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

CLIM 101: Weather, Climate and Global Society Uncertainty 2

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

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.

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

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

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.

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 1980-1999 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 5.5.2.1), 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 10.7.4). 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)

Global Sea Level  base period  Reconstructed fields since 1870 Figure 5.13. 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 2001. Error bars show 90% confidence intervals.  base period  Reconstructed fields since 1870 Coastal tide gauges Satellite altimetry

Global annual ocean heat content w. r. t Global annual ocean heat content w.r.t. 1961-1990 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

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)

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

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

The IPCC AR4

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

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 2100. A2 population more than double at 15 billion. GDP/capita is 47k$, 75k$, 16k$ for B1, A1B, A2. 0.6oC = 1.0oF 400

Projected Future Warming Figure 9.13, IPCC TAR

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Clouds: Still the Largest Source of Uncertainty Figure 10.11. 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 1999. 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.

CLIM 101: Weather, Climate and Global Society Feedback 21

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

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

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

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

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

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

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

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

A System Without Negative Feedbacks Catastrophic Warming! Temperature Time

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

Response to Oscillatory Energy Source Normal Behavior

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

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