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TRENDS REVISITED

This presentation delves into the analysis of climate trends in real-time forecasting settings, aiming to enhance trend forecasting tools and provide physical explanations for trends. Examining data from 2003 to understand the trend status over the US, the talk explores optimal climate normals and performance measures. The discussion includes weights of constructed analogues on global sea surface temperatures and the potential accuracy of inter-decadal climate variation. Insights on improving trend forecasts and tackling spatial and seasonal noise are also shared.

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TRENDS REVISITED

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  1. TRENDS REVISITED C P C Huug van den Dool Climate Prediction Center NCEP/NWS/NOAA CDPW Reno October, 22, 2003

  2. (Trends: not a straight line, LF ups and downs.) Trends: Diagnostics OR rather: How to ‘deal with trends’ in a real time forecast setting.? How to improve Trend forecast tools? How to physically explain Trends?

  3. Intro IWhere does 2003 stand over the US ‘trendwise’???Is it another warm year??

  4. Sofar, DJF thru JAS 2003:B N A at 102 US locations23 37 41%

  5. Intro II: The Great Performance Measure (PM)

  6. The PM (blue line)Retro-active OCN (pink line)

  7. What is OCN? (Optimal Climate Normals). Essentially a forecast in which one persists the average of the anomalies observed in the same named season over the last K years.Example of OCN for JFM 2004: The average anomaly for JFM over 1994-2003 (K=10; T; no space averaging)

  8. What might explain the skill of such simple forecasts?

  9. Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb 2001. An example.Yr(j) Wt(αj) Yr Wt Yr Wt Yr Wt 56 5 67 -8 78 -1 89 8 57 2 68 -5 79 -3 90 13 58 -4 69 -3 80 -4 91 7 59 -7 70 -5 81 -8 92 11 60 -3 71 -2 82 1 93 -6 61 1 72 6 83 0 94 2 62 -1 73 1 84 -1 95 7 63 -1 74 1 85 3 96 2 64 -3 75 2 86 12 97 14 65 -8 76 5 87 5 98 2 66 -5 77 1 88 0 99 26sum -24 sum -7 sum +4 sum +86---------------------------------------------------------------------------------------- • CA-SST(s) = 3 αj SST(s,j), where αj is given as in the Table. • j

  10. Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb 2001. An example.Yr(j) Wt(αj) Yr Wt Yr Wt Yr Wt 56 5 67 -8 78 -1 89 8 57 2 68 -5 79 -3 90 13 58 -4 69 -3 80 -4 91 7 59 -7 70 -5 81 -8 92 11 60 -3 71 -2 82 1 93 -6 61 1 72 6 83 0 94 2 62 -1 73 1 84 -1 95 7 63 -1 74 1 85 3 96 2 64 -3 75 2 86 12 97 14 65 -8 76 5 87 5 98 2 66 -5 77 1 88 0 99 26sum -24 sum -7 sum +4 sum +86---------------------------------------------------------------------------------------- CA-SST(s) = 3 αj SST(s,j), where αj is given as in the Table. j OCN-SST(s) = 3 αj SST(s,j), where αj=0 (+1/K) for older(recent) j. j

  11. Trends in lower boundary conditions?: global SST

  12. EOFs for JAS global SST 1948-2003

  13. Trends in lower boundary conditions?: global Soil Moisture

  14. Is the inter-decadal component of climate variation accurately known ???Probably not. Nature provides just one realization.

  15. Evidence: 1) 70% of skill of OCN over US can be obtained by replacing the K year average of T(s,m) by the annual mean spatial mean value, i.e. we can ignore some, if not most, of the spatial and seasonal dependence.

  16. 2) We can try to fight noise by : a) determining optimal K in EOF space ( Peitao Peng), i.e. build a smooth spatial dependence b) We could generate more data with a credible model

  17. Courtesy : Marty Hoerling

  18. DJF US Nationwide (NCDC)

  19. JJA US Nationwide (NCDC)

  20. East Anglia Climate Unit

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