From the “temperature bias only goes one way department” and the University of Montana:
Mountain system artificially inflates temperature increases at higher elevations
MISSOULA – In a recent study, University of Montana and Montana Climate Office researcher Jared Oyler found that while the western U.S. has warmed, recently observed warming in the mountains of the western U.S. likely is not as large as previously supposed.
His results, published Jan. 9 in the journal Geophysical Research Letters, show that sensor changes have significantly biased temperature observations from the Snowpack Telemetry (SNOTEL) station network.
More than 700 SNOTEL sites monitor temperature and snowpack across the mountainous western U.S. SNOTEL provides critical data for water supply forecasts. Researchers often use SNOTEL data to study mountain climate trends and impacts to mountain hydrology and ecology.
Oyler and his co-authors applied statistical techniques to account for biases introduced when equipment was switched at SNOTEL sites in the mid-1990s to mid-2000s.
His revised datasets reduced the biases to reveal that high-elevation minimum temperatures were warming only slightly more than minimum temperatures at lower elevations.
“Observations from other station networks clearly show that the western U.S. has experienced regional warming,” Oyler said, “but to assess current and future climate change impacts to snowpack and important mountain ecosystem processes, we need accurate observations from the high elevation areas only covered by the SNOTEL network. The SNOTEL bias has likely compromised our ability to understand the unique drivers and impacts of climate change in western U.S. mountains.”
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Co-authors on the paper “Artificial Amplification of Warming Trends Across the Mountains of the Western United States” include UM researchers Solomon Dobrowski, Ashley Ballantyne, Anna Klene and Steve Running. It is available online at http://onlinelibrary.wiley.com/enhanced/doi/10.1002/2014GL062803/.
This is just one more example of why satellite temperature data should be the primary source of global temperature records (post 1979) rather than ground temp station data.
There is so much bias built into terrestrial temperature databases (homogenization, inconsistent international temp record protocols, arbitrary in-filling, regional weighting, equipment change calibrations, discontinued temp stations, temp station relocations, UHI adjustments, interruptions of regional temp data, etc.) that the terrestrial data has become an incoherent mess.
Rather than making “educated guesses” (always towards warming), terrestrial data should just have large error bars to account for all these unquantifiable variables…
Based on available data, global temps have apparently risen around 0.75C since the end of Little Ice Age in 1850, for a global trend of just around 0.045C/decade, and no RSS satellite global warming trend for the past 18+ years (despite 30% of all manmade CO2 emissions since 1750 made over just the last 18 years)…
Where’s the beef?
Why is CAGW still taken seriously??
Why is CAGW still taken seriously??
Emotional, financial, and political investment.
From the NRCS website, job vacancies at their Portland, OR HQ.
Program Management
Meteorologist/Applied Climatologist Vacant
Resource Conservationist Vacant
GIS Specialist Vacant
Water and Climate Section
Electronics Technician Vacant
Statistical Assistant Vacant
If I were HMFWBIC (an old military term. figure it out), I would draft AW, WE, and BT onto some of those positions for a 24 month tour of duty to get them going on the straight narrow. Of course it would require some living in Portland and helicoptering up to & into the Rockies ato service and check on those SNOTEL sites in the summer. Rough duty. Do what you love, love what you do. The only down side would be living in Portland with the good beer and all.
[trimmed, off-subject. .mod]
WTF is that about, Int?
Enough of the paranoia. Never put down to dishonesty what can be ascribed to stupidity, incompetence or ar5e licking. If the boss says there is global warming you say how much do you want sir if you want to continue to work in the field?
I know because as an engineer I worked with some who would not give the right answer.
Reblogged this on sainsfilteknologi and commented:
Bias in temperature measurements
‘but to assess current and future climate change impacts…. we need accurate observations’
the key word is of course observations which models are definitely not but I would add a second part , we need to accept what these observations tells us without regarding ‘adjustments’ to be an automatic need when they tell us what we do not wish to hear.
This news is more consistent with our best measurement of the te4mperatuerr of the United States.
That comes from the pristine sited (no adjustments !) triple redundant platinum temp sensors used in the USCRN. (Google it or search on WUWT for USCRN-United States Climate Reference Network)
The past ten years data from this gold standard temperature measurement shows NO temperature increase at all.
None, zip, zilch, nada. This also confirms the “pause”, at least in the USA.
Much of touted warming is from adjustments.
Consistent.
You mean USHCN is consistent with our best measurements in US?
http://www.yaleclimateconnections.org/pics/0113_figure2.jpg
rooter,
Surely you must be aware that USHCN and related sites have been discredited?
“Goddard made two major errors in his analysis”
http://rankexploits.com/musings/2014/how-not-to-calculate-temperature/
dbstealey:
Discredited by the USCRN?
Your link then only shows that adjustments are warranted. Like adjustmets in the SNOTEL-network.
Too bad.
Along with inaccurate temp readings, I have doubts that their snow depth readings have much accuracy either. The depth of any given snowfall is dependent on the water content and resulting weight. Some snowy areas can have consistent kinds of snow, but some regions can vary quite a lot. Living in the Rocky Mtn area the snow was usually light, dry and fluffy, but some times heavy and wet. Living along Great Lakes snow belts it’s often heavy and wet, but sometime light and dry. After a short time compacting snow has to be challenging to measure accurately. Perhaps, like with temp measurements, there’s a certain degree of ‘adjusting’, guessing or making stuff up?
Perhaps this shows than adjustments and homogenization is necessary? Than “raw unadjusted” won’t cut it?
Anyhow. Why not check the Si:
“As discussed in the main text, these results are consistent with the differences in seasonal trends between SNOTEL and USHCN”.
They find something is wrong because they compare the trends to USCHN! The network used by the temperature indexes. Where they don’t find the breakpoints etc. This can be read as a validation of USHCN.
Ouch.
The SNOTEL Temperature Dataset
Randall P. Julander
Jan Curtis
Austin Beard
Real-time weather data and its archived “climate” dataset must be used with an appreciation of its original purpose and inherent limitations. By knowing a stations history/metadata (e.g., location, equipment, maintenance schedule, sensor changes, etc.), operators and researchers can then expect the highest degree of data quality and use. The SNOTEL system was initially installed in the late 1970’s primarily as a water supply forecasting hydro-climatic data collection network. To that end, sites were located specifically to forecast water supply in the western United States and in most cases, replaced existing manual snow courses that historically had good correlations between snow water-equivalent (SWE) and streamflow. There were limitations in the amount and type of data that the early system could process and transmit. Thus early on, only the SWE, precipitation and current air temperature data were initially collected. The observation times of these early data occurred without any uniformity. This situation was adequate for SWE and precipitation measurements but gave a much less than desired result for temperature. The data poll for individual sites typically started at midnight, 6:00 am, noon, 6:00 pm and could last for up to five or more hours. Thus air temperature data would be reported at the time an individual station was contacted and might vary from as early as shortly after midnight to as late as 5:00 am. Polls were conducted four times daily but in that early period, a station might report between zero and four times per day and the time stamp on those data would be dependent on when the station reported. Temperature data were to be used in a relational context to calculate or predict snow melt rates, predict the onset of melt and generally be used in a water supply context. As the data collection, processing and transmitting electronic components were improved, additional sensors were added to the system.
In the mid 1980’s, with the advent of better electronics, daily minimum, maximum and average temperature data were added as standard data collection to SNOTEL. Unfortunately, temperature sensors were not uniformly installed across the entire network and in fact, a rather poor job was done particularly in the mounting of these sensors. The first temperature sensors were generally Climatronics or Climet thermistors and were in small ~3X3 inch aluminum box shaped shields or in aspirated housings. Most of these sensors were mounted on or very close to the brown SNOTEL shelters. Others were mounted to the antenna tower. Various mounting configurations were used, mostly dictated by the ease of installation and not to any technical standards. In some cases, they were mounted horizontally across the face of the shelter about six to 12 inches below the shelter roof and about 24 inches from the side and in all cardinal directions. In other cases, an “S” shaped aluminum tube was used to mount the sensor vertically to the side of the shelter which put the thermistor about six inches from the shelter side and up to 12 inches above the roof line. In yet another configuration, the sensor was mounted vertically above the shelter at a distance of between three and six feet. Clearly any mounting configuration that put the sensor near the brown radiating shelter would have a net warming bias on the early dataset. Some of these early sensors were mounted to remote antenna towers then moved to a shelter mount to be moved again to the antenna tower and finally moved to the meteorological tower. Much of the early data from these sites are compromised by inappropriate mounting, sensor moves and various changes in sensors and aspirators. Another more isolated and easily identifiable problem with this mounting scheme is that occasionally, a shelter and its temperature sensor was completely buried in snow or the roof snow load encased the sensor in which case, the air temperature sensor became a snow temperature sensor. Later, some sensors were moved to the antenna towers, which in most cases is a better location. However, some towers were directly adjacent or attached to the shelter and the temperature data at these sites could be compromised to some degree, other towers, remote from the shelter should have reasonably consistent data.
In Photo 1, the SNOTEL site at Beaver Divide, Utah is shown with a standard YSI thermistor, a three gill aluminum aspirator and the “S” mount attached to the side of the shelter and extending above the roof line. This configuration is perhaps the worst of all mounting scenarios.
Photo 1.
Photo 2 shows the Rock Creek SNOTEL site and the impact on snow the brown shelter can have via long wave radiation. Notice the snowpack has melted to a distance of about 2 feet from the edge of the shelter and that the temperature sensor mounted horizontally across the top of the shelter in a northeastern direction is in a direct line above that obvious impact.
fr
Photo 2.
In photo 3, (Buck Flat, Utah) there is a standard YSI thermistor mounted on a remote tower. This sensor has subsequently been moved from this location to the meteorological tower some 20 feet distant, but the overall impact of this move would be small. Data
from these sites would be the most consistent in relation to current standard location and mounting practices.
Photo 3.
Temperature sensors gradually migrated to YSI thermistors with a range of aspirators including the most commonly used, a silver three vent aluminum model. There were tower mounted sensors and aspirators that were white, wind directed models and a variety of other configurations. In the mid 1990’s, snow depth was added to many sites as a standard sensor and this began the installation of standardized meteorological data collection towers. At that time, there was still a mix of temperature sensors mounted on shelters and on antenna towers with a wide variety of aspirators. Since snow accumulation is variable across the West, tower height is also variable with most meteorological towers being in the 10, 20 and 30 foot ranges, depending on snow depth. Sensor mountings are therefore also variable in height being at about seven, 17 and 27 feet respectively. The majority of all sensors are at the 17 foot height. That stated, during a significant portion of the year, the ground surface level is constantly changing due to the accumulation and ablation of the snowpack and the respective height of any individual sensor may range from 17 feet to as low as five feet or less.
Photo 4.
In photo 4, (Cascade Mountain, Utah) the current standard temperature mounting configuration is shown with the sensor at 17 feet, mounted three to six feet from the tower and in a white, six gill aspirator.
In the mid 1990’s as the installation of meteorological towers progressed, another sensor change was made from the standard YSI sensor to the extended range YSI sensor in order to capture temperature readings to minus 40 degrees F. The coldest sites were the first to get the extended range sensor. Personnel in the Idaho region had the foresight to run both the standard YSI and the extended range YSI side by side for several years with identical mounting and aspirator configurations and noticed a plus one degree C difference in a very large portion of the observed temperature range of the extended sensor compared to the standard sensor.
Chart 1.
Chart 1 displays a comparison of side by side mounted standard and extended range YSI temperature sensors, the results which show a one to two degree C difference between the two sensors, with the current extended range sensor warmer than the standard sensor. This difference is most noted at the lower end of the temperature scale whereas at the upper end, the difference becomes much less. This is consistent at all of the Idaho sites tested.
Physical site changes will continue to pose some problems in overall data consistency. Vegetation grows, and at times, dies yielding an ever changing solar view and site characteristics. At some sites, this could be dramatic and at others, not likely to be much of a source of an inhomogeneous dataset. The removal of one or several trees at a site can impact the canopy, solar window and evapotranspiration characteristics which could change the temperature regime over some or all of a daytime pattern including nighttime pattern. The same could be true of growing vegetation altering the periods of full sun or shade at any given site.
Photo 5.
In photo 5, (Camp Jackson, Utah) one can clearly see the proximity of the vegetation to the tower and the temperature sensor as opposed to the vegetation in photo 4. Vegetation height and proximity is constantly changing at some sites, while at others it tends to be relatively stable. At this site, the dominant species is Aspen (Populous Tremuloides) and has the added feature of being deciduous which changes the overall solar input as leaves are generated in the spring and subsequently lost in the fall. At other sites such as Big Flat, Utah, (photo 6) which is in a mature Spruce and Fir (Picea and Abies) forest, the vegetation is and has been very stable. However, should the current beetle and bud worm infestation spread with subsequent high spruce mortality, currently experienced in southern Utah, the vegetation at this site could change dramatically and hence, the solar window.
Photo 6.
Another source of potential inconsistency in the dataset is that of data editing and quality control. For the most part and with the exception of Idaho, the SNOTEL temperature dataset has undergone little in the way of systematic data quality control and verification. The data editing that is done is primarily the removal of howlers and screamers and focused on the daily maximum, minimum and average. Some areas such as Idaho have done more and have concentrated on a serially complete dataset complete with estimated data points but the techniques of data estimation and editing have been far from standardized system wide at this point. The NRCS is attempting to use spatial climate station comparison methodology to resolve suspected data and fill-in missing data by assigning quality control flags that are quantified by confidence probabilities.
An un-quantified source of data error is in the electronics of the system and could be either random, systematic or a combination of both. There have been a series of joint transceiver/receiver/data processors in combination with the series of different temperature sensors. These include the Secode, MCC 550A, MCC 550B and the current version, the MCC 545 coupled to a Cambell CR10X data logger. Each of these systems measure voltages from each sensor which are then equationally converted to meaningful engineering units. Errors may occur due to: error in the thermistor, resistive errors such as line loss, ground potentials, exitation voltages, and errors associated with the data logger reading the voltages. These errors may occur from something as mundane as the type of cable used or in the connections from the cable to an interface. Drift in the voltage reading device could be the source of some un-quantified error.
In summary, the historic temperature data from the SNOTEL network have some significant systematic and random bias. This bias includes poor mounting techniques, sensor changes, location changes, aspirator changes, vegetation changes and electronic errors. Vegetation changes can be in the form of 20 to 30 years of growth or instantaneous change due to fires, disease, or insects. Documentation of these changes has been inconsistent system wide and currently resides mostly on paper records difficult to access and digitize. Much of the very early record, from mid 1980’s to the early 1990’s will be difficult to salvage and much of those data are compromised by poor sensor mounting techniques and are suspected to record much warmer temperatures than actually occurred. General use of these early records as comparisons to current conditions is discouraged. Some records, particularly those sensors that were mounted to remote towers early on will have reasonable quality, subject only to observation time, aspirator and sensor changes and possibly some vegetation change. Useful metadata on dates of sensor, aspirator and location changes would facilitate the potential construction of a reasonable, corrected dataset for these specific sensors.
Currently, NRCS is moving to standardized temperature sensor mountings that are on a meteorological tower in a specified data collection area at a height of 7, 17 or 27 feet and a distance of four to six feet horizontally from the tower.
Addendum: November 9, 2007
The following was identified by Kevin Berghoff of the National Weather Service, NOAA and illustrates a serious potential systematic error in the SNOTEL temperature data set.
This graph shows the minimum daily temperatures from the Thunder Basin SNOTEL site in Washington for the years 1987 through 2003. The issue is a flat line of temperature data at 32 degrees starting in late November and continuing into April. The “minimum minimums” look good with a large range of data from -27 degrees up to near freezing conditions, however, the “maximum minimums” hit a ceiling at 32 degrees.
This chart shows the maximum data for the same site and time period. Notice that there is no ceiling in these data, both the “maximum maximums” and the “minimum maximums” show a very jagged edge on top and bottom with a large amplitude and defined sinusoidal pattern through the year. Obviously the sensor itself is sensing exactly what it sees – in other words, the sensor is operating correctly and the ceiling in the minimum temperature trace must be an artifact of the environment of the sensor.
The sensor environment.
This sensor is mounted at about 10 feet, on the shelter to the lee side of prevailing winds and just below the top of the shelter itself. When there is significant snow accumulation, this sensor is about 12 inches from an open ice box. Snow on top of this shelter could be several feet to potentially 4+ feet deep which means that the air temperature is actually measuring temperature near the snow surface. In the daytime, solar radiation from the shelter would allow increased maximum temperatures but at night, absent solar heating and given some protection from warm wind impacts, this sensor would not register values much above the snow surface values. The minimum minimums are free to fall, but the maximum minimums are constrained to near 32 degrees.
This sensor was moved to a location on the antenna tower at a height of 19 feet. This means that it is about 7 feet higher than the shelter roof at this point and given a snow depth on top of the roof of say, 5 feet, may still be compromised to some degree.
An analysis of the data post 2003 by Melissa Webb of the Oregon NRCS Snow Survey Office shows that the current mounting configuration is much better than the old.
This chart clearly shows that the 32 degree ceiling of the previous charts no longer exists. Given the current sensor location this site may still have some ceiling, the question becomes what that ceiling may be and what time duration it may exist.
This sensor mounting technique was very common throughout the SNOTEL system for many years. This specific problem of a minimum temperature “ceiling” has just been identified. There is a significant potential for this systematic bias to be misinterpreted. In the observed data, there can be this minimum temperature ceiling and when removed, as it has been by moving these sensors to a meterological tower remote from the shelter, there will be a net increase in minimum temperatures system-wide.
R Julander
[Good details. Thank you. .mod]
Much grateful appreciation owed to Randy Julander for this informative contribution.
very heartening to see an actual real climate scientist bringing real hard observational data to the table. i get the impression most spend the majority of their time in an office.
thank you for taking the time to post the information.
i hope you continue to enjoy a great job in a beautiful area.
I’ve been swimming off the coast of Brighton year round. I use magic seaweed for weather, tide, wind, swell, etc. I’ve been suspicious of their water temperature. It’s way above what it should be , historically. I’ve taken to wearing a cheap thermometer tied to my swimming trunks. Reckoned it might be off a few degrees but, would give an indication. It seems magic seaweed may be off 4-6℃. It’s much colder than they say. I think they base their predictions on models of the harbour and channel bouys.
this is the document that is the basis of the paper. sorry the photos and figures did not post. the snotel system and snow course system were designed to predict current year water supply and do a great job at that. its all point data relational to streamflow. if it does not correlate, we get rid of the site. point data are not space data – so all data critics that say… i dont believe the depth data either. really? go 10 feet in any direction and the point data are different. in all parameters. the purpose of this system is to be relational to streamflow. if you use it for any other purpose… its reseaarcher beware. people have to be responsible to know what data are useful for what purpose and what limitations every data set has. its freshman level data analysis. we have been open, honest and forthcoming with the limitations of these data – not our fault if people misuse the data.
Then too, should we leave the earth and go to the satellites, the two party evil money cult in Washington DC owns them, controls them and the read outs from them, heck that would be an easy fudge for them, just preload the instruments sent up, who could know.
It is not about the climate, it is about wealth redistribution.
Nothing unusual, unfortunately. Even when professional politicians are not involved, there’s plenty of “scientists” with agendas. Take a look at what happened to Halton Arp (astronomer). The “big bang” and “red shift” cosmologists are fighting tooth and nail to protect their dark matter, dark energy, dark flow, various new particles, etc., from the plasma physicists’ theory.
As Rooter points out, SNOTEL isn’t used in calculating U.S. temperatures or global temperatures by NOAA, NASA, or Hadley. Those stations are not included in GHCN-Monthly or USHCN, though they are included in GHCN-Daily.
As far as I know, Berkeley Earth is the only group that uses SNOTEL stations in their analysis. Looking up SNOTEL stations on the Berkeley website is interesting; all the ones I’ve found so far have pretty massive downward adjustments in recent years from the homogenization algorithm.
http://www.nrcs.usda.gov/wps/portal/nrcs/detail/ut/snow/?cid=nrcs141p2_034246
for those still interested – we have finally been able to restore the link to our bias in snotel and snow course data page. will have all the photos, graphs, etc. will make a lot more sense… and show that we have been diligent in trying to make bias in our data sets known to all… rj.