Monday, May 13, 2013

Climate Change and Weather Predictions: The differences in regards to Chaos Theory

A paper I wrote for my meteorology class on 5/11/2013:

            Back in 1963, a man named Lorenz was studying patterns of rising warm air in the atmosphere. His studies led to a model of chaos theory, the Lorenz Attractor (an attractor in math being a representation of space: “the smallest unit which cannot be itself decomposed into two or more attractors with distinct basins of attraction (Weisstein, 2013).”). This “strange attractor” came from his studies that showed how even a slight interference in pattern could cause the outcome to be completely unexpected. Take a look at the below, where blue and magenta travel together until one takes an unexpected diversion based on a very slight change in input:
Illustration of deterministic chaos. Imagine two systems started at slightly different initial conditions. They will follow each other closely for some time, but within a short time our ability to predict them breaks down (front and side view of the Lorenz attractor).
Illustration of deterministic chaos. Imagine two systems started at slightly different initial conditions. They will follow each other closely for some time, but within a short time our ability to predict them breaks down (front and side view of the Lorenz attractor).

What the Lorenz attractor models is the chaos of weather prediction (Axelsen, 2010).
            What Jacob Axelsen points out in his discussion of chaos theory and weather is that climate change predictions are not ruled by the same chaos (2010). Climate change can be predicted, but it will be the daily weather that results that remains hard to pin down. This is because the inputs for weather are sensitive and fragile – air, easily manipulated, and water systems. Climate prediction comes from much more predictable inputs such as radiation and molecules with predictable behaviours (Axelsen, 2010). Weather, essentially, is not going to be easy to figure out as climate change takes place, but the inputs from climate can be predicted.
            Much of the more consistent study of Earth’s past climate has come from looking at glacial, arctic and Antarctic ice cores. The use of ice cores determines two major components of climate change predictability: temperature and Carbon Dioxide (CO2) levels. CO2 from the atmosphere in the past can be measured by studying air bubbles in the ice cores. The temperature is determined by the way the ice is formed (Ferguson, 2013). The data from ice cores collected gives data from as far back as 80,000 years (Ferguson, 2013), so a good representation of past climate information is available to scientists in comparing the correlation of temperature to atmospheric CO2 levels.
            CO2 is one of several greenhouse gasses in Earth’s atmosphere. It is not the most plentiful; water vapor is a greenhouse gas and is much more plentiful in the atmosphere and actually absorbs more radiation (Aherns, 2012). However, the concern with CO2 is the amount that is generated by anthropogenic means through the relatively recent phenomenon of burning fossil fuels for power and technology. CO2 is a byproduct of spent fossil fuels.
            There has been more recent data taken directly from the atmosphere for the past few decades at the NOAA Earth System Research Laboratory at Mauna Loa, Hawaii. Below, you can see the data collected represented:
“The carbon dioxide data (red curve), measured as the mole fraction in dry air, on Mauna Loa constitute the longest record of direct measurements of CO2 in the atmosphere. They were started by C. David Keeling of the Scripps Institution of Oceanography in March of 1958 at a facility of the National Oceanic and Atmospheric Administration[Keeling, 1976]. NOAA started its own CO2 measurements in May of 1974, and they have run in parallel with those made by Scripps since then [Thoning, 1989]. The black curve represents the seasonally corrected data.”

            Based on this data, and the correlation of temperature and CO2 levels in ice cores, the prediction that the Earth’s surface temperature will increase makes sense. The warming of Earth makes some climatic changes easy to predict: the Northwest U.S. mountain ranges will experience more rainfall than snowfall, affecting water supplies. The tropical inland regions will get drier. Heat from the West African coast and the warming that the equatorial Atlantic experiences will add fuel to hurricane development, making them stronger and longer lasting. All these climatic predictions can be made in general (Ahrens, 2012).
            There is one major aspect of weather that could make climate change go either way. Cloud formation can be predicted to increase with climate change. What can’t be predicted is whether those clouds will increase or decrease Earth’s surface temperature. Scientists are currently trying to create models to figure this out. Clouds are large condensed bodies of water, which could have a greenhouse effect. They could also have an albedo effect and insulate the atmosphere from further radiation. Either way, the clouds and the likely increase in formation is one of the major sticking points in climate change predictability (Ahrens, 2012).
            In the end, there are two things that are certain: CO2 is increasing due to human activity and increasing CO2 adds greenhouse gases. There are questions, though, that remain in trying to predict whether climate change and warming will occur due to this phenomenon. Will increased CO2 create a bloom of plankton that can actually decrease CO2 levels? Will the increased cloud formations help cool or add to the warming? These may be the questions that chaos theory makes hard to answer when it comes to climate change.
Ahrens, C. D. (2012). Essentials of meteorology, an invitation to the atmosphere. (6th ed. ed.). Belmont: Brooks/Cole Pub Co.
Axelsen, J. (2010, July 9). Chaos theory and global warming: can climate be predicted. Retrieved from            
Ferguson , W. (2013, March 1). Ice core data help solve a global warming mystery. Retrieved from
Weisstein, E. W. (2013). Attractor from mathworld. Retrieved from