Jack Pincus- Stella.pdf

Jack Pincus- Stella.pdf - Jack Pincus – KGA320 –...

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Unformatted text preview: Jack Pincus – KGA320 – Assessment 3 1. Q1 a. This is a chart that is representing the East Pacific surface temperature. The crests and troughs represent the changing seasons. The changing seasons and the earths rotation around the sun vary the surface temperature as the years and seasons pass. T east: 1 -­ 1: 27.00 1 1 1 1: 24.50 1: 22.00 1 0.00 1825.00 Page 1 3650.00 Day s 5475.00 7300.00 11:09 AM Wed, 13 Sep 2017 T east b. Yes, it represents irregularity. Where there normally was steady peaks and troughs, there is now sporadic peaks and troughs. The randomization of wind has taken the uniformity out of this model and has created a more hectic layout. The graph that is shown above in example 1a is representative of an Earth without randomized wind which is not realistic. This randomization caused by wind can fluctuate temperatures. The the general increase and decrease of temperature still is maintained as there are still visible crests and troughs that follow the same pattern as the graph in question 1a, they are just less steady crests and troughs. T east: 1 -­ 1: 27.50 1 1 1: 24.50 1 1 1: Page 1 21.50 0.00 1825.00 3650.00 Day s T east 5475.00 7300.00 10:55 AM Wed, 13 Sep 2017 c. The two graphs below have been varied via rapid chaotic weather variations. By doubling the thermal damping rate and doubling the baseline wind stress you can add in rapid chaotic variations. The thermal damping rate is an increase in the pressure system and the baseline wind stress is used to maintain a contrast between the east and the west. I ran this model first without stochastic forcing (random wind stress) which is seen in the figure directly below. In this graph there is strong crests and troughs with the temperature in the first season, but as the day go on the temperature becomes more stable and the crests and troughs get smaller and smaller until the temperature is almost completely steady around 6000 days in. T east: 1 -­ 1: 27.00 1: 26.00 1: 25.00 1 1 1 1 0.00 1825.00 3650.00 Day s Page 1 5475.00 7300.00 11:02 AM Wed, 13 Sep 2017 With the stochastic forcing the temperature never stabilized. The crests and troughs are maintained throughout, however, the wavelength is increasing and they are far less consistent than a model without chaotic weather variations. The chaotic weather variations with stochastic forcing is a more life like representation of weather than the other models that were run. T east T east: 1 -­ 1: 28.50 1 1 1: 25.50 1 1 1: 22.50 0.00 1825.00 3650.00 Day s Page 1 5475.00 7300.00 11:04 AM Wed, 13 Sep 2017 T east 2. Q2 Below is the graph for the 20 different trials that were ran in this system. This is a messy graph so I have further organized the data on excel for a better look at it. T east: 1 -­ 2 -­ 3 -­ 4 -­ 5 -­ 6 -­ 7 -­ 8 -­ 9 -­ 10 -­ 11 -­ 12 -­ 13 -­ 14 -­ 15 -­ 16 -­ 17 -­ 18 -­ 19 -­ 20 -­ 21 -­ 22 -­ 23 -­ 24 -­ 25 -­ 1: 29.00 13 11 2 3 1 3 8 6 1 2 1: 6 9 25.00 8 4 5 10 7 2 7 1 4 12 14 13 3 4 9 5 15 6 7 1 12 2 17 11 16 5 10 20 4 14 3 9 10 15 19 5 8 18 1: Page 1 21.00 0.00 1827.50 3655.00 Day s T east 5482.50 7310.00 11:19 AM Wed, 13 Sep 2017 Temperature AVG 26.5 26 25.5 25 24.5 24 23.5 23 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Trial Standard Deviation STD 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Trial The standard deviation of this data remains fairly consistent but has a large spike at the 20th trial. With this information it cannot be concluded that the software is not reliable to run operations with over 20 trials because there was not a steady increase in the deviation, the 20th day could merely be an outlier. The average temperature had a steady drop as it moved towards the end of the 20 trials. A range of over 2 is not good for accurate standard deviation so retrials would need to be taken. The more data that can be collected the more accurate this would become. 3. Q3 a. The representation of this data via random noise is the best way to represent this data. Using this program that can randomize wind variables and demonstrate it on a model is a very accurate representation of how these systems work. It makes understanding patterns in the data and completely digesting the information slightly more difficult but the processes that are causing these irregular oscillations are captured because we can adjust one variable at a time and run multiple trials, determining what each component that we are adjusting is representing. b. An alternative to using random noise would be a systematic approach in which the program is run with direct inputs on how the wind and weather should operate. This method would only be practical if it was being used to understand the global climate system but it is not the best method to go about studying it. ...
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