Is global warming just a giant natural fluctuation? Nope

A press release from McGill University: An analysis of temperature data since 1500 all but rules out the possibility that global warming in the industrial era is just a natural fluctuation in the earth’s climate, according to a new study by McGill University physics professor Shaun Lovejoy.

The study, published online April 6 in the journal Climate Dynamics, represents a new approach to the question of whether global warming in the industrial era has been caused largely by man-made emissions from the burning of fossil fuels. Rather than using complex computer models to estimate the effects of greenhouse-gas emissions, Lovejoy examines historical data to assess the competing hypothesis: that warming over the past century is due to natural long-term variations in temperature.

“This study will be a blow to any remaining climate-change deniers,” Lovejoy says. “Their two most convincing arguments – that the warming is natural in origin, and that the computer models are wrong – are either directly contradicted by this analysis, or simply do not apply to it.”

Lovejoy’s study applies statistical methodology to determine the probability that global warming since 1880 is due to natural variability. His conclusion: the natural-warming hypothesis may be ruled out “with confidence levels great than 99%, and most likely greater than 99.9%.”

To assess the natural variability before much human interference, the new study uses “multi-proxy climate reconstructions” developed by scientists in recent years to estimate historical temperatures, as well as fluctuation-analysis techniques from nonlinear geophysics. The climate reconstructions take into account a variety of gauges found in nature, such as tree rings, ice cores, and lake sediments. And the fluctuation-analysis techniques make it possible to understand the temperature variations over wide ranges of time scales.

...While his new study makes no use of the huge computer models commonly used by scientists to estimate the magnitude of future climate change, Lovejoy’s findings effectively complement those of the International Panel on Climate Change (IPCC), he says. His study predicts, with 95% confidence, that a doubling of carbon-dioxide levels in the atmosphere would cause the climate to warm by between 2.5 and 4.2 degrees Celsius. That range is more precise than – but in line with -- the IPCC’s prediction that temperatures would rise by 1.5 to 4.5 degrees Celsius if CO2 concentrations double....

Ice floes shot by michael clarke stuff, Wikimedia Commons via Flickr, under the Creative Commons Attribution-Share Alike 2.0 Generic license 

New statistical models could lead to better predictions of ocean patterns and the impacts on weather, climate and ecosystems

A press release from the University of Missouri News Bureau: The world’s oceans cover more than 72 percent of the earth’s surface, impact a major part of the carbon cycle, and contribute to variability in global climate and weather patterns. However, accurately predicting the condition of the ocean is limited by current methods. Now, researchers at the University of Missouri have applied complex statistical models to increase the accuracy of ocean forecasting that can influence the ways in which forecasters predict long-range events such as El Nińo and the lower levels of the ocean food chain—one of the world’s largest ecosystems.

“The ocean really is the most important part of the world’s environmental system because of its potential to store carbon and heat, but also because of its ability to influence major atmospheric weather events such as droughts, hurricanes and tornados,” said Chris Wikle, professor of statistics in the MU College of Arts and Science. “At the same time, it is essential in producing a food chain that is a critical part of the world’s fisheries.”

The vastness of the world’s oceans makes predicting its changes a daunting task for oceanographers and climate scientists.  Scientists must use direct observations from a limited network of ocean buoys and ships combined with satellite images of various qualities to create physical and biological models of the ocean.  Wikle and Ralph Milliff, a senior research associate at the University of Colorado, adopted a statistical “Bayesian hierarchical model” that allows them to combine various sources of information as well as previous scientific knowledge. Their method helped improve the prediction of sea surface temperature extremes and wind fields over the ocean, which impact important features such as the frequency of tornadoes in tornado alley and the distribution of plankton in coastal regions—a critical first stage of the ocean food chain.

“Nate Silver of The New York Times combined various sources of information to understand and better predict the uncertainty associated with elections,” Wikle said. “So much like that, we developed more sophisticated statistical methods to combine various sources of data—satellite images, data from ocean buoys and ships, and scientific experience—to better understand the atmosphere over the ocean and the ocean itself. This led to models that help to better predict the state of the Mediterranean Sea, and the long-lead time prediction of El Nińo and La Nińa. Missouri, like most of the world, is affected by El Nińo and La Nińa (through droughts, floods and tornadoes) and the lowest levels of the food chain affect us all through its effect on Marine fisheries.”...

Rough seas off Ovingdean in the UK, shot by Peter Whitcomb, Wikimedia Commons via Geograph UK, under the Creative Commons Attribution-Share Alike 2.0 Generic license 

Statistics research could build consensus around climate predictions

Science Daily: Vast amounts of data related to climate change are being compiled by research groups all over the world. Data from these many and varied sources results in different climate projections; hence, the need arises to combine information across data sets to arrive at a consensus regarding future climate estimates.

In a paper published last December in the SIAM Journal on Uncertainty Quantification, authors Matthew Heaton, Tamara Greasby, and Stephan Sain propose a statistical hierarchical Bayesian model that consolidates climate change information from observation-based data sets and climate models.

"The vast array of climate data -- from reconstructions of historic temperatures and modern observational temperature measurements to climate model projections of future climate -- seems to agree that global temperatures are changing," says author Matthew Heaton. "Where these data sources disagree, however, is by how much temperatures have changed and are expected to change in the future. Our research seeks to combine many different sources of climate data, in a statistically rigorous way, to determine a consensus on how much temperatures are changing."

Using a hierarchical model, the authors combine information from these various sources to obtain an ensemble estimate of current and future climate along with an associated measure of uncertainty. "Each climate data source provides us with an estimate of how much temperatures are changing. But, each data source also has a degree of uncertainty in its climate projection," says Heaton. "Statistical modeling is a tool to not only get a consensus estimate of temperature change but also an estimate of our uncertainty about this temperature change."

The approach proposed in the paper combines information from observation-based data, general circulation models (GCMs) and regional climate models (RCMs).

…By combining information from multiple observation-based data sets, GCMs and RCMs, the model obtains an estimate and measure of uncertainty for the average temperature, temporal trend, as well as the variability of seasonal average temperatures. The model was used to analyze average summer and winter temperatures for the Pacific Southwest, Prairie and North Atlantic regions (seen in the image above) -- regions that represent three distinct climates. The assumption would be that climate models would behave differently for each of these regions. Data from each region was considered individually so that the model could be fit to each region separately.

"Our understanding of how much temperatures are changing is reflected in all the data available to us," says Heaton. "For example, one data source might suggest that temperatures are increasing by 2 degrees Celsius while another source suggests temperatures are increasing by 4 degrees. So, do we believe a 2-degree increase or a 4-degree increase? The answer is probably 'neither' because combining data sources together suggests that increases would likely be somewhere between 2 and 4 degrees. The point is that that no single data source has all the answers. And, only by combining many different sources of climate data are we really able to quantify how much we think temperatures are changing."…

This map (presented here just as a generic illustration) shows the projected impact of climate change in the 2080s on agricultural productivity across the world. Impacts are measured as a percentage change in agricultural productivity compared to 2003 levels. It is based on work by Cline (2007) (referred to by the European Environment Agency (EEA)

A new technique to simulate climate change

Space Daily via SPX: Scientists are using ever more complex models running on ever more powerful computers to simulate the earth's climate. But new research suggests that basic physics could offer a simpler and more meaningful way to model key elements of climate.

The research, published in the journal Physical Review Letters, shows that a technique called direct statistical simulation does a good job of modeling fluid jets, fast-moving flows that form naturally in oceans and in the atmosphere. Brad Marston, professor of physics at Brown University and one of the authors of the paper, says the findings are a key step toward bringing powerful statistical models rooted in basic physics to bear on climate science.

In addition to the Physical Review Letters paper, Marston will report on the work at a meeting of the American Physical Society to be held in Baltimore this later month.

The method of simulation used in climate science now is useful but cumbersome, Marston said. The method, known as direct numerical simulation, amounts to taking a modified weather model and running it through long periods of time. Moment-to-moment weather - rainfall, temperatures, wind speeds at a given moment, and other variables - is averaged over time to arrive at the climate statistics of interest. Because the simulations need to account for every weather event along the way, they are mind-bogglingly complex, take a long time run, and require the world's most powerful computers.

Direct statistical simulation, on the other hand, is a new way of looking at climate. "The approach we're investigating," Marston said, "is the idea that one can directly find the statistics without having to do these lengthy time integrations."...

A fresh breeze in the West wind belt, from Roald Amundsen's "The South Pole"

Climate change is not an all-or-nothing proposition, researcher says

Pam Frost Gorder in Ohio State University Research and Innovation Communications: An Ohio State University statistician says that the natural human difficulty with grasping probabilities is preventing Americans from dealing with climate change. In a panel discussion at the American Association for the Advancement of Science meeting on Feb. 15, Mark Berliner said that an aversion to statistical thinking and probability is a significant reason that we haven’t enacted strategies to deal with climate change right now.

Berliner, professor and chair of statistics at Ohio State, is the former co-chair of the American Statistical Association’s Advisory Committee on Climate Change Policy, and as such, he spent two years talking with U.S. Congressional staffers about climate change. As a result, he’s come to the conclusion that Americans need to understand that climate change is a range of possible events that are more or less likely. However, the negative impacts of climate change can be reduced by taking some moderate actions today, he said.

“The general public has an understanding of tipping points, the moment beyond which things become inevitable. But as soon as you start thinking of climate change as inevitable, it’s easy to throw up your hands and say, ‘it’s too late, so why bother to do anything?’” Berliner said. “It’s like a two-pack-a-day smoker deciding not to cut back on the cigarettes, because he’s as good as gone.”

“The situation is not hopeless. Instead of taking an extreme all-or-nothing view about climate change, we can think of it as a spectrum of possible problems, and look for a spectrum of practical solutions that will do the most good,” he said. From his own career in climate research, Berliner sees climate change as a collection of possible events: some extreme disasters that are unlikely to happen, but still possible; and less extreme events that are much more likely.

It’s the difference, he said, between the low possibility that a coastal town will flood permanently, versus the high possibility that high tides and periodic floods will force the town to close its beaches for more days during the year—a loss to valuable tourism.

It’s human nature to abhor uncertainty, he said, and climate research, like all research, is full of uncertainty. He hopes that opinion leaders will help the public understand the nature of science, and the idea that uncertainties diminish as data accumulates. There will never be a single right answer to the question “what will happen to Earth’s climate?”

“One of the criticisms of climate change research is that different computer models give different answers,” Berliner said. “But the key is not to pick the right climate model, but to pick the right elements out of each of the models.”

As he calculates the effectiveness of potential climate change mitigation strategies, Berliner has determined one thing for sure. “Compromise—if it leads to doing something—is better than doing nothing,” he said....

From 1767, Claude Joseph Vernet's "A storm on the Mediterranean coast"

'Black swans' and 'perfect storms' become lame excuses for bad risk management

Kelly Servick in Stanford University News: Elisabeth Paté-Cornell argues that a true 'black swan' - an event that is impossible to imagine because we've known nothing like it in the past - is extremely rare. The terms "black swan" and "perfect storm" have become part of the public vocabulary for describing disasters ranging from the 2008 meltdown in the financial sector to the terrorist attacks of Sept. 11, 2001. But according to Elisabeth Paté-Cornell, a Stanford professor of management science and engineering, people in government and industry are using these terms too liberally in the aftermath of a disaster as an excuse for poor planning.

Her research, published in the November issue of the journal Risk Analysis, suggests that other fields could borrow risk analysis strategies from engineering to make better management decisions, even in the case of once-in-a-blue-moon events where statistics are scant, unreliable or non-existent.

Paté-Cornell argues that a true "black swan" – an event that is impossible to imagine because we've known nothing like it in the past – is extremely rare. The AIDS virus is one of very few examples. Usually, there are important clues and warning signs of emerging hazards (e.g., a new flu virus) that can be monitored to guide quick risk management responses.

..."Risk analysis is not about predicting anything before it happens, it's just giving the probability of various scenarios," she said. She argues that systematically exploring those scenarios can help companies and regulators make smarter decisions before an event in the face of uncertainty.

An engineering risk analyst thinks in terms of systems, their functional components and their dependencies, Paté-Cornell said. ....Paté-Cornell says that a systematic approach is also relevant to human aspects of risk analysis.

....Paté-Cornell has found that human errors, far from being unpredictable, are often rooted in the way an organization is managed. "We look at how the management has trained, informed and given incentives to people to do what they do and assign risk based on those assessments," she said.

...."Lots of people don't like probability because they don't understand it," she said, "and they think if they don't have hard statistics, they cannot do a risk analysis." In fact, we generally do a system-based risk analysis because we do not have reliable statistics about the performance of the whole system.

Two black swans, shot by fir0002 | flagstaffotos.com.au, Wikimedia Commons, under the following Creative Commons license: Attribution NonCommercial Unported 3.0

Finding fingerprints in sea level rise

Terra Daily via SPX: It was used to help Apollo astronauts navigate in space, and has since been applied to problems as diverse as economics and weather forecasting, but Harvard scientists are now using a powerful statistical tool to not only track sea level rise over time, but to determine where the water causing the rise is coming from.

As described in the Proceedings of the National Academy of Sciences (PNAS), graduate students Eric Morrow and Carling Hay demonstrate the use of a statistical tool called a Kalman smoother to identify "sea level fingerprints" - tell-tale variations in sea level rise - in a synthetic data set. Using those fingerprints, scientists can determine where glacial melting is occurring.

"The goal was to establish a rigorous and precise method for extracting those fingerprints from this very noisy signal," Professor of Geophysics Jerry Mitrovica, who oversaw the research, said. "What Carling and Eric have come up with is very elegant and it provides a powerful method for detecting the fingerprints. In my view everyone is soon going to be using this method."

At the heart of the new technique is the idea, first proposed by Mitrovica and others more than a decade ago, that variability in sea level changes amount to a "fingerprint" researchers can use to identify the source of water pouring into the oceans.

With the public unconvinced about the effects of climate change, Mitrovica proposed the fingerprint idea as a way to refute the argument that melting ice sheets would cause a uniform sea level rise, he said, "like what you see when you turn on the tap in the bathtub - it all goes up uniformly." Rather than a uniform rise in sea level, skeptics pointed to records that showed levels rising in some areas and dropping in others as evidence that man-made climate change was a myth....

Photo by PD Photo.org, Wikimedia Commons, public domain

Statistical analysis projects future temperatures in North America

Ohio State Research: For the first time, researchers have been able to combine different climate models using spatial statistics - to project future seasonal temperature changes in regions across North America. They performed advanced statistical analysis on two different North American regional climate models and were able to estimate projections of temperature changes for the years 2041 to 2070, as well as the certainty of those projections.

The analysis, developed by statisticians at Ohio State University, examines groups of regional climate models, finds the commonalities between them, and determines how much weight each individual climate projection should get in a consensus climate estimate.

...Given the complexity and variety of climate models produced by different research groups around the world, there is a need for a tool that can analyze groups of them together, explained Noel Cressie, professor of statistics and director of Ohio State’s Program in Spatial Statistics and Environmental Statistics.

...“One of the criticisms from climate-change skeptics is that different climate models give different results, so they argue that they don’t know what to believe,” he said. “We wanted to develop a way to determine the likelihood of different outcomes, and combine them into a consensus climate projection. We show that there are shared conclusions upon which scientists can agree with some certainty, and we are able to statistically quantify that certainty.”

...Cressie cautioned that this first study is based on a combination of a small number of models. Nevertheless, he continued, the statistical computations are scalable to a larger number of models. The study shows that climate models can indeed be combined to achieve consensus, and the certainty of that consensus can be quantified....

Alfred Bierstadt's "Sunrise at Glacier Station"

Mitigating disasters by hunting down Dragon Kings

AlphaGalileo has a mysterious press release about something that could be worthwhile. You decide: Professional Dragon King hunter Didier Sornette from the Department of Management, Technology and Economics, ETH Zurich, Switzerland, together with his colleague Guy Ouillon, present the many facets of Dragon Kings in a review about to be published in EPJ ST. Their work will appear alongside nineteen other contributions exploring the ways in which this emerging field of statistical analysis could become further established.

Dragon Kings are events akin to catastrophes. They don't belong to the same power law regime as the more standard events. For example, they can be found in financial market bubbles ending in crashes, neuron-firing cascades leading to epileptic seizures, forest fires, the distribution of city sizes, insurance claims, and even in seemingly more mundane systems such as a stick balancing on a fingertip that eventually falls to one side or the other. Their name refers to the extreme behaviour of dragons stemming from their supernatural powers.

This review focuses on elucidating how Dragon Kings are created and can be detected. It also gives an overview of their empirical evidence in abnormal rainfall, hurricanes, and sudden events such as landslides and snow avalanches. The authors also outline the limitations of this sort of statistical analysis. For example, despite being sometimes interpreted as featuring characteristic events of Dragon Kings, great earthquakes may not be formally confirmed as such.

Finally, the authors share their views on the importance of devising prediction models that could become the basis for Dragon Kings simulators. These could be designed to help interpret the warning signs of complex systems evolving from their safe equilibrium into extreme events such as the subprime crisis, and to steer them into sustainability and ultimately avoid such crisis...

A dragon on a roof in the Imperial Enclosure, Hue, Vietnam, shot by AJ Oswald, Wikimedia Commons via Flickr, under the Creative Commons Attribution-Share Alike 2.0 Generic license