Making Climate Products Digestible Kelly Redmond Western Regional Climate Center

1. Making Climate Products Digestible Kelly Redmond Western Regional Climate Center Desert Research Institute 31st Climate Diagnostics and Prediction Workshop Earth System Research Laboratory Boulder CO, 2006 October 23-27

2. Climate questions span a large range of complexity and purposes (orders of magnitude), and span a range from past to future. In most cases, some kind of decision is involved. Decisions take place in the future, and involve future conditions. Actual forecasts pertinent to a specific question would be best, but most often such forecasts are not available, or would not be credible. Many requests for past data or information pertain to an upcoming decision. Thus past behavior is often treated as a de facto forecast. Past is prologue. The fact that something has happened once conveys considerable credibility.

3. A programmer’s perspective: If it’s easy to use, it was hard for the programmer. If it’s hard to use, it was easy for the programmer. (from an NPS software consultant) “The engineers who design cars never seem to have to fix them” parental observation “))^&*@*^$!_)!_)^&^%!#$#)(*&$%(*” supplemental observation

4. Products are much better if users, or user perspectives, are integrated into design. Better yet if designer has experience as both a user and a provider of information. Benevolent Dictator ? vs. Committee ?

5. Learning by doing. Start with a best guess. This can often be pretty good. Dictated by experience. Listen. Iterate. This approach can be slow and halting, but it has a rich track record.

7. Allowing for growth First time users – handholding and help Experienced users – shortcuts, and elaboration and sophistication Well designed web pages have both levels available at once. Fewer web pages to manage. Short Attention Span Syndrome (“Faster,” James Gleick) Even industrious people can be lazy about certain things Saving labor (minimal hand movement, clicking and scrolling) Ease and efficiency Compactness

8. CDC Composites page With thanks to Cathy Smith Many other examples as well

9. WRCC Divisional Data plot page

10. 2006 Oct 23 11:00 PDT

11. “ … think like a mountain …” (Aldo Leopold, A Sand County Almanac, 1949) With climate products: think like a user How to do this??? Know the user. Firsthand is better, not always practical on an industrial scale. Secondhand can work, but needs intermediaries. Provider push vs user pull Provider has (actually or potentially) useful information for user User wants certain information (perhaps needs, but does not recognize) Role of climate services provider Help the user to structure their thinking

12. Suppose your own personal resources were on the line … not just an abstraction! What would you do ? How would you decide ? How confident would you want, or need, to be ? Is this a voluntary decision, or a mandatory (imposed) decision ? What makes you trust information ? Track record is probably most frequently cited as desired information. “How do I know I am not getting a pig in a poke?” (recent question) What kind of track record information is most convincing? What kind of track record information is most relevant (objectively (?) )? According to a climate scientist ? According to an average user ? Skill measured how? Overall patterns, events, specific locations, specific circumstances, new or emergent phenomena How well can humans evaluate pattern similarities by eye ? (O’Lenic talk)

13. Precip (Fcst / Obs) Jul-Aug-Sep 2006 Temp (Fcst / Obs) A real track record based on real forecasts, of which this is just one. But, it is the most recent.

14. Precip (Fcst / Obs) Nov-Dec-Jan 2005-06 Temp (Fcst / Obs) A real track record based on real forecasts, of which this is just one. This one is from the previous October.

15. Monthly Oct init 700 mb Height Skill 1982-2003 Seasonal

16. All areas CFS monthly 2006 Oct 22 With skill mask

17. All areas CFS seasonal 2006 Oct 22 With skill mask

18. Monthly Oct init Precip Skill 1982-2003 Seasonal

19. Monthly Oct init Temp Skill 1982-2003 Seasonal

20. The track record of real forecasts rather than simulated forecasts. Here, CFS. Disadvantage: short record. Many other measures could also be utilized: Correlations Skill scores Percent “right” Percent of application-dependent events captured

23. A Noble Experiment: The Drought Outlook Most of the forecast consists of expansion, contraction, weakening or strengthening of existing areas, seldom new areas of formation. This is partly the nature of drought, and partly the nature of drought forecasters

24. An new source of concern What reference period should be used ?

27. Annual Mean Temperatures, 2000-2005. Departures from 1895-2000 Mean. Non-standardized. Units: Degrees F. Normalized (standard deviations). The West dominates recent U.S. warming.

28. Summer (Jun-Jul-Aug) Mean Temperatures, 2000-2006. Departures from 1895-2000 Mean. Non-standardized. Units: Degrees F. Normalized (standard deviations). The West dominates recent U.S. warming.

29. Autumn (Sep-Oct-Nov) Mean Temperatures, 2000-2005. Departures from 1895-2000 Mean. Non-standardized. Units: Degrees F. Normalized (standard deviations). The West dominates recent U.S. warming.

30. Winter (Dec-Jan-Feb) Mean Temperatures, 1999/2000-2005/2006. Departures from 1895-2000 Mean. Non-standardized. Units: Degrees F. Normalized (standard deviations). The West dominates recent U.S. warming.

31. Spring (Mar-Apr-May) Mean Temperatures, 2000-2006. Departures from 1895-2000 Mean. Non-standardized. Units: Degrees F. Normalized (standard deviations). The West dominates recent U.S. warming.

34. Climate events unfold slooooooowwwwwwwly The relation between verification time, and preparation time, is different than for daily weather forecasts. Weather: Three work shifts per day, each 8 hrs long. Four 12-hour forecasts updated every 6 hrs (4x per day). Approximately 0.75 work shift per forecast update. Climate: 90 days is 270 work shifts, or 90 daytime work shifts. One daytime work shift is 1/30=.033 forecast period (monthly), and 1/90=.011 daytime work shift per seasonal forecast. Thus, lots of time to watch the forecast verify. Or, not. Perhaps, more temptation to update. Also, time enough to forget forecast. Easier to intermingle observations and forecasts, the longer the duration and lead time of the phenomena being forecast. Aspects of the forecast system can evolve during as the verifying period is unfolding. Forecasts can be nudged, modified, or otherwise re-interpreted as they play out. This is more likely to occur the longer the interval covered: monthly, seasonal, annual, decadal … ENSO discussion: Forecasts less tentative when ENSO event appears to have started. Are these really forecasts? More like “observacasts”

35. What matters to users can be, and often is, very application-specific.

36. A small difference in circumstances or context, and it can be a whole new problem. Prof. Julien Clinton Sprott, UW Madison Physics

37. Provider and user relationship. A lock and key situation. Like biochemistry.

38. Steroid and nuclear hormones Human estrogen receptor–ligand binding domain hER-alpha-LBD Tanenbaum et al PNAS, 1998 Feb 18 Stereo Image Fix on estradiol: carbon black oxygen red

39. Evaluation Not of forecasts themselves, but of their use, and of system effectiveness Done properly, this takes lots of attention, and takes skill Attention and skill imply need for resources, perhaps significant Process has often expended most of its energy and budget by this point This part is often viewed as “extra” rather than integral Resource decision How to allocate resources to the forecast system Basic understanding (need for better forecasts) Better packaging and presentation of forecast information Better understanding of the use and decision environment How effectively is this knowledge plowed back into the forecast system?

40. Local applicability. User orientation. My back yard.

41. Whale Point (600 ft) and Highlands Peak (2500 ft), Big Sur. 2 miles apart. Whale Point 600 ft Highlands Peak 2500 ft

42. Whale Point, Big Sur, 600 ft, 10-min Temperature, July 2006 Heat Wave.

43. Highlands Pk, Big Sur, 2470 ft, 10-min Temperature, July 2006 Heat Wave.

44. Audiences for climate and climate forecast products General - casual users, minor economic decisions Interested - modest economic decisions (eg, whitewater guide company) Dependent - major decisions (eg, utilities, ag commodities, derivatives)

45. Water Year Temperature Departure 700 mb (10,000 ft) 1 Oct 2005 Through 31 Mar 2006

46. Warm Season Temperature Departure 700 mb (10,000 ft) 1 Apr 2006 Through 30 Sep 2006

47. Water Year Temperature Departure 700 mb (10,000 ft) 1 Oct 2005 Through 30 Sep 2006

48. Climate Test Bed offers many opportunities Hydrologic forecasts could potentially benefit, perhaps considerably NWS and NRCS, and many water managers. Other resource managers Providing model output that can link, or pipe directly, into applications In an atmosphere of limited total resources, the ultimate importance of developing a supportive constituency for maintaining the research and operational infrastructure should not be underestimated. Along with substance (assumed to be a given (!?!) ), the associated marketing salesmanship presentation all take unexpectedly large amounts of time and resources. A willingness to commit to this can mean the difference between success and failure.

49. What makes for a better dining experience? Taste or presentation? What makes for a better forecast use experience? Accuracy or presentation?

50. What’s for supper?