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.
Learning new climate science by opening the machine learning black boxZachary Labe
Department of Psychology (Invited): Cognitive Brownbag Series, Colorado State University, CO.
The popularity of machine learning, specifically neural networks, is rapidly growing in nearly all areas of science. The explosion of these methods also coincides with a growing influx of computationally expensive data sets and the need for high efficiency in solving predication problems. However, there is also some hesitancy in adopting the use of neural networks due to concerns about their reliability, reproducibility, and interpretability – thus, they are often described as “black boxes.”
In climate science, we often consider signal-to-noise problems to help disentangle human-caused climate change from natural variability. These applications typically involve complicated nonlinear relationships between different feedbacks at play in the ocean, land, and atmosphere. Recent work has shown that neural networks can be a promising tool for solving these types of statistical problems when combined with explainability techniques developed by the fields of computer science and image processing. Interestingly, these methods have revealed that neural networks often leverage regional patterns of climate change indicators in order to make their predictions. In this talk, I will share examples from climate science that use a few of these visualization methods to peer into the “black box” of neural networks, which help us to better understand their decision-making processes while also learning new science. The same machine learning visualization methods can be easily adapted for a wide variety of applications and other scientific fields of study.
Assessing climate variability and change with explainable neural networksZachary 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.
Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainab...Zachary Labe
26th Annual CESM Workshop - Machine Learning: CESM-Related Efforts
In this study, we use an explainable artificial intelligence met hod to identify climate signals that are found in a new set of single-forcing large ensembles from CESM1. To compare patterns between simulations, we adopt an artificial neural network (ANN) that predicts the year from input maps of near-surface temperature. We find that the North Atlantic Ocean is an important region for the ANN to make its prediction, especially for the simulation forced without time-evolving industrial aerosols.
Revealing climate change signals with explainable AIZachary Labe
1. The document discusses using explainable artificial intelligence methods to analyze climate model data and reveal patterns of climate change.
2. An artificial neural network was trained to predict decades from maps of surface temperature and showed greater skill at predicting decades when greenhouse gases were the only forcing compared to when aerosols were the only forcing.
3. Explainable AI techniques showed the North Atlantic region was particularly important for the neural network's predictions in climate model experiments forced by both aerosols and greenhouse gases.
Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Net...Zachary Labe
The relative roles of individual forcings on large-scale climate variability remain difficult to disentangle within fully-coupled global climate model simulations. Here, we train an artificial neural network (ANN) to classify the climate forcings of a new set of CESM1 initial-condition large ensembles that are forced by different combinations of aerosol (industrial and biomass burning), greenhouse gas, and land-use/land-cover forcings. As a result of learning the regional responses of internal variability to the different external forcings, the ANN is able to successfully classify the dominant forcing for each model simulation. Using recently developed explainable AI methods, such as layerwise relevance propagation, we then compare the patterns of climate variability identified by the ANN between different external climate forcings that are learned by the neural network. Further, we apply this ANN architecture on additional climate simulations from the multi-model large ensemble archive, which include all anthropogenic and natural radiative forcings. From this collection of initial-condition ensembles, the ANN is also able to detect changes in atmospheric internal variability between the 20th and 21st centuries by training on climate fields after the mean forced signal has already been removed. This ANN framework and its associated visualization tools provide a novel approach to extract complex patterns of observable and projected climate variability and trends in Earth system models. (from https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379553)
Using explainable neural networks for comparing climate model projectionsZachary Labe
1. The document discusses using explainable neural networks to compare climate model projections and evaluate which climate models best match observations.
2. Temperature maps from observations are input into a neural network trained on climate model data to classify each observation year with a climate model.
3. Layer-wise relevance propagation is used to explain the neural network's classifications and identify differences between climate models, which can help evaluate models, especially in regions with known biases like the Arctic.
Learning new climate science by opening the machine learning black boxZachary Labe
Department of Psychology (Invited): Cognitive Brownbag Series, Colorado State University, CO.
The popularity of machine learning, specifically neural networks, is rapidly growing in nearly all areas of science. The explosion of these methods also coincides with a growing influx of computationally expensive data sets and the need for high efficiency in solving predication problems. However, there is also some hesitancy in adopting the use of neural networks due to concerns about their reliability, reproducibility, and interpretability – thus, they are often described as “black boxes.”
In climate science, we often consider signal-to-noise problems to help disentangle human-caused climate change from natural variability. These applications typically involve complicated nonlinear relationships between different feedbacks at play in the ocean, land, and atmosphere. Recent work has shown that neural networks can be a promising tool for solving these types of statistical problems when combined with explainability techniques developed by the fields of computer science and image processing. Interestingly, these methods have revealed that neural networks often leverage regional patterns of climate change indicators in order to make their predictions. In this talk, I will share examples from climate science that use a few of these visualization methods to peer into the “black box” of neural networks, which help us to better understand their decision-making processes while also learning new science. The same machine learning visualization methods can be easily adapted for a wide variety of applications and other scientific fields of study.
Assessing climate variability and change with explainable neural networksZachary 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.
Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainab...Zachary Labe
26th Annual CESM Workshop - Machine Learning: CESM-Related Efforts
In this study, we use an explainable artificial intelligence met hod to identify climate signals that are found in a new set of single-forcing large ensembles from CESM1. To compare patterns between simulations, we adopt an artificial neural network (ANN) that predicts the year from input maps of near-surface temperature. We find that the North Atlantic Ocean is an important region for the ANN to make its prediction, especially for the simulation forced without time-evolving industrial aerosols.
Revealing climate change signals with explainable AIZachary Labe
1. The document discusses using explainable artificial intelligence methods to analyze climate model data and reveal patterns of climate change.
2. An artificial neural network was trained to predict decades from maps of surface temperature and showed greater skill at predicting decades when greenhouse gases were the only forcing compared to when aerosols were the only forcing.
3. Explainable AI techniques showed the North Atlantic region was particularly important for the neural network's predictions in climate model experiments forced by both aerosols and greenhouse gases.
Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Net...Zachary Labe
The relative roles of individual forcings on large-scale climate variability remain difficult to disentangle within fully-coupled global climate model simulations. Here, we train an artificial neural network (ANN) to classify the climate forcings of a new set of CESM1 initial-condition large ensembles that are forced by different combinations of aerosol (industrial and biomass burning), greenhouse gas, and land-use/land-cover forcings. As a result of learning the regional responses of internal variability to the different external forcings, the ANN is able to successfully classify the dominant forcing for each model simulation. Using recently developed explainable AI methods, such as layerwise relevance propagation, we then compare the patterns of climate variability identified by the ANN between different external climate forcings that are learned by the neural network. Further, we apply this ANN architecture on additional climate simulations from the multi-model large ensemble archive, which include all anthropogenic and natural radiative forcings. From this collection of initial-condition ensembles, the ANN is also able to detect changes in atmospheric internal variability between the 20th and 21st centuries by training on climate fields after the mean forced signal has already been removed. This ANN framework and its associated visualization tools provide a novel approach to extract complex patterns of observable and projected climate variability and trends in Earth system models. (from https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379553)
Using explainable neural networks for comparing climate model projectionsZachary Labe
1. The document discusses using explainable neural networks to compare climate model projections and evaluate which climate models best match observations.
2. Temperature maps from observations are input into a neural network trained on climate model data to classify each observation year with a climate model.
3. Layer-wise relevance propagation is used to explain the neural network's classifications and identify differences between climate models, which can help evaluate models, especially in regions with known biases like the Arctic.
This document discusses using satellite imagery to detect agricultural smoke pollution. It describes an algorithm to calculate aerosol optical thickness from SeaWiFS satellite data. A case study is presented analyzing smoke over Kansas from agricultural burning on April 10-13, 2003. Surface PM2.5 measurements are compared to the satellite-derived aerosol optical thickness values to correlate smoke pollution levels.
This document summarizes an experiment that measured the interior air temperature of a model doghouse facing south and west at different times of day.
In the morning (simulating winter conditions), the south-facing doghouse had a higher interior air temperature, while the west-facing doghouse was cooler. In the late afternoon (simulating summer), the south-facing doghouse was cooler, while the west-facing interior was warmer.
The results demonstrate that a structure oriented with its long side running east-west performs better for passive solar heating in winter and cooling in summer compared to other orientations. Some limitations included variable weather conditions and simplifications in the thermal model.
Lina Presented our CCNY work on the improvement of PM2.5 estimates for the NY state using satellite remote sensing Aerosol Optical Depth and meteorological information.
This document discusses using satellite and surface sensors to detect agricultural smoke. It describes using SeaWiFS satellite data to obtain surface reflectance and aerosol optical thickness (AOT) through an algorithm. A case study of agricultural fires in Kansas in April 2003 is presented, with images showing smoke patterns from Rayleigh corrected satellite data and AOT, along with surface PM2.5 measurements and wind vectors. The study correlates PM2.5 data with AOT values from satellite images at matching latitudes and longitudes.
Burlap, cheesecloth, and window screen were tested as canopy materials to reduce soil heating from solar radiation. Irradiance measurements found burlap allowed the least sunlight, followed by window screen then cheesecloth. Soil surface and depth temperatures followed this trend, with burlap maintaining the coolest temperatures. COMSOL modeling of heat flux agreed with experimental data. In conclusion, burlap most effectively reduced soil temperatures at all depths tested, demonstrating its utility for greenhouse shade coverings.
The document profiles Zachary Labe, a climate scientist at Colorado State University who studies Arctic climate change and communicates his research through simple, bold data visualizations. His work focuses on distinguishing climate signals from weather noise, placing current weather events in the context of long-term climate trends. He aims to use diverse voices and real-time climate data to tell stories about climate change impacts and the need for climate resilience and social justice.
Technical presentation documenting the process to classify land use at the Ce...Jason Schroeder
This document summarizes a study examining historical land cover classification at Cedarburg Bog in Wisconsin. The study aims to classify land cover in 1941 and 2000 from aerial photographs in order to identify changes over time in response to climate, land use, and human activities. The methods involve georeferencing and digitizing the 1941 aerial photos, classifying land cover into categories like forest, agriculture, water, and wetland, and comparing the results to published land cover maps from 1992 and 2001 to analyze major areas of land use change surrounding the bog over the past 60 years.
This document describes how rainfall estimates are created from raw radar data at the National Centers for Environmental Information (NCEI). It explains that multiple radars are combined and merged to get nationwide coverage with 2 minute resolution on a standardized grid. Raw radar returns are cleaned of contamination from birds and other sources. The vertical radar scans are integrated into a single rainfall estimate. The final product provides a consistent, high quality rainfall estimate compared to the original radar data which had poorer spatial and temporal resolution and quality issues.
The document summarizes the development of satellite modeling for the National Solar Radiation Database (NSRDB) to provide accurate surface solar radiation data. It describes the evolution from empirical to physical models using satellite measurements and ancillary data as inputs to radiative transfer models. Validation shows the new 2005-2012 dataset has a mean bias error of less than 5% for GHI and DNI compared to surface measurements, though uncertainty remains for cloudy cases. Future work aims to improve the model with higher resolution data and better representation of aerosols and surfaces.
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.
This document summarizes a study analyzing local climate change in Rio Branco, Brazil due to deforestation. The researchers used Landsat thermal band data and a land cover classification map to model temperature anomalies as distance from forest increases. They found some relationship between distance and temperature anomalies, but the direction and amount were not uniform, warranting further inquiry. The study aimed to test whether deforested areas have higher temperatures than forested areas when correcting for elevation differences.
The document discusses an ensemble generation scheme for ocean analysis systems that aims to provide uncertainty estimates of the ocean state. The scheme involves randomly perturbing both assimilated observations and surface forcing inputs to generate an ensemble of ocean state estimates. This allows the analysis system to account for uncertainties in observations and surface forcing when assessing the ocean state. The project developing this scheme is taking place from 2018 to 2020 at ECMWF.
This study compared the convection coefficients of two potato slices cooling via free and forced convection. One slice cooled freely on a rack while the other was cooled with a standing fan blowing air over it. Temperature probes measured the internal temperature of each slice over time. The slice under forced convection cooled faster, with its convection coefficient calculated as 20 W/(m2K) compared to 9.5 W/(m2K) for the freely cooling slice. A COMSOL model also showed the internal temperature distribution over time matched the measured data. The experiment validated that forced convection enhances heat transfer compared to free convection.
1) UAVSAR collected repeat-pass SAR data over agricultural fields in Canada during a soil moisture campaign to analyze the effect of soil moisture on polarimetric and interferometric measurements.
2) Both backscatter and interferometric phase correlations decreased as soil moisture changed more between passes, indicating soil moisture impacts the SAR measurements.
3) Models captured general backscatter and polarimetric phase trends with soil moisture but underestimated the observed variation in interferometric phase with changes in moisture.
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...grssieee
This document summarizes research generating fine-resolution leaf area index (LAI) maps for boreal forests in Finland using satellite imagery. Two methods were used: 1) an empirical model based on the reduced simple ratio vegetation index and 2) inversion of a forest reflectance model. Field measurements of LAI from over 1000 plots were used to develop and validate the models. The empirical model using scene-specific parameters produced LAI maps for 2000 and 2006 with realistic patterns, though some overestimation. Comparison to MODIS LAI showed generally good agreement but some systematic differences likely due to phenological and calibration issues. The empirical method was selected for producing the LAI maps.
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
This document discusses space agencies providing real-time analyses and forecasts of greenhouse gas fluxes, climate forcing factors like solar radiation, fire emissions, and anthropogenic emissions on global and regional scales. It also covers interim reanalyses and validated reanalyses of these in-situ components to inform policy on greenhouse gas fluxes and climate change.
Inter annual insolation variability (solar resource)chaudharichetan
This document presents new maps of solar resource interannual variability in the continental United States. It summarizes that solar resource variability plays a key role in energy yield and cash flow forecasting for PV systems. It then describes how the authors aggregated solar resource data from multiple sources by region and climate zone to reduce sampling errors and present more accurate estimates of interannual variability, given the limited available data. The authors find solar resource variability ranges from 1.3% to 3.9% across different climate zones in the US based on their analysis.
Exploring explainable machine learning for detecting changes in climateZachary 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
Forced climate signals with explainable AI and large ensemblesZachary Labe
1) The document discusses using artificial neural networks and explainable AI methods to analyze climate model simulations and detect climate signals related to different external forcings like greenhouse gases and aerosols.
2) A case study examines temperature trends in simulations with varying external forcings, and the neural network is able to more accurately predict observations when trained on a simulation with evolving greenhouse gases.
3) Explainable AI methods reveal the neural network relies more on patterns related to greenhouse gas forcing compared to aerosol or preindustrial forcing patterns when making predictions.
Using explainable machine learning for evaluating patterns of climate changeZachary Labe
22 February 2023…
Natural Sciences Group Seminar (Presentation): Using explainable machine learning for evaluating patterns of climate change, Washington State University Vancouver, 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
This document discusses using satellite imagery to detect agricultural smoke pollution. It describes an algorithm to calculate aerosol optical thickness from SeaWiFS satellite data. A case study is presented analyzing smoke over Kansas from agricultural burning on April 10-13, 2003. Surface PM2.5 measurements are compared to the satellite-derived aerosol optical thickness values to correlate smoke pollution levels.
This document summarizes an experiment that measured the interior air temperature of a model doghouse facing south and west at different times of day.
In the morning (simulating winter conditions), the south-facing doghouse had a higher interior air temperature, while the west-facing doghouse was cooler. In the late afternoon (simulating summer), the south-facing doghouse was cooler, while the west-facing interior was warmer.
The results demonstrate that a structure oriented with its long side running east-west performs better for passive solar heating in winter and cooling in summer compared to other orientations. Some limitations included variable weather conditions and simplifications in the thermal model.
Lina Presented our CCNY work on the improvement of PM2.5 estimates for the NY state using satellite remote sensing Aerosol Optical Depth and meteorological information.
This document discusses using satellite and surface sensors to detect agricultural smoke. It describes using SeaWiFS satellite data to obtain surface reflectance and aerosol optical thickness (AOT) through an algorithm. A case study of agricultural fires in Kansas in April 2003 is presented, with images showing smoke patterns from Rayleigh corrected satellite data and AOT, along with surface PM2.5 measurements and wind vectors. The study correlates PM2.5 data with AOT values from satellite images at matching latitudes and longitudes.
Burlap, cheesecloth, and window screen were tested as canopy materials to reduce soil heating from solar radiation. Irradiance measurements found burlap allowed the least sunlight, followed by window screen then cheesecloth. Soil surface and depth temperatures followed this trend, with burlap maintaining the coolest temperatures. COMSOL modeling of heat flux agreed with experimental data. In conclusion, burlap most effectively reduced soil temperatures at all depths tested, demonstrating its utility for greenhouse shade coverings.
The document profiles Zachary Labe, a climate scientist at Colorado State University who studies Arctic climate change and communicates his research through simple, bold data visualizations. His work focuses on distinguishing climate signals from weather noise, placing current weather events in the context of long-term climate trends. He aims to use diverse voices and real-time climate data to tell stories about climate change impacts and the need for climate resilience and social justice.
Technical presentation documenting the process to classify land use at the Ce...Jason Schroeder
This document summarizes a study examining historical land cover classification at Cedarburg Bog in Wisconsin. The study aims to classify land cover in 1941 and 2000 from aerial photographs in order to identify changes over time in response to climate, land use, and human activities. The methods involve georeferencing and digitizing the 1941 aerial photos, classifying land cover into categories like forest, agriculture, water, and wetland, and comparing the results to published land cover maps from 1992 and 2001 to analyze major areas of land use change surrounding the bog over the past 60 years.
This document describes how rainfall estimates are created from raw radar data at the National Centers for Environmental Information (NCEI). It explains that multiple radars are combined and merged to get nationwide coverage with 2 minute resolution on a standardized grid. Raw radar returns are cleaned of contamination from birds and other sources. The vertical radar scans are integrated into a single rainfall estimate. The final product provides a consistent, high quality rainfall estimate compared to the original radar data which had poorer spatial and temporal resolution and quality issues.
The document summarizes the development of satellite modeling for the National Solar Radiation Database (NSRDB) to provide accurate surface solar radiation data. It describes the evolution from empirical to physical models using satellite measurements and ancillary data as inputs to radiative transfer models. Validation shows the new 2005-2012 dataset has a mean bias error of less than 5% for GHI and DNI compared to surface measurements, though uncertainty remains for cloudy cases. Future work aims to improve the model with higher resolution data and better representation of aerosols and surfaces.
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.
This document summarizes a study analyzing local climate change in Rio Branco, Brazil due to deforestation. The researchers used Landsat thermal band data and a land cover classification map to model temperature anomalies as distance from forest increases. They found some relationship between distance and temperature anomalies, but the direction and amount were not uniform, warranting further inquiry. The study aimed to test whether deforested areas have higher temperatures than forested areas when correcting for elevation differences.
The document discusses an ensemble generation scheme for ocean analysis systems that aims to provide uncertainty estimates of the ocean state. The scheme involves randomly perturbing both assimilated observations and surface forcing inputs to generate an ensemble of ocean state estimates. This allows the analysis system to account for uncertainties in observations and surface forcing when assessing the ocean state. The project developing this scheme is taking place from 2018 to 2020 at ECMWF.
This study compared the convection coefficients of two potato slices cooling via free and forced convection. One slice cooled freely on a rack while the other was cooled with a standing fan blowing air over it. Temperature probes measured the internal temperature of each slice over time. The slice under forced convection cooled faster, with its convection coefficient calculated as 20 W/(m2K) compared to 9.5 W/(m2K) for the freely cooling slice. A COMSOL model also showed the internal temperature distribution over time matched the measured data. The experiment validated that forced convection enhances heat transfer compared to free convection.
1) UAVSAR collected repeat-pass SAR data over agricultural fields in Canada during a soil moisture campaign to analyze the effect of soil moisture on polarimetric and interferometric measurements.
2) Both backscatter and interferometric phase correlations decreased as soil moisture changed more between passes, indicating soil moisture impacts the SAR measurements.
3) Models captured general backscatter and polarimetric phase trends with soil moisture but underestimated the observed variation in interferometric phase with changes in moisture.
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...grssieee
This document summarizes research generating fine-resolution leaf area index (LAI) maps for boreal forests in Finland using satellite imagery. Two methods were used: 1) an empirical model based on the reduced simple ratio vegetation index and 2) inversion of a forest reflectance model. Field measurements of LAI from over 1000 plots were used to develop and validate the models. The empirical model using scene-specific parameters produced LAI maps for 2000 and 2006 with realistic patterns, though some overestimation. Comparison to MODIS LAI showed generally good agreement but some systematic differences likely due to phenological and calibration issues. The empirical method was selected for producing the LAI maps.
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
This document discusses space agencies providing real-time analyses and forecasts of greenhouse gas fluxes, climate forcing factors like solar radiation, fire emissions, and anthropogenic emissions on global and regional scales. It also covers interim reanalyses and validated reanalyses of these in-situ components to inform policy on greenhouse gas fluxes and climate change.
Inter annual insolation variability (solar resource)chaudharichetan
This document presents new maps of solar resource interannual variability in the continental United States. It summarizes that solar resource variability plays a key role in energy yield and cash flow forecasting for PV systems. It then describes how the authors aggregated solar resource data from multiple sources by region and climate zone to reduce sampling errors and present more accurate estimates of interannual variability, given the limited available data. The authors find solar resource variability ranges from 1.3% to 3.9% across different climate zones in the US based on their analysis.
Exploring explainable machine learning for detecting changes in climateZachary 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
Forced climate signals with explainable AI and large ensemblesZachary Labe
1) The document discusses using artificial neural networks and explainable AI methods to analyze climate model simulations and detect climate signals related to different external forcings like greenhouse gases and aerosols.
2) A case study examines temperature trends in simulations with varying external forcings, and the neural network is able to more accurately predict observations when trained on a simulation with evolving greenhouse gases.
3) Explainable AI methods reveal the neural network relies more on patterns related to greenhouse gas forcing compared to aerosol or preindustrial forcing patterns when making predictions.
Using explainable machine learning for evaluating patterns of climate changeZachary Labe
22 February 2023…
Natural Sciences Group Seminar (Presentation): Using explainable machine learning for evaluating patterns of climate change, Washington State University Vancouver, 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
Learning new climate science by thinking creatively with machine learningZachary Labe
Presentation for: GFDL/AOS Summer Internship Lecture Series
The popularity of machine learning is rapidly growing in nearly all areas of Earth science. However, there is also some hesitancy in adopting the use of these methods due to concerns about their reliability, reproducibility, and interpretability – thus, they are often described as “black boxes.”
In this talk, I will introduce a few simple examples from climate science that leverage new visualization methods to peer into the machine learning “black box,” which help us to better understand their predictions while also learning new science. These same machine learning visualization tools can be easily adapted for a wide variety of applications and other scientific fields of study.
Explainable AI for identifying regional climate change patternsZachary Labe
13 January 2023…
Scientific Machine Learning Community (Presentation): Explainable AI for identifying regional climate change patterns, University of Leeds, UK. Remote Presentation.
Machine learning for evaluating climate model projectionsZachary Labe
IEEE-Student Branch- IIT Indore, Tech-Talks 2.0. Remote Presentation (Dec 2022) (Invited).
The popularity of machine learning methods, such as neural networks, is rapidly expanding in nearly all areas of science. The interest in these tools also coincides with a growing influx of big data and the need for high efficiency in solving predication problems. However, there is also some hesitancy in adopting the use of neural networks due to concerns about their reliability, reproducibility, and interpretability.
In climate science, we often consider signal-to-noise problems to help disentangle human-caused climate change from natural variability. These applications typically involve complicated relationships between different feedbacks at play in the ocean, cryosphere, land, and atmosphere. Recent work has shown that neural networks can be a promising tool for solving these types of statistical problems when combined with explainability techniques developed by the fields of computer science and image processing. Interestingly, these methods have revealed that neural networks often leverage regional patterns of climate change in order to make their predictions. In this webinar, I will share examples from climate science that use a few of these visualization methods to peer into the “black box” of neural networks, which help us to better understand their decision-making process while also learning new science. The same machine learning visualization methods can be easily adapted for a wide variety of applications and other scientific fields of study.
Explainable AI approach for evaluating climate models in the ArcticZachary Labe
27 March 2024…
IARPC Collaborations, Modelers’ Community of Practice (Presentation): Explainable AI approach for evaluating climate models in the Arctic. Remote Presentation.
References...
Labe, Z. M., & Barnes, E. A. (2022). Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, 9(7), e2022EA002348, https://doi.org/10.1029/2022EA002348
Using explainable AI to identify key regions of climate change in GFDL SPEAR ...Zachary Labe
15 March 2023…
GFDL Lunchtime Seminar Series (Presentation): Using explainable AI to identify key regions of climate change in GFDL SPEAR large ensembles, Princeton, NJ.
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. https://doi.org/10.31223/X5394Z (submitted)
Applications of machine learning for climate change and variabilityZachary Labe
23 February 2024…
Department of Environmental Sciences Seminar (Presentation): Applications of machine learning for climate change and variability, Rutgers University, New Brunswick, NJ.
References:
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464
Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173
Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981
Using explainable machine learning to evaluate climate change projectionsZachary Labe
5 October 2023…
Atmosphere and Ocean Climate Dynamics Seminar (Presentation): Using explainable machine learning to evaluate climate change projections, Yale University, New Haven, CT. Remote Presentation.
References...
Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a, https://iopscience.iop.org/article/10.1088/1748-9326/acc81a
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
Creative machine learning approaches for climate change detectionZachary Labe
14 April 2023…
Resnick Young Investigators Symposium (Invited Presentation): Creative machine learning approaches for climate change detection, California Institute of Technology (Caltech), USA. https://resnick.caltech.edu/events/resnick-symposium/2023-symposium
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
Using artificial neural networks to predict temporary slowdowns in global war...Zachary Labe
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.
Using neural networks to predict temporary slowdowns in decadal climate warmi...Zachary Labe
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.
An intro to explainable AI for polar climate scienceZachary Labe
26 March 2024…
GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ.
References:
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464
Climate change extremes by season in the United StatesZachary Labe
11 September 2023…
Hershey Horticulture Society (Presentation): Climate change extremes by season in the United States, Hershey, PA, USA.
References...
Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1
Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2016), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, R. Clim Dyn, DOI:10.1007/s00382-016-3313-2
Labe, Z.M., N.C. Johnson, and T.L Delworth (2023). Changes in United States summer temperatures revealed by explainable neural networks. Preprint. DOI: 10.22541/essoar.168987129.98069596/v1
Evaluating and communicating Arctic climate change projectionZachary Labe
20 February 2023…
Climate Change and Agriculture Guest (Presentation): Evaluating and communicating Arctic climate change projections, Kansas State University, USA.
References...
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J. H., Dunne, K. A., ... & Zhao, M. (2020). SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001895
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088583
Peings, Y., Cattiaux, J., Vavrus, S. J., & Magnusdottir, G. (2018). Projected squeezing of the wintertime North-Atlantic jet. Environmental Research Letters, 13(7), 074016, https://iopscience.iop.org/article/10.1088/1748-9326/aacc79/meta
2003-12-04 Evaluation of the ASOS Light Scattering NetworkRudolf Husar
The document reports on an evaluation of the Automated Surface Observing System (ASOS) light scattering network. It analyzes data from 220 ASOS stations to evaluate the precision and performance of the ASOS visibility sensors. It finds that some stations show excellent correlation between duplicate sensors while others show poorer correlation or significant offsets. It also examines diurnal patterns and the effects of relative humidity on visibility readings.
Similar to Exploring climate change signals with explainable AI (20)
Reexamining future projections of Arctic climate linkagesZachary Labe
10 May 2024…
Atmospheric and Oceanic Sciences Student/Postdoc Seminar (Presentation): Reexamining future projections of Arctic climate linkages, Princeton University, USA.
References...
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss,
Geophysical Research Letters, DOI:10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation.
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
Techniques and Considerations for Improving Accessibility in Online MediaZachary Labe
3 April 2024…
United States Association of Polar Early Career Scientists (USAPECS) IDEA Training Course (Presentation): Accessibility and disability in online spaces. Remote Presentation.
Using accessible data to communicate global climate changeZachary Labe
25 March 2024…
Climate Communication Workshop: Learn How To Make Your Research Matter (Keynote Presentation): Using accessible data to communicate global climate change, Temple University, Philadelphia, PA.
Water in a Frozen Arctic: Cross-Disciplinary PerspectivesZachary Labe
14 March 2024…
United States Association of Polar Early Career Scientists (USAPECS) Webinar (Host): Water in a Frozen Arctic: Cross-Disciplinary Perspectives. Remote Panel.
Event Page: https://www.usapecs.org/post/webinar-water-frozen-arctic
data-driven approach to identifying key regions of change associated with fut...Zachary Labe
Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431300
Distinguishing the regional emergence of United States summer temperatures be...Zachary Labe
Labe, Z.M., N.C. Johnson, and T.L. Delworth. Distinguishing the regional emergence of United States summer temperatures between observations and climate model large ensembles, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431288
Researching and Communicating Our Changing ClimateZachary Labe
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 linkagesZachary Labe
16 November 2023…
Department Seminar (Presentation): Revisiting projections of Arctic climate change linkages, Tongji University, Shanghai, China. Remote Presentation.
References:
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI: 10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI: 10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?
Journal of Climate, DOI: 10.1175/JCLI-D-20-0613.1
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI: 10.1029/2022EA002348
Visualizing climate change through dataZachary Labe
18 November 2023…
NJ State Museum Planetarium (Presentation): Visualizing climate change through data, Trenton, NJ.
References...
Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1, https://journals.ametsoc.org/view/journals/clim/36/20/JCLI-D-22-0716.1.xml
Contrasting polar climate change in the past, present, and futureZachary Labe
28 September 2023…
Guest lecture for “Observing and Modeling Climate Change (EES 3506/5506)” (Presentation): Contrasting polar climate change in the past, present, and future, Temple University, Philadelphia, PA. Remote Presentation.
Guest Lecture: Our changing Arctic in the past and futureZachary Labe
22 August 2023…
Guest lecture for “Introduction to Global Climate Change (ESS 15)” (Invited): Our changing Arctic in the past and future, University of California, Irvine, CA. Remote Presentation.
References...
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J. H., Dunne, K. A., ... & Zhao, M. (2020). SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001895
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088583
Monitoring indicators of climate change through data-driven visualizationZachary Labe
19 June 2023…
La Uni Climática - IV Edition (Presentation): Monitoring indicators of climate change through data-driven visualization. Remote Presentation.
Career pathways and research opportunities in the Earth sciencesZachary Labe
20 April 2023…
Mercer County Community College (Presentation): Career pathways and research opportunities in the Earth sciences, West Windsor Township, NJ, USA.
24 March 2023…
NBCU Fellows Academy Workshop (Presentation): Telling data-driven climate stories, The City College of New York, USA. Remote Presentation.
PPT on Sustainable Land Management presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Sérgio Sacani
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDSSérgio Sacani
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Exploring climate change signals with explainable AI
1. Exploring climate change signals
with explainable AI
@ZLabe
Zachary M. Labe
with Elizabeth A. Barnes
in the Department of Atmospheric Science
at Colorado State University
9 December 2021
Carbon Club
NASA Jet Propulsion Laboratory (JPL)
5. Computer Science
Artificial Intelligence
Machine Learning
Deep Learning
Supervised
Learning
Unsupervised
Learning
Labeled data
Classification
Regression
Unlabeled data
Clustering
Dimension reduction
6. • Do it better
• e.g., parameterizations in climate models are not
perfect, use ML to make them more accurate
• Do it faster
• e.g., code in climate models is very slow (but we
know the right answer) - use ML methods to speed
things up
• Do something new
• e.g., go looking for non-linear relationships you
didn’t know were there
Very relevant for
research: may be
slower and worse,
but can still learn
something
WHY SHOULD WE CONSIDER
MACHINE LEARNING?
13. Machine learning for weather
IDENTIFYING SEVERE THUNDERSTORMS
Molina et al. 2021
Toms et al. 2021
CLASSIFYING PHASE OF MADDEN-JULLIAN OSCILLATION
SATELLITE DETECTION
Lee et al. 2021
DETECTING TORNADOES
McGovern et al. 2019
14. Machine learning for climate
FINDING FORECASTS OF OPPORTUNITY
Mayer and Barnes, 2021
PREDICTING CLIMATE MODES OF VARIABILITY
Gordon et al. 2021
TIMING OF CLIMATE CHANGE
Barnes et al. 2019
19. E.g.,
Establish robust,
responsible AI for
severe weather
detection
Tornado Warning
Special Marine Warning
Severe Thunderstorm Warning
Flash Flood Warning
23. Artificial Neural Networks [ANN]
X1
X2
W1
W2
∑
INPUTS
NODE
Linear regression with non-linear
mapping by an “activation function”
Training of the network is merely
determining the weights “w” and
bias/offset “b"
= factivation(X1W1+ X2W2 + b)
24. Artificial Neural Networks [ANN]
X1
X2
W1
W2
∑
INPUTS
NODE
= factivation(X1W1+ X2W2 + b)
ReLU Sigmoid Linear
26. Complexity and nonlinearities of the ANN allow it to learn many
different pathways of predictable behavior
Once trained, you have an array of weights and biases which can be
used for prediction on new data
INPUT
[DATA]
PREDICTION
Artificial Neural Networks [ANN]
29. We know some metadata…
+ What year is it?
+ Where did it come from?
TEMPERATURE
30. We know some metadata…
+ What year is it?
+ Where did it come from?
TEMPERATURE
Neural network learns nonlinear
combinations of forced climate
patterns to identify the year
31. ----ANN----
2 Hidden Layers
10 Nodes each
Ridge Regularization
Early Stopping
We know some metadata…
+ What year is it?
+ Where did it come from?
[e.g., Barnes et al. 2019, 2020]
[e.g., Labe and Barnes, 2021]
TIMING OF EMERGENCE
(COMBINED VARIABLES)
RESPONSES TO
EXTERNAL CLIMATE
FORCINGS
PATTERNS OF
CLIMATE INDICATORS
[e.g., Rader et al. 2021, in prep]
Surface Temperature Map Precipitation Map
+
TEMPERATURE
32. ----ANN----
2 Hidden Layers
10 Nodes each
Ridge Regularization
Early Stopping
We know some metadata…
+ What year is it?
+ Where did it come from?
[e.g., Barnes et al. 2019, 2020]
[e.g., Labe and Barnes, 2021]
TIMING OF EMERGENCE
(COMBINED VARIABLES)
RESPONSES TO
EXTERNAL CLIMATE
FORCINGS
PATTERNS OF
CLIMATE INDICATORS
[e.g., Rader et al. 2021, in prep]
Surface Temperature Map Precipitation Map
+
TEMPERATURE
34. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
Anomaly is relative to 1951-1980
35. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
CLIMATE MODEL
ENSEMBLES
36. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
CLIMATE MODEL
ENSEMBLES
Range of ensembles
= internal variability (noise)
Mean of ensembles
= forced response (climate change)
37. What is the annual mean temperature of Earth?
• Increasing greenhouse gases (CO2, CH4, N2O)
• Changes in industrial aerosols (SO4, BC, OC)
• Changes in biomass burning (aerosols)
• Changes in land-use & land-cover (albedo)
38. What is the annual mean temperature of Earth?
• Increasing greenhouse gases (CO2, CH4, N2O)
• Changes in industrial aerosols (SO4, BC, OC)
• Changes in biomass burning (aerosols)
• Changes in land-use & land-cover (albedo)
Plus everything else…
(Natural/internal variability)
40. Greenhouse gases fixed to 1920 levels
All forcings (CESM-LE)
Industrial aerosols fixed to 1920 levels
[Deser et al. 2020, JCLI]
Fully-coupled CESM1.1
20 Ensemble Members
Run from 1920-2080
Observations
41. So what?
Greenhouse gases = warming
Aerosols = ?? (though mostly cooling)
What are the relative responses
between greenhouse gas
and aerosol forcing?
44. INPUT LAYER
Surface Temperature Map
ARTIFICIAL NEURAL NETWORK (ANN)
Collection of nodes (neurons)
that adjust their weights and
biases across layers in order to
learn signals for making
predictions
Learns nonlinear processes
through selected parameters
in the model
45. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
ARTIFICIAL NEURAL NETWORK (ANN)
46. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
ARTIFICIAL NEURAL NETWORK (ANN)
47. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Layer-wise Relevance Propagation
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
ARTIFICIAL NEURAL NETWORK (ANN)
[Barnes et al. 2020, JAMES]
[Labe and Barnes 2021, JAMES]
48. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Volcano
Great White
Shark
Timber
Wolf
Image Classification LRP
https://heatmapping.org/
LRP heatmaps show regions
of “relevance” that
contribute to the neural
network’s decision-making
process for a sample
belonging to a particular
output category
Neural Network
WHY
WHY
WHY
Backpropagation – LRP
49. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Volcano
Great White
Shark
Timber
Wolf
Image Classification LRP
https://heatmapping.org/
LRP heatmaps show regions
of “relevance” that
contribute to the neural
network’s decision-making
process for a sample
belonging to a particular
output category
Neural Network
WHY
WHY
WHY
Backpropagation – LRP
50. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Volcano
Great White
Shark
Timber
Wolf
Image Classification LRP
https://heatmapping.org/
LRP heatmaps show regions
of “relevance” that
contribute to the neural
network’s decision-making
process for a sample
belonging to a particular
output category
Neural Network
WHY
WHY
WHY
Backpropagation – LRP
51. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Image Classification LRP
https://heatmapping.org/
NOT PERFECT
Crock
Pot
Neural Network
WHY
Backpropagation – LRP
52. [Adapted from Adebayo et al., 2020]
EXPLAINABLE AI IS
NOT PERFECT
THERE ARE MANY
METHODS
53. [Adapted from Adebayo et al., 2020]
THERE ARE MANY
METHODS
EXPLAINABLE AI IS
NOT PERFECT
55. Neural
Network
[0] La Niña [1] El Niño
[Toms et al. 2020, JAMES]
Input a map of sea surface temperatures
56. 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
57. OUTPUT LAYER
Layer-wise Relevance Propagation
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
WHY?
= LRP HEAT MAPS
[Labe and Barnes 2021, JAMES]
59. Layer-wise Relevance Propagation
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
WHY?
= LRP HEAT MAPS
Find regions of “relevance”
that contribute to the
neural network’s
decision-making process
[Labe and Barnes 2021, JAMES]
60. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
61. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
62. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
63. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
64. CLIMATE MODEL DATA PREDICT THE YEAR FROM MAPS OF TEMPERATURE
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL
[Labe and Barnes 2021, JAMES]
65. OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL
[Labe and Barnes 2021, JAMES]
66. OBSERVATIONS
SLOPES
PREDICT THE YEAR FROM MAPS OF TEMPERATURE
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL
[Labe and Barnes 2021, JAMES]
73. Higher LRP values indicate greater relevance
for the ANN’s prediction
AVERAGED OVER 1960-2039
Aerosol-driven
Greenhouse gas-driven
All forcings
Low High
[Labe and Barnes 2021, JAMES]
75. ----ANN----
2 Hidden Layers
10 Nodes each
Ridge Regularization
Early Stopping
TEMPERATURE
We know some metadata…
+ What year is it?
+ Where did it come from?
76. TEMPERATURE
We know some metadata…
+ What year is it?
+ Where did it come from?
Train on data from the
Multi-Model Large
Ensemble Archive
77. 2-m Temperature (°C)
THERE ARE MANY CLIMATE MODEL LARGE ENSEMBLES…
Annual mean 2-m temperature
7 global climate models
16 ensembles each
ERA5-BE (observations)
78. STANDARD EVALUATION OF
CLIMATE MODELS
Pattern correlation
RMSE
EOFs
Trends, anomalies, mean state
Climate modes of variability
79. STANDARD EVALUATION OF
CLIMATE MODELS
Pattern correlation
RMSE
EOFs
Trends, anomalies, mean state
Climate modes of variability
CORRELATION
[R]
80. STANDARD EVALUATION OF
CLIMATE MODELS
Pattern correlation
RMSE
EOFs
Trends, anomalies, mean state
Climate modes of variability
CORRELATION
[R]
81. STANDARD EVALUATION OF
CLIMATE MODELS
Pattern correlation
RMSE
EOFs
Trends, anomalies, mean state
Climate modes of variability
Negative Correlation Positive Correlation
PATTERN CORRELATION – T2M
82. TEMPERATURE
We know some metadata…
+ What year is it?
+ Where did it come from?
NEURAL NETWORK
CLASSIFICATION TASK
HIDDEN LAYERS
INPUT LAYER
101. WE CAN LEARN NEW SCIENCE
FROM EXPLAINABLE AI.
3)
102. KEY POINTS
Zachary Labe
zmlabe@rams.colostate.edu
@ZLabe
1. Machine learning is just another tool to add to our scientific workflow
2. We can use explainable AI (XAI) methods to peer into the black box of machine learning
3. We can learn new science by using XAI methods in conjunction with existing statistical tools