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Greg Bodeker and Stefanie Kremser Bodeker Scientific, Alexandra, New Zealand

A Demonstration of the Scientific Value of GRUAN Data: the use of GRUAN Uncertainty Estimates in Trend Analyses. Greg Bodeker and Stefanie Kremser Bodeker Scientific, Alexandra, New Zealand

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Greg Bodeker and Stefanie Kremser Bodeker Scientific, Alexandra, New Zealand

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  1. A Demonstration of the Scientific Value of GRUAN Data: the use of GRUAN Uncertainty Estimates in Trend Analyses Greg Bodeker and Stefanie Kremser Bodeker Scientific, Alexandra, New Zealand Presented at 17th Symposium on Meteorological Observation and Instrumentation, Westminster, 10 June 2014

  2. Overview • Very brief overview of GRUAN • GRUAN RS92 radiosonde data availability • RS92 radiosonde measurements at the Lindenberg site which is also the GRUAN Lead Centre • Things to worry about with trend analyses • Very first GRUAN trends (but too early)

  3. What is GRUAN? GCOSReference Upper Air Network (GCOS=Global Climate Observing System) Network for ground-based reference observations for climate in the free atmosphere in the frame of GCOS Currently ~15 stations, envisaged to be a network of 30-40 sites across the globe

  4. The goals of GRUAN • The purpose of GRUAN is to: • Provide long-term high quality climate records; • Constrain and calibrate data from more spatially-comprehensive global observing systems (including satellites and current radiosonde networks); and • Fully characterize the properties of the atmospheric column. • Four key user groups of GRUAN data products are identified: • The climate detection and attribution community. • The satellite community. • The atmospheric process studies community. • The numerical weather prediction (NWP) community.

  5. GRUAN RS92 radiosonde data availability Description of the product coming up shortly from Ruud Dirksen Flights available per month as of earlier this year

  6. What’s required to detect temperature trends at Lindenberg? Considered how uncertainty on monthly mean temperatures is determined as a function of sampling frequency, random error on each measurement, season and altitude/pressure. Used NCEPCFSR data set (1979-2010, 37 pressure levels, every 6 hours). You don’t lose much until your measurement random error exceeds 0.5 K.

  7. Dependence on sampling frequency and season GRUAN target is ≤0.2 K random error on instantaneous stratospheric temperature measurements.

  8. Dependence on altitude/pressure When sampling every 12 hours at midnight and noon, it can be seen that permissible random errors on individual measurements required to avoid increasing the uncertainty on the monthly mean by more than 10% above the uncertainty on the ‘true’ monthly mean. 0.5 K is OK in stratosphere but this reduces to 0.25 K at ~20 hPa and to 0.15 K in the free troposphere.

  9. Time to detect trends at Lindenberg at 10 hPa

  10. So what do you need to worry about when calculating trend? Why not just fit a straight line? Consider a perfectly sinusoidal signal with random noise added.

  11. Results when fitting the straight line… Rule 1: Fit for all known sources of variability → use a linear least squares regression model →http://how-to-do-mlr.wikidot.com/

  12. Typical regression model for temperature Value(t) = A(t) + B(t) x t + C(t) x QBO(t) + D(t) x QBOorthog(t) + E(t) x ENSO(t) + F(t) x Solar(t) + G(t) x Pinatubo(t) + H(t) x El Chichon(t) + R(t) Fit coefficients are shown in red and regression model basis functions are shown in green. Values to be regressed are weighted by 1/σ2 which also contributes, along with: The variance on the signal, The autocorrelation in the residuals, to the uncertainty on the trend.

  13. The danger of short periods Rule 2: be careful when the periodicity of the basis functions is longer than the period being fitted.

  14. First look at temperature trends at Lindenberg

  15. Example of regression model fit at 15 km

  16. Conclusions • There are lots of pitfalls on the path to robust trend determination. Be careful! • Uncertainties on GRUAN data allow for trend evaluation including robust uncertainties on the trends. • Reduced uncertainties on night-time temperature measurements from radiosondes should permit earlier detection of long-term trends. • The current GRUAN data record is still too short for trend detection.

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