This document summarizes research on modeling the "warm Arctic, cold Siberia" pattern under climate change. It finds that declining Arctic sea ice, especially sea ice thickness, reinforces warming over the Arctic and cooling over Siberia. The strength of this pattern also depends on the phase of the quasi-biennial oscillation. Specifically, declining sea ice leads to a stronger Siberian high pressure system and increased chances of cold extremes in Eurasia during the easterly QBO phase. Future projections using an ensemble of climate models suggest that both declining sea ice and rising greenhouse gases will continue intensifying the warm Arctic-cold Siberia pattern through the 21st century.
The Pan-Arctic Impacts of Thinning Sea IceZachary Labe
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.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to S...Zachary Labe
20th Conference on Middle Atmosphere at the 99th Annual Meeting of the American Meteorological Society (abstract: https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/352664)
Communicating Arctic climate change through data-driven storiesZachary Labe
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.
The Pan-Arctic Impacts of Thinning Sea IceZachary Labe
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.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to S...Zachary Labe
20th Conference on Middle Atmosphere at the 99th Annual Meeting of the American Meteorological Society (abstract: https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/352664)
Communicating Arctic climate change through data-driven storiesZachary Labe
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.
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)
Impact of Sea Level Rise from Storm Surge USADag Lohmann
We're quantifying the impact that a 30cm sea level rise has on losses from hurricanes for each individual location in the USA. We're also looking at losses from a hypothetical sea level in the year 1900. Summaries are shown by state and selected maps.
Summary of results: Based on current conditions of exposure (e.g. buildings and other economic assets) we have an annual average loss of about $5 Billion from our simulations. Given the sea level in 1900 that loss would go to $4 Billion. Current projections of sea level rise vary widely, but most have us exceed 30cm between 2040 and 2080. Some go much higher (many meters), while the most optimistic ones are around 30cm at the end of the century. Given the same exposure, same sea defenses, and same hurricanes, losses would go up to an average of $6.9 Billion / year (called the average annual loss or AAL).
Revisiting projections of Arctic climate change linkagesZachary Labe
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
Reexamining future projections of Arctic climate linkagesZachary Labe
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
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)
Impact of Sea Level Rise from Storm Surge USADag Lohmann
We're quantifying the impact that a 30cm sea level rise has on losses from hurricanes for each individual location in the USA. We're also looking at losses from a hypothetical sea level in the year 1900. Summaries are shown by state and selected maps.
Summary of results: Based on current conditions of exposure (e.g. buildings and other economic assets) we have an annual average loss of about $5 Billion from our simulations. Given the sea level in 1900 that loss would go to $4 Billion. Current projections of sea level rise vary widely, but most have us exceed 30cm between 2040 and 2080. Some go much higher (many meters), while the most optimistic ones are around 30cm at the end of the century. Given the same exposure, same sea defenses, and same hurricanes, losses would go up to an average of $6.9 Billion / year (called the average annual loss or AAL).
Revisiting projections of Arctic climate change linkagesZachary Labe
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
Reexamining future projections of Arctic climate linkagesZachary Labe
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
Melting Ice: Context, Causes, and Consequences of Polar AmplificationZachary Labe
Profound changes are ongoing at the ends of our planet. Thawing permafrost buried in ancient soils, melting lake and river ice-cover, thinning sea ice, and dwindling mountain glaciers are just a few indicators of climate change within the Arctic. Further, billions of tons of ice are now lost per year from the Greenland Ice Sheet, leaving our coastlines increasingly vulnerable to sea level rise. ‘Polar amplification’ refers to enhanced climate changes in the high latitudes compared to the rest of the globe in response to an external forcing. In the Arctic, air temperatures are rising at more than twice the rate of the global average. While changes in the Antarctic have been slower than the Arctic, the Antarctic ice sheets store enough freshwater to increase global sea levels by 58 m. Thus, Antarctica is often considered our sleeping giant.
Despite robust evidence of polar amplification in the past and present-day, the largest spread in future climate model projections is found in the Arctic and Antarctic. Moreover, quantifying the positive feedbacks contributing to polar amplification remains quite challenging. These large uncertainties are critical for understanding the impacts of future changes to ocean biogeochemistry and circulation, global sea level rise, and mid-latitude climate extremes and variability. This talk will provide an overview of polar amplification using present-day observational evidence and climate models simulations through the late 21st century. In particular, how do we separate the signal and noise in polar climate change and make evidence-based predictions in a warming world?
Evaluating and communicating Arctic climate change projectionZachary Labe
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
This is a pdf. due to file size we are not able to upload the PowerPoint presentation you can email info@thecccw.org.uk for a copy which includes video clips
Guest Lecture: Our changing Arctic in the past and futureZachary Labe
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
A hard-hitting lecture by Ranyl Rhydwen at the Centre for Alternative Technology in Wales - really 3 lectures crammed into one - explaining how our climate works, what the current science is saying about climate change, and thoughts on what to do about it. A very good, and important talk to listen to. Recorded November 2009, a month before the COP-15 Climate Conference in Copenhagen. Please note this lecture is copyright Centre for Alternative Technology (http://www.cat.org.uk)
Climate: Climatic Change - Evidence, Cycles and The Futuregeomillie
A PowerPoint used in class to cover the key forms of evidence you need to know for the Exam. Key Questions are likely to be focused on how we can gain information of past climatic change, and how it can be used to predict future, and I would expect you to be able to comment on the usefulness of the different types. For instance, Ice cores are highly accurate and quantifiable evidence, but gaining them is expensive, and only gives a climatic record for the site at which the snow formed. However, they do provide the longest record of change.
Antarctic climate history and global climate changesPontus Lurcock
Antarctic climate changes have been reconstructed from ice and sediment cores and numerical models (which also predict future changes). Major ice sheets first appeared 34 million years ago (Ma) and fluctuated throughout the Oligocene, with an overall cooling trend. Ice volume more than doubled at the Oligocene-Miocene boundary. Fluctuating Miocene temperatures peaked at 17–14 Ma, followed by dramatic cooling. Cooling continued through the Pliocene and Pleistocene, with another major glacial expansion at 3–2 Ma. Several interacting drivers control Antarctic climate. On timescales of 10,000–100,000 years, insolation varies with orbital cycles, causing periodic climate variations. Opening of Southern Ocean gateways produced a circumpolar current that thermally isolated Antarctica. Declining atmospheric CO2 triggered Cenozoic glaciation. Antarctic glaciations affect global climate by lowering sea level, intensifying atmospheric circulation, and increasing planetary albedo. Ice sheets interact with ocean water, forming water masses that play a key role in global ocean circulation.
Arctic climate through the lens of data visualizationZachary Labe
15 February 2023…
Rider University, Global Biogeochemistry Class Visit (Presentation): Arctic climate change through the lens of data visualization, NOAA GFDL, Princeton, 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., 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
Techniques and Considerations for Improving Accessibility in Online MediaZachary Labe
3 April 2024…
United States Association of Polar Early Career Scientists (USAPECS) IDEA Training Course (Presentation): Accessibility and disability in online spaces. Remote Presentation.
An intro to explainable AI for polar climate scienceZachary Labe
26 March 2024…
GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ.
References:
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. 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, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464
Using accessible data to communicate global climate changeZachary Labe
25 March 2024…
Climate Communication Workshop: Learn How To Make Your Research Matter (Keynote Presentation): Using accessible data to communicate global climate change, Temple University, Philadelphia, PA.
Water in a Frozen Arctic: Cross-Disciplinary PerspectivesZachary Labe
14 March 2024…
United States Association of Polar Early Career Scientists (USAPECS) Webinar (Host): Water in a Frozen Arctic: Cross-Disciplinary Perspectives. Remote Panel.
Event Page: https://www.usapecs.org/post/webinar-water-frozen-arctic
Explainable AI approach for evaluating climate models in the ArcticZachary Labe
27 March 2024…
IARPC Collaborations, Modelers’ Community of Practice (Presentation): Explainable AI approach for evaluating climate models in the Arctic. Remote Presentation.
References...
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
Explainable neural networks for evaluating patterns of climate change and var...Zachary Labe
12 March 2024…
Sharing Science – North American Webinar, Young Earth System Scientists (YESS) Community (Presentation): Explainable neural networks for evaluating patterns of climate change and variability. 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
Applications of machine learning for climate change and variabilityZachary Labe
23 February 2024…
Department of Environmental Sciences Seminar (Presentation): Applications of machine learning for climate change and variability, Rutgers University, New Brunswick, NJ.
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
data-driven approach to identifying key regions of change associated with fut...Zachary Labe
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
Distinguishing the regional emergence of United States summer temperatures be...Zachary Labe
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 ClimateZachary Labe
6 December 2023…
Mercer County Community College (Presentation): Meet the climate scientists: our journey and our science, West Windsor Township, NJ, USA.
Visualizing climate change through dataZachary Labe
18 November 2023…
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 projectionsZachary Labe
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
Contrasting polar climate change in the past, present, and futureZachary Labe
28 September 2023…
Guest lecture for “Observing and Modeling Climate Change (EES 3506/5506)” (Presentation): Contrasting polar climate change in the past, present, and future, Temple University, Philadelphia, PA. Remote Presentation.
Climate change extremes by season in the United StatesZachary Labe
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
Monitoring indicators of climate change through data-driven visualizationZachary Labe
19 June 2023…
La Uni Climática - IV Edition (Presentation): Monitoring indicators of climate change through data-driven visualization. Remote Presentation.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
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(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
4. An Overview of Sugarcane White Leaf Disease in Vietnam.pdf
Refining projections of the 'warm Arctic, cold Siberia' pattern in climate model simulations
1. Refining projections of the
'warm Arctic, cold Siberia'
pattern in climate model
simulations
Zachary Labe
Colorado State University
With
Yannick Peings & Gudrun Magnusdottir
10 September 2020
Yale University
Atmosphere and Ocean
Climate Dynamics
Seminar
20. [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”
21. 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]
22. 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]
23. 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]
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
[adapted/changed from
Cohen et al., 2014;
Nature Geosciences]
Polar Vortex
31. [ 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%)
32. [ 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%)
33. [ 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%)
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. !SIT = FIT – HIT
!SIC = FIC – HIC
!NET = FICT – HIT• Loss of sea-ice
thickness and
concentration
• Loss of sea-ice
thickness
• Loss of sea-ice
concentration
41. 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
42. 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?
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
50. 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)
51. 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)
52. 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)
53. 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)
54. 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
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
63. 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
64. 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
109. Dependence of the
Siberian High response on
polar mid-tropospheric
warming
Gray bar shows the
uncertainty range between
NCEP/NCAR R1 and ERA5
for 10-year epochs
110. 1. Climate models forced only by sea-ice anomalies do not
capture the vertical extent of Arctic warming
2. Increase in 1000-500 hPa layer is linked to a strengthening of
the Siberian High and cold anomalies in eastern Asia
3. Role of the stratosphere is unclear due to large internal
variability at future global warming levels of 2°C
Arctic amplification >> sea-ice loss
111. 1. Climate models forced only by sea-ice anomalies do not
capture the vertical extent of Arctic warming
2. Increase in 1000-500 hPa layer is linked to a strengthening of
the Siberian High and cold anomalies in eastern Asia
3. Role of the stratosphere is unclear due to large internal
variability at future global warming levels of 2°C
Arctic amplification >> sea-ice loss