Climate Observations in
Pacific Northwest National Parks
Kelly T. Redmond
Western Regional Climate Center
Desert Research Institute
2215 Raggio Parkway
Reno Nevada 89512-1095
775-674-7011 voice
775-674-7016 fax
krwrcc@dri.edu
National Weather Service Cooperative Network
Approximately 5000 daily max/min temperature stations, 8000 daily
precipitation stations, 3000 automated hourly precipitation stations.
Volunteer Observers.
Standard raingage
8” wide, 20 inch capacity,
This one with funnel
(summer configuration).
Max/Min Temperature
System (MMTS) in
background (electronic
thermometers).
Longest individual
observer: 76 years,
Cottage Grove Oregon
(not the person pictured).
344 U.S. Climate Divisions.
http://www.wrcc.dri.edu/spi/divplot1map.html
Washington Statewide Temperature 1895 – 2003. Jan, Feb Mar Apr
Washington Statewide Temperature 1895 – 2003. May Jun Jul Aug
Washington Statewide Temperature 1895 – 2003. Sep Oct Nov Dec
Washington Statewide Temperature 1895 – 2003. Winter Spring Summer Autumn
Washington Statewide Temperature 1895 – 2003. Oct-Mar, Apr-Sep, Jul-Jun, Jan-Dec
Remote Automated Weather Stations
RAWS
1100 Active
700 Inactive
Snowfall Telemetry
SNOTEL
700 Active
RCC Site Survey Regions
North
West
Toulumne Meadows
(Gaylor Meadow)
Yosemite, 9270 feet
East
South
National Park Service Protocol Considerations (stemming from CHIS)
Why ?
•
- Why are the measurements being made?
Immediate project needs (a few months to a few years duration)
•
“General purpose” - often turn out to be very important & heavily
used. Should assume this as the default case.
•
Data from many networks are being used for far more than their
original purpose or motivation
Sustainability - Acquisition and deployment are the easy and cheap part.
Think ahead: Arguments for equipment is usually more effective than
arguments for maintenance, quality control, archive, access,
summary.
Maintenance – the bane of all systematic measurement programs, and
(the lack thereof) often responsible for the death of measurement
efforts
Automated systems still require considerable human attention.
Automation is not a panacea, or a replacement for people.
Automation introduces new types of errors.
What constitutes a good climate record? - One that records real climate
variability rather than fake climate variability.
Real Climate Variability: All variations seen in the record are due to
things that happened in the atmosphere. And, we usually hope, in the
larger scale atmosphere. (Though not always … depends on the purpose
of the measurements.)
Fake Climate Variability:
Sensor changes
Replacements, degradations, drift, insects, birds.
Site changes
Ground cover and land use changes
Obstructions / blocking
Anything affecting the local energy balance
Observer and methodological changes
How observations are recorded (conventions followed?)
a.m. / p.m. issues. How instruments are reset.
“Climate quality” measurements require a higher standard than “weather
quality” measurements.
Representativeness - What spatial and temporal scales are the
measurements intended to represent?
Siting. Choosing a site. What factors, at what spatial scales, will
influence the measurements? The scales vary from millimeters to
megameters. What is the site representative of? Practical and scientific
considerations both matter, can be at odds, and often lead to
compromise.
Precipitation – Assume that all precipitation will occur in frozen form,
even if just occasional. Can the equipment handle this?
Link into existing systems
• Instrumentation, communication, dissemination
• Archival, retrieval, analysis
•Local versus external dependencies. Develop or rely on internal
mechanisms and expertise? Develop relations with or rely on external
mechanisms and expertise?
Quality control begins at the station. This bears repeating.
Quality control begins at the station.
•The best form of quality control is to not let bad data leave the station at
all.
• Up-front attention to equipment quality, installation quality, robustness
to disruption, backup systems.
• The weather is usually the most important reason that weather
measurements are lost.
• The weather that knocks out weather stations is often the weather we
most want to know about. (Extremes, and disturbances.)
• Design for the worst conditions.
• Once the data values leave the station, essentially only bad things can
happen them.
• “Cheap” is often not cheap.
Two-way communication. More desirable. Enables re-transmission of
data, resetting of data loggers, better debugging of problems.
Documentation (“Metadata – data about data”)
•Need history of whatever factors that can affect the interpretation of the
measurements. These include sensors, site physical circumstances
(including vegetation), observing practices and reporting conventions,
numerical pre-processing, averaging intervals, gage shielding, etc.
•Most perishable: that which is carried in human heads, best retrieved
before or retirement, or more final outcomes. Filing cabinets, old
diskettes, etc, also contain much interesting material, but often separated
from their originator.
•GPS positions.
•Full photo sets, repeated periodically, approximately 1-2 years. Typically
8 point compass, wide angle, skylines and foreground, plus any other
helpful angles which show local influences, and changes through time.
•Changes affecting local climate, within 200-300 meters. Irrigation
starting or stopping, vegetative regrowth or trimming, pavement or
change in surface status, additions or subtractions of obstructions.
•Digital versions of the above, accessible with the data.
Observing strategies
•Is the emphasis on learning about temporal behavior, temporal behavior,
or both at once?
•Dependence on spatial correlation structure of temporal behavior of
climate elements. (How well does time-varying behavior correlate in
space, at various combinations of spatial and temporal scales?) This
varies widely, depending on topographic complexity (height, slope,
aspect), proximity to water bodies, as well as latitude and season.
• *** Catch 22: Chicken and egg! We need the observations to tell us
what observations we need! *** Solution: Experience, brains, intuition.
•A few very good and reliable sites, or lots of mediocre sites?
•Vulnerability assessment. Where could key failures in measurement
result in loss of valuable information or knowledge? Example: manual or
simple backup of automated precipitation gages, or, saving of water after
initial precipitation measurement (we might lose the timing but know the
total).
•Redundancy is not a weakness. A degree of redundancy is good.
Observing strategies (continued)
•Station density. Optimum density depends on spatial correlation
structure mentioned above. General consideration: are all of the
principal climate regimes adequately sampled?
•A mixed strategy seems best: A few reference quality sites that get lots
of attention, produce complete records, have needed backup, and are
thoroughly documented, interspersed with more numerous “satellite”
observations of perhaps lesser quality, or less protected from their
various vulnerabilities.
Adaptive monitoring. Smarter electronics have made it possible for
observing systems to modify their sensing rate, in response to
environmental cues. Systematic measurements are always needed, but
these can be interspersed with rates that vary according to the behavior
of interest. Of course, we don’t always know what behavior will turn out
to be of interest, especially if new.
Retrospective capability. We often do not learn that a system has
experienced a change until well after the fact. We are therefore
dependent on systematic measurement to reconstruct the past.
Added considerations raised during meeting.
• Public interface needed. Web access to reach broad
range of public, research, and other user communities.
• Public outreach / education. There is a great deal of
public interest in climate. Capitalize on that as part of the
park mission.
• Administrative environment. Take it as given that
personnel are busy, overwhelmed, have multiple duties.
• “Expectationless” monitoring. The most useful
monitoring has no special hypotheses in mind.
• Ten commandments for climate monitoring
• Partnering / leveraging. Why re-invent the wheel? Share
resources and expertise. Be careful to pick partners with
staying power.
Added considerations raised during meeting (continued).
• Monitoring the monitoring. Is the monitoring performing as
needed or intended? Is it functioning at all? How healthy is
this activity? Refered to as "network health”.
• Snow issues. Many of these, in higher, northern parks. Far
more challenging, difficult, expensive, labor intensive, to
obtain quality records.
• High elevation monitoring. Badly needed. High elevations
may not vary in time like nearby low elevations. Separate
monitoring needed. Needs high standards, rugged
equipment, good communication, highly reliable instruments.
• The world changes. Landscape, vegetation, growth can
alter locally measured climate significantly. Fires destroy,
then grow backi. Clearcuts: slow changes in small scale
climate. Mt St Helens: Destruction, then regrowth. Local
temp & precip climate will slowly change (and separately).
Ten Principles for Climate Monitoring
For the past few years, with respect to climate data issues, frequent
mention is made of a set of climate monitoring principles enunciated
in 1995 by Tom Karl, director of NCDC. In some quarters (following Gene
Rasmusson, for one), they have also been informally referred to as the
"Ten Commandments of Climate Monitoring". Both versions are given here.
collated by Kelly Redmond, Western Regional Climate Center, August 2000.
--------------------------------------------------------------------------------------------------------------------------------------------Cliff's Notes version. "Ten Commandments of Climate Monitoring"
1. Assess the impact of new observing systems or changes to existing
systems prior to implementation.
"Thou shalt properly manage network change." (Assess effect of proposed
changes.)
2. Require a suitable period of overlap for new and old observing systems.
"Thou shalt conduct parallel testing." (Of old and replacement systems.)
3. Treat the results of calibration, validation, algorithm changes, and
data homogeneity assessments with the same care as the data.
"Thou shalt collect metadata." (Full documentation of system and
operating prodecures.)
\
4. Ensure a capability for routine assessments of quality and homogeneity,
including high resolution data for extreme events.
"Thou shalt assure data quality and continuity." (Assess as part of
routine operating procedures.)
5. Integrate assessments, like those of the International Panel on
Climate change, into global observing priorities.
"Thou shalt anticipate use of the data." (e.g., integrated environmental
assessment; anticipate data use as part of operating system plan.)
6. Maintain long-term stations.
"Thou shalt worship historical significance." (Maintain long term
observing systems which provide homogeneous data sets.)
7. Put a high priority on increasing observations in data-poor regions
and regions sensitive to change and variability.
"Thou shalt acquire complementary data." (New sites to fill in
observational gaps.)
8. Provide network operators, designers, and instrument engineers
with long-term requirements at the outset of the design and
implementation phases of new systems.
"Thou shalt specify climate requirements." (Designers of networks
be aware of monitoring requirements for climate usage.)
9. Think through the transition from research observing systems
to long-term operations carefully.
"Thou shalt have continuity of purpose." (Stable, long-term
commitments.)
10. Focus on data management systems that facilitate access, use,
and interpretation of weather data and metadata.
"Thou shalt provide data and metadata access.“
--------------------------------------------------------------------------------------------------------------------------------------------From Karl et al 1996. Full version:
1. The effects on the climate record of changes in instruments,
observing practices, observation locations, sampling rates, etc. must
be known prior to implementing such changes. This can be ascertained
through a period of overlapping measurements between old and new
obwerving systems or sometimes by comparison of the old and new
observing systems with a reference standard. Site stability for
in-situ measurements, both in terms of physical location and changes
in the nearby environment, should also be a key criterion in site
selection. Thus, many synoptic network stations, primarily used in
weather forecasting but which provide valuable climate data, and all
dedicated climatolotical stations intended to be operational for
extended periods, must be subject to such a policy.
2. The processing algorithms and changes in these algorithms must be
well documented. Documentation of these changes should be carried
along with the data throughout the data archiving process.
3. Knowledge of instrument, station and/or platform history is
essential for data interpretation and use. Changes in instrument
sampling time, local environmental conditions for in-situ
measurements, and any other factors pertinent to the interpretation of
the observations and measurements should be recorded as a mandatory
part of the observing routine and be archived with the original data.
4. In-situ and other observations with a long uninterrupted record
should be maintained. Every effort should be applied to protect the
data sets that have provided long-term homogeneous observations.
"Long-term" for space-based measurements is measured in decades, but
for more conventional measurements "long-term" may be a century or
more. Each element of the observations system should develop a list
of prioritized sites or observations based on their contribution to
long-term climate monitoring.
5. Calibration, validation and maintenance facilities are a critical
requirement for long-term climateic data sets. Climate record
homogeneity must be routinely assessed, and corrective action must
become part of the archived record.
6. Where feasible, some level of "low-technology" backup to
"high-technology" observing systems should be developed to safeguard
against unexpected operational failures.
7. Data poor regions, variables and regions sensitive to change, and
key measurements with inadequate spatial and temporal resolution
should be given the highest priority in the design and implementation
of new climate observing systems.
8. Network designers and instrument engineers must be provided
long-term climate requirements at the outset of network design. This
is particularly important because most observing systems have been
desigined for purposes other than long-term climate monitoring.
Instruments must have adequate accuracy with biases small enough to
document climate variations and chaqnges.
9. Much of the development of new observation capabilities and much
of the evidence supporting the value of these observationss stem from
research-oriented needs or programs. A lack of stable, long-term
commitment to these observations, and lack of a clear transition plan
from research to operations, are two frequent limitations in the
development of adequate long-term monitoring capabilities. The
difficulties of securing a long-term commitment must be overcome if
the climate observing system is to be improved in a timely manner with
minimum interruptions.
10. Data management systems that facilitate access, use, and
interpretation are essential. Freedom of access, low cost, mechanisms
which facilitate use (directories, catalogs, browse capabilities,
availability of metadata on station histories, algorithm accessibility
and documentation, etc.) and quality control should guide data
management. International cooperation is critical for successful
management of data used to monitor long-term climate change and
variability.
--------------------------------------------------------------------------------------------------------------------------------------------Climate Monitoring Guidelines.
Sources:
Thomas R. Karl, V.E. Derr, D.R. Easterling, C.K. Folland,
D.J. Hoffman, S. Levitus, N. Nicholls, D.E. Parker, and G.W. Withee,
1996. Critical Issues for Long-Term Climate Monitoring. pp 55-92, in
"Long Term Climate Monitoring by the Global Climate Observing System",
T.R. Karl, ed, Kluwer, 518 pp.
National Research Council, 1998. Guidelines and Principles for
Climate Monitoring. Appendix F, p 63, in Future of the National
Weather Service Cooperative Observer Network. National Academy Press,
65 pp.
Eugene Rasmusson, 2000. Workshop notes. Climate Services: A vision for
the future. NAS/NRC/BASC, Woods Hole MA.
-------------------------------------------------------------------------Dr. Kelly T. Redmond
Regional Climatologist/Deputy Director
775-674-7011 voice
Western Regional Climate Center
775-674-7016 fax
Desert Research Institute
email: krwrcc@dri.edu
2215 Raggio Parkway
http://www.wrcc.dri.edu
Reno, Nevada 89512-1095
ftp.wrcc.dri.edu/pub
Magnitudes and perceptions.
In a relative sense, climate change is expected to be smaller, perhaps
much smaller, than the fluctuations of climate and weather that we
already know and love.
However, it is also expected to be fairly systematic. Thus, many causes
and consequences would likely express themselves as subtle systematic
biases in time series of properties of the climate itself and of affected
physical and biological systems. These would be interspersed with
changes in the likelihood of more visible and memorable occurrences of
extremes and disturbances.
As cosmologist Philip Morrison pointed out in his book “Nothing is Too
Wonderful to be True,” change in the universe occurs approximately
equally in quiet and unassuming forms, and in explosive and dramatic
forms. But even the latter often build in a slow and innocuous manner.
“Subtle does not mean unimportant.”
Prof Mike Wallace
UW Atmospheric Sciences
Seattle, July 14, 1997
The End