Tag Archives: climate

Asian ozone pollution in Hawaii is tied to climate variability (Nature Geoscience)

Asian air pollution

Asian pollution drifts east toward North America in 2010. Hawaii is denoted by the star. (Source: Nature Geoscience)

By Joanne Curcio, Program in Atmospheric and Oceanic Sciences

Air pollution from Asia has been rising for several decades but Hawaii had seemed to escape the ozone pollution that drifts east with the springtime winds. Now a team of researchers has found that shifts in atmospheric circulation explain the trends in Hawaiian ozone pollution.

Ozone levels during autumn 1975-2012

Researchers found that ozone levels measured during autumn at Mauna Loa Observatory in Hawaii (black line) accurately reflect the trend in rising Asian air pollution from 1975 to 2012. The researchers demonstrated that the autumnal rise in ozone could be explained by atmospheric and climatic shifts over periods of decades. Using a chemistry-climate model, the researchers modeled this autumnal variation in ozone using constant (red) and time-varying (purple) emissions of ozone precursors. (Source: Nature Geoscience.)

The researchers found that since the mid-1990s, these shifts in atmospheric circulation have caused Asian ozone pollution reaching Hawaii to be relatively low in spring but rise significantly in autumn. The study, led by Meiyun Lin, an associate research scholar in the Program in Atmospheric and Oceanic Sciences (NOAA) at Princeton University and a scientist at the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory, was published in Nature Geoscience.

“The findings indicate that decade-long variability in climate must be taken into account when attributing U.S. surface ozone trends to rising Asian emissions,” Lin said. She conducted the research with Larry Horowitz and Songmiao Fan of GFDL, Samuel Oltmans of the University of Colorado and the NOAA Earth System Research Laboratory in Boulder; and Arlene Fiore of the Lamont-Doherty Earth Observatory at Columbia University.

Although protective at high altitudes, ozone near the Earth’s surface is a greenhouse gas and a health-damaging air pollutant. The longest record of ozone measurements in the U.S. dates back to 1974 in Hawaii. Over the past few decades, emissions of ozone precursors in Asia has tripled, yet the 40-year Hawaiian record revealed little change in ozone levels during spring, but a surprising rise in autumn.

Through their research, Lin and her colleagues solved the puzzle. “We found that changing wind patterns ‘hide’ the increase in Asian pollution reaching Hawaii in the spring, but amplify the change in the autumn,” Lin said.

Using chemistry-climate models and observations, Lin and her colleagues uncovered the different mechanisms driving spring versus autumn changes in atmospheric circulation patterns. The findings indicate that the flow of ozone-rich air from Eurasia towards Hawaii during spring weakened in the 2000s as a result of La-Niña-like decadal cooling in the equatorial Pacific Ocean. The stronger transport of Asian pollution to Hawaii during autumn since the mid-1990s corresponds to a positive pattern of atmospheric circulation variability known as the Pacific-North American pattern.

“This study not only solves the mystery of Hawaiian ozone changes since 1974, but it also has broad implications for interpreting trends in surface ozone levels globally,” Lin said. “Characterizing shifts in atmospheric circulation is of paramount importance for understanding the response of surface ozone levels to a changing climate and evolving global emissions of ozone precursors,” she said.

The work was supported by NOAA’s Cooperative Institute for Climate Science at Princeton University. Ozone measurements were obtained at Mauna Loa Observatory, operated by NOAA’s Earth System Research Laboratory.

Read the abstract

Meiyun Lin, Larry W. Horowitz, Samuel J. Oltmans, Arlene M. Fiore, Songmiao Fan. Tropospheric ozone trends at Mauna Loa Observatory tied to decadal climate variability. Nature Geoscience, Published Online: 26 January, 2014, http://dx.doi.org/10.1038/ngeo2066.

How will crops fare under climate change? Depends on how you ask (Global Change Biology)

Research image

Mechanistic (top row) and empirical (bottom row) simulations compared recent, or “baseline,” maize production in South Africa (1979-99) to projected future production under climate change (2046-65). While both models showed a reduction in output, the third column shows that the empirical model estimated a widespread yield loss of around 10 percent (in yellow), while the mechanistic model showed several areas of increased production (in green). (Image by Lyndon Estes)

Research image 2

For wheat, the mechanistic model (top row) projected greater wheat yields, while the empirical model (bottom row) suggested that wheat-growing areas would expand by almost 50 percent. (Image by Lyndon Estes)

By Morgan Kelly, Office of Communications

The damage scientists expect climate change to do to crop yields can differ greatly depending on which type of model was used to make those projections, according to research based at Princeton University. The problem is that the most dire scenarios can loom large in the minds of the public and policymakers, yet neither audience is usually aware of how the model itself influenced the outcome, the researchers said.

The report in the journal Global Change Biology is one of the first to compare the agricultural projections generated by empirical models — which rely largely on field observations — to those by mechanistic models, which draw on an understanding of how crop growth and development are affected by the environment. Building on similar studies from ecology, the researchers found yet more evidence that empirical models may show greater losses as a result of climate change, while mechanistic models may be overly optimistic.

The researchers ran an empirical and a mechanistic model to see how maize and wheat crops in South Africa — the world’s ninth largest maize producer, and sub-Saharan Africa’s second largest source of wheat — would fare under climate change in the years 2046 to 2065. Under the hotter, wetter conditions projected by the climate scenarios they used, the empirical model estimated that maize production could drop by 3.6 percent, while wheat output could increase by 6.2 percent. Meanwhile, the mechanistic model calculated that maize and wheat yields might go up by 6.5 and 15.2 percent, respectively.

In addition, the empirical model estimated that suitable land for growing wheat would drop by 10 percent, while the mechanistic model found that it would expand by 9 percent. The empirical model projected a 48 percent expansion in wheat-growing areas, but the mechanistic reported only 20 percent growth. In regions where the two models overlapped, the empirical model showed declining yields while the mechanistic model showed increases. These wheat models were less accurate, but still indicative of the vastly different estimates empirical and mechanistic can produce, the researchers wrote.

Disparities such as these aren’t just a concern for climate-change researchers, said first author Lyndon Estes, an associate research scholar in the Program in Science, Technology and Environmental Policy in Princeton’s Woodrow Wilson School of Public and International Affairs. Impact projections are crucial as people and governments work to understand and address climate change, but it also is important that people understand how they are generated and the biases inherent in them, Estes said. The researchers cite previous studies that suggest climate change will reduce South African maize and wheat yields by 28 to 30 percent — according to empirical studies. Mechanistic models project a more modest 10 to 19 percent loss. What’s a farmer or government minister to believe?

“A yield projection based only on empirical models is likely to show larger yield losses than one made only with mechanistic models. Neither should be considered more right or wrong, but people should be aware of these differences,” Estes said. “People who are interested in climate-change science should be aware of all the sources of uncertainty inherent in projections, and should be aware that scenarios based on a single model — or single class of models — are not accounting for one of the major sources of uncertainty.”

The researchers’ work relates to a broader effort in recent years to examine the biases introduced into climate estimates by the models and data scientists use, Estes said. For instance, a paper posted Aug. 7 by Global Change Biology — and includes second author and 2011 Princeton graduate Ryan Huynh — challenges predictions that higher global temperatures will result in the widespread extinction of cold-blooded forest creatures, particularly lizards. These researchers say that a finer temperature scale than existing projections use suggests that many cold-blooded species would indeed thrive on a hotter Earth.

Scientists are aware of the differences between empirical and mechanistic models, said Estes, who was prompted by a similar comparison that showed an empirical-mechanistic divergence in tree-growth models. Yet, only one empirical-to-mechanistic comparison (of which Estes also was first author) has been published in relation to agriculture — and it didn’t even examine the impact of climate change.

The solution would be to use both model classes so that researchers could identify each class’s biases and correct for it, Estes said. Each model has different strengths and weaknesses that can be complementary when combined.

Simply put, empirical models are built by finding the relationship between observed crop yields and historical environmental conditions, while mechanistic models are built on the physiological understanding of how the plant grows and reproduces in response to a range of conditions. Empirical models, which are simpler and require fewer inputs, are a staple in studying the possible effects of climate change on ecological systems, where the data and knowledge about most species is largely unavailable. Mechanistic models are more common in studying agriculture because there is a much greater wealth of data and knowledge that has accumulated over several thousand years of agricultural development, Estes said.

“These two model classes characterize different portions of the environmental space, or niche, that crops and other species occupy,” Estes said. “Using them together gives us a better sense of the range of uncertainty in the projections and where the errors and limitations are in the data and models. Because the two model classes have such different structures and assumptions, they also can improve our confidence in scenarios where their findings agree.”

Read the abstract.

Estes, Lyndon D., Hein Beukes, Bethany A. Bradley, Stephanie R. Debats, Michael Oppenheimer, Alex C. Ruane, Roland Schulze and Mark Tadross. 2013. Projected climate impacts to South African maize and wheat production in 2055: A comparison of empirical and mechanistic modeling approaches. Global Change Biology. Accepted, unedited article first published online: July 17, 2013. DOI: 10.1111/gcb.12325

The work was funded by the Princeton Environmental Institute‘s Grand Challenges Program.

Effects of climate and land management on the type and location of vegetation in wetlands (PNAS)

Periodic floods are a normal occurrence in wetlands. To find out how these floods impact niches of different plant species in wetlands, Princeton researchers studied plant species in Everglades National Park (ENP) in Florida. They found that the sizes of the clusters of each species follow a power law probability distribution and that such clusters have well-defined fractal characteristics. They modeled the effect that periodic flooding and neighboring vegetation have on plant clusters. They found that climate and land management have a predictable impact on the type of vegetation and its spatial organization in wetlands. The findings are highly relevant for the management of wetland ecosystems.

R Foti, M Del Jesus, A Rinaldo, and I Rodriguez-Iturbe. Hydroperiod regime controls the organization of plant species in wetlands.
PNAS, November 13, 2012

Read the abstract