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# Global Warming

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Forecasting increase in temperature by the end of this century.

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### Global Warming

1. 1. Hosted by Decisioneering, Inc. July 19, 2007 Global Warming To listen to the session on your phone , follow the instructions in the “ Join Teleconference ” pop up dialog box which will appear in a few moments. To listen to the session on your computer speakers instead of your phone, follow the instructions in the “ Join Internet Phone ” pop up dialog box which will appear in a few moments. Please DO NOT join both, as this is redundant. Guest Speaker Gaetan ‘Guy’ Lion
2. 2. Objective: simulate temperature increase over next century using IPCC timeframe <ul><li>Starting point: temperature 1980 – 1999 period. </li></ul><ul><li>Ending point: 2090 – 2099 period. </li></ul>
3. 3. Basic model structure <ul><li>If not for IPCC timeframe intricacies, the model would be simple. </li></ul><ul><li>Record most current temperature (14.6 degree Celsius). </li></ul><ul><li>Next, simulate CO2 concentration by 2099 (i.e. 600 ppm) </li></ul><ul><li>Using regression, convert CO2 concentration into temperature level (16.3 degree Celsius). </li></ul><ul><li>Calculate temperature increase: 16.3 – 14.6 = 1.7 deg. Celsius. </li></ul>
4. 4. The Variables
5. 5. Simulating CO2 concentration by 2099 Forecast
6. 6. The Regression Models: Log & Linear Until recently, climatologists debated whether the relationship between CO2 and temperature was logarithmic or linear.
7. 7. The Natural Log Model Simulation Year CO2 conc . The model simulates temperature level by 2090 – 2099 two ways. One way just picks a year at random within the decade. The other way calculates the average over the decade.
8. 8. The Linear Model Simulation Year CO2 conc .
9. 9. Output: Temperature increase by end of 21 st century
10. 10. IPCC Scenarios Definitions Source: IPCC Summary for Policymakers. Technological energy emphasis seems to be the most influential factor in determining CO2 concentration CAGR.
11. 11. Models vs IPCC Scenarios
12. 12. Are some IPCC scenarios higher because of other greenhouse gases? No. CO2 accounts for nearly 100% of the net radiative forcing from all sources.
13. 13. IPCC Scenarios are higher because of much higher CO2 concentration levels CO2 concentration level in ppm Historic. rate B1 A1T B2 A1B A2 A1FI 2005 380 380 380 380 380 380 380 2010 387 389 393 396 397 406 410 2015 395 399 406 412 415 433 444 2020 403 410 420 430 434 463 480 2025 411 420 435 448 454 495 519 2030 419 431 450 467 475 529 561 2035 428 442 465 487 497 565 607 2040 436 454 482 507 519 603 656 2045 445 465 498 529 543 645 709 2050 454 477 515 551 568 689 767 2055 463 490 533 574 594 736 829 2060 473 502 552 599 621 786 897 2065 482 515 571 624 650 840 970 2070 492 528 591 650 679 898 1049 2075 502 542 611 678 711 959 1134 2080 512 556 632 707 743 1025 1226 2085 522 570 654 736 777 1095 1326 2090 533 585 677 768 813 1170 1433 2095 543 600 700 800 850 1250 1550 CAGR 0.40% 0.51% 0.68% 0.83% 0.90% 1.33% 1.58%
14. 14. IPCC Scenarios – CO2 concentration
15. 15. Temp. Increase. Models vs 3 IPCC scenarios Among the IPCC scenarios, B1 is the low scenario, A1B is the mid level one, and A1FI is the high one. Watch carefully for the scale of the Y axes here.
16. 16. Testing our regression coefficients vs IPCC scenarios The coefficients of the natural log model replicate reasonably well the IPCC best estimates up to CO2 concentration of 850 ppm. Linear model LN model
17. 17. Why is volatility so much higher for IPCC Scenarios?
18. 18. Another view of volatility. LN model vs B1 The natural log model and scenario B1 (IPCC) generate about the same best estimate in temperature increase (~ 1.8 degree Celsius). But, the confidence interval for the B1 scenario (green) is much wider at 1.8 degree Celsius vs only 0.68 degree Celsius for the log model (orange). B1 scenario LN model
19. 19. Why volatility is higher in IPCC scenarios <ul><li>The IPCC estimates rely on numerous model sets that feed into each other. </li></ul><ul><li>Algorithms capture all gases mentioned earlier. Each gas radiative forcing is associated with uncertainty (random variable). </li></ul><ul><li>They capture many physical phenomenons such as cloud formation, precipitation, ice melting, ocean heat absorption, convection, radiation, etc… </li></ul>
20. 20. Generating higher Volatility In this log model, I used the standard errors of the intercept and slope as random variables instead of the standard error of the regression. To moderate excessive volatility I used a high negative correlation (-0.95) between the two standard errors. But, resulting volatility was still way too high with a Confidence Interval that is too wide including large decrease in temperature.
21. 21. Thank you for attending the Web Seminar Global Warming Gaetan “Guy” Lion E-mail: gaetanlion@gmail.com July 19, 2007 Decisioneering, Inc. 1515 Arapahoe St., Ste 1311 Denver, Colorado 80202 303-534-1515 www.crystalball.com