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Climate Data for Citizen Scientists

Posted on 10 February 2011 by D Kelly O

Guest post by Kelly O'Day from Climate Charts & Graphs

I find that charting climate trend data helps me to understand the interplay of climate factors more effectively than just reading an article or post . As an example, using online monthly climate agency data I made this chart to help me understand how the long term temperature trends are affected by major volcanic eruptions and  El Nino - La Nina events. Click image to  enlarge

You can visit this link to learn more about this chart. 

Building this chart helped me to learn about volcanoes, their release of aerosols to the stratosphere with resulting reduction of sunlight transmission through the atmosphere. I could clearly see the downward impact major volcanoes had on the GISS temperature anomalies as well as the impacts  El Nino (upward) - La Nina (downward) have on the GISS temperature anomalies.

Climate Agency Data

Climate agencies provide a great service by putting their data files online, however, the organization and format of these files often makes it challenging for citizen scientists to compare series. To see the problem, look at the different formats for the 5 major global land & ocean temperature anomaly series:  GISS, RSS, Hadley, NOAA, UAH.  It takes considerable data manipulation to consolidate these 5 anomaly series into a single file.

A citizen scientists who wants to compare global SSTA and Nino34 trends has to take several steps:

  1. Find online data files – even with Google this can take time. Wood For Trees and Climate Explorer  are two great resources to start your search
  2. Download files
  3. Merge 2 files to get data  into a usable format – source files have different formats
  4. Perform analysis
  5. Reach conclusions

Steps 1-3 can be very time consuming, so many users just don’t bother checking out their ideas, rather, they may rely on climate blog comments or guesswork.

There should be a better way!

Climate Time Series File  (CTS.csv) Online

I have consolidated 18 monthly climate time series into a single online Climate Time Series file (CTS.csv). Here’s a snap shot of the first 6 rows of the CTS.csv file. The data extends from 1880 until the most recent month. Click image to enlarge


Several series do not start until 1950 or 1979. These series have NA (not available) entries until the series start.

This page lists the source agency and data links for each of the climate data series.

How can CTS.csv Help Do-It-Yourself Citizen Climate Scientists?

My goal is to make it as easy as possible for citizen climate scientists to:

  • Check temperature anomalies trends by series (GISS, HAD, NOAA, RSS, UAH)
  • Assess climate oscillation (AMO, AO, MEI, Nino34,  PDO)  trends
  • Evaluate CO2 versus temperature anomaly relationships
  • Evaluate relationship between Sunspot numbers and temperature anomaly trends
  • Compare atmospheric transmission, SATO index  and volcanic activity
  • Assess impact of volcanoes on temperature anomaly trends
  • Compare MEI versus Nino 34 El Nino-Southern Oscillation indicators
  • Assess lower stratospheric trends using RSS’s TLS series

By having these climate time series in a single csv file, R and/ or Excel users can analyze up-to-date climate data in a convenient form. I update this file monthly as the climate agencies release their latest data.

Data & RClimate Scripts Are All Open Book

I have a family of R scripts to retrieve, analyze, chart climate data; I call them RClimate scripts. All of the RClimate scripts that I use to produce the CTS.csv are available online at this link. Climate agency data links are available here and they are included in the RClimate.txt function for each series.

I plan to add more series to the file. Please feel free to make suggestions or comments. Check for updates on both my RClimate scripts and CTS.csv at Climate Charts & Graphs.

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Comments 1 to 17:

  1. Great resource, I highly recommend Kelly's site.
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  2. There's a peer-reviewed paper that matches the temperature curve by combining volcanoes, El Nino, Sunspots, and anthropogenic emissions: Lean & Rind (2009), Geophysical Research Letters 36, L15708. See
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  3. Thanks, Kelly! I visit your site daily during the Arctic melt season; weekly at this time of year.

    Highly recommended.

    The Yooper
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  4. You should also include TSI, Total Solar Irradiance, in your time series suite. For recent values, PMOD observations are best. If you want to go farther back than 1978, the reconstruction of Lean et. al. can be found on Climate Explorer.

    The biggest problem with the Lean reconstruction is that it doesn't model the very deep decline seen at the end of cycle 23 very well. Which means that for recent values, PMOD observations are still better than any reconstruction. It is possible to create a fairly seamless composite dataset (PMOD 1978-present, Lean further back) by regressing Lean against PMOD during the overlap period 1978-2000, i.e., before cycle 23 reached its peak. You can then modify Lean's reconstruction by the regression slope and intercept (r>.96) and get a fairly unified monthly dataset going back to 1882.
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  5. I should also mention: I've found that Nino4 has slightly higher correlations with global temps than Nino34 (as you're using) when best lags are used ... you might want to try both.
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  6. Dealing with all sorts of different file formats is a problem for professional scientists, not just citizen scientists! There is nothing like providing code to read the files to help make analysis easier, so thanks for making yours available.
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  7. Great work. This will making climate communication easier.

    On comment on normalisation:
    From past experience with the temperaturedatasets: As far as I know do they use different normal period as reference. I cannot see any part of your text above commenting on this. My question is: Do you use the agencies normal period, og do you adjust the series to a common period?
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  8. A series of arctic ice coverage (and ice volume: would have been great.
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  9. hohygen

    "Do you use the agencies normal period, or do you adjust the series to a common period?"

    I use the agencies normal baseline period. I document this here.
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  10. Kelly, thank you so much. There are many other data files that could be added, of course. My nomination would be sea-level data from U. Colorado.
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  11. There's been so much so-called skeptical diversion and misinformation on here recently that it's a pleasure to see a post and an informative link from someone (Spencer Weart, above) who actually knows what they are talking about !
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  12. Jeff T

    Here is a link to a post on U of Colorado sea level trend ( link). I update the chart monthly. The post includes a link to the RClimate script for retrieving and plotting the U of Colorado NetCDF file.

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  13. Thank you for this compilation. If I use this how should I refer to this? Anyway I'm not planning to publish anything based on this in a serious scientific journal so the WUWT style of reference (i.e. none credible) might be enough?
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  14. How about adding predicted sunspot numbers for the next cycle?

    Data here:

    Given that the next maximum is currently looking to be very weak indeed (ssn of 58 vs 118 in 2000) this is very relevent to climate over the next 5-10 years.
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  15. Thought that the citizen-science thread would be a good place to post this.

    Not enough material to justify a full guest post, so I'm just leaving a little note here.

    I've added some new features to my "Quick and Dirty" global temperature anomaly app. It now performs a simple gridded average (geospatial weighting) that produces results that are remarkably similar to NASA's "Meteorological Stations" temperature index (NASA results here: -- scroll down to the Global Temperature (meteorological stations) plot).

    The app now allows you to generate urban vs. rural results side-by-side (it reads the GHCN v2 metadata file to extract rural and urban stations separately). You can now debunk the "Urban Heat Island" talking-point in real-time.

    The all also allows you to generate ensembles of results, each computed from a different set of randomly-chosen temperature stations. You can throw out 90 percent of the temperature stations and still get results that are quite consistent with the results you get when you process all of the data.

    Here's a plot of the program's output vs. NASA's equivalent results (note: I used GHCN "raw" monthly-mean data):

    For those who are interested, the latest source-code can be found here:

    (Run the app without any arguments to get a semi-helpful "usage" message).

    Now, back onto the soapbox for a bit:

    It was surprisingly easy to implement the gridding/geospatial-weighting routine (much easier that I originally thought that it would be). The result posted above is what popped out on my first "full-up" run -- i.e. not tweaking or data-fiddling required. Only a few modest changes to my app would be required to perform all the temperature-data analysis/processing that Anthony Watts has been promising (but has failed to deliver) for years!

    Getting a crude version of this app up and running took something like a weekend in my spare time -- the bulk of the time spent was cleaning it up and getting it into a form where others might find it useful.

    The one major thing that I learned from this project is how breathtakingly inane and brain-dead the denialists' campaign against the surface temperature record has been. A major part of the "climategate" campaign against the CRU was based on the claim that nobody could verify the CRU's global temperature results because the CRU supposedly was "hiding" a small fraction of the data used in their global temperature calculations.

    That, of course, is completely absurd -- what my exercise demonstrated is that someone with basic programming skills and the ability to read documentation should have no trouble validating the CRU's results with just a weekend's worth of effort. There is enough public temperature data and plenty of free software tools to enable any technically competent person to debunk the entire basis of the so-called "climategate" scandal with just a couple of days worth of effort.

    The "climategate" campaign truly has taken "stupid" to unprecedented heights.

    I'm keeping my app handy so that the next time some loudmouth starts spouting off to me about NASA's/CRU's "cooked" data, I'll be able to whip out my laptop and shut him up. I've found that having someone see me generate results "on the fly" from my own app makes a bigger impression than handing him/her a link to a NASA web-page.

    Hopefully, others can find this app useful in the same way. I've given it a fair bit of testing, so I don't think that there are any "showstopper" bugs (but no guarantees!).
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    Moderator Response: [DB] Converted URL's to links.
  16. Caerbannog - I liken "skeptics" to dogs that chase after cars. They get all excited and noisy, but when the car stops they have no idea what to do. Same with skeptics and the raw data.
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  17. Nice work Caerbannog!

    I had one interesting idea on a variant of the baselining technique: Calculate a baseline on the whole period rather than 1950-1980 (which will therefore be poor), then calculate the geographically averaged monthly anomaly. Then go back and recalculate the baseline for each station subtracting the monthly average to get a better set of baselines. Calculate the monthly anomaly again. Calculate baselines again. Iterate until stable. The result should be a poor-man's approximation to Nick Stokes' TempLS algorithm.
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