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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ZLabe_AMS2020_presentation_01142020
- 1. Detection of Signal in the Large- Scale Circulation Response to Arctic Sea-Ice Decline Zachary Labe Yannick Peings and Gudrun Magnusdottir Department of Earth System Science at the University of California, Irvine 14 January 2020 100th AMS Annual Meeting @ZLabe
- 2. LENS (RCP 8.5) Mean December - Relative to 1981-2010 Climatology
- 6. Is the circulation response to Arctic sea-ice loss actually robust in the context of internal variability? 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
- 7. 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 Is the circulation response to Arctic sea-ice loss actually robust in the context of internal variability?
- 8. Complex. 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
- 9. 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”
- 10. 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”
- 11. 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”
- 14. Net Surface Heat Flux
- 28. Surface Troposphere Stratosphere December-March Zonal Wind Labe et al. 2018, GRL
- 29. Surface Troposphere Stratosphere December-March Zonal Wind Labe et al. 2018, GRL
- 34. two-sided Student's t test 𝜶 = 0.05
- 35. False Discovery Rate 𝜶 𝑭𝑫𝑹 = 0.05
- 39. SURFACE AIR TEMPERATURE NONE SIGNAL!
- 41. 1. Small signal in dynamical response to sea-ice decline relative to internal variability and climatology 2. Strong surface warming and increase in precipitation mostly confined to Arctic Ocean 3. AGCM experiments need even larger ensembles (>200 members) to address the noise Is the circulation response to Arctic sea-ice loss actually robust in the context of internal variability?
- 42. 1. Small signal in dynamical response to sea-ice decline relative to internal variability and climatology 2. Strong surface warming and increase in precipitation mostly confined to Arctic Ocean 3. AGCM experiments need even larger ensembles (>200 members) to address the noise Is the circulation response to Arctic sea-ice loss actually robust in the context of internal variability?
- 43. 1. Small signal in dynamical response to sea-ice decline relative to internal variability and climatology 2. Strong surface warming and increase in precipitation mostly confined to Arctic Ocean 3. AGCM experiments need even larger ensembles (>200 members) to address the noise Is the circulation response to Arctic sea-ice loss actually robust in the context of internal variability?
- 44. Zachary Labe zlabe@uci.edu @ZLabe Atmospheric response very sensitive to changes in Arctic sea-ice (thickness) variability and background state (QBO) Role of sea ice is small relative to internal variability