Using artificial neural networks to predict temporary slowdowns in global warming trends

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1. Researchers used an artificial neural network to predict slowdowns in the rate of decadal global warming by analyzing patterns in ocean heat content anomalies. 2. Explainable AI techniques showed the neural network was leveraging tropical ocean heat content to make predictions. 3. Transitions between phases of the Interdecadal Pacific Oscillation were often associated with periods of slower warming in climate model simulations, consistent with observed temperature trends.

Ciências

USING ARTIFICIAL NEURAL NETWORKS
TO PREDICT TEMPORARY SLOWDOWNS
IN GLOBAL WARMING TRENDS
@ZLabe
Zachary M. Labe1
with Elizabeth A. Barnes2
1NOAA GFDL and Princeton University; Atmospheric and Oceanic Sciences
2
Colorado State University; Department of Atmospheric Science
9 January 2023
22nd Conference on Artificial Intelligence for Environmental Science
103rd AMS Annual Meeting in Denver, CO

Global Warming
Hiatus?
…in the media, etc.

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
[Labe and Barnes, 2022; GRL]

Calculate 10-year
moving (linear) trends
2-m TEMPERATURE
ANOMALY
[Labe and Barnes, 2022; GRL]

Plot the slope of the
linear trends
START OF 10-YEAR
TEMPERATURE TREND
2-m TEMPERATURE
ANOMALY
[Labe and Barnes, 2022; GRL]

Calculate a threshold
for defining a slowdown
in decadal warming
[Labe and Barnes, 2022; GRL]

Repeat this exercise for
each ensemble
member in CESM2-LE
[Labe and Barnes, 2022; GRL]

Compare warming
slowdowns with
reanalysis (ERA5)
[Labe and Barnes, 2022; GRL]

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…
[Labe and Barnes, 2022; GRL]

OCEAN HEAT CONTENT – 100 M
INPUT LAYER
Start with anomalous ocean heat…
[Labe and Barnes, 2022; GRL]

OCEAN HEAT CONTENT – 100 M
INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
YES
SLOWDOWN
NO
SLOWDOWN
Will a slowdown begin?
[Labe and Barnes, 2022; GRL]

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?
[Labe and Barnes, 2022; GRL]

Neural
Network
[0] La Niña [1] El Niño
[Toms et al. 2020; JAMES]
Input a map of sea surface temperature

Visualizing something we already know…
Input maps of sea surface
temperatures to identify
El Niño or La Niña
Use ‘LRP’ to see how the
neural network is making
its decision
[Toms et al. 2020; JAMES]
Layer-wise Relevance Propagation
Composite Observations
LRP [Relevance]
SST Anomaly [°C]
0.00 0.75
0.0 1.5
-1.5

[Adapted from Adebayo et al., 2020]
EXPLAINABLE AI IS
NOT PERFECT
THERE ARE MANY
METHODS

[Adapted from Adebayo et al., 2020]
THERE ARE MANY
METHODS
EXPLAINABLE AI IS
NOT PERFECT

So how well does the neural network do?
[Labe and Barnes, 2022; GRL]

Low High Colder Warmer
[Labe and Barnes, 2022; GRL]

What about observations?
Future (2012-)
so-called “hiatus”
Comparing
observations to
the IPO
[Labe and Barnes, 2022; GRL]

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

What about observations?
Colder Warmer
[2003, 2004] [2016, 2017]
[Labe and Barnes, 2022; GRL]

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

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fruit fly, this slide mainly made for pumpkin fruit fly, this is also known as drosophila melangastor, this type of fruit fly destroyed the mainly vegetables crops. if you want to known examples this types of fly which is destroy the pumpkin, tomato, brinjal, potato, bottle guard, ridge guard, bitter guard, cucumber, water melon, musk melon, bean, long bean and other many vegetables which has fruits. they distryed fruit fly. thank you...

pumpkin fruit fly, water melon fruit fly, cucumber fruit fly

PRADYUMMAURYA1

Clean In Place(CIP).pptx .

Poonam Aher Patil

www.seribangash.com The Mariana Trench is one of the most remarkable geological features on Earth. Here are some details about it: Location: The Mariana Trench is located in the western Pacific Ocean, east of the Mariana Islands. It stretches for about 2,550 kilometers (1,580 miles) and is known as the deepest part of the world's oceans. Depth: The trench reaches incredible depths, with its deepest point known as the Challenger Deep, which plunges down to approximately 10,984 meters (36,037 feet) below sea level. To put this into perspective, if Mount Everest, the tallest mountain on Earth, were placed at the bottom of the Challenger Deep, its peak would still be over 2 kilometers (1.25 miles) underwater. Formation: The Mariana Trench was formed by the subduction of the Pacific Plate beneath the Mariana Plate. This process creates a deep trench as the heavier Pacific Plate is forced beneath the lighter Mariana Plate. Geological Features: The trench is characterized by steep, V-shaped valleys, and its walls are composed of highly compressed sedimentary rock. At the bottom of the trench, there are also large amounts of marine sediment. Pressure: The pressure at the bottom of the Mariana Trench is immense, reaching over 1,000 times the pressure at the surface. This extreme pressure creates a challenging environment for exploration and makes it difficult for organisms to survive. Exploration: Despite its extreme conditions, the Mariana Trench has been the subject of numerous scientific expeditions and explorations. One of the most famous explorations was the dive to the Challenger Deep by Swiss scientist Jacques Piccard and U.S. Navy Lieutenant Don Walsh in 1960. More recently, in 2012, filmmaker James Cameron made a solo dive to the bottom of the Challenger Deep in the Deepsea Challenger submersible. Biological Discoveries: Despite the harsh conditions, the Mariana Trench is home to a surprising variety of life forms, including unique species of deep-sea fish, crustaceans, and microbial life. Some organisms have adapted to survive in the extreme pressure and darkness of the trench. Environmental Importance: Studying the Mariana Trench provides valuable insights into the geology, biology, and oceanography of the deep sea. It also helps scientists better understand the processes that shape the Earth's crust and the distribution of life in the oceans. Conservation: Due to its remote location and extreme depths, the Mariana Trench has remained relatively untouched by human activity. However, there is growing concern about the potential impacts of deep-sea mining and pollution on this fragile ecosystem, highlighting the need for conservation efforts to protect this unique environment. https://seribangash.com/barber-shop-business-complete-guide-for-beginners/ https://seribangash.com/legend-virat-kohli-in-cricket-history/

The Mariana Trench remarkable geological features on Earth.pptx

seri bangash

Theoretical predictions and observational data indicate a class of sub-Neptune exoplanets may have water-rich interiors covered by hydrogen-dominated atmospheres. Provided suitable climate conditions, such planets could host surface liquid oceans. Motivated by recent JWST observations of K2-18 b, we self-consistently model the photochemistry and potential detectability of biogenic sulfur gases in the atmospheres of temperate sub-Neptune waterworlds for the first time. On Earth today, organic sulfur compounds produced by marine biota are rapidly destroyed by photochemical processes before they can accumulate to significant levels. Domagal-Goldman et al. suggest that detectable biogenic sulfur signatures could emerge in Archean-like atmospheres with higher biological production or low UV flux. In this study, we explore biogenic sulfur across a wide range of biological fluxes and stellar UV environments. Critically, the main photochemical sinks are absent on the nightside of tidally locked planets. To address this, we further perform experiments with a 3D general circulation model and a 2D photochemical model (VULCAN 2D) to simulate the global distribution of biogenic gases to investigate their terminator concentrations as seen via transmission spectroscopy. Our models indicate that biogenic sulfur gases can rise to potentially detectable levels on hydrogen-rich water worlds, but only for enhanced global biosulfur flux (20 times modern Earth’s flux). We find that it is challenging to identify DMS at 3.4 μm where it strongly overlaps with CH4, whereas it is more plausible to detect DMS and companion byproducts, ethylene (C2H4) and ethane (C2H6), in the mid-infrared between 9 and 13 μm. Unified Astronomy Thesaurus concepts: Exoplanet atmospheres (487); Exoplanet

Biogenic Sulfur Gases as Biosignatures on Temperate Sub-Neptune Waterworlds

Sérgio Sacani

Using artificial neural networks to predict temporary slowdowns in global warming trends

1. USING ARTIFICIAL NEURAL NETWORKS TO PREDICT TEMPORARY SLOWDOWNS IN GLOBAL WARMING TRENDS @ZLabe Zachary M. Labe1 with Elizabeth A. Barnes2 1NOAA GFDL and Princeton University; Atmospheric and Oceanic Sciences 2 Colorado State University; Department of Atmospheric Science 9 January 2023 22nd Conference on Artificial Intelligence for Environmental Science 103rd AMS Annual Meeting in Denver, CO

4. NASA/GISS/GISTEMPv4 “Hiatus”

5. Global Warming Hiatus? …in research

6. Global Warming Hiatus? …in research

7. Global Warming Hiatus? …in research

8. Global Warming Hiatus? …in research

9. Global Warming Hiatus? …in research

10. Global Warming Hiatus? …in research

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

12. 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

13. Select one ensemble member and calculate the annual mean global mean surface temperature (GMST) 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]

14. Calculate 10-year moving (linear) trends 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]

15. Plot the slope of the linear trends START OF 10-YEAR TEMPERATURE TREND 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]

16. Calculate a threshold for defining a slowdown in decadal warming [Labe and Barnes, 2022; GRL]

17. Repeat this exercise for each ensemble member in CESM2-LE [Labe and Barnes, 2022; GRL]

18. Compare warming slowdowns with reanalysis (ERA5) [Labe and Barnes, 2022; GRL]

19. [Labe and Barnes, 2022; GRL]

20. INPUT [DATA] PREDICTION Machine Learning

21. INPUT [DATA] PREDICTION Machine Learning

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

23. OCEAN HEAT CONTENT – 100 M Start with anomalous ocean heat… [Labe and Barnes, 2022; GRL]

24. OCEAN HEAT CONTENT – 100 M INPUT LAYER Start with anomalous ocean heat… [Labe and Barnes, 2022; GRL]

25. OCEAN HEAT CONTENT – 100 M INPUT LAYER HIDDEN LAYERS OUTPUT LAYER YES SLOWDOWN NO SLOWDOWN Will a slowdown begin? [Labe and Barnes, 2022; GRL]

26. 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? [Labe and Barnes, 2022; GRL]

27. Visualizing something we already know…

28. Neural Network [0] La Niña [1] El Niño [Toms et al. 2020; JAMES] Input a map of sea surface temperature

29. Visualizing something we already know… Input maps of sea surface temperatures to identify El Niño or La Niña Use ‘LRP’ to see how the neural network is making its decision [Toms et al. 2020; JAMES] Layer-wise Relevance Propagation Composite Observations LRP [Relevance] SST Anomaly [°C] 0.00 0.75 0.0 1.5 -1.5

30. [Adapted from Adebayo et al., 2020] EXPLAINABLE AI IS NOT PERFECT THERE ARE MANY METHODS

31. [Adapted from Adebayo et al., 2020] THERE ARE MANY METHODS EXPLAINABLE AI IS NOT PERFECT

32. 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? [Labe and Barnes, 2022; GRL]

33. So how well does the neural network do? [Labe and Barnes, 2022; GRL]

34. Low High Colder Warmer [Labe and Barnes, 2022; GRL]

35. Low High Colder Warmer [Labe and Barnes, 2022; GRL]

36. Low High Colder Warmer [Labe and Barnes, 2022; GRL]

37. What about observations? Future (2012-) so-called “hiatus” Comparing observations to the IPO [Labe and Barnes, 2022; GRL]

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

39. What about observations? Colder Warmer [2003, 2004] [2016, 2017] [Labe and Barnes, 2022; GRL]

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

Using artificial neural networks to predict temporary slowdowns in global warming trends

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