This question is important in order to assess the robustness of the trends and variability in the surface temperature records. A new paper was recently accepted by the AMS Journal of Atmospheric and Ocean Technology that addresses this issue. It is titled: “Comparison of Co-Located Automated (NCECONet) and Manual (COOP) Climate Observations in North Carolina” by Christopher Holder, Ryan Boyles, Ameenulla Syed, Dev Niyogi, and Sethu Raman. A draft copy is available at the link above.
Even though the study is focused over North Carolina, we believe the findings can be considered generic enough and provide a feel for the uncertainty in the datasets. The abstract states,
“The National Weather Service’s cooperative observer network (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where thirteen of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product-moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm per day. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root mean square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data (such as from NCECONet) can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.”
Some interesting findings from our study suggest:
It is not possible to state which is correct but does provide insights into the uncertainty and differences one could expect just from differences in the measurements from collocated stations. The errors or uncertainty that will be caused by station location is another issue we could not address but is an important issue for data representativeness.
This brings back the old adage “Everyone but the observer believes in the observations; nobody except the modeler believes in his/her model results!” , which for the climate scenarios seem to be flipped over and people often may start looking at model results as “reality”.
Hey folks, i see you’re interested in climate change- check out “Mr. Luna’s Bright Idea” to help stop global warming and cut electric costs too by going to http://thebrightidea.blogspot.com. Thanks so much for your help, ken (Mr. Luna)
Comment by Ken — December 31, 2005 @ 6:13 pm
Good idea to save energy, but if you read this weblog you can see that the impact on the natural cycles of warming and cooling that we call climate change will be nil.
Comment by Paul Biggs — January 1, 2006 @ 9:34 am
Concerning uncertainty in homogeneity adjustments, it seems to me that the adjustments using a group of nearby stations are dependent upon the order in which a researcher does them, much like vector addition as opposed to scalar addition. The order of the adjustments is subjective and different researchers could obtain different adjusted time series starting with the same raw data. I would suggest that no attempt be made to apply these homogeneity adjustments; rather, each time an adjustment is normally required, a new time series should be started. This approach would leave you with many thousands of short time series, but it would still allow long-term regional and approximate global time series to be constructed. It would also be easier to place error bars on these reconstructions.
If one follows present day procedures, the magnitude and timing of the homogeneity adjustments should not be hidden from those interested in the time series.
The above approach still only addresses known and discovered discontinuities above a certain threshold and does not address subtle long-term changes in micro-climate.
Comment by Douglas Hoyt — January 2, 2006 @ 8:04 am
Douglas, I agree with your comment that there is a need for raw as well as series of corrected datasets available for climate studies as is for instance found at the the Historical Climate Network site. I further agree that we are only correcting for the errors or factors we know and there are still various unknowns the most significant being the microscale and sesonally changing exposure which is difficult to document or quantify and yet may be a first order effect on the quality of the data we get from the sites. The important take home message still remains that there is significant uncertainty in our surface datasets for assessing climate trends and variability and temperature, unfortunately - and I state unfortunately because it is being used by the policymakers for assessing climate change - may be most vulnerable to such uncertainties. Thanks.
Comment by Dev — January 2, 2006 @ 10:55 am
It strikes me that it would be more informative to put out a chain of stations located say at 0m, 1m, 10m, 100m, etc from each other in a flat area (Kansas?) You could get at some the issues Douglas Hoyt raised that way.
Comment by Eli Rabett — January 2, 2006 @ 4:33 pm
Your conclusion:
“For our study, the manual stations recorded…more rainfall than the automated sensors”
does not support the statement that,
“It is not possible to state which is correct…”
Unless you have serious doubts about the manufacturing and calibration of manual gauges. The automated guages routinely under-collect intense rainfall, as I understand it. I will grant, however, that human reading errors of manual gauges do produce random errors on the order of hundredths of inches per event.
Comment by Kenneth Blumenfeld — January 4, 2006 @ 6:08 pm