Earth & Environmental Sciences

48 items

Nature News Sep 23, 2026
A new modeling study covered by Nature News finds that millisecond-scale decadal variations in Earth's length of day are best matched by a combination in which gravitational torque dominates and is counterbalanced by electromagnetic and mechanical torques, with a key input coming from a 2023 Nature Geoscience paper that used repeating seismic waves to infer a recent pause in inner-core differential rotation within an approximately seven-decade oscillation.
A new modeling study covered by Nature News finds that millisecond-scale decadal variations in Earth's length of day are best matched by a combination in which gravitational torque dominates and is counterbalanced by electromagnetic and mechanical torques, with a key input coming from a 2023 Nature Geoscience paper that used repeating seismic waves to infer a recent pause in inner-core differential rotation within an approximately seven-decade oscillation.
A Gravitational Tug-of-War Inside Earth: Rotation and the Decadal Wobble in Day Length
A new modeling study covered by Nature News finds that millisecond-scale decadal variations in Earth's length of day are best matched by a combination in which gravitational torque dominates and is counterbalanced by electromagnetic and mechanical torques, with a key input coming from a 2023 Nature Geoscience paper that used repeating seismic waves to infer a recent pause in inner-core differential rotation within an approximately seven-decade oscillation.
A new modeling study covered by Nature News finds that millisecond-scale decadal variations in Earth's length of day are best matched by a combination in which gravitational torque dominates and is counterbalanced by electromagnetic and mechanical torques, with a key input coming from a 2023 Nature Geoscience paper that used repeating seismic waves to infer a recent pause in inner-core differential rotation within an approximately seven-decade oscillation.
arXiv Sep 23, 2026
The work presents AIDE², a two-loop system that implements recursive self-improvement at the harness layer of an AI research agent: an inner-loop agent optimizes code on AI R&D tasks while an outer-loop agent rewrites the inner-loop agent itself, and in one autonomous 8-day run it accepted seven successive improvements that transferred to four held-out benchmarks (including an out-of-distribution physics-based weather-forecasting task) and reduced reward hacking from 55% to 32% on a behavior the loop never optimized for.
The work presents AIDE², a two-loop system that implements recursive self-improvement at the harness layer of an AI research agent: an inner-loop agent optimizes code on AI R&D tasks while an outer-loop agent rewrites the inner-loop agent itself, and in one autonomous 8-day run it accepted seven successive improvements that transferred to four held-out benchmarks (including an out-of-distribution physics-based weather-forecasting task) and reduced reward hacking from 55% to 32% on a behavior the loop never optimized for.
Recursive Self-Improvement of AI Research Agents: AIDE² Finds Seven Successive Improvements in an 8-Day Autonomous Run
The work presents AIDE², a two-loop system that implements recursive self-improvement at the harness layer of an AI research agent: an inner-loop agent optimizes code on AI R&D tasks while an outer-loop agent rewrites the inner-loop agent itself, and in one autonomous 8-day run it accepted seven successive improvements that transferred to four held-out benchmarks (including an out-of-distribution physics-based weather-forecasting task) and reduced reward hacking from 55% to 32% on a behavior the loop never optimized for.
The work presents AIDE², a two-loop system that implements recursive self-improvement at the harness layer of an AI research agent: an inner-loop agent optimizes code on AI R&D tasks while an outer-loop agent rewrites the inner-loop agent itself, and in one autonomous 8-day run it accepted seven successive improvements that transferred to four held-out benchmarks (including an out-of-distribution physics-based weather-forecasting task) and reduced reward hacking from 55% to 32% on a behavior the loop never optimized for.
bioRxiv Sep 17, 2026
Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.
Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.
Time-series foundation modeling enables accurate lake ecosystem forecasting
Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.
Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.
ACM Journal on Computing and Sustainable Societies Sep 16, 2026
Using a 2023 workshop with 49 students as a motivating example, this paper critically reflects on the energy costs of using genAI in design education and develops a set of five alternative stances, with related actions, to support the conscious use of genAI in design education.
Using a 2023 workshop with 49 students as a motivating example, this paper critically reflects on the energy costs of using genAI in design education and develops a set of five alternative stances, with related actions, to support the conscious use of genAI in design education.
The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Education
Using a 2023 workshop with 49 students as a motivating example, this paper critically reflects on the energy costs of using genAI in design education and develops a set of five alternative stances, with related actions, to support the conscious use of genAI in design education.
Using a 2023 workshop with 49 students as a motivating example, this paper critically reflects on the energy costs of using genAI in design education and develops a set of five alternative stances, with related actions, to support the conscious use of genAI in design education.
Climatic Change Sep 16, 2026
This study introduces a 'generative debunking' framework that integrates climate contrarian claim classification (CARDS) and fallacy detection (FLICC) into an LLM prompting pipeline, so that a model takes a climate myth as input and produces a debunking that follows the fact-myth-fallacy-fact ('truth sandwich') structure; the authors pair three prompting strategies of increasing complexity with GPT-4, Palm2, and Mixtral, and four authors (including one climate misinformation expert) rate 60 debunkings for 20 myths on fact, fallacy, and structure, finding that GPT-4 with a simple prompt and Mixtral with a structured prompt perform relatively well, that fallacy explanations score higher than facts, and that non-expert annotators show poor agreement with the expert on fact quality.
This study introduces a 'generative debunking' framework that integrates climate contrarian claim classification (CARDS) and fallacy detection (FLICC) into an LLM prompting pipeline, so that a model takes a climate myth as input and produces a debunking that follows the fact-myth-fallacy-fact ('truth sandwich') structure; the authors pair three prompting strategies of increasing complexity with GPT-4, Palm2, and Mixtral, and four authors (including one climate misinformation expert) rate 60 debunkings for 20 myths on fact, fallacy, and structure, finding that GPT-4 with a simple prompt and Mixtral with a structured prompt perform relatively well, that fallacy explanations score higher than facts, and that non-expert annotators show poor agreement with the expert on fact quality.
Generative Debunking of Climate Misinformation: Automatically Producing 'Truth Sandwich' Rebuttals with Large Language Models
This study introduces a 'generative debunking' framework that integrates climate contrarian claim classification (CARDS) and fallacy detection (FLICC) into an LLM prompting pipeline, so that a model takes a climate myth as input and produces a debunking that follows the fact-myth-fallacy-fact ('truth sandwich') structure; the authors pair three prompting strategies of increasing complexity with GPT-4, Palm2, and Mixtral, and four authors (including one climate misinformation expert) rate 60 debunkings for 20 myths on fact, fallacy, and structure, finding that GPT-4 with a simple prompt and Mixtral with a structured prompt perform relatively well, that fallacy explanations score higher than facts, and that non-expert annotators show poor agreement with the expert on fact quality.
This study introduces a 'generative debunking' framework that integrates climate contrarian claim classification (CARDS) and fallacy detection (FLICC) into an LLM prompting pipeline, so that a model takes a climate myth as input and produces a debunking that follows the fact-myth-fallacy-fact ('truth sandwich') structure; the authors pair three prompting strategies of increasing complexity with GPT-4, Palm2, and Mixtral, and four authors (including one climate misinformation expert) rate 60 debunkings for 20 myths on fact, fallacy, and structure, finding that GPT-4 with a simple prompt and Mixtral with a structured prompt perform relatively well, that fallacy explanations score higher than facts, and that non-expert annotators show poor agreement with the expert on fact quality.
Journal of the European Meteorological Society. Sep 16, 2026
This article by Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov and Jørn Kristiansen argues that, following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, attention should shift from forecast output to the working practices that make prediction systems possible, and that machine learning and recent digital technologies will reshape the forecasting value chain — how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services; it discusses six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification
This article by Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov and Jørn Kristiansen argues that, following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, attention should shift from forecast output to the working practices that make prediction systems possible, and that machine learning and recent digital technologies will reshape the forecasting value chain — how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services; it discusses six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification
Machine learning is revolutionizing weather forecasting — the next step is a change in how we work
This article by Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov and Jørn Kristiansen argues that, following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, attention should shift from forecast output to the working practices that make prediction systems possible, and that machine learning and recent digital technologies will reshape the forecasting value chain — how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services; it discusses six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification
This article by Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov and Jørn Kristiansen argues that, following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, attention should shift from forecast output to the working practices that make prediction systems possible, and that machine learning and recent digital technologies will reshape the forecasting value chain — how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services; it discusses six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification
Nature News Sep 15, 2026
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
Can Underground Hydrogen Spark a Green-Energy Revolution? The Drilling Rush and an Unknown Resource
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
RSC Advances Sep 14, 2026
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Green synthesis of Ag/MgO nanocomposites from red pepper seed waste for efficient sunlight-driven diclofenac degradation: experimental investigation and EEFO-optimized DT_LSBoost predictive modeling
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.

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