Group members preparing summary: Will Myers, Michelle Johnson, and Joe HerringOnline Unit Summary:
Unit 2-1 deals with the climate system and its predictability. The learning objectives for this unit are:
- To understand the elements that comprise the climate system and how they interact
- To explore the concept of predictability and understand the difference between weather forecasts and climate simulation
The definition of climate is given as being "the average behavior of the land, ocean, atmosphere, cryosphere system over relatively long periods of time." Climate, is of course, relative to weather, which is by definition the "day-to-day values of temperature, rainfall, pressure, winds, etc." There are three main types of climate change that are presented: gradual change, changes in variability, and unexpected surprises. Since we know that the climate has always been changing and will change in the future, the most important questions concerning climate change are asking: which elements are changing, at what rate and in what direction they are changing, and what the magnitude and impact of future & additional changes might be. Specifically, the climate system is made up of many different physical components that interact together, all according to different time scales. The atmosphere, ocean, earth, land, ice-sheets & snow, bio-mass, sea-ice, and space all comprise the Earth's climate system. Different components have different time scales, which can be considered either changing or unchanging components of climate. For example, the Antarctic ice sheet has a very long time scale (in terms of elemental residence time), so it would be considered unchanging. By defining external and internal influences of the climate system, we can better organize the factors that relate to climate. External influences do not respond to changes occurring within the defined climate system, while internal conditions are those within the system that respond to external forces. Examples of both types .
In considering climate predictability, we must acknowledge an inherent property of climate known as almost-intransitivity. The climate system is thought to have multiple states of this property which ultimately can cause a specific climate or some component of the climate system to change abruptly into a different state of stability, then change back for no reason. Almost-intransitivity of climates can be explained by a climate's description using a set of time-dependent, nonlinear differential equations. The property of almost-intransitivity is not accounted for when predicting climates. To differentiate between climate predictions and weather predictions, and to understand the concept of a climate model this course assumes the use of the "pinball machine analogy." Basically, a weather forecast for a specific day would compare to an individual path downward through the matrix, while a climate prediction would compare to the distribution of balls at the bottom of the matrix. The pegs represent equations or conditions, and in our case, can represent increases of greenhouse gases. A complete description of the use of the pinball machine analogy.
Online Discussion Summary:
A total of three initial posts were made in this unit discussion, all classified as brainstorming. The first posting addressed the question of how climates of several thousand years past are determined by scientists when studying climate change. Two students reacted to this question, the first noting that by measuring the amounts of Oxygen and Hydrogen that's trapped in air bubbles of ice cores, the temperature at which snow fell can be determined. This student provided a website to refer to. The other reaction added to the first by implying that study of ancient tree ring samples could tell us the growing conditions of that time. The second initial post brainstormed about the amount of data (in years) needed to be able to study and accurately predict climate change. One student replied by saying she/he felt that several decades of data collection would provide the most accurate results. Another student took the opportunity to brainstorm under the same topic, asking how the extremes in a data set would affect overall climate change predictability. The final initial posting was by a student who stated that he/she had noticed an increase in weather-related natural disasters when compared to past times, including tornadoes, hurricanes, etc. Responses to this idea stated opinions that there is simply more awareness of these events globally by individuals, due to increased media coverage.