Climate Variability: El Niño - Southern Oscillation
More creative ways to present statistical results / data y-axis or the worst graphs ever !? The next examples are taken from a web-page that shares educational material for teachers (the graphs were actually published in newspapers and magazines) http://dpcdsb-gains.wikispaces.com/file/view/
Worst+Graphs+Ever.pdf/126543183/Worst %20Graphs%20Ever.pdf (retrieved March 2014) x-axis More creative ways to present statistical results / data y-axis
Note: When talking about regression We say y is regressed on x x-axis x=10yr
x=10yry=4miles x=10yry=0.5mile Distance (y-axis) Time (x-axis) Distortion factor ( Lie-factor) And the objective presentation of the data
Some more creative ways to hide or distort the statistical results Climate Variability: El Nio - Southern Oscillation El Nio region SST departures (anomalies) (oC) measured in different regions of the tropical Pacific
El Nio Region SST Departures (oC) Recent Evolution Climate Variability: El Nio - Southern Oscillation Fossil corals Image source:
Climate Variability: El Nio - Southern Oscillation Red: Observed SST anomalies Black : Coral reconstructions (oxygen isotopes) Cobb, K.M., C.D. Charles, H. Cheng & R.L. Edwards, 2003,El Nio-Southern Oscillation and
tropical Pacific climate during the last millennium. Nature, Vol. 424, No. 6946, pp. 271 - 276 (17 Climate Variability: El Nio - Southern Oscillation Cobb, K.M., C.D. Charles, H. Cheng & R.L. Edwards, 2003,El Nio-Southern Oscillation and tropical Pacific climate during the last millennium. Nature, Vol. 424, No. 6946, pp.
271 - 276 (17 July 2003). http://www.ncdc.noaa.gov/paleo/pubs/ cobb2003/cobb2003.html Reconstructed Climate Variability: A.D. 1320-1480 http://www.ncdc.noaa.gov/paleo/pubs/ cobb2003/cobb2003.html
From Prof. Aiguo Dais paper in Geophysical Research Letters (2000) Effect of ENSO on Global Rainfall Global Teleconnection Pattern http://precip.gsfc.nasa.gov/rain_pages/ el_nino_vsn2.html U.S. Temperature and Precipitation Departures During the Last 30 and 90 Days
Effect of ENSO on Global Rainfall Last 30 Days 30-day (ending 22 Mar 2014) temperature departures (degree C) 30-day (ending 22 Mar 2014) % of average precipitation Last 90 Days 90-day (ending 22 Mar 2014) % of average precipitation
http://precip.gsfc.nasa.gov/rain_pages/ el_nino_vsn2.html 90-day (ending 22 Mar 2014) temperature departures (degree C) R-scripts and data update We will work in the next weeks with ENSO and local climate data.
We will explore if we find correlations between rainfall and temperatures in the state of New York and ENSO. Please update the following files in your local scripts-directory (If you have not done so already in class (April 27 th, 2014)): myfunctions.R climatology.R plot_climatology.R class12.R class15.R http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/
R-scripts and data update Please update the following file in your local data-directory (If you have not done so already in class (April 27 th, 2014)): create a local subdirectory named NY (for New York State) then download some of the USW station data files NOTE: ghcnd-stations-NY.csv you open in R-studio (or text edito) To see a list of stations with geographic locations and the name of th station. http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/data/NY/ Processing new station data
1) Calculate the 1981-2010 climatology with climatology.R ( input is e.g. USW00094789_tavg_mon_mean.asc) This creates two output files (a) the monthly mean climatology) ( e.g. USW00094789_tavg_mon_mean_climc_1981-2010.csv) (b) the monthly mean anomalies ( e.g. USW00094789_tavg_mon_mean_ano.asc) Processing new station data Processing new station data
2) Use plot_climatology.R to see the climatological cycle Processing new station data 3) Use class15.R To work the newly created anomaly data files to compare the time evolution and study the correlation between two stations.
Processing new station data 3) Use class15.R To work the newly created anomaly data files to compare the time evolution and study the correlation between two stations. Note: If there are gaps in the data, the program does not do the calculation
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