1. The heat is on! Peter Guttorp peter.guttorp@nr.no guttorp@uw.edu

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 ( ) realized that Earth ought to be a lot cooler than it is. John Tyndall ( ) found that water vapor and CO2 are greenhouse gases Svante Arrhenius ( ) calculated how changes in CO2 can heat the planet
Why does the heat not just bounce back out? CO2 regulates water vapor in atmosphere - Warmer air holds more water so more greenhouse effect

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?
After taking out annual (monthly) medians, a ltm process remains mle.d lower.d upper.d
summertime
correction
screen painted white?
miscalibrated
thermometer
urbanization

9. Global temperature measurements
Oldest record Berlin cts from 1701
Marine data

10. Comparison between estimates
HadCRU red NOAA green GISS purple

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

12. Models

13. Climate modeling
IPSL model. Gridded solution to coupled pdes.

14. The issue of gridding Hurricanes Clouds Glaciers
250 km grid square in climate model
18 km in hurricane model
Shading surface temp, contours pressure, vectors wind

15. Comparing global climate models to data
38 CMIP5 models

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?
Reason for minima—q-q plot

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.
Volcanic eruptions – cooling effect

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
Slope 1.3
Global (20th century) 1.7 mm/yr so est 2.2 mm/yr, slight decrease rel to land rise

29. Projections
33 models that compute temperature projections for this scenario
Upper limit of models is Norwegian planning rule

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
Based on 15 European cities (not including Bergen)

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
exp(10.3)=
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: P. F. Craigmile and P. Guttorp (2013): Can a regional climate model reproduce observed extreme temperatures? Statistica 73: P. Guttorp (2014): Statistics and Climate. Annual Reviews of Statistics and its Applications 1: 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:

38. Uncertainty in cumulative damage