained without external forcing, and very unlikely (less than 10 percent) that it is due to known natural external causes alone.”
Humanity’s effect on climate is broad. Of most concern to climate scientists and environmentalists are increasing carbon dioxide levels in the Earth’s atmosphere. According to the IPCC AR4, two-thirds of the anthropogenic carbon dioxide emissions come from fossil fuel combustion, while the remaining third comes from land-use changes such as irrigation, deforestation, and ranching. Carbon dioxide is naturally present in the atmosphere along with water vapor, nitrous oxide, methane, ozone, and other trace gases collectively referred to as greenhouse gases. As their name suggests, these gases warm the Earth by trapping energy from sunlight just as a greenhouse does. Ever since the Industrial Revolution, greenhouse-gas emissions have increased, warming the Earth and affecting regional weather patterns.
The terms “climate” and “weather” describe the atmosphere through different timeframes. Climate refers to average, or statistical, behavior of the global atmosphere over a long period of time—say, year to year—whereas weather is a measure of day-to-day local atmospheric conditions. Through cutting-edge models, Balaji’s project, dubbed CHiMES (for Coupled High-Resolution Modeling of the Earth System), aims to explore the relationship between climate and weather.
CHiMES is a collaborative effort between DOE and NOAA. If climate can be predicted at all, the project endeavors to forecast change over time periods of interest to resource managers (e.g., decades) and to understand the responses of phenomena like tropical storms to a warming climate.
Anatomy of a climate model
Climate models are composed of mathematical sets of instructions called differential equations. These equations assess interactions among various components that influence the Earth’s climate—atmosphere, oceans, land, and ice. In short, climate models calculate the solar energy our planet absorbs and the radiation it emits, some of which escapes into space while some remains trapped by Earth’s atmosphere. Differential equations that govern the system are solved in accordance with different boundary conditions, such as specifying sea surface temperature distributions, carbon dioxide concentrations, and atmospheric aerosol distributions.
Balaji’s model—GFDL Flexible Modeling System—is actually two models built upon a flexible framework that allows different components of the climate system to be modeled by multiple scientists and code developers and assembled in a variety of ways. The atmospheric model, Finite Volume Cubed-Sphere, runs at a resolution of 25 kilometers. The oceanic model MOM4 (short for Modular Ocean Model) is what climate scientists call a quarter-degree model, which translates into resolution of roughly 25 kilometers as well. The resolution of the models can be increased
in specific areas, and Balaji and colleagues increased the granularity to 10-kilometer resolution in areas of particular interest.
The equations of any climate model are calculated at individual points on a grid covering the Earth. Traditionally, this grid has been dictated by lines of longitude and latitude in which each cell of the grid is defined as the intersection of a latitude line and a longitude line. Though easy to manage, a latitude/longitude-based grid creates problems for climate scientists. The distance between meridians (lines of longitude) decreases as they draw closer to the poles, requiring the dynamical algorithms to use exceedingly short time steps to keep the solutions stable in the polar regions. Although this stabilizes the calculation in the polar regions, it constrains the time step throughout the global domain, decreasing the overall efficiency of the calculation.
Balaji’s team conquers this issue in its coupled model by using a cubed-sphere grid that projects a three-dimensional cubed grid onto the Earth, removing the clustered points around the poles and providing quasi-uniform resolution at each grid cell. Using a tripolar grid for the ocean model overcomes this “pole problem” by placing three “poles” over landmasses on the grid: one at the South Pole (as there is no ocean there), one over the Asian continent, and one over North America.
The atmospheric and oceanic models employ separate grids and solve different algorithms. The majority of the time, the models run independently but concurrently. A third grid is used to exchange data between the two models every 2 hours.
“Many other climate models only exchange data once a day or not at all, so anything that happens faster than a 24-hour timeframe will be missed,” said Balaji. “Peaks in wind can change the ocean’s circulation [in less than 24 hours], and we are able to resolve that.”
Coupling climate models is computationally expensive, but the Jaguar XT5—the fastest supercomputer in the world at 2.33 quadrillion calculations per second and featuring nearly a quarter of a million processors—gave Balaji’s team the power and speed it needed to frequently link its climate models.
The most exciting results from the CHiMES project come from the team’s study of tropical storm response to global climate change. In 2009 the team used 20 million processor hours on Jaguar to run approximately 500 years’ worth of coupled-model simulations through an INCITE program allocation. Scaling its high-resolution models from 60,000 to 100,000 cores, Balaji’s team was able to realistically duplicate year-to-year behavior of hurricanes, accurately simulating their seasonal peak in September.
The team is also resolving an issue that has plagued coupled climate models since their inception. The issue pertains to an area around the equator where winds originating in the northern and southern hemispheres meet, affecting the wet and dry seasons of many equatorial nations. The region is called the Intertropical Convergence Zone (ITCZ), and it appears in coupled climate models as two peaks in rainfall. In reality the ITCZ has only one peak in rainfall. As the team continues to increase the resolution of its models, it is slowly coming closer to eliminating the second “peak.”
The team also hopes to use the ocean as a way to forecast climate on the scale of decades. The ocean influences the climate on a longer scale than do Earth’s ice, land, and atmosphere. If models can capture the long-term behavior of the oceans, they can also capture the short-term behavior of Earth’s other climate-influencing components.
While Balaji is unsure of whether his models are capable of forecasting climate on the decadal scale, the team has been awarded another 20 million hours on Jaguar through INCITE for 2010. Balaji’s team plans to complete runs to determine if decadal predictability is possible, but it also hopes to bring even higher-resolution models online—ones capable of resolving fine-scale weather such as cloud convection and shifting winds.
Ultimately Balaji and his colleagues hope to demonstrate that global climate change is not a remote concept, but a tangible problem with significant consequences that the world’s experts must solve together.
“Local changes will be far more intense; you might get warming here and cooling there,” he explained. “The term global warming seems like a temperature problem, but precipitation patterns change—some areas will get wetter, some dryer. We have to make [climate change] more relevant to people by bringing it down to the regional scale. In order to help people adapt, we want to tell them what to be prepared for.”
—by Caitlin Rockett