Using neural networks to predict temporary slowdowns in decadal climate warming trends

•

0 likes•101 views

27th Annual CESM Workshop - Earth System Prediction Working Group (ESPWG) To explore the predictability of temporary slowdowns in future climate warming, we apply an artificial neural network (ANN) to data from CESM2-LE and observations. Here, an ANN is tasked with predicting the onset of a slowdown in the rate of the global mean surface temperature trend by using maps of upper ocean heat content anomalies. Through a machine learning explainability method, we identify key regional patterns the ANN is learning to make its slowdown predictions.

Science

USING NEURAL NETWORKS TO PREDICT
TEMPORARY SLOWDOWNS IN DECADAL
CLIMATE 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
16 June 2022
27th Annual CESM Workshop
Earth System Prediction Working Group (ESPWG)

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?

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

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

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

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

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

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.

Decadal warming slowdown predictions by an artificial neural network

Zachary Labe

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.

Guest Lecture: Our changing Arctic in the past and future

Zachary 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

Exploring climate change signals with explainable AI

Zachary Labe

This document provides an overview of using machine learning and explainable AI techniques to analyze climate model data and make predictions about climate signals and forcings. It discusses: 1) The growing use of machine learning tools like neural networks in weather and climate research to better understand models, find relationships in data, and make predictions. 2) An example of using a neural network to classify decade from surface temperature maps and explaining the predictions using Layer-wise Relevance Propagation. 3) Results showing the neural network had greater skill in identifying patterns related to greenhouse gas forcing versus aerosol forcing, and LRP helped identify the relevant regions driving these predictions.

How to explain global warming The question of Attribution

PazSilviapm

How to explain global warming? The question of Attribution You learned about the evidence that proves anthropogenic climate change is taking place. Now, let’s talk about how we explain the phenomena of global warming. Previously, you viewed this figure from the IPCC’s assessment report, showing various factors that contribute to climate change. The next slide will include further detail about each forcing component. This figure is also from the IPCC’s assessment report. LOSU means ‘level of scientific understanding’. In this figure, two different forcing components are shown; anthropogenic and natural forcings. It is important to remember that not only anthropogenic forcings, natural forcings also drive climate change. For example, glacial/Interglacial cycles we observed from the ice core samples earlier this semester that recorded atmospheric conditions over last 450,000 years are clearly caused by natural forcings as we, homo sapiens, did not exist that time! In this figure, each radiative forcing is associated with a value (watts per square meter) quantifying how much each forcing contributes to climate change. Some forcings have a negative number (contribute to cooling), whereas others have a positive number (contribute to warming). The total net forcing is currently a positive value. Thus, the climate trend is currently warming. IPCC report As shown in the previous figure, natural forcing can change climate. The dominant energy source to change Earth’s climate, the sun, also varies its energy emission. This figure shows natural changes in solar irradiance from 1874 to 1988. Solar irradiance is the amount of energy per unit area received from the Sun. In recent decades, solar activity has been measured by satellites, while before it was estimated using a proxy variation. Without satellite observation, energy differences were too small to detect. Solar irradiance is higher during a period called “solar maximum”, which appears almost every 11 years. During a solar maximum, interesting features that appears on the Sun’s surface… (continue) Solar luminosity Sunspot cycle (~11 year period, ~0.1% change in radiation output) (continued) …are sunspots! Sunspots are relatively dark areas on the radiating surface of the Sun, where intense magnetic activity inhibits convection and cools the photosphere. Luminosity is the total amount of energy emitted by the Sun. To summarize, more sunspot appears during a period of solar maximum, when the Sun presents more intense magnetic activity (therefore higher luminosity). Although solar irradiance was only recently measured by satellite, sunspots have been observed for a very long time! The first such recording was made by Galileo Galilei in the 17th century when he created the first telescope. In addition, there are well documented historical records that captured solar activity by Chinese astronomers. All records combined confirm ...

Using neural networks to explore regional climate patterns in single-forcing ...

Zachary Labe

Evaluating and communicating Arctic climate change projection

Zachary 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

Swanston - Climate change Frequently Asked Questions

Northern Institute of Applied Climate Science

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

Making effective science figures

Zachary Labe

Exploring explainable machine learning for detecting changes in climate

Zachary Labe

9 February 2023… Department of Earth, Ocean, and Atmospheric Science Colloquium (Presentation): Exploring explainable machine learning for detecting changes in climate, Florida State University, USA. Remote Presentation. 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 Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming. Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119

CLIM Undergraduate Workshop: Applications in Climate Context - Michael Wehner...

The Statistical and Applied Mathematical Sciences Institute

Climate statisticians analyze observational climate data and model simulations to detect trends, attribute causes, and quantify uncertainties. They use statistical methods like linear regression to attribute observed warming to human and natural factors. They also use extreme value theory to describe rare weather events and project how these extremes may change with continued warming. A key task is quantifying various sources of uncertainty in climate projections, like different model sensitivities and emissions scenarios.

Global Warming Sec 2324

Chen Yugin

The document describes a scenario where a team of 4 scientists are transported 101 years into the future to the year 2110 to study the effects of global warming. Their mission is to investigate how global warming has impacted the Earth and report back to "Central Command" with recommendations for actions that can be taken today to protect future inhabitants from the consequences of climate change. Each scientist is assigned a different role - climate scientist, policy analyst, economist, energy expert, or urban planner - to research the issues from various perspectives.

Global Warming Sec 2324

Chen Yugin

The document describes a scenario where a team of four scientists are transported 101 years into the future to the year 2110 to study the effects of global warming. Their mission is to investigate how global warming has impacted the Earth and report back to "Central Command" with recommendations for actions that can be taken today to protect future inhabitants from the consequences of climate change. Each scientist is assigned a different role - as a climate scientist, policy analyst, economist, energy expert, or urban planner - to research the issues from various perspectives.

Explainable AI for identifying regional climate change patterns

Zachary Labe

Lecture 2 anthropogenic climate change - the current state of knowledge and...

polylsgiedx

This document discusses climate change and global warming. It begins by explaining the greenhouse effect and how the Earth's climate system works. It then discusses evidence of a changing climate, such as rising temperatures, melting ice sheets and glaciers, rising sea levels, and increasing carbon dioxide levels in the atmosphere. The document addresses skepticism around human-caused climate change and argues that climate science has been thoroughly tested and validated through induction, deduction, and consistency of evidence from multiple independent sources. It outlines how doubt has been deliberately spread by certain groups for political or economic reasons, similar to how the tobacco industry denied the health risks of smoking. In the end, the evidence overwhelmingly shows that human activity such as fossil fuel burning is disrupting the

Climate Change in the Arctic

Zachary Labe

The document discusses climate change in the Arctic. It notes that the Arctic is warming faster than the rest of the globe, with sea ice extent and thickness declining significantly. Climate models project that Arctic warming and sea ice loss will continue through the 21st century. Improving observations and models can help reduce uncertainties about future climate impacts in the Arctic and how changes may influence remote weather patterns. Action is needed to reduce emissions and limit global temperature rise in order to prevent the worst effects of climate change in the Arctic.

Terra Seismic : Global Earthquake Prediction Systems

Oleg Elshin (3,100+ connections)

Terra Seismic can predict most major earthquakes (M6.2 or greater) at least 2 - 5 months before they will strike. Global earthquake prediction is based on determinations of the stressed areas that will start to behave abnormally before major earthquakes. The size of the observed stressed areas roughly corresponds to estimates calculated from Dobrovolsky’s formula. To identify abnormalities and make predictions, Terra Seismic applies various methodologies, including satellite remote sensing methods and data from ground-based instruments. We currently process terabytes of information daily, and use more than 80 different multiparameter prediction systems. Alerts are issued if the abnormalities are confirmed by at least five different systems. We observed that geophysical patterns of earthquake development and stress accumulation are generally the same for all key seismic regions. Thus, the same earthquake prediction methodologies and systems can be applied successfully worldwide. Our technology has been used to retrospectively test data gathered since 1970 and it successfully detected about 90 percent of all significant quakes over the last 50 years.

Applications of machine learning for climate change and variability

Zachary 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

Using explainable machine learning to evaluate climate change projections

Zachary 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

Telling data-driven climate stories

Zachary Labe

Changes in the atmosphere

cdenef

This document summarizes data from multiple scientific organizations on trends in global surface air temperatures and atmospheric greenhouse gas levels. The key points are: 1) Six independent scientific groups find that average global land and sea surface temperatures have steadily increased over the past century, with the most rapid rise occurring over the past 50 years. 2) Regional temperature changes are often larger than global averages, with greater warming at northern high latitudes and Arctic regions. 3) Satellite and weather balloon data also indicate warming of the lower troposphere and cooling of the lower stratosphere. 4) Atmospheric levels of greenhouse gases like carbon dioxide and methane have risen substantially since the industrial revolution due to human activities like fossil fuel burning

2014 Climate Change Connections - Workshop Content & Activities

Teresa Eastburn

The 14th Summer Environmental Health Sciences Institute took place in Houston, TX the week of 7/14/2014. This workshop on climate change, comes from educational designers from the National Center for Atmospheric Research. While you may not have been able to join us, you can still review content and download all the activities at our website: https://scied.ucar.edu/events/clone-climate-change-connections-2014

Climate Change: Frequently Asked Questions

Northern Institute of Applied Climate Science

The document discusses frequently asked questions about climate change. It summarizes that there is overwhelming scientific evidence that climate change is occurring and is caused by human activities. While models have uncertainties, they consistently project warming trends that are useful for considering risks over decades or more. The best approach is to use multiple models and consider a range of possible futures, as uncertainties are inherent in long-term climate projections. Questions about the topic are encouraged, but the evidence clearly shows the climate is changing due to human emissions of greenhouse gases.

COUNTER-INTUITY OF COMPLEX SYSTEMS: WEATHER VS. CLIMATE

Paul H. Carr

2013 Climate Change Connections to our Weather, Environment, and Health

Teresa Eastburn

The Science of Global Warming.pdf

BillyCripe1

This document provides an overview of a course on global warming science. It introduces key concepts like climate vs. weather, examples of past climate variability, evidence that the earth is warming due to human activity, and tools used to study and model climate. It outlines central questions the course will explore around physical processes controlling climate and how climate has and could change in the future.

Assessing climate variability and change with explainable neural networks

Zachary Labe

I. Explainable neural networks can reveal patterns of climate change in large ensembles simulated with different combinations of external forcing. II. Neural networks can identify unique model differences and biases between large ensembles and observations. III. The presentation raises the question of what neural networks can reveal about predictability in the climate system, using the example of the debated "global warming hiatus" period.

Welcome to GFDL for Take Your Child To Work Day

Zachary Labe

Explainable AI for distinguishing future climate change scenarios

Zachary Labe

Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria (Apr 2024). Abstract... https://meetingorganizer.copernicus.org/EGU24/EGU24-9110.html References... Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. (submitted; preprint - https://doi.org/10.22541/essoar.171288901.17027965/v1)

Similar to Using neural networks to predict temporary slowdowns in decadal climate warming trends

Climate change extremes by season in the United States

Zachary Labe

Making effective science figures

Zachary Labe

Exploring explainable machine learning for detecting changes in climate

Zachary Labe

CLIM Undergraduate Workshop: Applications in Climate Context - Michael Wehner...

The Statistical and Applied Mathematical Sciences Institute

Global Warming Sec 2324

Chen Yugin

Global Warming Sec 2324

Chen Yugin

Explainable AI for identifying regional climate change patterns

Zachary Labe

Lecture 2 anthropogenic climate change - the current state of knowledge and...

polylsgiedx

Climate Change in the Arctic

Zachary Labe

Terra Seismic : Global Earthquake Prediction Systems

Oleg Elshin (3,100+ connections)

Applications of machine learning for climate change and variability

Zachary Labe

Using explainable machine learning to evaluate climate change projections

Zachary Labe

Telling data-driven climate stories

Zachary Labe

Changes in the atmosphere

cdenef

2014 Climate Change Connections - Workshop Content & Activities

Teresa Eastburn

Climate Change: Frequently Asked Questions

Northern Institute of Applied Climate Science

COUNTER-INTUITY OF COMPLEX SYSTEMS: WEATHER VS. CLIMATE

Paul H. Carr

2013 Climate Change Connections to our Weather, Environment, and Health

Teresa Eastburn

The Science of Global Warming.pdf

BillyCripe1

Assessing climate variability and change with explainable neural networks

Zachary Labe

Similar to Using neural networks to predict temporary slowdowns in decadal climate warming trends (20)

Climate change extremes by season in the United States

Making effective science figures

Exploring explainable machine learning for detecting changes in climate

CLIM Undergraduate Workshop: Applications in Climate Context - Michael Wehner...

Global Warming Sec 2324

Explainable AI for identifying regional climate change patterns

Lecture 2 anthropogenic climate change - the current state of knowledge and...

Climate Change in the Arctic

Terra Seismic : Global Earthquake Prediction Systems

Applications of machine learning for climate change and variability

Using explainable machine learning to evaluate climate change projections

Telling data-driven climate stories

Changes in the atmosphere

2014 Climate Change Connections - Workshop Content & Activities

Climate Change: Frequently Asked Questions

COUNTER-INTUITY OF COMPLEX SYSTEMS: WEATHER VS. CLIMATE

2013 Climate Change Connections to our Weather, Environment, and Health

The Science of Global Warming.pdf

Assessing climate variability and change with explainable neural networks

More from Zachary Labe

Welcome to GFDL for Take Your Child To Work Day

Zachary Labe

Explainable AI for distinguishing future climate change scenarios

Zachary Labe

Reexamining future projections of Arctic climate linkages

Zachary 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

Techniques and Considerations for Improving Accessibility in Online Media

Zachary Labe

An intro to explainable AI for polar climate science

Zachary 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 change

Zachary Labe

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Zachary Labe

Explainable AI approach for evaluating climate models in the Arctic

Zachary 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

data-driven approach to identifying key regions of change associated with fut...

Zachary Labe

Distinguishing the regional emergence of United States summer temperatures be...

Zachary Labe

Researching and Communicating Our Changing Climate

Zachary Labe

Zachary Labe is a postdoc researcher at NOAA GFDL and Princeton University who studies climate variability and change. His research uses tools like artificial intelligence and climate models to disentangle the signal of climate change from natural weather noise. He conducts field work including Arctic expeditions and uses supercomputers to run complex climate models that generate huge amounts of data.

Revisiting projections of Arctic climate change linkages

Zachary 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

Visualizing climate change through data

Zachary 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

Contrasting polar climate change in the past, present, and future

Zachary Labe

Climate Projections - What Really is Business as Usual?

Zachary Labe

Monitoring indicators of climate change through data-driven visualization

Zachary Labe

Sea Ice Anomalies

Zachary Labe

Techniques and Considerations for Improving Accessibility in Online Media

Zachary Labe

Career pathways and research opportunities in the Earth sciences

Zachary Labe

More from Zachary Labe (20)

Welcome to GFDL for Take Your Child To Work Day

Explainable AI for distinguishing future climate change scenarios

Reexamining future projections of Arctic climate linkages

Techniques and Considerations for Improving Accessibility in Online Media

An intro to explainable AI for polar climate science

Using accessible data to communicate global climate change

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Explainable AI approach for evaluating climate models in the Arctic

Explainable neural networks for evaluating patterns of climate change and var...

data-driven approach to identifying key regions of change associated with fut...

Distinguishing the regional emergence of United States summer temperatures be...

Researching and Communicating Our Changing Climate

Revisiting projections of Arctic climate change linkages

Visualizing climate change through data

Contrasting polar climate change in the past, present, and future

Climate Projections - What Really is Business as Usual?

Monitoring indicators of climate change through data-driven visualization

Sea Ice Anomalies

Techniques and Considerations for Improving Accessibility in Online Media

Career pathways and research opportunities in the Earth sciences

Recently uploaded

Compexometric titration/Chelatorphy titration/chelating titration

Vandana Devesh Sharma

Classification Metal ion ion indicators Masking and demasking reagents Estimation of Magnisium sulphate Calcium gluconate Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types- 1.Direct Titration 2.Back Titration 3.Replacement Titration 4.Indirect Titration Masking agent, Demasking agents formation of complex comparition between masking and demasking agents, Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators, Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators Instrumental Techniques-Photometry Potentiometry Miscellaneous methods. Complex titration with EDTA.

TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptx

shubhijain836

Sustainable Land Management - Climate Smart Agriculture

International Food Policy Research Institute- South Asia Office

Physiology of Nervous System presentation.pptx

fatima132662

Gadgets for management of stored product pests_Dr.UPR.pdf

PirithiRaju

Insectsplayamajorroleinthedeteriorationoffoodgrainscausingbothquantitativeandqualitativelosses Wellprovedthatnogranariescanbefilledwithgrainswithoutinsectsastheharvestedproducecontainegg(or)larvae(or)pupae(or)adultinsectinthembecauseoffieldcarryoverinfestationwhichcannotbeavoidedindevelopingcountrieslikeIndia Simpletechnologiesfortimelydetectionofinsectsinthestoredproduceandtherebyplantimelycontrolmeasures

Male reproduction physiology by Suyash Garg .pptx

suyashempire

23PH301 - Optics - Optical Lenses.pptx

RDhivya6

Methods of grain storage Structures in India.pdf

PirithiRaju

•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc., •Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores. •InIndiaoutofthetotalproduction,about30%ismarketablesurplus •Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable •TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

Areesha Ahmad

The cost of acquiring information by natural selection

Carl Bergstrom

This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome. It's based on the first part of this research paper: The cost of information acquisition by natural selection Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577

快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样

hozt8xgk

学校原件一模一样【微信：741003700 】《(UAM毕业证书)马德里自治大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Direct Seeded Rice - Climate Smart Agriculture

International Food Policy Research Institute- South Asia Office

Summary Of transcription and Translation.pdf

vadgavevedant86

LEARNING TO LIVE WITH LAWS OF MOTION .pptx

yourprojectpartner05

Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...

Sérgio Sacani

We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS + 53.13485 − 27.82088 with a host spectroscopic redshift of 2.903 ± 0.007 . The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red ( � ⁢ ( � − � ) ∼ 0.9 ) despite a host galaxy with low-extinction and has a high Ca II velocity ( 19 , 000 ± 2 , 000 km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low- � Ca-rich population. Although such an object is too red for any low- � cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement ( ≲ 1 ⁢ � ) with Λ CDM. Therefore unlike low- � Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high- � truly diverge from their low- � counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.

Introduction_Ch_01_Biotech Biotechnology course .pptx

QusayMaghayerh

AJAY KUMAR NIET GreNo Guava Project File.pdf

AJAY KUMAR

Farming systems analysis: what have we learnt?.pptx

Frédéric Baudron

Immersive Learning That Works: Research Grounding and Paths Forward

Leonel Morgado

We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Sérgio Sacani

Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation

Recently uploaded (20)

Compexometric titration/Chelatorphy titration/chelating titration

TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptx

Sustainable Land Management - Climate Smart Agriculture

Physiology of Nervous System presentation.pptx

Gadgets for management of stored product pests_Dr.UPR.pdf

Male reproduction physiology by Suyash Garg .pptx

23PH301 - Optics - Optical Lenses.pptx

Methods of grain storage Structures in India.pdf

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

The cost of acquiring information by natural selection

快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样

Direct Seeded Rice - Climate Smart Agriculture

Summary Of transcription and Translation.pdf

LEARNING TO LIVE WITH LAWS OF MOTION .pptx

Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...

Introduction_Ch_01_Biotech Biotechnology course .pptx

AJAY KUMAR NIET GreNo Guava Project File.pdf

Farming systems analysis: what have we learnt?.pptx

Immersive Learning That Works: Research Grounding and Paths Forward

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Using neural networks to predict temporary slowdowns in decadal climate warming trends

1. USING NEURAL NETWORKS TO PREDICT TEMPORARY SLOWDOWNS IN DECADAL CLIMATE 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 16 June 2022 27th Annual CESM Workshop Earth System Prediction Working Group (ESPWG)

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. Visualizing something we already know…

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

28. 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

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

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

31. 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?

32. So how well does the neural network do?

33. Low High Colder Warmer

34. Low High Colder Warmer

35. Low High Colder Warmer

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

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

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

39. 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 neural networks to predict temporary slowdowns in decadal climate warming trends

Recommended

Recommended

More Related Content

Similar to Using neural networks to predict temporary slowdowns in decadal climate warming trends

Similar to Using neural networks to predict temporary slowdowns in decadal climate warming trends (20)

More from Zachary Labe

More from Zachary Labe (20)

Recently uploaded

Recently uploaded (20)

Using neural networks to predict temporary slowdowns in decadal climate warming trends