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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.

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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


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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.


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A programmer’s perspective: purposes (orders of magnitude), and span a range from past to future.

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


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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 ?


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Learning by doing. integrated into design.

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.


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Allowing for growth integrated into design.

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


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CDC Composites page integrated into design.

With thanks to

Cathy Smith

Many other examples as well


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WRCC Divisional Data plot page integrated into design.


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2006 Oct 23 11:00 PDT integrated into design.


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“ … 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


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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)


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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.


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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.







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The track record of Seasonal

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


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A Noble Experiment: The Drought Outlook Seasonal

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


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An new source of concern Seasonal

What reference period should be used ?


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Annual Mean Temperatures, 2000-2005. Seasonal

Departures from 1895-2000 Mean.

Non-standardized. Units: Degrees F. Normalized (standard deviations).

The West dominates recent U.S. warming.


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Summer (Jun-Jul-Aug) Mean Temperatures, 2000-2006. Seasonal

Departures from 1895-2000 Mean.

Non-standardized. Units: Degrees F. Normalized (standard deviations).

The West dominates recent U.S. warming.


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Autumn (Sep-Oct-Nov) Mean Temperatures, 2000-2005. Seasonal

Departures from 1895-2000 Mean.

Non-standardized. Units: Degrees F. Normalized (standard deviations).

The West dominates recent U.S. warming.


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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.


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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.


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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”



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A small difference in circumstances or context, and it can be a whole new problem.

Prof. Julien Clinton Sprott, UW Madison Physics



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Steroid and nuclear hormones Like biochemistry.

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


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Evaluation Like biochemistry.

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?



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

Whale Point

600 ft

Highlands Peak

2500 ft




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Audiences for climate and climate forecast products 2006 Heat Wave.

General - casual users, minor economic decisions

Interested - modest economic decisions (eg, whitewater guide company)

Dependent - major decisions (eg, utilities, ag commodities, derivatives)


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Water Year 2006 Heat Wave.

Temperature

Departure

700 mb (10,000 ft)

1 Oct 2005

Through

31 Mar 2006


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Warm Season 2006 Heat Wave.

Temperature

Departure

700 mb (10,000 ft)

1 Apr 2006

Through

30 Sep 2006


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Water Year 2006 Heat Wave.

Temperature

Departure

700 mb (10,000 ft)

1 Oct 2005

Through

30 Sep 2006


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Climate Test Bed offers many opportunities 2006 Heat Wave.

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.


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What makes for a better dining experience? 2006 Heat Wave.

Taste or presentation?

What makes for a better forecast use experience?

Accuracy or presentation?


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What’s for supper? 2006 Heat Wave.


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