Revisiting projections of Arctic climate change linkages
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16 November 2023… Department Seminar (Presentation): Revisiting projections of Arctic climate change linkages, Tongji University, Shanghai, China. Remote Presentation. 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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Revisiting projections of Arctic climate change linkages
- 1. Revisiting projections of Arctic climate change linkages Zachary M. Labe Postdoc in Seasonal-to-Decadal Variability & Predictability Division at NOAA GFDL and AOS at Princeton University with… Elizabeth Barnes Gudrun Magnusdottir Yannick Peings 16 November 2023 Tongji University
- 2. The Arctic is warming more than 3 times faster than the global average!
- 3. The Arctic is warming more than 3 times faster than the global average!
- 10. [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”
- 11. Vihma, 2014
- 12. Cohen et al. 2014
- 13. Barnes and Screen, 2015
- 14. Overland et al. 2016
- 15. Francis, 2017
- 16. Francis et al. 2017
- 17. Screen et al. 2018
- 18. CLIVAR Working Group, 2018
- 19. Screen et al. 2018
- 20. Vavrus, 2018
- 21. Coumou et al. 2018
- 22. Smith et al. 2019
- 23. Cohen et al. 2020
- 24. 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 Polar Vortex [adapted/changed from Cohen et al., 2014; Nature Geosciences]
- 25. 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 Polar Vortex [adapted/changed from Cohen et al., 2014; Nature Geosciences]
- 26. 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 Polar Vortex [adapted/changed from Cohen et al., 2014; Nature Geosciences]
- 27. 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 Polar Vortex [adapted/changed from Cohen et al., 2014; Nature Geosciences]
- 28. 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 Polar Vortex [adapted/changed from Cohen et al., 2014; Nature Geosciences]
- 29. 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/changed from Cohen et al., 2014; Nature Geosciences] Polar Vortex
- 34. R/V Lance – Greenland Sea – May 2017
- 35. R/V Lance – Greenland Sea – May 2017 Turbulent heat fluxes [ SIC ]
- 36. R/V Lance – Greenland Sea – May 2017 Turbulent heat fluxes [ SIC + SIT ]
- 37. WACCM4 Whole Atmosphere Community Climate Model version 4 – Specified Chemistry “high top” chemistry-climate atmosphere model Physical parameterizations from CAM4 • 66 vertical levels – extending to 5 x 10-6 hPa (140 km) • 1.9° latitude x 2.5° longitude • QBO prescribed from radiosonde observations • Improved representation of sudden stratospheric warming (SSW) events • fixed radiative forcings from year 2000
- 38. Sea-Ice Thickness LABE ET AL. 2018, GRL
- 39. Sea-Ice Concentration LABE ET AL. 2018, GRL
- 40. Future Arctic How does sea-ice thickness decline influence the large- scale atmospheric response? Significant thermodynamic response over Arctic Ocean Poleward weakening of jet LABE ET AL. 2018, GRL
- 41. Future Arctic Significant thermodynamic response over Arctic Ocean Poleward weakening of jet LABE ET AL. 2018, GRL How does sea-ice thickness decline influence the large- scale atmospheric response?
- 42. LABE ET AL. 2018, GRL
- 43. Loss of sea-ice thickness reinforces large-scale response LABE ET AL. 2018, GRL + =
- 46. Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss Composite response by QBO phase (~67 years) Modulation by QBO Sea ice experiments
- 47. Modulation by QBO Sea ice experiments Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss
- 48. Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss Composite response by QBO phase (~67 years) Modulation by QBO Sea ice experiments Future (2051-2080) Historical (1975-2005)
- 49. Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss Composite response by QBO phase (~67 years) Modulation by QBO Sea ice experiments Future (2051-2080) Historical (1975-2005)
- 50. Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss Modulation by QBO Sea ice experiments Composite response by QBO phase (~67 years) Easterly (QBO-E) Westerly (QBO-W)
- 51. Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss Modulation by QBO Sea ice experiments Composite response by QBO phase (~67 years) Easterly (QBO-E) Westerly (QBO-W)
- 52. Assess the role of the Quasi-biennial Oscillation (QBO) on the atmospheric response to Arctic sea-ice loss Sea ice experiments Composite response by QBO phase (~67 years) Modulation by QBO Surface (thermodynamic) Troposphere/Stratosphere
- 56. PLUMB FLUX LABE ET AL. 2019, GRL
- 57. PLUMB FLUX LABE ET AL. 2019, GRL
- 58. PLUMB FLUX QBO-E at 150 hPa Anomalous WAFz over Siberia LABE ET AL. 2019, GRL
- 59. COLD EXTREMES LABE ET AL. 2019, GRL
- 60. MOTIVATION ARCTIC SEA ICE MID-LATITUDE WEATHER Sea-ice thickness variability is important for reinforcing the atmospheric response Strength of Siberian High closely related to Eurasia cold spells QBO can modulate teleconnections due to Arctic sea-ice loss
- 61. MOTIVATION ARCTIC SEA ICE MID-LATITUDE WEATHER Sea-ice thickness variability is important for reinforcing the atmospheric response Strength of Siberian High closely related to Eurasia cold spells QBO can modulate teleconnections due to Arctic sea-ice loss
- 62. MOTIVATION ARCTIC SEA ICE MID-LATITUDE WEATHER Sea-ice thickness variability is important for reinforcing the atmospheric response Strength of Siberian High closely related to Eurasia cold spells QBO can modulate teleconnections due to Arctic sea-ice loss
- 66. WHAT IS THE EFFECT OF SEA-ICE LOSS RELATIVE TO ARCTIC AMPLIFICATION?
- 67. OBSERVATIONS
- 69. Polar Amplification Model Intercomparison Project [Smith et al., 2018; GCMD] Model: SC-WACCM4 Model: E3SM Setup: Constant SST Setup: Sea ice change Forcing(s): 2°C Forcing(s): Present Forcing(s): Pre-indust. Ensembles: 300 “Response to sea ice”
- 70. Polar Amplification Model Intercomparison Project [Smith et al., 2018; GCMD] Model: SC-WACCM4 Model: E3SM Setup: Constant SST Setup: Sea ice change Forcing(s): 2°C Forcing(s): Present Forcing(s): Pre-indust. Ensembles: 300 “Response to sea ice”
- 71. Polar Amplification Model Intercomparison Project [Smith et al., 2018; GCMD] Model: SC-WACCM4 Model: E3SM Setup: Constant SST Setup: Sea ice change Forcing(s): 2°C Forcing(s): Present Forcing(s): Pre-indust. Ensembles: 300 “Response to sea ice”
- 72. AA-2030 AA-2060 AA-2090 • 2020-2039 Arctic Amplification Arctic Amplification Experiments RCP8.5 Forcing – LENS Peings et al. 2019, GRL • 2050-2069 Arctic Amplification • 2080-2099 Arctic Amplification
- 73. Arctic 45°N Arctic 45°N Arctic 45°N Sea-ice loss Arctic amplification LABE ET AL. 2020, GRL
- 74. ΔSEA LEVEL PRESSURE Arctic amplification LABE ET AL. 2020, GRL
- 75. ΔSEA LEVEL PRESSURE Arctic sea-ice loss LABE ET AL. 2020, GRL
- 78. Arctic amplification > sea-ice loss
- 79. Arctic amplification > sea-ice loss What about internal variability?
- 80. 1-100 101-200 201-300 RESPONSE TO SEA-ICE FORCING AT 2°C OF GLOBAL WARMING Coupled ocean-atmosphere, high-top model (SC-WACCM4) 14-month simulations Initial-condition large ensembles (300 members) Response to Arctic sea-ice forcing at 2°C of global warming SURFACE RESPONSE PEINGS ET AL. 2021, JCLI
- 81. 1-100 101-200 201-300 RESPONSE TO SEA-ICE FORCING AT 2°C OF GLOBAL WARMING IN BOREAL WINTER Ensemble #
- 82. 1-100 101-200 201-300 Ensemble # RESPONSE TO SEA-ICE FORCING AT 2°C OF GLOBAL WARMING IN BOREAL WINTER
- 83. 1-100 101-200 201-300 Ensemble # RESPONSE TO SEA-ICE FORCING AT 2°C OF GLOBAL WARMING IN BOREAL WINTER
- 84. 1-100 101-200 201-300 Coupled ocean-atmosphere, high-top model (SC-WACCM4) 14-month simulations Initial-condition large ensembles (300 members) Response to Arctic sea-ice forcing at 2°C of global warming STRATOSPHERIC RESPONSE PEINGS ET AL. 2021, JCLI
- 85. 1-100 101-200 201-300 POLAR VORTEX SHIFT Ensemble #
- 86. 1-100 101-200 201-300 POLAR VORTEX WEAKENING Ensemble #
- 87. 1-100 101-200 201-300 NO CHANGE Ensemble #
- 88. ∆SLP ∆Z50 How many ensembles? PEINGS ET AL. 2021, JCLI
- 89. Arctic amplification > sea-ice loss What about internal variability? What about other climate models?
- 90. 2-m Temperature (°C) THERE ARE MANY CLIMATE MODELS… Annual mean 2-m temperature 7 global climate models 16 ensembles each ERA5-BE (observations)
- 91. STANDARD EVALUATION OF CLIMATE MODELS Pattern correlation RMSE EOFs Trends, anomalies, mean state Climate modes of variability Negative Correlation Positive Correlation PATTERN CORRELATION – T2M
- 93. ----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
- 94. 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
- 95. NEURAL NETWORK CLASSIFICATION TASK HIDDEN LAYERS INPUT LAYER OUTPUT LAYER TEMPERATURE MAP LABE AND BARNES 2022, ESS
- 96. APPLY SOFTMAX OPERATOR IN THE OUTPUT LAYER RANK LABE AND BARNES 2022, ESS
- 97. 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
- 98. 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
- 99. 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
- 100. RANKING CLIMATE MODEL PREDICTIONS FOR EACH YEAR IN OBSERVATIONS LABE AND BARNES 2022, ESS
- 101. COMPARING CLIMATE MODELS IN THE ARCTIC High Low RECENT ARCTIC AMPLIFICATION LABE AND BARNES 2022, ESS
- 102. High Low HISTORICAL PERIOD COMPARING CLIMATE MODELS IN THE ARCTIC LABE AND BARNES 2022, ESS
- 103. High Low DIFFERENCE IN LAYER-WISE RELEVANCE PROPAGATION COMPARING CLIMATE MODELS IN THE ARCTIC LABE AND BARNES 2022, ESS
- 104. Atmospheric response sensitive to changes in Arctic sea-ice thickness variability and background state (QBO) Role of sea ice is small relative to Arctic amplification QUESTIONS! Zachary Labe zachary.labe@noaa.gov 16 November 2023 – Tongji University 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
- 105. Atmospheric response sensitive to changes in Arctic sea-ice thickness variability and background state (QBO) Role of sea ice is small relative to Arctic amplification QUESTIONS! Zachary Labe zachary.labe@noaa.gov 16 November 2023 – Tongji University 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