I decided to do myself something that so far NOAA has refused to do: give a CONUS average temperature for the United States from the new ‘state of the art’ United States Climate Reference Network (USCRN). After spending millions of dollars to put in this new network from 2002 to 2008, they are still giving us data from the old one when they report a U.S. national average temperature. As readers may recall, I have demonstrated that old COOP/USHCN network used to monitor U.S. climate is a mishmash of urban, semi-urban, rural, airport and non-airport stations, some of which are sited precariously in observers backyards, parking lots, near air conditioner vents, airport tarmac, and in urban heat islands. This is backed up by the 2011 GAO report spurred by my work.
Here is today’s press release from NOAA, “State of the Climate” for July 2012 where they say:
The average temperature for the contiguous U.S. during July was 77.6°F, 3.3°F above the 20th century average, marking the hottest July and the hottest month on record for the nation. The previous warmest July for the nation was July 1936 when the average U.S. temperature was 77.4°F. The warm July temperatures contributed to a record-warm first seven months of the year and the warmest 12-month period the nation has experienced since recordkeeping began in 1895.
OK, that average temperature for the contiguous U.S. during July is easy to replicate and calculate using NOAA’s USCRN network of stations, shown below:
The U.S. Climate Reference Network (USCRN) consists of 114 stations developed, deployed, managed, and maintained by the National Oceanic and Atmospheric Administration (NOAA) in the continental United States for the express purpose of detecting the national signal of climate change. The vision of the USCRN program is to maintain a sustainable high-quality climate observation network that 50 years from now can with the highest degree of confidence answer the question: How has the climate of the nation changed over the past 50 years? These stations were designed with climate science in mind. Three independent measurements of temperature and precipitation are made at each station, insuring continuity of record and maintenance of well-calibrated and highly accurate observations. The stations are placed in pristine environments expected to be free of development for many decades. Stations are monitored and maintained to high standards, and are calibrated on an annual basis. In addition to temperature and precipitation, these stations also measure solar radiation, surface skin temperature, and surface winds, and are being expanded to include triplicate measurements of soil moisture and soil temperature at five depths, as well as atmospheric relative humidity. Experimental stations have been located in Alaska since 2002 and Hawaii since 2005, providing network experience in polar and tropical regions. Deployment of a complete 29 station USCRN network into Alaska began in 2009. This project is managed by NOAA’s National Climatic Data Center and operated in partnership with NOAA’s Atmospheric Turbulence and Diffusion Division.
So clearly, USCRN is an official effort, sanctioned, endorsed, and accepted by NOAA, and is of the highest quality possible. Here is what a typical USCRN station looks like:
A few other points about the USCRN:
- Temperature is measured with triple redundant air aspirated sensors (Platinum Resistance Thermometers) and averaged between all three sensors. The air aspirated shield exposure system is the best available.
- Temperature is measured continuously and logged every 5 minutes, ensuring a true capture of Tmax/Tmin
- All stations were sited per Leroy 1999 siting specs, and are Class 1 or Class 2 stations by that siting standard. (see section 2.2.1 here of the USCRN handbook PDF)
- The data goes through quality control, to ensure an errant sensor hasn’t biased the values, but is otherwise unchanged.
- No stations are near any cities, nor have local biases of any kind that I have observed in any of my visits to them.
- Unlike the COOP/USHCN network where they fought me tooth and nail, NOAA provided station photographs up front to prove the “pristine” nature of the siting environment.
- All data is transmitted digitally via satellite uplink direct from the station.
So this means that:
- There are no observer or transcription errors to correct.
- There is no time of observation bias, nor need for correction of it.
- There is no broad scale missing data, requiring filling in data from potentially bad surrounding stations. (FILNET)
- There are no needs for bias adjustments for equipment types since all equipment is identical.
- There are no need for urbanization adjustments, since all stations are rural and well sited.
- There are no regular sensor errors due to air aspiration and triple redundant lab grade sensors. Any errors detected in one sensor are identified and managed by two others, ensuring quality data.
- Due to the near perfect geospatial distribution of stations in the USA, there isn’t a need for gridding to get a national average temperature.
Knowing this, I wondered why NOAA has never offered a CONUS monthly temperature from this new network. So, I decided that I’d calculate one myself.
The procedure for a CONUS monthly average temperature from USCRN:
- Download each station data set from here: USCRN Quality Controlled Datasets.
- Exclude stations that are part of the USHCN-M (modernized USHCN) or USRCRN-Lite stations which are not part of the 114 station USCRN master set.
- Exclude stations that are not part of the CONUS (HI and AK)
- Load all July USCRN 114 station data into an Excel Spreadsheet, available here: CRN_CONUS_stations_July2012_V1.2
- Note stations that have missing monthly totals data. Three in July 2012, Elgin, AZ, (4 missing days) Avondale, PA,(5 missing days) McClellanville, SC, (7 missing days) and set their data aside to be dealt with separately.
- Do sums and calculate CONUS area averages from the Tmax, Tmin, Tavg and Tmean data provided for each station.
- Do a separate calculation to see how much difference the stations with missing/partial data make for the entire CONUS.
Here are the results:
USA Monthly Mean for July 2012: 75.72°F
(111 stations)
USA Monthly Average for July 2012: 75.51°F
(111 stations)
USA Monthly Mean for July 2012: 75.74°F
(114 stations, 3 w/ partial missing data, difference 0.02)
USA Monthly Average for July 2012: 75.55°F
(114 stations, 3 w/ partial missing data, difference 0.04)
============================
Comparison to NOAA’s announcement today:
Using the old network, NOAA says the USA Average Temperature for July 2012 is: 77.6°F
Using the NOAA USCRN data, the USA Average Temperature for July 2012 is: 75.5°F
The difference between the old problematic network and new USCRN is 2.1°F cooler.
This puts July 2012, according to the best official climate monitoring network in the USA at 1.9°F below the 77.4°F July 1936 USA average temperature in the NOAA press release today, not a record by any measure. Dr. Roy Spencer suggested earlier today that he didn’t think so either, saying:
So, all things considered (including unresolved issues about urban heat island effects and other large corrections made to the USHCN data), I would say July was unusually warm. But the long-term integrity of the USHCN dataset depends upon so many uncertain factors, I would say it’s a stretch to to call July 2012 a “record”.
This result also strongly suggests, that a well sited network of stations, as the USCRN is designed from inception to be, is totally free of the errors, biases, adjustments, siting issues, equipment issues, and UHI effects that plague the older COOP USHCN network that is a mishmash of problems that the new USCRN was designed to solve.
It suggests Watts et al 2012 is on the right track when it comes to pointing out the temperature measurement differences between stations with and without such problems. I don’t suggest that my method is a perfect comparison to the older COOP/USHCN network, but the fact that my numbers come close, within the bounds of the positive temperature bias errors noted in Leroy 1999, and that the more “pristine” USCRN network is cooler for absolute monthly temperatures (as would be expected) suggests my numbers aren’t an unreasonable comparison.
NOAA never mentions this new pristine USCRN network in any press releases on climate records or trends, nor do they calculate and display a CONUS value for it. Now we know why. The new “pristine” data it produces is just way too cool for them.
Look for a regular monthly feature using the USCRN data at WUWT. Perhaps NOAA will then be motivated to produce their own monthly CONUS Tavg values from this new network. They’ve had four years to do so since it was completed.
UPDATE: Some people questioned what is the difference between the mean and average temperature values. In the monthly data files from USCRN, there are these two values:
T_MONTHLY_MEAN
T_MONTHLY_AVG
http://www.ncdc.noaa.gov/crn/qcdatasets.html
The mean is the monthly (max+min)/2, and the average is the average of all the daily averages.
UPDATE2: I’ve just sent this letter to NCDC – to ncdc.info@ncdc.noaa.gov
Hello,
I apologize for not providing a proper name in the salutation, but none was given on the contact section of the referring web page.
I am attempting to replicate the CONUS temperature average of 77.6 degrees Fahrenheit for July 2012, listed in the August 8th 2012, State of the Climate Report here: http://www.ncdc.noaa.gov/sotc/
Pursuant to that, would you please provide the following:
1. The data source of the surface temperature record used.
2. The list of stations used from that surface temperature record, including any exclusions and reasons for exclusions.
3. The method used to determine the CONUS average temperature, such as simple area average, gridded average, altitude corrections, bias corrections, etc. Essentially what I’m requesting is the method that can be used to replicate the resultant 77.6F CONUS average value.
4. A flowchart of the procedures in step 3 if available.
5. Any other information you deem relevant to the replication process.
Thank you sincerely for your consideration.
Best Regards,
Anthony Watts
===================================================
Below is the response I got to the email address provided in the SOTC release, some email addresses redacted to prevent spamming.
===================================================
—–Original Message—–
From: mailer-daemon@xxxx.xxxx.xxx
Date: Thursday, August 09, 2012 3:22 PM
To: awatts@xxxxxxx.xxx
Subject: Undeliverable: request for methods used in SOTC press release
Your message did not reach some or all of the intended recipients.
Sent: Thu, 9 Aug 2012 15:22:43 -0700
Subject: request for methods used in SOTC press release
The following recipient(s) could not be reached:
ncdc.info@ncdc.noaa.gov
Error Type: SMTP
Error Description: No mail servers appear to exists for the recipients address.
Additional information: Please check that you have not misspelled the recipients email address.
hMailServer
===============================
UPDATE3: 8/10/2012. This may put the issue to rest about straight averaging -vs- some corrected method. From http://www.ncdc.noaa.gov/temp-and-precip/us-climate-divisions.php
It seems they are using TCDD (simple average) still. I’ve sent an email to verify…hopefully they get it.
Traditional Climate Divisional Database
Traditionally, climate division values have been computed using the monthly values for all of the Cooperative Observer Network (COOP) stations in each division are averaged to compute divisional monthly temperature and precipitation averages/totals. This is valid for values computed from 1931 to the present. For the 1895-1930 period, statewide values were computed directly from stations within each state. Divisional values for this early period were computed using a regression technique against the statewide values (Guttman and Quayle, 1996). These values make up the traditional climate division database (TCDD).
Gridded Divisional Database
The GHCN-D 5km gridded divisional dataset (GrDD) is based on a similar station inventory as the TCDD however, new methodologies are used to compute temperature, precipitation, and drought for United States climate divisions. These new methodologies include the transition to a grid-based calculation, the inclusion of many more stations from the pre-1930s, and the use of NCDC’s modern array of quality control algorithms. These are expected to improve the data coverage and the quality of the dataset, while maintaining the current product stream.
The GrDD is designed to address the following general issues inherent in the TCDD:
- For the TCDD, each divisional value from 1931-present is simply the arithmetic average of the station data within it, a computational practice that results in a bias when a division is spatially undersampled in a month (e.g., because some stations did not report) or is climatologically inhomogeneous in general (e.g., due to large variations in topography).
- For the TCDD, all divisional values before 1931 stem from state averages published by the U.S. Department of Agriculture (USDA) rather than from actual station observations, producing an artificial discontinuity in both the mean and variance for 1895-1930 (Guttman and Quayle, 1996).
- In the TCDD, many divisions experienced a systematic change in average station location and elevation during the 20th Century, resulting in spurious historical trends in some regions (Keim et al., 2003; Keim et al., 2005; Allard et al., 2009).
- Finally, none of the TCDD’s station-based temperature records contain adjustments for historical changes in observation time, station location, or temperature instrumentation, inhomogeneities which further bias temporal trends (Peterson et al., 1998).
The GrDD’s initial (and more straightforward) improvement is to the underlying network, which now includes additional station records and contemporary bias adjustments (i.e., those used in the U.S. Historical Climatology Network version 2; Menne et al., 2009).
The second (and far more extensive) improvement is to the computational methodology, which now addresses topographic and network variability via climatologically aided interpolation (Willmott and Robeson, 1995). The outcome of these improvements is a new divisional dataset that maintains the strengths of its predecessor while providing more robust estimates of areal averages and long-term trends.
The NCDC’s Climate Monitoring Branch plans to transition from the TCDD to the more modern GrDD by 2013. While this transition will not disrupt the current product stream, some variances in temperature and precipitation values may be observed throughout the data record. For example, in general, climate divisions with extensive topography above the average station elevation will be reflected as cooler climatology. A preliminary assessment of the major imapacts of this transition can be found in Fenimore, et. al, 2011.
Several days late to this thread, so perhaps no one will see this comment, but I have a question. The comparison between the CRN and USHCN networks seems to be to determine the accuracy of the average reported by NOAA, as compared to the “real” average temperature of the CONUS. The point being that the newer, better sited CRN sites better reflect “true” temperatures needing no adjustments.
Nick Stokes then complained that the CRN sites were at a higher average altitude than the USHCN sites, making the comparison invalid without adjustment for altitude. But, again, it seems the whole point of the comparison is to determine the accuracy of the USHCN sites against real temps.
As a later commenter pointed out, the U.S. Geological Survey estimates the mean U.S. elevation at 2500 feet, while Nick Stokes claims the average elevation of CRN sites is 2263 feet, and the USHCN sites’ average elevation is 1,681 feet.
Wouldn’t that mean that, based on Stokes’ analysis, the CRN sites overstate the real average temperature since they are on average 237 feet below the real mean elevation of the U.S.? And wouldn’t that also mean that the USHCN sites overstate temperature even more since they are on average 819 feet below the mean elevation of the U.S.?
Not only are the CRN stations better cited to avoid urbanization and other influences, but it seems to me they are much better sited as far as average elevation as well. I think Nick Stokes’ argument makes this article an even stronger critique of the NOAA announcement, whatever the full method of their calculation.
RE: your Update #3. From NOAA: The NCDC’s Climate Monitoring Branch plans to transition from the TCDD to the more modern GrDD by 2013. While this transition will not disrupt the current product stream, some variances in temperature and precipitation values may be observed throughout the data record.
For what time period will results of TCDD and GrDD overlap?
How long will TCDD remain available for study.
There was a subtle change in focus in the course of the main post and updates.
Anthony Watts rightly pointed out that USCRN data is seldom used by NOAA or other researchers when making pronouncements about the “warmest ever…” such records. Despite having a very short history, USCRN should be used to confirm or contrast statements and conclusions based upon the dirtier, more problematic, more adjusted USHCN.
The subtle change above was in the discussion of the transition from TCDD to GrDD. These are global databases. Since USCRN is a high-quality, but non-global network and therefore, even if it is included in GrDD, it will be swamped by the more poorly sited stations. The Fenimore et al 2011 paper linked above makes no mention of USCRN or “Reference” Network. It does say this:
So I have to wonder if transition to a new database with new adjustments to old untrustworthy, UHI contaminated stations, is really an exercise in rearranging the deck chairs on the Titanic. When analyzing GrDD, check for holes below the waterline.
This from Stu Ostro’s Twitter stream: https://nes.ncdc.noaa.gov/pls/prod/f?p=100:1:4202314326918058::::P1_ARTICLE_SEARCH:360
Stu Ostro tweeted: https://nes.ncdc.noaa.gov/pls/prod/f?p=100:1:4202314326918058::::P1_ARTICLE_SEARCH:360
Hi all. This is my first post but I’ve been reading for a while. I hope I’m not too late to the party…
Wayne’s comment about hourly temperature readings in the 30s vs daily max piqued my interest. It seems it was ultimately determined to be a non-issue but if so I don’t understand why. The USCRN data descriptions explain the sampling rate and how the data is averaged and, to some extent “passed up.”
Excerpts of relevant descriptions are below. To summarize, as I undestand it, max temp is recorded every 5 minutes from the average of samplings taken every 10 seconds. If so, then any spike that lasted at least 10 seconds would be captured and result in a higher maximum than if the sampling rate were 5 minutes or anything less frequent.
Here are the descriptions.
– – – – –
NOTE: Each of the descriptions below also include the following note on the site that I eleiminated here for brevity: “Note: USCRN/USRCRN stations have multiple co-located temperature sensors that record independent measurements. This value is a single temperature number that is calculated from the multiple independent measurements.”
From their hourly data set:
T_CALC: Average temperature, in degrees C, during the last 5 minutes of the hour.
T_HR_AVG: Average temperature, in degrees C, during the entire hour.
T_MAX: Maximum temperature, in degrees C, during the hour. [Note] The independent measurements are the maximum for each sensor of 5-minute average temperatures measured every 10 seconds during the hour.
From their daily data set:
T_DAILY_MAX: Maximum temperature, in degrees C, during the day. [Note] The independent measurements are the maximum for each sensor of 5-minute average temperatures measured every 10 seconds during the day.
From their monthly set:
T_MONTHLY_MAX: The maximum air temperature, in degrees C, for the month. This maximum is calculated as the average of all available day-maximums. To be valid there must be less than 4 consecutive day maximums missing, and no more than 5 total day maximums missing.
– – – –
It appears that T_CALC has the data needed to compute a T_DAILY_HR_MAX based on an hourly reading (specifically, the average of 10 second readings for the last 5 min of each hour). And from that to compute a T_MONTHLY_HR_MAX. If of any value, When time allows I want to do this and see how that compares to the monthly average they are now computing. If that would be a waste of time, I won’t bother. Can anyoen let me know?
Ty, I think any information in this area would be welcomed, it sounds like you might even have access to the 5-minute data and it would be great to see what that was reporting on a record afternoon, such as Aug.3 in Oklahoma, looked like in the late afternoon at either KOKC (Will Roger Int’l) or KPWA (Wiley Post Airport) stations. Glad to see someone see’s what I seem to question. It’s not that new sensors are incorrect, it is just that older Stevenson type cages may have been much slower to change and then not to register fast transient ups and downs in the temperature on hot afternoons. Those transients seem to occur on very mildly gusty days especially with cumulus clouds present. You can see the temperature vacillate when either between or conversely underneath clouds marking the thermal bases. Get’s a bit into vertical ‘potential temperature’ changes in the thermals. Oklahoma’s a great place for this if you’re into the sport of sailplanes. (I haven’t since the 80’s and really miss it, your engine *is* these temperature variances and they are always in flux, minute by minute)