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

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 I Where does 2003 stand over the US ‘trendwise’??? Is it another warm year??

14. Sofar, DJF thru JAS 2003: BNAat 102 US locations 233741%

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

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

18. 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)

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

35. Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb 2001. An example. Yr(j)Wt( α j )YrWtYrWtYr Wt 56 567-878-189 8 57 268-579-39013 58-469-380-491 7 59-770-581-89211 60-371-282 193-6 61 172 683 094 2 62-173 184-195 7 63-174 185 396 2 64-375 286129714 65-876 587 598 2 66-577 188 09926 sum -24 sum -7 sum+4 sum +86 ---------------------------------------------------------------------------------------- CA-SST(s) = 3 α j SST(s,j), where α j is given as in the Table. j

36. Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb 2001. An example. Yr(j)Wt( α j )YrWtYrWtYr Wt 56 567-878-189 8 57 268-579-39013 58-469-380-491 7 59-770-581-89211 60-371-282 193-6 61 172 683 094 2 62-173 184-195 7 63-174 185 396 2 64-375 286129714 65-876 587 598 2 66-577 188 09926 sum -24 sum -7 sum+4 sum +86 ---------------------------------------------------------------------------------------- CA-SST(s) = 3 α j SST(s,j), where α j is given as in the Table. j OCN-SST(s) = 3 α j SST(s,j), where α j =0 (+1/K) for older(recent) j. j

37. Trends in lower boundary conditions?: global SST

38. EOFs for JAS global SST 1948- 2003

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

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

44. 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.

45. 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

46. Courtesy : Marty Hoerling

48. DJF US Nationwide (NCDC)

49. JJA US Nationwide (NCDC)

50. East Anglia Climate Unit

52. Closing comments LF (inter)decadal variability (“trends”) are important for seasonal forecasts, even at short leads. Are there any situations we can identify a-priori where trend tools should be played down ? Trends over the US appear related to trends in the NH, even worldwide

53. Do trends have spatial patterns, and seasonality ? (probably yes) Can we extract such patterns (and seasonality) from limited observations ? (probably, barely) So either we fight noise by EOF or other spatial smoothing, OR We generate ‘artificial’ data by running a trustworthy GCM

54. Explaining trends may require understanding ‘global change’ Shall we start forecasts for K year averages ?? (Regress from the global mean ?)