1. Uncertainty and Feedback
CLIM 101: Weather, Climate and Global Society
Uncertainty and Feedback

2. CLIM 101: Weather, Climate and Global Society
Uncertainty 2

3. Sources of Uncertainty: Observations
Instrument error
Sparse, infrequent measurements - inadequate sampling or sampling bias
Observing system change over time
Mixing direct measurements and proxy measurements

4. Increased post-WWII pollution in NH ------- Little change ----
Cooling 
Increased post-WWII pollution in NH
Little change 
Variability due to solar changes, volcanism
Warming 
Increasing GHG
FAQ 3.1, Figure 1. Annual global mean observed temperatures1 (black dots) along with simple fits to the data. The left hand axis shows anomalies relative to the 1961 to 1990 average and the right hand axis shows the estimated actual temperature (°C). Linear trend fits to the last 25 (yellow), 50 (orange), 100 (purple) and 150 years (red) are shown, and correspond to 1981 to 2005, 1956 to 2005, 1906 to 2005, and 1856 to 2005, respectively. Note that for shorter recent periods, the slope is greater, indicating accelerated warming. The blue curve is a smoothed depiction to capture the decadal variations. To give an idea of whether the fluctuations are meaningful, decadal 5% to 95% (light grey) error ranges about that line are given (accordingly, annual values do exceed those limits). Results from climate models driven by estimated radiative forcings for the 20th century (Chapter 9) suggest that there was little change prior to about 1915, and that a substantial fraction of the early 20th-century change was contributed by naturally occurring influences including solar radiation changes, volcanism and natural variability. From about 1940 to 1970 the increasing industrialisation following World War II increased pollution in the Northern Hemisphere, contributing to cooling, and increases in carbon dioxide and other greenhouse gases dominate the observed warming after the mid-1970s.

5. Slope = 1.02
Slope = 1.67
Slope = 1.01
Slope = 1.82
Synthetic time series example: Need large samples to avoid “end effects” in estimating linear trends

6. URBAN HEAT ISLAND EFFECT
full US Historical Climatology Network (USHCN) data
USHCN data without the 16% of the stations with populations of over 30,000 within 6 km in the year 2000
UHI and changes in land use can be important for DTR at the regional scale
The global land warming trend is unlikely to be influenced significantly by increasing urbanization.
Figure 3.3. Anomaly (°C) time series relative to the 1961 to 1990 mean of the full US Historical Climatology Network (USHCN) data (red), the USHCN data without the 16% of the stations with populations of over 30,000 within 6 km in the year 2000 (blue), and the 16% of the stations with populations over 30,000 (green). The full USHCN set minus the set without the urban stations is shown in magenta. Both the full data set and the data set without the high-population stations had stations in all of the 2.5° latitude by 3.5° longitude grid boxes during the entire period plotted, but the subset of high-population stations only had data in 56% of these grid boxes. Adapted from Peterson and Owen (2005).
USHCN data for the 16% of the stations with populations over 30,000
Full USHCN set minus the set without the urban stations

7. observations in each 1° grid box at 250 m depth
Figure 5.A.1 The number of ocean temperature observations in each 1° grid box at 250 m depth for two periods: (a) 1955 to 1959, with a low density of observations, and (b) 1994 to 1998, with a high density of observations. A blue dot indicates a 1° grid box containing 1 observation, a green dot 2 to 5 observations, an orange dot 6 to 20 observations, and a red dot more than 20 observations.

8. Global mean sea level (deviation from the 1980-1999 mean)
FAQ 5.1, Figure 1. Time series of global mean sea level (deviation from the mean) in the past and as projected for the future. For the period before 1870, global measurements of sea level are not available. The grey shading shows the uncertainty in the estimated long-term rate of sea level change (Section 6.4.3). The red line is a reconstruction of global mean sea level from tide gauges (Section ), and the red shading denotes the range of variations from a smooth curve. The green line shows global mean sea level observed from satellite altimetry. The blue shading represents the range of model projections for the SRES A1B scenario for the 21st century, relative to the 1980 to 1999 mean, and has been calculated independently from the observations. Beyond 2100, the projections are increasingly dependent on the emissions scenario (see Chapter 10 for a discussion of sea level rise projections for other scenarios considered in this report). Over many centuries or millennia, sea level could rise by several metres (Section ).
Uncertainty in estimated long-term rate of sea-level change
Based on tide gauges
Based on satellite altimetry
Range of model projections (SRES A1B scenario)

9. Global Sea Level  base period  Reconstructed fields since 1870
Figure Annual averages of the global mean sea level (mm). The red curve shows reconstructed sea level fields since 1870 (updated from Church and White, 2006); the blue curve shows coastal tide gauge measurements since 1950 (from Holgate and Woodworth, 2004) and the black curve is based on satellite altimetry (Leuliette et al., 2004). The red and blue curves are deviations from their averages for 1961 to 1990, and the black curve is the deviation from the average of the red curve for the period 1993 to Error bars show 90% confidence intervals.
 base period 
Reconstructed fields since 1870
Coastal tide gauges
Satellite altimetry

10. Global annual ocean heat content w. r. t
Global annual ocean heat content w.r.t mean for the 0 to 700 m layer
Figure 5.1. Time series of global annual ocean heat content (1022 J) for the 0 to 700 m layer. The black curve is updated from Levitus et al. (2005a), with the shading representing the 90% confidence interval. The red and green curves are updates of the analyses by Ishii et al. (2006) and Willis et al. (2004, over 0 to 750 m) respectively, with the error bars denoting the 90% confidence interval. The black and red curves denote the deviation from the 1961 to 1990 average and the shorter green curve denotes the deviation from the average of the black curve for the period 1993 to 2003.
Update of Levitus et al. (2005) … shading represents 90% confidence interval
Update of Ishii et al. (2006) … error bar represents 90% confidence interval
Update of Willis et al. (2004; 0 to 750 m) … error bar represents 90% confidence interval

12. Sources of Uncertainty: Models
Input data (forcing) uncertainty
Differing assumptions with respect to relevant processes
Differing estimates of model parameters
Intrinsic unpredictability
Unpredictability of external phenomena (e.g. volcanoes)

13. Climate models without volcanic Forcing
OHC - ocean heat content
ThSL: Thermosteric sea level change
(density changes induced by temperature change)
Domingues et al. 2008 13

14. Climate models with volcanic Forcing
ThSL: Thermosteric sea level change
(density changes induced by temperature change)
Domingues et al. 2008

15. The IPCC AR4

16. IPCC SRES Emission Scenarios (IPCC Special Report on Emission Scenarios)
Pg (Petagram) = 1015 g = Gt (Gigaton)

17. What is in store for the future and what has already been committed
Global warming will increase if GHGs concentration increase. Even if GHGs were kept constant at current levels, there is a “commitment” of 0.6°C of additional warming by 2100.
CO2 Eq
3.4oC = 6.1oF
850
2.8oC = 5.0oF
600
1.8oC = 3.2oF
B1 and A1B have similar population projections – ca 7 billion at A2 population more than double at 15 billion. GDP/capita is 47k$, 75k$, 16k$ for B1, A1B, A2.
0.6oC = 1.0oF
400

18. Projected Future Warming
Figure 9.13, IPCC TAR

19. 19

20. Clouds: Still the Largest Source of Uncertainty
Figure Changes in (a) global mean cloud radiative forcing (W m–2) from individual models (see Table 10.4 for the list of models) and (b) multi-model mean diurnal temperature range (°C). Changes are annual means for the SRES A1B scenario for the period 2080 to 2099 relative to 1980 to Stippling denotes areas where the magnitude of the multi-model ensemble mean exceeds the inter-model standard deviation. Results for individual models can be seen in the Supplementary Material for this chapter.

21. CLIM 101: Weather, Climate and Global Society
Feedback 21

23. Ice-Albedo Feedback (1)
Cooling
Ice Increases
Albedo Increases
Absorption of sunlight decreases

24. Ice-Albedo Feedback (2)
Warming
Ice Decreases
Albedo Decreases
Absorption of sunlight increases

25. Positive vs. Negative Feedback
Something triggers a small system change
The system responds to the change
Feedback
Positive Feedback: The response accelerates the original change
Negative Feedback: The response damps the original change

26. Water Vapor Feedback (1)
Warming
Evaporation from the Oceans Increases
Atmospheric Water Vapor Increases
Stronger Greenhouse Effect

27. Water Vapor Feedback (2)
Cooling
Evaporation from the Oceans Decreases
Atmospheric Water Vapor Decreases
Weaker Greenhouse Effect
Water Vapor Feedback is Positive

28. Effect of Positive Feedback (1)
With positive feedbacks
Temperature
If no feedbacks present
Time

29. Effect of Positive Feedback (2)
If no feedbacks present
Temperature
With positive feedbacks
Time

30. The Need for Negative Feedbacks
Positive feedbacks are destabilizing - they tend to drive the system away from equilibrium
Negative feedbacks are required to restore equilibrium

31. A System Without Negative Feedbacks
Catastrophic Warming!
Temperature
Time

32. The Way Physical Systems Usually Behave
Temperature
Warming Accelerating
Warming Decelerating
Time

33. Response to Oscillatory Energy Source
Normal Behavior

34. Response to Oscillatory Energy Source
Response with weakened negative feedbacks – increased amplitude

35. Feedbacks - Summary
Positive feedbacks tend to increase the amplitude of the system response
Negative feedbacks tend to reduce the amplitude of the system response