Climate Change in the Arctic

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Zachary Labe

Presentation for the CASSINI Hackathon Finland and Nordic Startup School

Science

CLIMATE CHANGE IN
THE ARCTIC
Zachary Labe
Postdoc at Colorado State University
1 November 2021
CASSINI Hackathon Finland
Nordic Startup School
@ZLabe

ZACHARY LABE, PH.D.
Climate Scientist at Colorado State University, USA
zmlabe@rams.colostate.edu
RESEARCHER
Climate signal vs. weather noise
@ZLabe
COMMUNICATOR
RESEARCHER
Arctic climate change
STORYTELLER
Simple, bold data visualization

Warmer
Colder
Now!
Start of
satellite-era

Polar Amplification:
acceleration of warming in
high latitudes relative to
the rest of the globe

WHY?
Sea ice extent is dropping
at a rate of nearly
0.8 million km2/decade
Temperatures rising >3x as
fast as the globally
averaged mean

Climate Variability
R
e
c
e
n
t
A
r
c
t
i
c
A
m
p
l
i
f
i
c
a
t
i
o
n

[ 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%)

MOSAiC – 2019/2020
https://mosaic-expedition.org/science/

The extent of Arctic sea ice is decreasing…

The thickness of Arctic sea ice is decreasing…

The volume of Arctic sea ice is decreasing…

The age of Arctic sea ice is getting younger…

Climate model projection of summer sea ice through 2100

1920-1962 1980-1997
1998-2015
2006-2042
1963-2005
2043-2080
LENS
LABE ET AL. 2018, JCLI
Historical
Record
Future
Projections
A CLIMATE MODEL

JET STREAM
[Visualization
by
NASA/JPL
Hyperwall]

WHY?
How does Arctic amplification
influence extreme weather
events?
Has it?
Will it?
Can it?
Necessary to understand
mechanisms of Arctic climate
variability before assessing
future local/remote responses
Barnes and
Screen [2015]

[International
Arctic
Research
Center
[IARC;
University
of
Alaska,
Fairbanks]

Increasing
economic
activity in
the Arctic

October-November – Relative to the years of 1951-1980
Our planet
without change…

ARCTIC
NORTHERN
HEMISPHERE
[Modified from SWIPA, 2017]
[0°C]
[14°C]
[7°C]
High Emissions
Low Emissions
Climate Model Projections Climate Model Projections

KEY POINTS
Zachary Labe
zmlabe@rams.colostate.edu
@ZLabe
Climate change has 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.

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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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On 26 January 2023, a military pilot reported four potential unidentified anomalous phenomena (UAP) while operating in the Eglin Air Force Base training range off the coast of Florida. Through the onboard radar system, the pilot initially observed that the four objects were aloft between 16,000 – 18,000 feet and appeared to be flying in formation. However the pilot observed only one of the four objects visually and captured two images of the single object via the aircraft’s electro-optical/infrared (EO/IR) sensor (see Figures 1A and 1B). The pilot could not record video of the event because the aircraft’s video recording equipment was inoperable prior to and during the aircraft’s flight. The pilot observed this single object aloft at 16,000 feet. The following case information is based on the pilot’s initial report and a subsequent discussion that AARO held with the pilot to seek additional details about the pilot’s observation of the reported object.

All-domain Anomaly Resolution Office U.S. Department of Defense (U) Case: “Eg...

Sérgio Sacani

Small size (20-300 nm) / bacteria (size measured in μm) Viral genome – single type of nucleic acid: DNA – dezoxyriboviruses RNA - riboviruses Totally dependant on living cells for all metabolic processes May infect humans, animals, plants, bacteria, fungi,parasites, insects Virion = viral corpuscle = elementary, infectious unit. Only visible by electron microscopy. Laboratory diagnosis of viral infections Methods: • Cytology • Electron microscopy • Cultivation • Detection of viral proteins • Serology • Molecular diagnosis (detection of genetic material i.e. nucleic acids) PPT prepared by Dr. Prince C P

VIRUSES structure and classification ppt by Dr.Prince C P

PRINCE C P

Chemistry is the scientific study of matter, its properties, composition, structure, and the changes it undergoes. It's often divided into various sub-disciplines such as organic chemistry, inorganic chemistry, physical chemistry, analytical chemistry, and biochemistry. Chemistry plays a crucial role in understanding the world around us and has applications in various fields such as medicine, materials science, environmental science, and industry.

Chemistry 4th semester series (krishna).pdf

Sumit Kumar yadav

Context. Determining the size distribution of asteroids is key to understanding the collisional history and evolution of the inner Solar System. Aims. We aim to improve our knowledge of the size distribution of small asteroids in the main belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) archive and subsequently their absolute magnitudes and sizes. Methods. Asteroids appear as curved trails in HST images because of the parallax induced by the fast orbital motion of the spacecraft. Taking into account the trajectory of this latter, the parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the absolute magnitude of an object and an estimation of its size assuming an albedo value, along with some boundaries for its orbital parameters. Results. In this work, we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST archive. Images were captured with the ACS/WFC and WFC3/UVIS instruments. A machine learning algorithm (trained with the results of a citizen science project) was used to detect objects in these images as part of a previous study. Our raw data consist of 1031 asteroid trails from unknown objects, not matching any entries in the Minor Planet Center (MPC) database using their coordinates and imaging time. We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST asteroid set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential main belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution logN(H > H0) ∝ αlog(H0) confirms the previously reported slope change for 15 < H < 18, from α ≈ 0.56 to α ≈ 0.26, maintained in our case down to absolute magnitudes of around H ≈ 20, and therefore expanding the previous result by approximately two magnitudes. Conclusions. HST archival observations can be used as an asteroid survey because the telescope pointings are statistically randomly oriented in the sky and cover long periods of time. They allow us to expand the current best samples of astronomical objects at no extra cost in regard to telescope time.

Hubble Asteroid Hunter III. Physical properties of newly found asteroids

Sérgio Sacani

COST ESTIMATION FOR A RESEARCH PROJECT.pptx

FarihaAbdulRasheed

HotJupiters are amongthebest-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5–12µ 5–12µ 5–12µm with JWST’s Mid-Infrared Instrument (MIRI). 1524 ±35 1524 ±35 and 863±23 The spectra reveal a large day–night temperature contrast (with average brightness temperatures of 1524 ± 35 863 ±23 863 ±23Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ∼100mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Con3trary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2σ upper limit of 1–6 parts per million, depending on model assumptions).

Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b

Sérgio Sacani

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Climate Change in the Arctic

1. CLIMATE CHANGE IN THE ARCTIC Zachary Labe Postdoc at Colorado State University 1 November 2021 CASSINI Hackathon Finland Nordic Startup School @ZLabe

2. ZACHARY LABE, PH.D. Climate Scientist at Colorado State University, USA zmlabe@rams.colostate.edu RESEARCHER Climate signal vs. weather noise @ZLabe COMMUNICATOR RESEARCHER Arctic climate change STORYTELLER Simple, bold data visualization

5. Warmer Colder Now! Start of satellite-era

6. Polar Amplification: acceleration of warming in high latitudes relative to the rest of the globe

7. Polar Amplification: acceleration of warming in high latitudes relative to the rest of the globe

8. The Arctic.

9. The Arctic.

10.

11. WHY? Sea ice extent is dropping at a rate of nearly 0.8 million km2/decade Temperatures rising >3x as fast as the globally averaged mean

12.

13. Satellite-era Uncertainties!

14. Climate Variability R e c e n t A r c t i c A m p l i f i c a t i o n

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%)

18. [Goosse et al. 2018]

19. Ice-covered No Ice

20. Observing ice.

21. SEA-ICE EXTENT 2 km

22. SEA-ICE THICKNESS 1.5 m

23. NASA Scientific Visualization Studio

24. We also use climate models!

25. Modeling vs. Field Work

26. R/V Lance – Greenland Sea – May 2017

27.

28.

29. Snow pits

30. MOSAiC – 2019/2020 https://mosaic-expedition.org/science/

31. Trends & Variability.

32. The extent of Arctic sea ice is decreasing…

33. The thickness of Arctic sea ice is decreasing…

34. The volume of Arctic sea ice is decreasing…

35. The age of Arctic sea ice is getting younger…

36. Future projections.

37. Climate model projection of summer sea ice through 2100

38. 1920-1962 1980-1997 1998-2015 2006-2042 1963-2005 2043-2080 LENS LABE ET AL. 2018, JCLI Historical Record Future Projections A CLIMATE MODEL

39. Changes to weather.

40. JET STREAM [Visualization by NASA/JPL Hyperwall]

41. WHY? How does Arctic amplification influence extreme weather events? Has it? Will it? Can it? Necessary to understand mechanisms of Arctic climate variability before assessing future local/remote responses Barnes and Screen [2015]

42. COLD EXTREMES LABE ET AL. 2019, GRL

43. Regional impacts.

44.

45.

46.

47.

48.

49.

50.

51.

52. [International Arctic Research Center [IARC; University of Alaska, Fairbanks]

53. Increasing economic activity in the Arctic

54. Looking back.

55. https://www.oldweather.org/

56.

57. Looking ahead.

58. October-November – Relative to the years of 1951-1980 Our planet without change…

59. ARCTIC NORTHERN HEMISPHERE [Modified from SWIPA, 2017] [0°C] [14°C] [7°C] High Emissions Low Emissions Climate Model Projections Climate Model Projections

60. It’s not too late!

61. PLOT BY ED HAWKINS

62. KEY POINTS Zachary Labe zmlabe@rams.colostate.edu @ZLabe Climate change has 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.

Climate Change in the Arctic

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