Decadal warming slowdown predictions by an artificial neural network

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1. An artificial neural network was developed to predict slowdowns in the rate of decadal global warming by analyzing patterns in ocean heat content anomalies. 2. The neural network found that transitions in the phase of the Interdecadal Pacific Oscillation often preceded warming slowdowns in climate model simulations. 3. Using explainable AI techniques, researchers determined the neural network was leveraging tropical patterns of ocean heat content to make its predictions of when decadal warming rates might slowdown.

Ciencias

DECADAL WARMING
SLOWDOWN PREDICTIONS
BY AN ARTIFICIAL NEURAL NETWORK
@ZLabe
Zachary M. Labe
with Elizabeth A. Barnes
Colorado State University
Department of Atmospheric Science
29 October 2021
Young Scientist Symposium on Atmospheric Research (YSSAR)

Global Warming
Hiatus?
…in the media, etc.

Global Warming
Hiatus?
>300 papers, to-date

Are slowdowns (“hiatus”) in decadal
warming predictable?
• Statistical construct?
• Lack of surface temperature observations in the Arctic?
• Phase transition of the Interdecadal Pacific Oscillation (IPO)?
• Influence of volcanoes and other aerosol forcing?
• Weaker solar forcing?
• Lower equilibrium climate sensitivity (ECS)?
• Other combinations of internal variability?
FUTURE
WARMING

Select one ensemble
member and calculate
the annual mean
global mean surface
temperature (GMST)
2-m TEMPERATURE
ANOMALY

Calculate 10-year
moving (linear) trends
2-m TEMPERATURE
ANOMALY

Plot the slope of the
linear trends
START OF 10-YEAR
TEMPERATURE TREND
2-m TEMPERATURE
ANOMALY

Calculate a threshold
for defining a slowdown
in decadal warming

Repeat this exercise for
each ensemble
member in CESM2-LE

Compare warming
slowdowns with
reanalysis (ERA5)

INPUT
[DATA]
PREDICTION
Machine
Learning

INPUT
[DATA]
PREDICTION
Machine
Learning
Explainable AI
Learn new
science!

OCEAN HEAT CONTENT – 100 M
Start with anomalous ocean heat…

OCEAN HEAT CONTENT – 100 M
INPUT LAYER
Start with anomalous ocean heat…

OCEAN HEAT CONTENT – 100 M
INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
YES
SLOWDOWN
NO
SLOWDOWN
Will a slowdown begin?

OCEAN HEAT CONTENT – 100 M
INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
YES
SLOWDOWN
NO
SLOWDOWN
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
LAYER-WISE RELEVANCE PROPAGATION
Will a slowdown begin?

What about observations?
Future (2012-)
so-called “hiatus”
Comparing
observations to
the IPO

What about observations?
Future (2012-)
so-called “hiatus”
2021 (preliminary)
Looking ahead
to the near-
future…
?

What about observations?
Colder Warmer
[2003, 2004] [2016, 2017]

KEY POINTS
Zachary Labe
zmlabe@rams.colostate.edu
@ZLabe
1. An artificial neural network predicts the onset of slowdowns in decadal warming trends of
global mean surface temperature
2. Transitions in the phase of the Interdecadal Pacific Oscillation are frequently associated with
warming slowdown trends in CESM2-LE
3. Explainable AI reveals the neural network is leveraging tropical patterns of ocean heat content
anomalies to make its predictions

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Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Sérgio Sacani

GBSN - Microbiology (Unit 3)

Areesha Ahmad

Forensic Biology & Its biological significance.pdf

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Proteomics: types, protein profiling steps etc.

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Bollworms are among the most damaging pests in cotton cultivation, affecting the bolls where the cotton fibers are formed. There are several species of bollworms, each capable of causing significant yield loss and quality degradation if not effectively managed. Here’s a detailed look at the primary bollworm species affecting cotton: Cotton Bollworm (Helicoverpa armigera): Also known as the corn earworm or the Old World bollworm, this pest is found in many regions around the world. It is highly polyphagous (feeds on many different plants) and poses a threat not only to cotton but also to maize, tomatoes, and legumes. The larvae bore into the cotton bolls, feeding on the developing seeds and fibers, which can lead to boll rot. Pink Bollworm (Pectinophora gossypiella): A significant pest of cotton, the pink bollworm larvae infest the cotton bolls, feeding on the seeds and lint. This can severely damage or destroy the bolls. In regions where pink bollworms are prevalent, they have been a major driver for the adoption of genetically engineered Bt cotton, which expresses a bacterium gene toxic to certain insects. Tobacco Budworm (Heliothis virescens): Closely related to the cotton bollworm, the tobacco budworm primarily attacks tobacco but is also a common pest in cotton. It primarily damages the flowers and bolls of the cotton plant. Differentiating between the tobacco budworm and the cotton bollworm based on appearance can be challenging, but it is crucial for effective management. American Bollworm (Helicoverpa zea): Known in some regions as the corn earworm, it is similar in behavior to Helicoverpa armigera and poses a threat to a variety of crops, including cotton. The larvae attack the cotton bolls, leading to direct damage to the cotton lint and seeds. Management Strategies: Cultural Controls: Crop rotation, destruction of crop residues, and deep plowing can help break the pest’s life cycle. Timing of planting can also be adjusted to avoid peak pest infestation. Biological Controls: Natural enemies like Trichogramma wasps, which parasitize bollworm eggs, and predators such as lacewings and ladybugs can be encouraged. Bacillus thuringiensis (Bt) products can also be sprayed, which are particularly effective against young larvae. Chemical Controls: Insecticides may be required when infestation levels exceed economic thresholds. However, resistance management must be considered, alternating modes of action to avoid developing resistance. Genetic Approaches: Bt cotton, genetically modified to express Bacillus thuringiensis toxin, has been highly effective in controlling bollworms and has dramatically reduced the reliance on chemical insecticides. Monitoring and Scouting: Regular field scouting and using pheromone traps to monitor adult populations can help in timely and targeted application of control measures. The effective management of bollworms often requires an integrated approach

Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf

PirithiRaju

Mustard, as a crop, is susceptible to a variety of pests that can affect its growth and yield. Here’s a rundown of some common pests that target mustard plants: Aphids: These small, sap-sucking insects can cause significant damage by feeding on the leaves and stems. Aphids also excrete a sticky substance known as honeydew, which can lead to the growth of sooty mold on the plants. Flea Beetles: These tiny beetles jump like fleas when disturbed and chew small holes in the leaves. They are particularly damaging in the early growth stages of the plant. Cabbage Loopers: The larvae of a type of moth, these caterpillars are known for their distinctive looping movement. They chew large holes in the leaves and can defoliate plants if present in large numbers. Diamondback Moth Larvae: Another caterpillar pest, these larvae chew small holes in the leaves and can cause extensive damage, especially when infestations are heavy. Whiteflies: These are tiny, winged insects that feed on plant sap and can quickly become a problem in greenhouse or close planting conditions. Like aphids, they also secrete honeydew. Cutworms: These are the larvae of certain types of moths and are known for cutting young plants at the stem base at ground level. They are most destructive during the night. Root Maggots: The larvae of root maggot flies, these pests attack the roots of mustard plants, causing wilting and potentially killing young plants. Harlequin Bugs: These are colorful stink bugs that suck the sap from mustard plant stems and leaves, causing the leaves to become stippled, wilt, and eventually die if the infestation is severe. Mustard Sawfly: The larvae of the mustard sawfly can cause considerable defoliation, as they feed voraciously on the leaves. Clubroot: Caused by a fungus-like organism, clubroot affects the roots, causing them to swell and distort. While technically a disease, it is often associated with pest management because controlling it involves similar preventative strategies. Control Measures: Managing pests in mustard involves a combination of cultural, biological, and chemical methods. Crop rotation, resistant varieties, timely sowing, maintaining plant health, and using natural predators like ladybugs and parasitic wasps can help keep pest populations under control. Chemical pesticides should be used as a last resort due to their potential impact on the environment and non-target species.

Pests of mustard_Identification_Management_Dr.UPR.pdf

PirithiRaju

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Module for Grade 9 for Asynchronous/Distance learning

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Decadal warming slowdown predictions by an artificial neural network

1. DECADAL WARMING SLOWDOWN PREDICTIONS BY AN ARTIFICIAL NEURAL NETWORK @ZLabe Zachary M. Labe with Elizabeth A. Barnes Colorado State University Department of Atmospheric Science 29 October 2021 Young Scientist Symposium on Atmospheric Research (YSSAR)

2. Global Warming Hiatus? …in research

3. Global Warming Hiatus? …in research

4. Global Warming Hiatus? …in research

5. Global Warming Hiatus? …in research

6. Global Warming Hiatus? …in research

7. Global Warming Hiatus? …in research

8. Global Warming Hiatus? …in the media, etc.

9. Global Warming Hiatus? >300 papers, to-date

10. Global Warming Hiatus?

11. Are slowdowns (“hiatus”) in decadal warming predictable? • Statistical construct? • Lack of surface temperature observations in the Arctic? • Phase transition of the Interdecadal Pacific Oscillation (IPO)? • Influence of volcanoes and other aerosol forcing? • Weaker solar forcing? • Lower equilibrium climate sensitivity (ECS)? • Other combinations of internal variability? FUTURE WARMING

12. Select one ensemble member and calculate the annual mean global mean surface temperature (GMST) 2-m TEMPERATURE ANOMALY

13. Calculate 10-year moving (linear) trends 2-m TEMPERATURE ANOMALY

14. Plot the slope of the linear trends START OF 10-YEAR TEMPERATURE TREND 2-m TEMPERATURE ANOMALY

15. Calculate a threshold for defining a slowdown in decadal warming

16. Repeat this exercise for each ensemble member in CESM2-LE

17. Compare warming slowdowns with reanalysis (ERA5)

18.

19. INPUT [DATA] PREDICTION Machine Learning

20. INPUT [DATA] PREDICTION Machine Learning

21. INPUT [DATA] PREDICTION Machine Learning Explainable AI Learn new science!

22. OCEAN HEAT CONTENT – 100 M Start with anomalous ocean heat…

23. OCEAN HEAT CONTENT – 100 M INPUT LAYER Start with anomalous ocean heat…

24. OCEAN HEAT CONTENT – 100 M INPUT LAYER HIDDEN LAYERS OUTPUT LAYER YES SLOWDOWN NO SLOWDOWN Will a slowdown begin?

25. OCEAN HEAT CONTENT – 100 M INPUT LAYER HIDDEN LAYERS OUTPUT LAYER YES SLOWDOWN NO SLOWDOWN BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI LAYER-WISE RELEVANCE PROPAGATION Will a slowdown begin?

26. So how well does the neural network do?

27. Low High Colder Warmer

28. Low High Colder Warmer

29. Low High Colder Warmer

30. What about observations? Future (2012-) so-called “hiatus” Comparing observations to the IPO

31. What about observations? Future (2012-) so-called “hiatus” 2021 (preliminary) Looking ahead to the near- future… ?

32. What about observations? Colder Warmer [2003, 2004] [2016, 2017]

33. KEY POINTS Zachary Labe zmlabe@rams.colostate.edu @ZLabe 1. An artificial neural network predicts the onset of slowdowns in decadal warming trends of global mean surface temperature 2. Transitions in the phase of the Interdecadal Pacific Oscillation are frequently associated with warming slowdown trends in CESM2-LE 3. Explainable AI reveals the neural network is leveraging tropical patterns of ocean heat content anomalies to make its predictions

Decadal warming slowdown predictions by an artificial neural network

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