You are going to Tombouctou next August, and would like to know whether to bring an umbrella

• On the page "monthly station data", select "precipitation" under "GHCN (all)" and type "Tomb" (you are not sure about the spelling of the rest of the name) into the field after "stations with a name containing". Press [Get stations].
• This gives a list of eight stations, the third one is TOMBOUCTOU MALI (MALI), coordinates: 16.80°N, 3.00°W. Follow the link "get data".
• The following page gives a time series of precipitation at Tombouctou from 1897-2012 with a gap at 1915-1921 and incomplete observations the last few years.
  TOMBOUCTOU MALI (MALI), coordinates: 16.80N, -3.00E, 263m, WMO station code: 61223 TOMBOUCTOU , prcp from v2.prcp [mm/month]
  
• The second plot is more interesting, it shows that the annual rainfall is concentrated in August, with an average rainfall of 77.4mm and 95% interval from 15mm to 175mm, quite variable (click on "raw data" to read off the numbers). The is almost the same as August in De Bilt, both in the mean (83mm) as in variability (15mm to 187mm). Do take an umbrella.
  Two annual cycles computed with all data available, not 30 years
• Clicking on "View per month" (right-hand side) reveals that the August rainfall has decadal variability: the 1970s and 1980s were drier than the average (associated with the Atlantic Multidecadal Oscillation and maybe other causes). However, in the 1990s rainfall came back to normal. No clear case to leave the umbrella at home. Go back.
• If you want to investigate the connection with El Niño, follow the link "Correlate with other time series" in the right-hand menu. Select "NINO3" under "system-defined timeseries", and under "common options" select the starting month [Jul] and press [Correlate]. The resulting scatter plot shows that the very wettest years have all been La Niña years, but the two driest years have also been during a La Niña. The tercile plot also shows that there is not much you can say about the chances of a wet/normal/dry July at Tombouctou if you can predict the NINO3 index.
• The situation is slightly better if you look at the whole rain season, July-September. Go back to the "common options" and select [averaging] over [3] months and press [Correlate]. With a strong El Niño at this time of the year (NINO3>+1.25) there were five years with drier than average conditions, and one with wetter than average (1905), so it seems likely it will be drier next time. The tercile plot at the bottom gives a forecast of 50:30:20 for a NINO3 index of 1.4 for drier, normal or wetter conditions. During a La Niña the spread is greater, with a 20:40:40 forecast for NINO3 at -1.
• Finally, just to make sure, go back to "common options" and change "lag" to [0:24]. The lag correlations show the NINO3-autocorrelation function in reverse; nothing interesting here.
• Are there other oceans where the surface temperature was a predictor for Timbouctou rainfall? Go back to the time series and now follow the link "Correlate with a field", "only observations". Select the HadCRUT4 temperature dataset, and again select starting month [Jul], [averaging] over [3] months (to cut down the noise), it is wise to also choose lag: [3] months to avoid cause/effect ambiguities. Press [correlate] and wait a while. Besides the ENSO-region, SST in the Mediterranean and North Atlantic stand out. However, these disappear when one takes year-on-year differences ("difference"); so the interannual variability seems (tenuously) connected to ENSO only. The time series are probably not long enough, and the data not homogeneous enough, to say much about the decadal variability. Please send me the article if you feel you can do something here.

What have been the patterns associated with global warming over the past 100 years?

• Go to the page with "monthly climate indices" in the right-hand menu.
• Follow one of the links "global average temperature" (they are all very similar).
• Follow the link on "correlate with a field" "only observations" in the right-hand menu.
• Select under "observations" the GISS "1200 km" temperature anomalies, and choose to plot the "regression". Under "options" select starting month [Jan], [averaging] over [12] months for yearly averages.
• The automatic colour-chosing scheme is suboptimal due to the huge regression coefficient at Svelbard (Spitsbergen), go back and set the contours from -2.5 to 2.5.
• Now one can see that the world has warmed relatively uniformly (regression=1). In the Arctic the regression is much larger than one (vertical mixing and snow-albedo feedback); and the Pacific decadal pattern and the North Atlantic have been cooling when the rest of the world became warmer. Also the equatorial eastern Pacific correlates with the global mean temperature, but here the causality is reversed: El Niño causes a higher global mean temperature.
• You can compare this to the patterns that climate models predict. In the right-hand menu, follow the link "Monthly CMIP5 scenario runs".
• Choose your favorite model, select "tas" (temperature of the atmosphere at the surface). Press the button [select field] at the top (or press the key enter in Firefox).
• First generate the world average temperature by filling out [-90], [90], [0], [360] in the form under "Extract time series"; press [Make time series].
• Under "investigate this field" in the right-hand menu follow the link "correlate with a time series"
• Select the globally averaged temperature you just generated, and make yearly averages again (if you are registered this has already been filled in).
• Compare the plot with the one for the observations.

What were the effects of El Niño and La Niña over the last hundred years?

• Select the NINO3_rel series in the list of "monthly climate indices"
• Follow the link "Correlate with a field" "only observations"
• Select the CRU Hulme precipitation anomalies, starting month [all], [averaging] over [3] months (traditional seasons), and press [correlate]. Wait a while.
• Note the very strong seasonality in most signals.
• Do the same for temperature.
• Split up the effects of El Niño (index>0) and La Niña (index<0) if you are in a particularly thorough mood.
• Compare the DJF and JJA temperature and precipitation plots with the canonical pictures of the effects of El Niño and La Niña based on the Ropelweski and Halpert articles. Note that the correlation plots are also under Effects of ENSO .