Evaluating and communicating Arctic climate change projection
•
0 j'aime•20 vues
20 February 2023… Climate Change and Agriculture Guest (Presentation): Evaluating and communicating Arctic climate change projections, Kansas State University, USA. 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 Peings, Y., Cattiaux, J., Vavrus, S. J., & Magnusdottir, G. (2018). Projected squeezing of the wintertime North-Atlantic jet. Environmental Research Letters, 13(7), 074016, https://iopscience.iop.org/article/10.1088/1748-9326/aacc79/meta
Recommandé
Recommandé
Contenu connexe
Similaire à Evaluating and communicating Arctic climate change projection
Similaire à Evaluating and communicating Arctic climate change projection (20)
Plus de Zachary Labe
Plus de Zachary Labe (20)
Dernier
Dernier (20)
Evaluating and communicating Arctic climate change projection
- 1. Evaluating and communicating Arctic climate change projections @ZLabe Zachary Labe Postdoc at Princeton/GFDL 20 February 2023 Kansas State University Climate Change and Agriculture
- 2. RESEARCHER Climate signal vs. weather noise @ZLabe COMMUNICATOR RESEARCHER Arctic climate change STORYTELLER Simple, bold data visualization ZACHARY LABE Climate Scientist at Princeton University & NOAA GFDL zachary.labe@noaa.gov https://zacklabe.com/
- 6. The Arctic.
- 7. The Arctic.
- 12. Polar Amplification: acceleration of warming in high latitudes relative to the rest of the globe
- 14. Observing ice.
- 15. [ SIT ] Sea Ice Thickness Depth between sea surface and ice/snow layer [ SIC ] Sea Ice Concentration Fraction (%) of seawater covered by ice Snow Ice [ SIE ] Sea Ice Extent Area of seawater covered by any amount of ice (>15%)
- 16. [ SIT ] Sea Ice Thickness Depth between sea surface and ice/snow layer [ SIC ] Sea Ice Concentration Fraction (%) of seawater covered by ice Snow Ice [ SIE ] Sea Ice Extent Area of seawater covered by any amount of ice (>15%)
- 17. [ SIT ] Sea Ice Thickness Depth between sea surface and ice/snow layer [ SIC ] Sea Ice Concentration Fraction (%) of seawater covered by ice Snow Ice [ SIE ] Sea Ice Extent Area of seawater covered by any amount of ice (>15%)
- 20. LABE ET AL. 2018, JCLI
- 21. Modeling vs. Field Work
- 22. R/V Lance – Greenland Sea – May 2017
- 25. Snow pits
- 27. 7 Feb. 2010
- 30. Vihma, 2014
- 31. Cohen et al. 2014
- 32. Barnes and Screen, 2015
- 33. Overland et al. 2016
- 34. Francis, 2017
- 35. Francis et al. 2017
- 36. Screen et al. 2018
- 37. CLIVAR Working Group, 2018
- 38. Screen et al. 2018
- 39. Vavrus, 2018
- 40. Coumou et al. 2018
- 41. Smith et al. 2019
- 42. Cohen et al. 2020
- 43. [Newson, 1973; Nature] “…great warming of the lower layers of the troposphere over the Arctic basin... In fact, there is a lowering of mid-latitude continental temperatures near the surface”
- 48. Why? What is the perturbation?
- 49. R/V Lance – Greenland Sea – May 2017
- 50. R/V Lance – Greenland Sea – May 2017 Turbulent heat fluxes [ SIC ]
- 51. R/V Lance – Greenland Sea – May 2017 Turbulent heat fluxes [ SIC + SIT ]
- 52. Global climate change Northern Hemisphere mid-latitude weather Arctic Amplification Changes in: + Storm tracks + Jet stream + Planetary waves Natural Variability + Internal modes + Solar cycle + Volcanoes Northern Hemisphere cryosphere changes + Summer and early fall Arctic sea-ice loss + Fall Eurasian snow cover increases + Late fall and winter Arctic sea-ice loss [adapted from Cohen et al., 2014; Nature Geosciences] Polar Vortex
- 54. LENS 2100-2070 minus 1981-2010 [JFM] Adapted from Peings et al. 2018, ERL Troposphere Stratosphere Antarctic Equator Arctic CLIMATE MODEL PROJECTION
- 55. Adapted from Peings et al. 2018, ERL AA UTW LENS Stratosphere Troposphere 2100-2070 minus 1981-2010 [JFM] Antarctic Equator Arctic CLIMATE MODEL PROJECTION
- 56. WHAT IS THE EFFECT OF SEA-ICE LOSS RELATIVE TO ARCTIC AMPLIFICATION?
- 58. Arctic amplification > sea-ice loss LABE ET AL. 2020, GRL
- 59. Looking ahead.
- 60. October-November – Relative to the years of 1951-1980 Our planet without change…
- 63. Observed Arctic temperatures from 1950 to 2021 Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 64. Simulated historical Arctic temperatures from 1930 to 2014 using a climate model Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 65. Simulated Arctic temperatures from 1930 to 2100 using a climate model WITHOUT human- caused climate change Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 66. What influences of climate change do you see on temperatures in the Arctic? Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 67. Projected future Arctic temperatures from 2015 to 2100 using a climate model with increases in fossil fuel development Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 68. Projected future Arctic temperatures from 2015 to 2100 using a climate model with moderate progress in mitigation and other sustainability goals Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 69. Projected future Arctic temperatures from 2015 to 2100 using a climate model with a rapid reduction in current emissions globally Climate Model – GFDL SPEAR (30 ensemble members); Delworth et al. 2020
- 72. THE REAL WORLD (Observations) Map of temperature
- 73. THE REAL WORLD (Observations) Anomaly is relative to 1951-1980
- 74. THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change)
- 75. Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change) But let’s remove climate change…
- 76. Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change) After removing the forced response… anomalies/noise!
- 77. 2-m Temperature (°C) THERE ARE MANY CLIMATE MODEL LARGE ENSEMBLES… Annual mean 2-m temperature 7 global climate models 16 ensembles each ERA5-BE (observations)
- 78. STANDARD EVALUATION OF CLIMATE MODELS Pattern correlation RMSE EOFs Trends, anomalies, mean state Climate modes of variability
- 79. STANDARD EVALUATION OF CLIMATE MODELS Pattern correlation RMSE EOFs Trends, anomalies, mean state Climate modes of variability CORRELATION [R]
- 80. STANDARD EVALUATION OF CLIMATE MODELS Pattern correlation RMSE EOFs Trends, anomalies, mean state Climate modes of variability CORRELATION [R]
- 81. STANDARD EVALUATION OF CLIMATE MODELS Pattern correlation RMSE EOFs Trends, anomalies, mean state Climate modes of variability Negative Correlation Positive Correlation PATTERN CORRELATION – T2M
- 83. ----ANN---- 2 Hidden Layers 10 Nodes each Ridge Regularization Early Stopping TEMPERATURE We know some metadata… + What year is it? (Labe & Barnes, 2021) + Where did it come from? LABE AND BARNES 2022, ESS
- 84. TEMPERATURE We know some metadata… + What year is it? (Labe & Barnes, 2021) + Where did it come from? Train on data from the Multi-Model Large Ensemble Archive LABE AND BARNES 2022, ESS
- 85. NEURAL NETWORK CLASSIFICATION TASK HIDDEN LAYERS INPUT LAYER OUTPUT LAYER TEMPERATURE MAP LABE AND BARNES 2022, ESS
- 86. COMPARING CLIMATE MODELS IN THE ARCTIC High Low RECENT ARCTIC AMPLIFICATION LABE AND BARNES 2022, ESS
- 87. High Low HISTORICAL PERIOD COMPARING CLIMATE MODELS IN THE ARCTIC LABE AND BARNES 2022, ESS
- 88. High Low DIFFERENCE IN LAYER-WISE RELEVANCE PROPAGATION COMPARING CLIMATE MODELS IN THE ARCTIC LABE AND BARNES 2022, ESS
- 89. APPLY SOFTMAX OPERATOR IN THE OUTPUT LAYER RANK LABE AND BARNES 2022, ESS
- 90. APPLY SOFTMAX OPERATOR IN THE OUTPUT LAYER [ 0.71 ] [ 0.05 ] [ 0.01 ] [ 0.01 ] [ 0.03 ] [ 0.11 ] [ 0.08 ] RANK LABE AND BARNES 2022, ESS
- 91. APPLY SOFTMAX OPERATOR IN THE OUTPUT LAYER [ 0.71 ] [ 0.05 ] [ 0.01 ] [ 0.01 ] [ 0.03 ] [ 0.11 ] [ 0.08 ] RANK [ 1 ] [ 4 ] [ 7 ] [ 6 ] [ 5 ] [ 2 ] [ 3 ] LABE AND BARNES 2022, ESS
- 92. APPLY SOFTMAX OPERATOR IN THE OUTPUT LAYER [ 0.71 ] [ 0.05 ] [ 0.01 ] [ 0.01 ] [ 0.03 ] [ 0.11 ] [ 0.08 ] RANK [ 1 ] [ 4 ] [ 7 ] [ 6 ] [ 5 ] [ 2 ] [ 3 ] Confidence/Probability LABE AND BARNES 2022, ESS
- 93. RANKING CLIMATE MODEL PREDICTIONS FOR EACH YEAR IN OBSERVATIONS LABE AND BARNES 2022, ESS
- 94. RANKING CLIMATE MODEL PREDICTIONS FOR EACH YEAR IN OBSERVATIONS LABE AND BARNES 2022, ESS
- 95. DATA VISUALIZATION IS STORY-TELLING. Arctic temperature anomalies from 1950 to 2021
- 97. PLOT BY ED HAWKINS
- 98. 2016 RIO OLYMPICS OPENING CEREMONY
- 101. DON’T BE SUCH A SCIENTIST WE ARE DATA SCIENTISTS ART BY JILL PELTO
- 102. 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 “ ”
- 103. THE CLIMATE IS CHANGING IN REAL-TIME. Considering a global view of temperatures relative to average – placing weather in the context of climate
- 104. 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.
- 105. THIS IS AN OPPORTUNITY TO COMMUNICATE
- 107. 2016 Average
- 108. [International Arctic Research Center [IARC; University of Alaska, Fairbanks] Start a conversation!
- 110. Crystal Polar Cruise, Aug. 2016 We need scientists. We need educators. We need innovators. We need communicators.
- 111. KEY POINTS Climate change effects have already emerged in the Arctic. Improvements to observations and models will reduce uncertainty in future climate projections. We can still prevent the worst of the impacts in the Arctic. Zachary Labe zachary.labe@noaa.gov @ZLabe https://zacklabe.com/