Improving scientific graphics of climate (change) data

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Creating visuals of data is a critical part of our jobs as scientists. We use figures for journal publications, presentations, posters, and science communication. This week we'll discuss a framework for making better figures, particularly in the climate sciences. I will also give examples of what not to do, and how we can improve these figures moving forward. e

Improving scientific graphics of
climate (change) data
https://github.com/zmlabe/ClimatePython
PALETTABLE CUBEHELIX

WarmerColder
NOW
Start of
satellite-era

WarmerColder
NOW
Start of
satellite-era

Polar Amplification:
acceleration of warming in
high latitudes relative to
the rest of the globe

DANISHMETEOROLOGICALINSTITUTE

@ZLabe

1. https://betterfigures.org/
2. https://www.climate-lab-book.ac.uk/
3. http://colorbrewer2.org/
RESOURCES

LABEETAL.2017,CLIMATEDYNAMICS

LINE GRAPHSTemperatureAnomaly(°C)

MAP PLOTS

YIKES – COLOR!

YIKES – THE FONT!

YIKES – SOFTEN COASTLINES!

YIKES – FIRE IN THE ARCTIC!

OKAY – MINOR DETAILS

MAP PLOTS

YIKES!

YIKES – FONT IS BLEH!

YIKES – COLOR!

YIKES – MAP PROJECTION!

HAVE FUN

Senator Sheldon Whitehouse (D-CT) - Time to Wake Up: Climate Progress Speech (3/21/2018)

https://sites.uci.edu/zlabe/arctic-sea-ice-figures/

Adjusting axes (spines)

Changing font styles

#endrainbow
Check out: https://betterfigures.org/2018/06/04/playing-hunt-the-discontinuity/

EGU 2018 Conference https://betterfigures.org/

#endrainbow

#endrainbow

#endrainbow
Not all color-blind vision is the same!

SCHNEIDERANDNOCKE,2017

SCHNEIDERANDNOCKE,2017

SCHNEIDERANDNOCKE,2017

SCHNEIDERANDNOCKE,2017

SEQUENTIAL

QUALITATIVE

DIVERGING

Adapted from Ed Hawkins at betterfigures.org

NCL TABLE

NCL Color Table

OTHER OPTIONS
THYNG ET AL. 2016; OCEANOGRAPHY

CMOCEAN

1. Seaborn.
2. Plotly.
3. ggplot.
4. Matplotlib v3.
ALTERNATIVES

ACCESSIBILITY

ACCESSIBILITY

ACCESSIBILITY

https://www.climate-lab-book.ac.uk/visualisation-resources/

VISUALIZING
GLOBAL
TEMPERATURE
CHANGE

PLOT BY ED HAWKINS

2016 RIO OLYMPICS OPENING CEREMONY

PLOT BY ED HAWKINS

https://showyourstripes.info/
PLOT BY ED HAWKINS

CALIFORNIA

PLOT BY ED HAWKINS

PLOT BY NEIL KAYE

PLOT BY NASA EARTH OBSERVATORY
JOSHUA STEVENS

CHECK
Simple is best.
Color has meaning.
Data is visual.
PLOT BY ED HAWKINS

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5 October 2023… Atmosphere and Ocean Climate Dynamics Seminar (Presentation): Using explainable machine learning to evaluate climate change projections, Yale University, New Haven, CT. Remote Presentation. References... Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a, https://iopscience.iop.org/article/10.1088/1748-9326/acc81a

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14 April 2023… Resnick Young Investigators Symposium (Invited Presentation): Creative machine learning approaches for climate change detection, California Institute of Technology (Caltech), USA. https://resnick.caltech.edu/events/resnick-symposium/2023-symposium References: Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464 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 Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming. Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119

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An intro to explainable AI for polar climate science

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Explainable AI approach for evaluating climate models in the Arctic

Explainable AI approach for evaluating climate models in the Arctic

Explainable AI approach for evaluating climate models in the Arctic

Explainable neural networks for evaluating patterns of climate change and var...

Explainable neural networks for evaluating patterns of climate change and var...

Explainable neural networks for evaluating patterns of climate change and var...

Applications of machine learning for climate change and variability

Applications of machine learning for climate change and variability

Applications of machine learning for climate change and variability

data-driven approach to identifying key regions of change associated with fut...

data-driven approach to identifying key regions of change associated with fut...

data-driven approach to identifying key regions of change associated with fut...

Distinguishing the regional emergence of United States summer temperatures be...

Distinguishing the regional emergence of United States summer temperatures be...

Distinguishing the regional emergence of United States summer temperatures be...

Researching and Communicating Our Changing Climate

Researching and Communicating Our Changing Climate

Researching and Communicating Our Changing Climate

Revisiting projections of Arctic climate change linkages

Revisiting projections of Arctic climate change linkages

Revisiting projections of Arctic climate change linkages

Visualizing climate change through data

Visualizing climate change through data

Visualizing climate change through data

Using explainable machine learning to evaluate climate change projections

Using explainable machine learning to evaluate climate change projections

Using explainable machine learning to evaluate climate change projections

Climate Projections - What Really is Business as Usual?

Climate Projections - What Really is Business as Usual?

Climate Projections - What Really is Business as Usual?

Sea Ice Anomalies

Sea Ice Anomalies

Sea Ice Anomalies

Techniques and Considerations for Improving Accessibility in Online Media

Techniques and Considerations for Improving Accessibility in Online Media

Techniques and Considerations for Improving Accessibility in Online Media

Career pathways and research opportunities in the Earth sciences

Career pathways and research opportunities in the Earth sciences

Career pathways and research opportunities in the Earth sciences

Creative machine learning approaches for climate change detection

Creative machine learning approaches for climate change detection

Creative machine learning approaches for climate change detection

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Bacterial Identification and Classifications

Bacterial Identification and Classifications

Bacterial Identification and Classifications

Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

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Module for Grade 9 for Asynchronous/Distance learning

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FarihaAbdulRasheed

Proteomics: types, protein profiling steps etc.

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GBSN - Microbiology (Unit 1)

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Theoretical predictions and observational data indicate a class of sub-Neptune exoplanets may have water-rich interiors covered by hydrogen-dominated atmospheres. Provided suitable climate conditions, such planets could host surface liquid oceans. Motivated by recent JWST observations of K2-18 b, we self-consistently model the photochemistry and potential detectability of biogenic sulfur gases in the atmospheres of temperate sub-Neptune waterworlds for the first time. On Earth today, organic sulfur compounds produced by marine biota are rapidly destroyed by photochemical processes before they can accumulate to significant levels. Domagal-Goldman et al. suggest that detectable biogenic sulfur signatures could emerge in Archean-like atmospheres with higher biological production or low UV flux. In this study, we explore biogenic sulfur across a wide range of biological fluxes and stellar UV environments. Critically, the main photochemical sinks are absent on the nightside of tidally locked planets. To address this, we further perform experiments with a 3D general circulation model and a 2D photochemical model (VULCAN 2D) to simulate the global distribution of biogenic gases to investigate their terminator concentrations as seen via transmission spectroscopy. Our models indicate that biogenic sulfur gases can rise to potentially detectable levels on hydrogen-rich water worlds, but only for enhanced global biosulfur flux (20 times modern Earth’s flux). We find that it is challenging to identify DMS at 3.4 μm where it strongly overlaps with CH4, whereas it is more plausible to detect DMS and companion byproducts, ethylene (C2H4) and ethane (C2H6), in the mid-infrared between 9 and 13 μm. Unified Astronomy Thesaurus concepts: Exoplanet atmospheres (487); Exoplanet

Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds

Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds

Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds

Mustard, as a crop, is susceptible to a variety of pests that can affect its growth and yield. Here’s a rundown of some common pests that target mustard plants: Aphids: These small, sap-sucking insects can cause significant damage by feeding on the leaves and stems. Aphids also excrete a sticky substance known as honeydew, which can lead to the growth of sooty mold on the plants. Flea Beetles: These tiny beetles jump like fleas when disturbed and chew small holes in the leaves. They are particularly damaging in the early growth stages of the plant. Cabbage Loopers: The larvae of a type of moth, these caterpillars are known for their distinctive looping movement. They chew large holes in the leaves and can defoliate plants if present in large numbers. Diamondback Moth Larvae: Another caterpillar pest, these larvae chew small holes in the leaves and can cause extensive damage, especially when infestations are heavy. Whiteflies: These are tiny, winged insects that feed on plant sap and can quickly become a problem in greenhouse or close planting conditions. Like aphids, they also secrete honeydew. Cutworms: These are the larvae of certain types of moths and are known for cutting young plants at the stem base at ground level. They are most destructive during the night. Root Maggots: The larvae of root maggot flies, these pests attack the roots of mustard plants, causing wilting and potentially killing young plants. Harlequin Bugs: These are colorful stink bugs that suck the sap from mustard plant stems and leaves, causing the leaves to become stippled, wilt, and eventually die if the infestation is severe. Mustard Sawfly: The larvae of the mustard sawfly can cause considerable defoliation, as they feed voraciously on the leaves. Clubroot: Caused by a fungus-like organism, clubroot affects the roots, causing them to swell and distort. While technically a disease, it is often associated with pest management because controlling it involves similar preventative strategies. Control Measures: Managing pests in mustard involves a combination of cultural, biological, and chemical methods. Crop rotation, resistant varieties, timely sowing, maintaining plant health, and using natural predators like ladybugs and parasitic wasps can help keep pest populations under control. Chemical pesticides should be used as a last resort due to their potential impact on the environment and non-target species.

Pests of mustard_Identification_Management_Dr.UPR.pdf

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pumpkin fruit fly, water melon fruit fly, cucumber fruit fly

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Improving scientific graphics of climate (change) data

1. Improving scientific graphics of climate (change) data https://github.com/zmlabe/ClimatePython PALETTABLE CUBEHELIX

2.

3. WarmerColder NOW Start of satellite-era

4. WarmerColder NOW Start of satellite-era

5. Polar Amplification: acceleration of warming in high latitudes relative to the rest of the globe

6. DANISHMETEOROLOGICALINSTITUTE

8. 1. https://betterfigures.org/ 2. https://www.climate-lab-book.ac.uk/ 3. http://colorbrewer2.org/ RESOURCES

9. LABEETAL.2017,CLIMATEDYNAMICS

10. LINE GRAPHSTemperatureAnomaly(°C)

11.

12.

13.

14.

15.

17.

18.

19.

21. YIKES – COLOR!

22. YIKES – THE FONT!

23. YIKES – SOFTEN COASTLINES!

24. YIKES – FIRE IN THE ARCTIC!

25. OKAY – MINOR DETAILS

29. YIKES – FONT IS BLEH!

30. YIKES – COLOR!

31. YIKES – MAP PROJECTION!

34.

35.

36.

37.

38.

39.

40.

41.

42. Senator Sheldon Whitehouse (D-CT) - Time to Wake Up: Climate Progress Speech (3/21/2018)

43. https://sites.uci.edu/zlabe/arctic-sea-ice-figures/

44. Adjusting axes (spines)

45. Changing font styles

46. #endrainbow Check out: https://betterfigures.org/2018/06/04/playing-hunt-the-discontinuity/

47. EGU 2018 Conference https://betterfigures.org/

48. #endrainbow

49. #endrainbow

50. #endrainbow Not all color-blind vision is the same!

51.

52.

53.

54.

55.

56.

57. SCHNEIDERANDNOCKE,2017

58. SCHNEIDERANDNOCKE,2017

59. SCHNEIDERANDNOCKE,2017

60. SCHNEIDERANDNOCKE,2017

62. QUALITATIVE

64. Adapted from Ed Hawkins at betterfigures.org

66. NCL Color Table

67. OTHER OPTIONS THYNG ET AL. 2016; OCEANOGRAPHY

68.

70. 1. Seaborn. 2. Plotly. 3. ggplot. 4. Matplotlib v3. ALTERNATIVES

71. ACCESSIBILITY

72. ACCESSIBILITY

73. ACCESSIBILITY

74.

75. https://www.climate-lab-book.ac.uk/visualisation-resources/

76. VISUALIZING GLOBAL TEMPERATURE CHANGE

77. PLOT BY ED HAWKINS

78. 2016 RIO OLYMPICS OPENING CEREMONY

79.

80. PLOT BY ED HAWKINS

81. https://showyourstripes.info/ PLOT BY ED HAWKINS

83. PLOT BY ED HAWKINS

84.

85.

86. PLOT BY NEIL KAYE

87.

88. PLOT BY NASA EARTH OBSERVATORY JOSHUA STEVENS

89.

90. CHECK Simple is best. Color has meaning. Data is visual. PLOT BY ED HAWKINS