Introduction: Atmospheric circulation patterns exert a tremendous influence on regional weather and climate especially in the mid-latitudes in the Northern Hemisphere1. However, there is a significant debate over how patterns will respond to climate change. In this experiment, I will analyze climate models using a specific metric called sinuosity to determine how climate change will affect circulation patterns in the mid-latitudes. This information is impactful and important as changes in circulation patterns may raise the probability for extreme weather like heat waves. Through this research process, I want to learn more about the effects of climate change and how climate modeling is used to predict future impacts.
Background: Circulation …show more content…
This metric identifies an isohype (a line of equal geopotential height that spans horizontally) and determines its perimeter2. A perfectly straight line has a sinuosity of 1 which is the baseline measurement. If it has a more sinuous (wavy) flow, it will have a sinuosity greater than 1. Figure 1 demonstrates what an isohype looks like and its associated sinuosity. Sinuosity is an important metric because it explains why trends may occur such as more extreme or persistent weather because it reflects how the circulation patterns are changing. Other metrics like blocking indices are often Boolean and will only respond if weather behavior reaches a certain threshold such as 4 straight days of the same weather. This strict definition can underestimate changes in atmospheric circulation patterns. Sinuosity is non-Boolean and is not subject to a …show more content…
CMIP5 is a collection of different climate models developed from different researchers and governments. Two ensembles will be analyzed. The first ensemble will be analyzed with pre-industrial levels of greenhouse gasses to determine a baseline prediction of what sinuosity would look like without climate change. The second ensemble will be analyzed with Representative Concentration Pathway (RCP) 8.5 which is a scenario in which greenhouse gas emissions continue to rise through the 21st century. From each of these ensembles, I will take time scales of the seasonal average of sinuosity and time scales of the seasonal standard deviation of sinuosity. Changes in the seasonal averages between the first and second ensemble would indicate that climate change is affecting the mean-state of the sinuosity and atmospheric circulation patterns. Changes in the standard deviation would indicate that variability is increasing or decreasing from climate change. Variability is an important indicator because the mean may stay the same, but the probability of more extreme events on the outer edges of the probability distribution tails may increase. Seasonal variations and trends are important because changes in sinuosity seasonally can result in different weather extremes. In the summer, it could lead to more heat extreme weather
Background: Circulation …show more content…
This metric identifies an isohype (a line of equal geopotential height that spans horizontally) and determines its perimeter2. A perfectly straight line has a sinuosity of 1 which is the baseline measurement. If it has a more sinuous (wavy) flow, it will have a sinuosity greater than 1. Figure 1 demonstrates what an isohype looks like and its associated sinuosity. Sinuosity is an important metric because it explains why trends may occur such as more extreme or persistent weather because it reflects how the circulation patterns are changing. Other metrics like blocking indices are often Boolean and will only respond if weather behavior reaches a certain threshold such as 4 straight days of the same weather. This strict definition can underestimate changes in atmospheric circulation patterns. Sinuosity is non-Boolean and is not subject to a …show more content…
CMIP5 is a collection of different climate models developed from different researchers and governments. Two ensembles will be analyzed. The first ensemble will be analyzed with pre-industrial levels of greenhouse gasses to determine a baseline prediction of what sinuosity would look like without climate change. The second ensemble will be analyzed with Representative Concentration Pathway (RCP) 8.5 which is a scenario in which greenhouse gas emissions continue to rise through the 21st century. From each of these ensembles, I will take time scales of the seasonal average of sinuosity and time scales of the seasonal standard deviation of sinuosity. Changes in the seasonal averages between the first and second ensemble would indicate that climate change is affecting the mean-state of the sinuosity and atmospheric circulation patterns. Changes in the standard deviation would indicate that variability is increasing or decreasing from climate change. Variability is an important indicator because the mean may stay the same, but the probability of more extreme events on the outer edges of the probability distribution tails may increase. Seasonal variations and trends are important because changes in sinuosity seasonally can result in different weather extremes. In the summer, it could lead to more heat extreme weather