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The heat is on!

Explore the greenhouse effect, climate measurements, modeling, and projections to understand the impact of climate change. Analyze uncertainties in projecting sea level rise and assess adaptation measures.

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The heat is on!

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  1. The heat is on! Peter Guttorp peter.guttorp@nr.no guttorp@uw.edu http://www.stat.washington.edu/peter

  2. The greenhouse effect • Heat comes in from the sun • Shortwave radiation • Earth gets warmed up by the heat • Earth radiates heat back • Longwave radiation • Greenhouse gases absorb much energy in radiating heat • Atmosphere warms (15°C instead of -18°C) • Main greenhouse gases: • Water vapor • Carbon dioxide • Methane

  3. The greenhouse effect • Joseph Fourier (1768-1830) realized that Earth ought to be a lot cooler than it is. • John Tyndall (1820-1893) found that water vapor and CO2 are greenhouse gases • Svante Arrhenius (1859-1927) calculated how changes in CO2 can heat the planet

  4. What is climate? • Climate is what you expect; weather is what you get. • Heinlein: Notebooks of Lazarus Long (1978)

  5. Outline • Measurements • Models • Local impact • Projections

  6. Measurements

  7. Measuring global surface temperature

  8. Homogenization summertime correction screen painted white? miscalibrated thermometer urbanization

  9. Global temperature measurements Marine data

  10. Comparison between estimates

  11. Is there a trend? • An Ac t of Dog • Global temperature

  12. Models

  13. Climate modeling

  14. The issue of gridding Hurricanes Clouds Glaciers

  15. Comparing global climate models to data

  16. 30-year distributions

  17. Local impact

  18. Comparing climate model output to weather data • Global models are very coarse • Regional models are driven by boundary conditions given by global model runs • In either case, describes distribution of weather, not actual weather • Consider a regional model driven by “actual weather” • Stockholm 50 km x 50 km grid, 3 hr resolution (SMHI-RCA3; ERA40)

  19. Stockholm data issues Location was moved twice (1875, 1960) Calibration (1826: 0 reads as +0.75; 1858,1915; annual thereafter)

  20. How well does the climate model reproduce data?

  21. Model problem? • Annual average temperature over the grid square containing the Stockholm site is about 1.7°C warmer than the observed average • Model calculates separately open air, forest, and water/ice. • Do we need finer resolution?

  22. Open air predictions • Using 12.5 km version of RCA3, forced by ERA40, looking at only open air predictions (77% of grid square is open air)

  23. Is the station really in open air?

  24. Comparison to forested model output

  25. Projections

  26. Why not predictions? • Climate models need input of greenhouse gases, solar radiation, land use etc. To use climate models for prediction, must predict also these input variables. • Instead, set up scenarios (reasonable values of the input variables). Run models with these inputs. We call that projections.

  27. Projecting sea level rise • Sea levels rise due to • warming of oceans • melting of land ice • Most climate models do not output sea level • Strategy: • relate global mean temperature to global mean sea level • relate global to local sea level • Use projections of temperature to project local sea level

  28. Bergen • Cultural Heritage Site • Storm surges up to 1.4m • Land rise 2.6 mm/year

  29. Projections

  30. Components of uncertainty

  31. Using uncertainty in decision making • Do Bergen authorities need to address sea level rise? If so, when? • Adaptation costs: • Outer barrier 30B NOK (5B CAD) • Inner barriers 1.1B (0.2B) • Need cumulative storm surge damage costs.

  32. Current storm surge damage costs

  33. Change due to sea level rise

  34. Simulate damages • Draw random annual cost • Draw random increase factor path • Draw random sea level path • Accumulate costs over time • Look at upper 95th percentile of cumulative costs

  35. When is an adaptation measure beneficial? Outer barrier Inner barrier Outer barrier Inner barriers

  36. Some references • P. Guttorp and J. Xiu (2011): Climate change, trends in extremes, and model assessment for a long temperature time series from Sweden. Environmetrics 22: 456-463. • P. F. Craigmile and P. Guttorp (2013): Can a regional climate model reproduce observed extreme temperatures?Statistica 73: 103-122. • P. Guttorp (2014): Statistics and Climate. Annual Reviews of Statistics and its Applications1: 87-101. • P. Guttorp, D. Bolin, A. Januzzi, D. Jones, M. Novak, H. Podschwit, L. Richardson, A. Särkkä, C. Sowder and A Zimmerman (2014): Assessing the uncertainty in projecting local mean sea level from global temperature. Journal of Applied Meteorology and Climatology 53: 2163-2170.

  37. Uncertainty in cumulative damage

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