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On Improving NOAA’S Climate Normals: An Introduction to ‘Optimal Normals’ of Temperature

On Improving NOAA’S Climate Normals: An Introduction to ‘Optimal Normals’ of Temperature. Anthony Arguez NOAA National Climatic Data Center Anthony.Arguez@noaa.gov Phone: (828) 271- 4338. Optimal Normals: Brief Overview.

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On Improving NOAA’S Climate Normals: An Introduction to ‘Optimal Normals’ of Temperature

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  1. On Improving NOAA’S Climate Normals:An Introduction to‘Optimal Normals’ of Temperature Anthony Arguez NOAA National Climatic Data Center Anthony.Arguez@noaa.gov Phone: (828) 271- 4338 OPTIMAL NORMALS

  2. Optimal Normals: Brief Overview • A suite of Experimental Products that supplement the Traditional 30-Year Normals • Monthly Temperature (Max/Min/Mean) for now • Version 1.0 (computed through 2008): • Annual Updates • OCN • Hinge Fit • Later Versions • More advanced techniques • More variables • Improved data source (discussed by Dr. Menne) Tuesday, June 2, 2009 OPTIMAL NORMALS

  3. This webcast is co-hosted by the AMS Energy Committee and NOAA’s National Climatic Data Center • Jon Davis, Chesapeake Energy: Chair of the AMS Energy Committee • Anthony Arguez, NCDC: project lead on Optimal Normals, NCDC’s User Engagement Lead for Energy, Member of the AMS Energy Committee. • Matthew Menne, NCDC: climate scientist involved in the creation of the dataset used for Optimal Normals Tuesday, June 2, 2009 OPTIMAL NORMALS

  4. You may be interested in this…. Call for Papers • First Conference on Weather, Climate, and the New Energy Economy • American Meteorological Society Annual Meeting • 17–21 January 2010, Atlanta, Georgia • More information? Contact Jon Davis jon.davis@chk.comor visit http://www.ametsoc.org Tuesday, June 2, 2009 OPTIMAL NORMALS

  5. History of this Project • Letters, informal discussion, anecdotal evidence: Is the 30-year Normal the best we can do? • May 2007 Teleconference: Listening • September 2007 Webcast: Proposal • June 2009 Webcast (now): Produce • Future: • Feedback, Perpetual Engagement • Release of Optimal Normals to all users Tuesday, June 2, 2009 OPTIMAL NORMALS

  6. Traditional Climate NormalsIssues in a Changing Climate Two main issues: • Is the 30-year average representative of the current state of the climate? • Consider this: Normals are only updated every 10 years! • What if there is a prominent trend? Are they obsolete? Is an average the best method? Climate Normals are calculated retrospectively, but are used prospectively for planning  OPTIMAL NORMALS

  7. Why Optimal Normals? • Explicit Acknowledgment: No method is always perfect for all applications • Provide alternatives to the Traditional 30-Year Normals • Experimental Products • Supplement, Not Replace, 30-Year Normals • Evaluate these alternatives (later) • NOAA Leadership on this issue • Livezey et al. (2007) Recommendations • Version 1.0 essentially follows these recommendations OPTIMAL NORMALS

  8. OPTIMAL NORMALS Gray: Not Significant

  9. OPTIMAL NORMALS Gray: Not Significant

  10. Annual Updates • A moving, or rolling, 30-year average • Why? Official Normals are only computed once per decade. • e.g., 1979-2008 instead of 1971-2000 • Computed once per year (every January) → Update Still an average Tuesday, June 2, 2009 OPTIMAL NORMALS

  11. OCN • Tool developed by NOAA’s Climate Prediction Center in the 1990s – they called it ‘Optimal Climate Normals’ • Determine the ‘optimal’ averaging period: N Years • the maximum correlation between the forecast anomaly and observed anomaly during the verification period • Initially utilized in a sub-optimal fashion: fixed averaging periods, 10-year average for monthly temperature, 15-year average for monthly precipitation • ‘Optimal’ averaging period (N) can be computed per station, per variable, per monthly time series based on the residual lag-1 autocorrelation (g) and the linear trend (β). • Livezey et al. 2007 (JAMC) Still an average Tuesday, June 2, 2009 OPTIMAL NORMALS

  12. Hinge Fit • Piecewise continuous with no change from 1940-1976 and linear change thereafter. • Modeled after underlying global warming signal. • Reduces sampling error greatly from linear fit to last three decades. • Outperforms the linear fit, and OCN except for small trends. • The line represents a time-dependent normal – there is no average involved Not an average Tuesday, June 2, 2009 OPTIMAL NORMALS top provided by Bob Livezey

  13. Data and Methods: Overview • We use data from 1940-2008 • 9168 stations in total • Flags indicate at least 20% of values were interpolated • All methods are applied to annually-sampled monthly time series • e.g. a January time series Tuesday, June 2, 2009 OPTIMAL NORMALS

  14. Data Processing Steps • Start with daily data sources (DSI-3200; DSI-3206; DSI-3210) Apply quality assurance (QA) checks; compute monthly values when no more than 9 daily values are missing • Merge these monthly values together to form one “superset” of monthly data Apply additional QA checks to the monthly values • Apply algorithm to adjust for bias associated with changes to the time of observation (all monthly values set to conform to a midnight to midnight observation hour) • Apply adjustments to account for changes in instrumentation, station moves, etc. • Create estimates for missing and/or flagged values using values from surrounding stations (FILNET) Each of these steps is described on the U.S. HCN Version 2 web site and in a forthcoming article Menne, Williams and Vose (2009) Bulletin of the American Meteorological Society (for early online release see http://ams.allenpress.com/archive/1520-0477/preprint/2009/pdf/10.1175_2008BAMS2613.1.pdf)

  15. U.S. HCN Processing Steps are applied to the full Cooperative Observer Network to produce Normals dataset

  16. The Time of Observation Bias

  17. The Time of Observation Bias 1950s 1960s 1970s 1980s 2000- 2006 1990s Hour of observation histograms at bottom of each U.S. decadal map (Figure courtesy of Xioamoa Lin, University of Nebraska)

  18. Impact of Time of Observation Adjustments Average year by year difference over the conterminous United States between the Time of Observation Bias (TOB)-adjusted data and the unadjusted (raw) data.

  19. Homogenization

  20. Adjusted Unadjusted °F Year Chula Vista annual maximum temperature departure from long-term average minus the average from 10 nearby stations. The Chula Vista station moved on January 1, 1982 (from 32°36'N, 117°06'W, Elev 9 feet to 32°36'N, 117°06'W, Elev 56 feet).

  21. (a) Mean annual unadjusted and fully adjusted minimum temperatures at Reno, Nevada. Error bars indicating the magnitude of uncertainty (±1 standard error) were calculated via 100 Monte Carlo simulations that sampled within the range of the pairwise estimates for the magnitude of each inhomogeneity; (b) difference between minimum temperatures at Reno and the mean from its 10 nearest neighbors

  22. Maximum Temperature Trends – Raw (unadjusted)(1950 to 2007)

  23. Maximum Temperature Trends – TOB Adjusted(1950 to 2007)

  24. Maximum Temperature Trends – Fully Adjusted(1950 to 2007)

  25. Future • Our plan is to vertically integrate future updates of the Optimal Normals monthly data with the Global Historical Climatology Network – Daily dataset (http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/). • This will result in changes to the periods of record at some stations; however, the processing steps will be the same.

  26. Computation of Optimal Normals:Additional Details • Annual Updates: Simple Arithmetic Average over the 1979-2008 period • If more than 6 values are interpolated, the value is flagged. • OCN: An N-Year Average • To determine N, we compute the scaled slope and residual lag-1 autocorrelation. The latter is computed by first subtracting each time series by the Hinge Fit, yielding the residual time series. • Flagged: If 20% of 1940-1975 are interpolated OR 20% of 1976-2008. • Hinge Fit: A Constrained Linear Fit • The Hinge point is fixed at 1975. The same flag criteria as for OCN. Tuesday, June 2, 2009 OPTIMAL NORMALS

  27. Caveats • The data set is different from that used to compute NOAA’s official 1971-2000 Normals. Use care. • Two deviations from the Livezey et al. (2007) recommendations • (1) The article recommends a “hybrid” approach that selects either the Hinge Fit or OCN based on certain time series characteristics. We simply provide both results individually. • (2) The authors set all negative lag-1 autocorrelations to zero. We do not. This is described further in a document called negative-lag1corr.doc Tuesday, June 2, 2009 OPTIMAL NORMALS

  28. Accessing the Products andAncillary Files Two Options (1) Anonymous FTP: ftp://ftp.ncdc.noaa.gov/pub/data/aarguez/optimal-normals/ * This may not work if your firewall is too strict * (2) HTTP: http://www1.ncdc.noaa.gov/pub/data/aarguez/optimal-normals/ Look for the readme.txt file Tuesday, June 2, 2009 OPTIMAL NORMALS

  29. Directory Contents • Data Files • xxx-yyy-2008.dat • “xxx” can be “ann” or “ocn” or “hin” • “yyy” can be “avg” or “max” or “min” • stations.dat → station list, metadata: lat, lon, ele, name • Word Documents • ams-talk-2009.doc • January 2009 talk at AMS • negative-lag1corr.doc • Discussion of the retention of negative residual lag1-autocorrelations Tuesday, June 2, 2009 OPTIMAL NORMALS

  30. JUL Tmax Optimal Normals vs. 1971-2000 JAN Tmin Annual Update OCN Hinge Fit OPTIMAL NORMALS

  31. Concluding Thoughts • Optimal Normals Version 1.0 is now officially released as an experimental product. • Please provide feedback. • Please read the ‘readme.txt’ file carefully. • Compare to Official 1971-2000 with care • Remember: Optimal Normals are experimental products that supplement the 30-Year Normals Tuesday, June 2, 2009 OPTIMAL NORMALS

  32. On Improving NOAA’S Climate Normals:An Introduction to ‘Optimal Normals’ of Temperature Anthony Arguez NOAA National Climatic Data Center Anthony.Arguez@noaa.gov Phone: (828) 271- 4338 Comments or Questions? OPTIMAL NORMALS

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