Sea Ice Anomalies
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13 June 2023… EUMET/Copernicus Earth Observation – Data Visualization Workshop Series (Presentation): Climate Extremes: Heatwaves, Changes in Ice, Drought, Sea Ice Anomalies. Remote Presentation.
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You learned about the evidence that proves anthropogenic climate change is
taking place. Now, let’s talk about how we explain the phenomena of global
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Previously, you viewed this figure from the IPCC’s assessment report, showing
various factors that contribute to climate change. The next slide will include
further detail about each forcing component.
This figure is also from the IPCC’s assessment report. LOSU means ‘level of
scientific understanding’. In this figure, two different forcing components are
shown; anthropogenic and natural forcings. It is important to remember that
not only anthropogenic forcings, natural forcings also drive climate change. For
example, glacial/Interglacial cycles we observed from the ice core samples
earlier this semester that recorded atmospheric conditions over last 450,000
years are clearly caused by natural forcings as we, homo sapiens, did not exist
that time!
In this figure, each radiative forcing is associated with a value (watts per square
meter) quantifying how much each forcing contributes to climate change. Some
forcings have a negative number (contribute to cooling), whereas others have a
positive number (contribute to warming). The total net forcing is currently a
positive value. Thus, the climate trend is currently warming.
IPCC report
As shown in the previous figure, natural forcing can change climate. The
dominant energy source to change Earth’s climate, the sun, also varies its
energy emission. This figure shows natural changes in solar irradiance from
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from the Sun. In recent decades, solar activity has been measured by satellites,
while before it was estimated using a proxy variation. Without satellite
observation, energy differences were too small to detect.
Solar irradiance is higher during a period called “solar maximum”, which
appears almost every 11 years. During a solar maximum, interesting features
that appears on the Sun’s surface…
(continue)
Solar luminosity
Sunspot cycle (~11 year period,
~0.1% change in radiation
output)
(continued)
…are sunspots! Sunspots are relatively dark areas on the radiating surface of the
Sun, where intense magnetic activity inhibits convection and cools the
photosphere. Luminosity is the total amount of energy emitted by the Sun.
To summarize, more sunspot appears during a period of solar maximum, when the
Sun presents more intense magnetic activity (therefore higher luminosity).
Although solar irradiance was only recently measured by satellite, sunspots
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While an equatorward shift of the eddy-driven jet is found in boreal winter, the response to future sea-ice loss is small relative to climatology and highly sensitive to the number of ensemble members considered. On average, a sea ice-forced signal in the large-scale circulation cannot be distinguished from atmospheric internal variability in our simulations. A low signal-to-noise ratio is also demonstrated in the stratosphere, where the sign of the polar vortex response can be interpreted differently depending on the ensemble size. However, the local thermodynamic effects are statistically significant with strong surface warming and increases in precipitation found in the vicinity of newly ice-free areas. This warming is generally confined to the Arctic, and there is little response in the midlatitudes. Our results highlight the important role of internal variability in the extratropics and emphasize the need for especially large ensembles (>150-200 members) when assessing the dynamical response to both present-day and future Arctic sea-ice loss. (from https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/367289)
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References:
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI: 10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI: 10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?
Journal of Climate, DOI: 10.1175/JCLI-D-20-0613.1
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI: 10.1029/2022EA002348
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The document discusses climate change in the Arctic through data visualization. It notes that Arctic temperatures are rising more than 3 times faster than the global average. Sea ice extent is declining at a rate of nearly 0.8 million km2 per decade. Future projections show continued sea ice loss and Arctic warming, which could influence weather patterns in mid-latitudes through impacts on jet streams and storm tracks. Improved understanding of these connections and their dependence on factors like the quasi-biennial oscillation could help constrain predictions. Data visualization is presented as an important tool for communicating climate science.
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How to explain global warming?
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You learned about the evidence that proves anthropogenic climate change is
taking place. Now, let’s talk about how we explain the phenomena of global
warming.
Previously, you viewed this figure from the IPCC’s assessment report, showing
various factors that contribute to climate change. The next slide will include
further detail about each forcing component.
This figure is also from the IPCC’s assessment report. LOSU means ‘level of
scientific understanding’. In this figure, two different forcing components are
shown; anthropogenic and natural forcings. It is important to remember that
not only anthropogenic forcings, natural forcings also drive climate change. For
example, glacial/Interglacial cycles we observed from the ice core samples
earlier this semester that recorded atmospheric conditions over last 450,000
years are clearly caused by natural forcings as we, homo sapiens, did not exist
that time!
In this figure, each radiative forcing is associated with a value (watts per square
meter) quantifying how much each forcing contributes to climate change. Some
forcings have a negative number (contribute to cooling), whereas others have a
positive number (contribute to warming). The total net forcing is currently a
positive value. Thus, the climate trend is currently warming.
IPCC report
As shown in the previous figure, natural forcing can change climate. The
dominant energy source to change Earth’s climate, the sun, also varies its
energy emission. This figure shows natural changes in solar irradiance from
1874 to 1988. Solar irradiance is the amount of energy per unit area received
from the Sun. In recent decades, solar activity has been measured by satellites,
while before it was estimated using a proxy variation. Without satellite
observation, energy differences were too small to detect.
Solar irradiance is higher during a period called “solar maximum”, which
appears almost every 11 years. During a solar maximum, interesting features
that appears on the Sun’s surface…
(continue)
Solar luminosity
Sunspot cycle (~11 year period,
~0.1% change in radiation
output)
(continued)
…are sunspots! Sunspots are relatively dark areas on the radiating surface of the
Sun, where intense magnetic activity inhibits convection and cools the
photosphere. Luminosity is the total amount of energy emitted by the Sun.
To summarize, more sunspot appears during a period of solar maximum, when the
Sun presents more intense magnetic activity (therefore higher luminosity).
Although solar irradiance was only recently measured by satellite, sunspots
have been observed for a very long time! The first such recording was made
by Galileo Galilei in the 17th century when he created the first telescope. In
addition, there are well documented historical records that captured solar
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ZLabe_AMS2020_presentation_01142020
Substantial disagreement continues between modeling studies in attributing midlatitude climate extremes to Arctic sea-ice anomalies. This is a result of uncertainties due to internal variability, nonlinear interactions, model biases, or more likely a combination of these effects. In this study, we use large ensembles from two high-top atmospheric general circulation models (SC-WACCM4 and E3SM) to separate the sea ice-forced signal from atmospheric internal variability (noise). Following protocol for the Polar Amplification Model Intercomparison Project (PAMIP), each simulation is prescribed with either pre-industrial, present-day, or future levels of sea-ice concentration, which are associated with global warming projections of 2°C. We use 300 ensemble members per simulation to obtain large sample sizes for robust statistics in the context of internal variability.
While an equatorward shift of the eddy-driven jet is found in boreal winter, the response to future sea-ice loss is small relative to climatology and highly sensitive to the number of ensemble members considered. On average, a sea ice-forced signal in the large-scale circulation cannot be distinguished from atmospheric internal variability in our simulations. A low signal-to-noise ratio is also demonstrated in the stratosphere, where the sign of the polar vortex response can be interpreted differently depending on the ensemble size. However, the local thermodynamic effects are statistically significant with strong surface warming and increases in precipitation found in the vicinity of newly ice-free areas. This warming is generally confined to the Arctic, and there is little response in the midlatitudes. Our results highlight the important role of internal variability in the extratropics and emphasize the need for especially large ensembles (>150-200 members) when assessing the dynamical response to both present-day and future Arctic sea-ice loss. (from https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/367289)
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Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI: 10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI: 10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?
Journal of Climate, DOI: 10.1175/JCLI-D-20-0613.1
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Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019GL083095
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In this presentation, I will discuss the power of sharing Arctic climate change information through accessible and engaging data visualizations. In particular, I will focus on using social media (Twitter) as one tool for communicating science to broad audiences.
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The document discusses climate change in the Arctic. It notes that the Arctic is warming faster than the rest of the globe, with sea ice extent and thickness declining significantly. Climate models project that Arctic warming and sea ice loss will continue through the 21st century. Improving observations and models can help reduce uncertainties about future climate impacts in the Arctic and how changes may influence remote weather patterns. Action is needed to reduce emissions and limit global temperature rise in order to prevent the worst effects of climate change in the Arctic.
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The document summarizes recent changes in Arctic sea ice extent based on satellite observations. Summer Arctic sea ice is declining at a rate of 11.6% per decade since 1979, with record low extents in 2007, 2008, 2009 and 2010. Sea ice is also getting younger and thinner. Continued sea ice loss is projected under business as usual warming scenarios, and could eliminate summer sea ice by 2100. This would have significant biological, economic and climate impacts.
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The Arctic is rapidly changing. However, long-term observations of trends in Arctic sea-ice thickness are still quite limited. In this presentation, Zachary will discuss the different methods (satellite instruments and climate model simulations) of observing sea-ice thickness in order to understand changes in the recent Arctic amplification era. He will also highlight the far-reaching environmental and societal impacts from a thinning Arctic sea-ice cover.
Meier clean presentation_pdf
Presentation for CLEAN, 9 May 2011.
Walt Meier, NSIDC
PDF version
(Note animations will not work in this version)
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The document discusses evidence that global warming is occurring rather than global cooling. It notes that the Earth's temperature has risen 1.4°F over the past century due to increased greenhouse gases from fossil fuel burning. This is causing Arctic ice to melt 5% per decade and glaciers worldwide to retreat. Rising sea levels and more extreme weather are threatening coastal communities. While some argue for global cooling, statistics show the last decade was the hottest on record and temperatures continue increasing, indicating the planet is warming rather than cooling.
Regional climate: Australia and New Zealand
1) Australian and New Zealand land areas have warmed around 1.4°C and 1.1°C respectively between 1910-2020, with temperature changes emerging above natural variability.
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3) Heavy rainfall and flooding are projected to increase, as is the frequency and intensity of extreme fire weather.
Reexamining future projections of Arctic climate linkages
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References...
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss,
Geophysical Research Letters, DOI:10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation.
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
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Explainable AI for distinguishing future climate change scenarios
Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria (Apr 2024).
Abstract... https://meetingorganizer.copernicus.org/EGU24/EGU24-9110.html
References...
Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. (submitted; preprint - https://doi.org/10.22541/essoar.171288901.17027965/v1)
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Arctic Science Summit Week 2021 (Session 2: “The 4 Essential Cs - Coordination, Communication, Community, and Collaboration”):
In this presentation, I will discuss the power of sharing Arctic climate change information through accessible and engaging data visualizations. In particular, I will focus on using social media (Twitter) as one tool for communicating science to broad audiences.
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The document discusses climate change in the Arctic. It notes that the Arctic is warming faster than the rest of the globe, with sea ice extent and thickness declining significantly. Climate models project that Arctic warming and sea ice loss will continue through the 21st century. Improving observations and models can help reduce uncertainties about future climate impacts in the Arctic and how changes may influence remote weather patterns. Action is needed to reduce emissions and limit global temperature rise in order to prevent the worst effects of climate change in the Arctic.
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The Arctic is rapidly changing. However, long-term observations of trends in Arctic sea-ice thickness are still quite limited. In this presentation, Zachary will discuss the different methods (satellite instruments and climate model simulations) of observing sea-ice thickness in order to understand changes in the recent Arctic amplification era. He will also highlight the far-reaching environmental and societal impacts from a thinning Arctic sea-ice cover.
Meier clean presentation_pdf
Presentation for CLEAN, 9 May 2011.
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PDF version
(Note animations will not work in this version)
Global warming
The document discusses evidence that global warming is occurring rather than global cooling. It notes that the Earth's temperature has risen 1.4°F over the past century due to increased greenhouse gases from fossil fuel burning. This is causing Arctic ice to melt 5% per decade and glaciers worldwide to retreat. Rising sea levels and more extreme weather are threatening coastal communities. While some argue for global cooling, statistics show the last decade was the hottest on record and temperatures continue increasing, indicating the planet is warming rather than cooling.
Regional climate: Australia and New Zealand
1) Australian and New Zealand land areas have warmed around 1.4°C and 1.1°C respectively between 1910-2020, with temperature changes emerging above natural variability.
2) Heat extremes have increased and cold extremes have decreased across Australasia, and these trends are projected to continue.
3) Heavy rainfall and flooding are projected to increase, as is the frequency and intensity of extreme fire weather.
Reexamining future projections of Arctic climate linkages
10 May 2024…
Atmospheric and Oceanic Sciences Student/Postdoc Seminar (Presentation): Reexamining future projections of Arctic climate linkages, Princeton University, USA.
References...
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss,
Geophysical Research Letters, DOI:10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation.
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
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Reexamining future projections of Arctic climate linkages
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NOAA GFDL Take Your Child to Work Day (Presentation/Volunteer): Welcome to GFDL, Princeton, NJ, USA.
Explainable AI for distinguishing future climate change scenarios
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Abstract... https://meetingorganizer.copernicus.org/EGU24/EGU24-9110.html
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Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. (submitted; preprint - https://doi.org/10.22541/essoar.171288901.17027965/v1)
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Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
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IARPC Collaborations, Modelers’ Community of Practice (Presentation): Explainable AI approach for evaluating climate models in the Arctic. Remote Presentation.
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Labe, Z. M., & Barnes, E. A. (2022). Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, 9(7), e2022EA002348, https://doi.org/10.1029/2022EA002348
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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
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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
Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981
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Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431300
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Labe, Z.M., N.C. Johnson, and T.L. Delworth. Distinguishing the regional emergence of United States summer temperatures between observations and climate model large ensembles, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431288
Researching and Communicating Our Changing Climate
Zachary Labe is a postdoc researcher at NOAA GFDL and Princeton University who studies climate variability and change. His research uses tools like artificial intelligence and climate models to disentangle the signal of climate change from natural weather noise. He conducts field work including Arctic expeditions and uses supercomputers to run complex climate models that generate huge amounts of data.
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NJ State Museum Planetarium (Presentation): Visualizing climate change through data, Trenton, NJ.
References...
Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1, https://journals.ametsoc.org/view/journals/clim/36/20/JCLI-D-22-0716.1.xml
Using explainable machine learning to evaluate climate change projections
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Atmosphere and Ocean Climate Dynamics Seminar (Presentation): Using explainable machine learning to evaluate climate change projections, Yale University, New Haven, CT. Remote Presentation.
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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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Climate change extremes by season in the United States
11 September 2023…
Hershey Horticulture Society (Presentation): Climate change extremes by season in the United States, Hershey, PA, USA.
References...
Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1
Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2016), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, R. Clim Dyn, DOI:10.1007/s00382-016-3313-2
Labe, Z.M., N.C. Johnson, and T.L Delworth (2023). Changes in United States summer temperatures revealed by explainable neural networks. Preprint. DOI: 10.22541/essoar.168987129.98069596/v1
Guest Lecture: Our changing Arctic in the past and future
22 August 2023…
Guest lecture for “Introduction to Global Climate Change (ESS 15)” (Invited): Our changing Arctic in the past and future, University of California, Irvine, CA. Remote Presentation.
References...
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J. H., Dunne, K. A., ... & Zhao, M. (2020). SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001895
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088583
Climate Projections - What Really is Business as Usual?
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AOS Isaac Held Blog Post Meeting (Presentation): What really is business as usual? Princeton University, NJ.
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GFDL/AOS Summer Internship Lecture Series (Presentation): Making effective science figures, Princeton University, NJ.
Monitoring indicators of climate change through data-driven visualization
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La Uni Climática - IV Edition (Presentation): Monitoring indicators of climate change through data-driven visualization. Remote Presentation.
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Explainable AI for distinguishing future climate change scenarios
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如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
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Micronuclei test.M.sc.zoology.fisheries.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
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Sea Ice Anomalies
- 1. Zachary M. Labe; Postdoc at Princeton University & NOAA GFDL https://zacklabe.com/ @ZLabe Climate Extremes: Heatwaves, Changes in Ice, Drought, Floods -Sea Ice Anomalies-
- 2. Daily Arctic temperature in 2018 (red) compared to every year since 1958 in the month of February. Average is shown by the white line. THE CLIMATE IS CHANGING IN REAL-TIME. Considering a global view of temperatures relative to average – placing weather in the context of climate
- 3. Daily Arctic temperature in 2018 (red) compared to every year since 1958 in the month of February. Average is shown by the white line. THE ARCTIC IS CHANGING IN REAL-TIME. Daily Arctic temperature in 2018 (red) compared to every year since 1958 in the month of February. Average is shown by the white line.
- 4. Monitoring Arctic sea ice anomalies in real-time
- 5. Monitoring Antarctic sea ice anomalies in real-time
- 6. THIS IS AN OPPORTUNITY TO COMMUNICATE
- 7. Seasonal cycle of Arctic sea ice
- 8. Seasonal cycle of Arctic sea ice
- 11. Example of an ongoing anomalous sea ice extreme event
- 14. Changing baselines is very confusing for science communication, especially around climate change! 1981-2010 1991-2020
- 15. Changing baselines is very confusing for science communication, especially around climate change!
- 18. Getting started!
- 19. Getting started!
- 20. Accessibility in Visualization Storytelling 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
- 21. We need scientists. We need educators. We need innovators. We need communicators.
- 22. Sharing Arctic climate change and sea ice extremes in real-time. Supporting Arctic climate resilience and environmental justice. Identifying sea ice impacts with data-driven visualizations. Using a diversity of voices to communicate. Zachary Labe | 13 June 2023 | zachary.labe@noaa.gov @ZLabe https://zacklabe.com/ Questions!