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Making effective and accessible figures

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Making effective and accessible figures

  1. 1. MAKING EFFECTIVE & ACCESSIBLE FIGURES Zachary Labe| 12 July 2022| REU Professional Development| Colorado State University
  2. 2. Zack Labe Pioneer Coal Mine Blue Whale of Catoosa Centralia Underground Fire Roadside America Greenland Sea (81°N) Enjoy: roadside oddities, diners, and horror movies Hobbies: gardening, #scicomm ( @ZLabe), hiking, traveling to lighthouses Graduate work: Arctic – midlatitude climate variability (sea-ice changes) Recent work: using ANNs to detect patterns of climate variability/change Now at GFDL I am thinking about: detection and attribution of extreme events Relevance LENS X-Single Forcing Runs Hometown – Linglestown, Pennsylvania BSc – Atmospheric Science at Cornell University PhD – Earth System Science at UC Irvine Postdoc – climate variability using AI at Colorado State U. Postdoc – climate attribution with T. Delworth & N. Johnson he/him
  3. 3. Polar Amplification: acceleration of warming in high latitudes relative to the rest of the globe
  4. 4. NOW Start of satellite-era
  5. 5. DATA VISUALIZATION IS STORY-TELLING.
  6. 6. DON’T BE SUCH A SCIENTIST WE ARE DATA SCIENTISTS ART BY JILL PELTO
  7. 7. Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Jill Pelto|http://www.jillpelto.com/landscape-of-change “ ”
  8. 8. BEGINNING WITH OUR DATA.
  9. 9. LINE GRAPHS Temperature Anomaly (°C)
  10. 10. :)
  11. 11. MAP PLOTS
  12. 12. YIKES!
  13. 13. YIKES – FONT IS BLEH!
  14. 14. YIKES – COLOR!
  15. 15. MAP PROJECTION!
  16. 16. :)
  17. 17. HAVE FUN!
  18. 18. LEVERAGING ALL DIMENSIONS OF YOUR DATA.
  19. 19. I have a 2D-array of sea ice thickness anomaly data [year,day]… Each line is one day Number of Years Thickness Anomaly (m)
  20. 20. https://seaborn.pydata.org/examples/index.html
  21. 21. SURPRISINGLY EFFECTIVE.
  22. 22. Senator Sheldon Whitehouse (D-CT) - Time to Wake Up: Climate Progress Speech (3/21/2018)
  23. 23. DANISH METEOROLOGICAL INSTITUTE
  24. 24. Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173 TELLING A STORY.
  25. 25. Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173 TELLING A STORY.
  26. 26. Select one ensemble member and calculate the annual mean global mean surface temperature (GMST) 2-m TEMPERATURE ANOMALY LABE AND BARNES, 2022, GRL
  27. 27. Calculate 10-year moving (linear) trends 2-m TEMPERATURE ANOMALY LABE AND BARNES, 2022, GRL
  28. 28. Plot the slope of the linear trends START OF 10-YEAR TEMPERATURE TREND 2-m TEMPERATURE ANOMALY LABE AND BARNES, 2022, GRL
  29. 29. Calculate a threshold for defining a slowdown in decadal warming LABE AND BARNES, 2022, GRL
  30. 30. Repeat this exercise for each ensemble member in CESM2-LE LABE AND BARNES, 2022, GRL
  31. 31. Compare warming slowdowns with reanalysis (ERA5) LABE AND BARNES, 2022, GRL
  32. 32. SCIENCE OF DESIGN.
  33. 33. cmap = ‘jet’
  34. 34. STOELZLE AND STEIN, 2021, HESS
  35. 35. 1998!
  36. 36. Citations on Google Scholar…
  37. 37. Check out: https://betterfigures.org/2018/06/04/playing-hunt-the-discontinuity/
  38. 38. 2004!
  39. 39. SCHNEIDER AND NOCKE, 2017
  40. 40. SCHNEIDER AND NOCKE, 2017
  41. 41. Adapted from Ed Hawkins at betterfigures.org
  42. 42. Crameri, F. (2018). Scientific colour maps. Zenodo. http://doi.org/10.5281/zenodo.1243862 Crameri, F. (2018), Geodynamic diagnostics, scientific visualisation and StagLab 3.0, Geosci. Model Dev., 11, 2541- 2562, doi:1 0.5194/gmd-11-2541-2018 Crameri, F., G.E. Shephard, and P.J. Heron (2020), The misuse of colour in science communication, Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7 Palettable: Color palettes for Python
  43. 43. Crameri, F. (2018). Scientific colour maps. Zenodo. http://doi.org/10.5281/zenodo.1243862 Crameri, F. (2018), Geodynamic diagnostics, scientific visualisation and StagLab 3.0, Geosci. Model Dev., 11, 2541- 2562, doi:1 0.5194/gmd-11-2541-2018 Crameri, F., G.E. Shephard, and P.J. Heron (2020), The misuse of colour in science communication, Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7
  44. 44. CMOCEAN THYNG ET AL. 2016; OCEANOGRAPHY
  45. 45. CMASHER “Scientific colormaps for making accessible, informative and cmashing plots”
  46. 46. Adjusting axes (spines)
  47. 47. Changing font styles
  48. 48. https://github.com/zmlabe
  49. 49. 1. Seaborn. 2. Plotly. 3. ggplot. 4. Matplotlib v3. RESOURCES
  50. 50. 1. https://betterfigures.org/ 2. https://www.climate-lab-book.ac.uk/ 3. http://colorbrewer2.org/ RESOURCES
  51. 51. MAKING ACCESSIBLE FIGURES.
  52. 52. ACCESSIBILITY No jargon Tell a story Alternative text Color contrast ratio Label data directly Avoid flashing GIFs Include figure titles Avoid data overlays Provide data references
  53. 53. CHECK: Simple. Bold. Stories. @ZLabe Questions! ZACHARY LABE | 12 JULY 2022| REU PROFESSIONAL DEVELOPMENT | COLORADO STATE UNIVERSITY

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