1. Designing Systems to Address Outstanding Issues in Climate Change Betsy Weatherhead

3. SPM 1a Variations of the Earth’s surface temperature for the past 140 years

4. M.S.U. Channel 2

5. Trends in Surface and Tropospheric Temperature Existing Satellite and Surface Measurements are not in agreement. Existing Satellite and Surface Measurements are not in agreement. Satellites have a difficult time measuring low in the atmosphere. Satellites have a difficult time measuring low in the atmosphere. Interpretation of satellite measurements requires assumptions about the vertical and chemical structure of the atmosphere. Interpretation of satellite measurements requires assumptions about the vertical and chemical structure of the atmosphere.

8. Future Temperature Trends Temperature trends are predicted by a number of different models. Temperature trends are predicted by a number of different models. Can we identify more accurate models? Can we identify more accurate models?

9. Detection of Trends Fundamentally: a signal to noise problem. Fundamentally: a signal to noise problem. We don’t control the signal.We don’t control the signal. We don’t control the noise.We don’t control the noise.

13. We can control only four aspects of monitoring to detect trends Where we monitor Where we monitor What frequency What frequency What accuracy What accuracy What we monitor What we monitor

15. Where do we monitor? Some places are inherently better for detecting trends than others. Some places are inherently better for detecting trends than others. Monitoring by satellite involves averaging over height, longitude and latitude. Monitoring by satellite involves averaging over height, longitude and latitude. Measurement smoothing can damage our ability to detect trendsMeasurement smoothing can damage our ability to detect trends

18. How does spatial redundancy affect our ability to detect trends?

19. 82 Station Subset of HCN Network (1.75º “Distance” Factor)

20. 225 Station Subset of HCN Network

22. Where do we monitor: global coverage Interpretation of raw signals can be difficult. Interpretation of raw signals can be difficult. Inversion methods can be dependent on all other parameters not changing. Inversion methods can be dependent on all other parameters not changing. Footprint size as well as vertical resolution are critical to detection of trends. Footprint size as well as vertical resolution are critical to detection of trends.

23. Where do we monitor: global coverage

26. Where we monitor Well designed in situ measurements can offer Well designed in situ measurements can offer Monitoring in critical, unmonitored areas;Monitoring in critical, unmonitored areas; Unprecedented accuracy;Unprecedented accuracy; Critical information on climate processes.Critical information on climate processes.

28. We can control only four aspects of monitoring to detect trends Where we monitor Where we monitor What frequency What frequency What accuracy What accuracy What we monitor What we monitor

29. What frequency? Inherent memory in environmental data results in redundancy of measurements. Inherent memory in environmental data results in redundancy of measurements. Daily data may be more than needed. Daily data may be more than needed. Less than daily measurements may obscure diurnal trends Less than daily measurements may obscure diurnal trends

32. How do the trends change when we take data less frequently than every day?

34. How long will it take to detect trends?

36. Decreasing the data frequency We can optimize data collection frequency to assure efficiency. We can optimize data collection frequency to assure efficiency. Decreasing the data frequency can reduce our ability to: Decreasing the data frequency can reduce our ability to: Detect extreme eventsDetect extreme events Detect diurnal (or perhaps seasonal) signalsDetect diurnal (or perhaps seasonal) signals

38. We can control only four aspects of monitoring to detect trends Where we monitor Where we monitor What frequency What frequency What accuracy What accuracy What we monitor What we monitor

39. What accuracy? Relative accuracy is all that’s needed for trend detection. Relative accuracy is all that’s needed for trend detection. Relative accuracy is extremely hard to maintain for decades without absolute accuracy. Relative accuracy is extremely hard to maintain for decades without absolute accuracy. Improved accuracy may save decades in monitor or may be irrelevant. Improved accuracy may save decades in monitor or may be irrelevant.

40. Case Example Uncertainty: ±2% ; Trend: 4% per decade Result: –First ten years of data are still unsubstantial Improving Accuracy to ±1% saves five years of monitoring

41. Measurement Uncertainty is Not Generally Random Trends generally require decades to detect Trends generally require decades to detect Reference instruments and calibration mechanisms often change over the period of several decades Reference instruments and calibration mechanisms often change over the period of several decades Most materials for both instrumentation and calibration drift or shift preferentially in one direction Most materials for both instrumentation and calibration drift or shift preferentially in one direction

43. Accuracy directly influences our ability to detect trends In some cases, our measurement uncertainty is considerably larger than the signal we want to detect. In some cases, our measurement uncertainty is considerably larger than the signal we want to detect. Estimating appropriate measurement uncertainty over decades of monitoring is extremely difficult. Estimating appropriate measurement uncertainty over decades of monitoring is extremely difficult.

45. We can control only four aspects of monitoring to detect trends Where we monitor Where we monitor What frequency What frequency What accuracy What accuracy What we monitor What we monitor

46. What we monitor Changes are predicted for a large variety of parameters: Changes are predicted for a large variety of parameters: Temperature, humidity, cloud cover, tropopause height, precipitation, mesopause height, sea ice, snow cover extent, extrememe events, ENSO, tropical cyclones.Temperature, humidity, cloud cover, tropopause height, precipitation, mesopause height, sea ice, snow cover extent, extrememe events, ENSO, tropical cyclones.

47. Is there a canary parameter? Proposed Canaries The Arctic – change may be greatest The subtropics – small changes are easy to detect The stratosphere – very responsive The tropopause height – integrative response The ionosphere – changes can be very large

48. Is there a canary parameter? What is meant by this? A parameter where the signal is considerably larger than the variability.* A parameter where change can only imply anthropogenic influence - this requires considerably understanding over long time scales. A parameter where a change can imply significant changes at the Earth’s surface. * and measurement uncertainty?

50. What we monitor Tropospheric parameters, particularly temperature are canary parameters. Tropospheric parameters, particularly temperature are canary parameters. Free troposphere is considerably better for detecting trends than the surface. Free troposphere is considerably better for detecting trends than the surface. Explanatory variables can offer insight to mechanisms. Explanatory variables can offer insight to mechanisms.

51. Integration We make choices about all four of the parameters we control. We make choices about all four of the parameters we control. These choices have direct impact on how long we will likely need to monitor in order to detect trends. These choices have direct impact on how long we will likely need to monitor in order to detect trends. Optimal choices exist. Optimal choices exist. All choices will affect our ability to detect trends and the scientific questions we may ask of the emerging data. All choices will affect our ability to detect trends and the scientific questions we may ask of the emerging data.

52. Conclusion 1. Trends are difficult to detect: Predicted trends are smallPredicted trends are small natural variability is largenatural variability is large Measurement uncertainty can be largeMeasurement uncertainty can be large 2. We can control only four aspects to detect trends: What we monitor; Where we monitor;What we monitor; Where we monitor; What frequency; What accuracyWhat frequency; What accuracy 3. We can optimize systems to detect trends most efficiently with the following benefits: Answering scientific questions earlierAnswering scientific questions earlier Confirming, improving modelsConfirming, improving models Allowing for earliest policy decisionsAllowing for earliest policy decisions Maintaining prudent use of available fundsMaintaining prudent use of available funds

53. Understanding the climate system is more important than detecting trends.

57. Next Steps We can work to identify true canaries. We can work to identify true canaries. We can examine existing networks for efficiency. We can examine existing networks for efficiency. We can determine savings due to: We can determine savings due to: Improved accuracyImproved accuracy Improved spatial informationImproved spatial information Improved temporal informationImproved temporal information Optimization of existing networks can allow scientific, environmental and policy relevant results earlier. Optimization of existing networks can allow scientific, environmental and policy relevant results earlier. New networks can be established in a defensible, efficient manner. New networks can be established in a defensible, efficient manner.

62. Visual Example How many years does it take to detect a trend in ozone? How many years does it take to detect a trend in ozone? Use our understanding of variability; Use our understanding of variability; Use our understanding of the predicted trends Use our understanding of the predicted trends Estimate visually how long it will take to detect a trend. Estimate visually how long it will take to detect a trend.

69. Changing local observation time leads to aliasing of diurnal signal into long term trends Corrected Global Time Series Uncorrected Global Time Series Difference (expanded scale): 0.15K over 20 years Courtesy Frank Wertz Effect of Diurnal Correction on MSU Channel 2