Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick
Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use weather forecasts to plan their activities around precipitation, reliable air quality forecasts could help individuals reduce their exposure to air pollution. In the present work, we evaluate several existing forecasts of fine particular matter (PM2.5) within the continental United States in the context of individual decision-making. Our comparison suggests there is meaningful room for improvement in air pollution forecasting, which might be realized by incorporating more data sources and using machine learning tools. To facilitate future machine learning development and benchmarking, we set up a framework to evaluate and compare air pollution forecasts for individual decision making. We introduce a new loss to capture decisions about when to use mitigation measures. We highlight the importance of visualizations when comparing forecasts. Finally, we provide code to download and compare archived forecast predictions.
{"title":"A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making","authors":"Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick","doi":"arxiv-2409.05866","DOIUrl":"https://doi.org/arxiv-2409.05866","url":null,"abstract":"Wildfire frequency is increasing as the climate changes, and the resulting\u0000air pollution poses health risks. Just as people routinely use weather\u0000forecasts to plan their activities around precipitation, reliable air quality\u0000forecasts could help individuals reduce their exposure to air pollution. In the\u0000present work, we evaluate several existing forecasts of fine particular matter\u0000(PM2.5) within the continental United States in the context of individual\u0000decision-making. Our comparison suggests there is meaningful room for\u0000improvement in air pollution forecasting, which might be realized by\u0000incorporating more data sources and using machine learning tools. To facilitate\u0000future machine learning development and benchmarking, we set up a framework to\u0000evaluate and compare air pollution forecasts for individual decision making. We\u0000introduce a new loss to capture decisions about when to use mitigation\u0000measures. We highlight the importance of visualizations when comparing\u0000forecasts. Finally, we provide code to download and compare archived forecast\u0000predictions.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud observables such as precipitation efficiency and cloud lifetime are key quantities in weather and climate, but understanding their quantitative connection to initial conditions such as initial cloud water mass or droplet size remains challenging. Here we study the evolution of cloud droplets with a bin microphysics scheme, modeling both gravitational coagulation as well as fallout, and develop analytical formulae to describe the evolution of bulk cloud and rain water. We separate the dynamics into a mass-conserving and fallout-dominated regime, which reveals that the overall dynamics are governed by a single non-dimensional parameter $mu$, the ratio of accretion and sedimentation time scales. Cloud observables from the simulations accordingly collapse as a function of $mu$. We also find an unexpected relationship between cloud water and accumulated rain, and that fallout can be modeled with a bulk fall speed which is constant in time despite an evolving raindrop distribution.
{"title":"Single-parameter effective dynamics of warm cloud precipitation","authors":"Shai Kapon, Nadir Jeevanjee, Anna Frishman","doi":"arxiv-2409.05398","DOIUrl":"https://doi.org/arxiv-2409.05398","url":null,"abstract":"Cloud observables such as precipitation efficiency and cloud lifetime are key\u0000quantities in weather and climate, but understanding their quantitative\u0000connection to initial conditions such as initial cloud water mass or droplet\u0000size remains challenging. Here we study the evolution of cloud droplets with a\u0000bin microphysics scheme, modeling both gravitational coagulation as well as\u0000fallout, and develop analytical formulae to describe the evolution of bulk\u0000cloud and rain water. We separate the dynamics into a mass-conserving and\u0000fallout-dominated regime, which reveals that the overall dynamics are governed\u0000by a single non-dimensional parameter $mu$, the ratio of accretion and\u0000sedimentation time scales. Cloud observables from the simulations accordingly\u0000collapse as a function of $mu$. We also find an unexpected relationship\u0000between cloud water and accumulated rain, and that fallout can be modeled with\u0000a bulk fall speed which is constant in time despite an evolving raindrop\u0000distribution.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate weather forecasting is critical for science and society. Yet, existing methods have not managed to simultaneously have the properties of high accuracy, low uncertainty, and high computational efficiency. On one hand, to quantify the uncertainty in weather predictions, the strategy of ensemble forecast (i.e., generating a set of diverse predictions) is often employed. However, traditional ensemble numerical weather prediction (NWP) is computationally intensive. On the other hand, most existing machine learning-based weather prediction (MLWP) approaches are efficient and accurate. Nevertheless, they are deterministic and cannot capture the uncertainty of weather forecasting. In this work, we propose CoDiCast, a conditional diffusion model to generate accurate global weather prediction, while achieving uncertainty quantification with ensemble forecasts and modest computational cost. The key idea is to simulate a conditional version of the reverse denoising process in diffusion models, which starts from pure Gaussian noise to generate realistic weather scenarios for a future time point. Each denoising step is conditioned on observations from the recent past. Ensemble forecasts are achieved by repeatedly sampling from stochastic Gaussian noise to represent uncertainty quantification. CoDiCast is trained on a decade of ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results demonstrate that our approach outperforms several existing data-driven methods in accuracy. Our conditional diffusion model, CoDiCast, can generate 3-day global weather forecasts, at 6-hour steps and $5.625^circ$ latitude-longitude resolution, for over 5 variables, in about 12 minutes on a commodity A100 GPU machine with 80GB memory. The open-souced code is provided at url{https://github.com/JimengShi/CoDiCast}.
{"title":"CoDiCast: Conditional Diffusion Model for Weather Prediction with Uncertainty Quantification","authors":"Jimeng Shi, Bowen Jin, Jiawei Han, Giri Narasimhan","doi":"arxiv-2409.05975","DOIUrl":"https://doi.org/arxiv-2409.05975","url":null,"abstract":"Accurate weather forecasting is critical for science and society. Yet,\u0000existing methods have not managed to simultaneously have the properties of high\u0000accuracy, low uncertainty, and high computational efficiency. On one hand, to\u0000quantify the uncertainty in weather predictions, the strategy of ensemble\u0000forecast (i.e., generating a set of diverse predictions) is often employed.\u0000However, traditional ensemble numerical weather prediction (NWP) is\u0000computationally intensive. On the other hand, most existing machine\u0000learning-based weather prediction (MLWP) approaches are efficient and accurate.\u0000Nevertheless, they are deterministic and cannot capture the uncertainty of\u0000weather forecasting. In this work, we propose CoDiCast, a conditional diffusion\u0000model to generate accurate global weather prediction, while achieving\u0000uncertainty quantification with ensemble forecasts and modest computational\u0000cost. The key idea is to simulate a conditional version of the reverse\u0000denoising process in diffusion models, which starts from pure Gaussian noise to\u0000generate realistic weather scenarios for a future time point. Each denoising\u0000step is conditioned on observations from the recent past. Ensemble forecasts\u0000are achieved by repeatedly sampling from stochastic Gaussian noise to represent\u0000uncertainty quantification. CoDiCast is trained on a decade of ERA5 reanalysis\u0000data from the European Centre for Medium-Range Weather Forecasts (ECMWF).\u0000Experimental results demonstrate that our approach outperforms several existing\u0000data-driven methods in accuracy. Our conditional diffusion model, CoDiCast, can\u0000generate 3-day global weather forecasts, at 6-hour steps and $5.625^circ$\u0000latitude-longitude resolution, for over 5 variables, in about 12 minutes on a\u0000commodity A100 GPU machine with 80GB memory. The open-souced code is provided\u0000at url{https://github.com/JimengShi/CoDiCast}.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mark DeMaria, James L. Franklin, Galina Chirokova, Jacob Radford, Robert DeMaria, Kate D. Musgrave, Imme Ebert-Uphoff
In just the past few years multiple data-driven Artificial Intelligence Weather Prediction (AIWP) models have been developed, with new versions appearing almost monthly. Given this rapid development, the applicability of these models to operational forecasting has yet to be adequately explored and documented. To assess their utility for operational tropical cyclone (TC) forecasting, the NHC verification procedure is used to evaluate seven-day track and intensity predictions for northern hemisphere TCs from May-November 2023. Four open-source AIWP models are considered (FourCastNetv1, FourCastNetv2-small, GraphCast-operational and Pangu-Weather). The AIWP track forecast errors and detection rates are comparable to those from the best-performing operational forecast models. However, the AIWP intensity forecast errors are larger than those of even the simplest intensity forecasts based on climatology and persistence. The AIWP models almost always reduce the TC intensity, especially within the first 24 h of the forecast, resulting in a substantial low bias. The contribution of the AIWP models to the NHC model consensus was also evaluated. The consensus track errors are reduced by up to 11% at the longer time periods. The five-day NHC official track forecasts have improved by about 2% per year since 2001, so this represents more than a five-year gain in accuracy. Despite substantial negative intensity biases, the AIWP models have a neutral impact on the intensity consensus. These results show that the current formulation of the AIWP models have promise for operational TC track forecasts, but improved bias corrections or model reformulations will be needed for accurate intensity forecasts.
{"title":"Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models","authors":"Mark DeMaria, James L. Franklin, Galina Chirokova, Jacob Radford, Robert DeMaria, Kate D. Musgrave, Imme Ebert-Uphoff","doi":"arxiv-2409.06735","DOIUrl":"https://doi.org/arxiv-2409.06735","url":null,"abstract":"In just the past few years multiple data-driven Artificial Intelligence\u0000Weather Prediction (AIWP) models have been developed, with new versions\u0000appearing almost monthly. Given this rapid development, the applicability of\u0000these models to operational forecasting has yet to be adequately explored and\u0000documented. To assess their utility for operational tropical cyclone (TC)\u0000forecasting, the NHC verification procedure is used to evaluate seven-day track\u0000and intensity predictions for northern hemisphere TCs from May-November 2023.\u0000Four open-source AIWP models are considered (FourCastNetv1,\u0000FourCastNetv2-small, GraphCast-operational and Pangu-Weather). The AIWP track forecast errors and detection rates are comparable to those\u0000from the best-performing operational forecast models. However, the AIWP\u0000intensity forecast errors are larger than those of even the simplest intensity\u0000forecasts based on climatology and persistence. The AIWP models almost always\u0000reduce the TC intensity, especially within the first 24 h of the forecast,\u0000resulting in a substantial low bias. The contribution of the AIWP models to the NHC model consensus was also\u0000evaluated. The consensus track errors are reduced by up to 11% at the longer\u0000time periods. The five-day NHC official track forecasts have improved by about\u00002% per year since 2001, so this represents more than a five-year gain in\u0000accuracy. Despite substantial negative intensity biases, the AIWP models have a\u0000neutral impact on the intensity consensus. These results show that the current\u0000formulation of the AIWP models have promise for operational TC track forecasts,\u0000but improved bias corrections or model reformulations will be needed for\u0000accurate intensity forecasts.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria J. Molina, Amy McGovern, Jhayron S. Perez-Carrasquilla, Robin L. Tanamachi
Although generative artificial intelligence (AI) is not new, recent technological breakthroughs have transformed its capabilities across many domains. These changes necessitate new attention from educators and specialized training within the atmospheric sciences and related fields. Enabling students to use generative AI effectively, responsibly, and ethically is critically important for their academic and professional preparation. Educators can also use generative AI to create engaging classroom activities, such as active learning modules and games, but must be aware of potential pitfalls and biases. There are also ethical implications in using tools that lack transparency, as well as equity concerns for students who lack access to more sophisticated paid versions of generative AI tools. This article is written for students and educators alike, particularly those who want to learn more about generative AI in education, including use cases, ethical concerns, and a brief history of its emergence. Sample user prompts are also provided across numerous applications in education and the atmospheric and related sciences. While we don't have solutions for some broader ethical concerns surrounding the use of generative AI in education, our goal is to start a conversation that could galvanize the education community around shared goals and values.
{"title":"Using Generative Artificial Intelligence Creatively in the Classroom: Examples and Lessons Learned","authors":"Maria J. Molina, Amy McGovern, Jhayron S. Perez-Carrasquilla, Robin L. Tanamachi","doi":"arxiv-2409.05176","DOIUrl":"https://doi.org/arxiv-2409.05176","url":null,"abstract":"Although generative artificial intelligence (AI) is not new, recent\u0000technological breakthroughs have transformed its capabilities across many\u0000domains. These changes necessitate new attention from educators and specialized\u0000training within the atmospheric sciences and related fields. Enabling students\u0000to use generative AI effectively, responsibly, and ethically is critically\u0000important for their academic and professional preparation. Educators can also\u0000use generative AI to create engaging classroom activities, such as active\u0000learning modules and games, but must be aware of potential pitfalls and biases.\u0000There are also ethical implications in using tools that lack transparency, as\u0000well as equity concerns for students who lack access to more sophisticated paid\u0000versions of generative AI tools. This article is written for students and\u0000educators alike, particularly those who want to learn more about generative AI\u0000in education, including use cases, ethical concerns, and a brief history of its\u0000emergence. Sample user prompts are also provided across numerous applications\u0000in education and the atmospheric and related sciences. While we don't have\u0000solutions for some broader ethical concerns surrounding the use of generative\u0000AI in education, our goal is to start a conversation that could galvanize the\u0000education community around shared goals and values.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"59 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elena MasciadriINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Alessio TurchiINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Luca FiniINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy
In this contribution we present preliminary results of a study applied to the Observatories of Roque de Los Muchachos (La Palma) and Teide (Tenerife) in Canary Islands aiming to investigate the possibility to implement an automatic system for the optical turbulence forecasting for the European Solar Telescope (EST) telescope. The study has been carried out in the context of the SOLARNET project and the two mentioned sites were the pre-selected sites for EST. This analysis aimed to investigate the possibility to extend the methodology of the forecast of the optical turbulence developed by our team and performed on top-class ground-based telescopes dedicated to night time observations such as ALTA (@ LBT) and FATE (@ VLT) to the day-time regime. As an ancillary output our very preliminary analysis concludes, that the two sites of Roque de Los Muchachos Observatory (ORM) and Teide Observatory (TO) show comparable characteristics during the day time. Considering that the site of EST has been already identified to be at ORM this can be considered a very useful information from a scientific point of view.
在这篇论文中，我们介绍了在加那利群岛罗克-德洛斯-穆查乔斯（拉帕尔马）和特内里费（特内里费）观测站进行的一项研究的初步结果，该研究旨在探讨为欧洲太阳望远镜（EST）实施光学湍流预报自动系统的可能性。这项研究是在 SOLARNET 项目背景下进行的，上述两个地点是 EST 的预选地点。这项分析旨在研究是否有可能将我们团队开发的、在专门用于夜间观测的顶级地基望远镜（如ALTA（@ LBT）和FATE（@ VLT））上进行的光学湍流预测方法扩展到昼间观测。我们的初步分析结果表明，Roque de LosMuchachos 天文台（ORM）和 Teide 天文台（TO）的两个观测点在白天显示出相似的特征。考虑到 EST 的地点已经确定在罗克-德洛斯穆查科斯天文台（ORM），从科学的角度来看，这可以说是一个非常有用的信息。
{"title":"Optical turbulence forecast for the European Solar Telescope (EST): the challenge of the day-time regime","authors":"Elena MasciadriINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Alessio TurchiINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Luca FiniINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy","doi":"arxiv-2409.05149","DOIUrl":"https://doi.org/arxiv-2409.05149","url":null,"abstract":"In this contribution we present preliminary results of a study applied to the\u0000Observatories of Roque de Los Muchachos (La Palma) and Teide (Tenerife) in\u0000Canary Islands aiming to investigate the possibility to implement an automatic\u0000system for the optical turbulence forecasting for the European Solar Telescope\u0000(EST) telescope. The study has been carried out in the context of the SOLARNET\u0000project and the two mentioned sites were the pre-selected sites for EST. This\u0000analysis aimed to investigate the possibility to extend the methodology of the\u0000forecast of the optical turbulence developed by our team and performed on\u0000top-class ground-based telescopes dedicated to night time observations such as\u0000ALTA (@ LBT) and FATE (@ VLT) to the day-time regime. As an ancillary output\u0000our very preliminary analysis concludes, that the two sites of Roque de Los\u0000Muchachos Observatory (ORM) and Teide Observatory (TO) show comparable\u0000characteristics during the day time. Considering that the site of EST has been\u0000already identified to be at ORM this can be considered a very useful\u0000information from a scientific point of view.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"159 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elena MasciadriINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Alessio TurchiINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Luca FiniINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Alberto OrtolaniLaMMA, Firenze, Italy, Valerio CapecchiLaMMA, Firenze, Italy, Francesco PasiLaMMA, Firenze, Italy, Angel OtarolaESO, Santiago, Chile, Steffen MieskeESO, Santiago, Chile
In this contribution we report the on-going progresses of the project FATE, an operational automatic forecast system conceived to deliver forecasts of a set of astroclimatic and atmospheric parameters having the aim to support the science operations (i.e. the Service Mode) at the Very Large Telescope. The project has been selected at conclusion of an international open call for tender opened by ESO and it fits with precise technical specifications. In this contribution we will present the ultimate goals of this service once it will be integrated in the VLT operations, the forecasts performances at present time and the state of the art of the project. FATE is supposed to draw the roadmap towards the optical turbulence forecast for the ELT.
{"title":"FATE -- an operational automatic system for optical turbulence forecasting at the Very Large Telescope","authors":"Elena MasciadriINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Alessio TurchiINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Luca FiniINAF - Osservatorio Astrofisico di Arcetri, Florence, Italy, Alberto OrtolaniLaMMA, Firenze, Italy, Valerio CapecchiLaMMA, Firenze, Italy, Francesco PasiLaMMA, Firenze, Italy, Angel OtarolaESO, Santiago, Chile, Steffen MieskeESO, Santiago, Chile","doi":"arxiv-2409.05133","DOIUrl":"https://doi.org/arxiv-2409.05133","url":null,"abstract":"In this contribution we report the on-going progresses of the project FATE,\u0000an operational automatic forecast system conceived to deliver forecasts of a\u0000set of astroclimatic and atmospheric parameters having the aim to support the\u0000science operations (i.e. the Service Mode) at the Very Large Telescope. The\u0000project has been selected at conclusion of an international open call for\u0000tender opened by ESO and it fits with precise technical specifications. In this\u0000contribution we will present the ultimate goals of this service once it will be\u0000integrated in the VLT operations, the forecasts performances at present time\u0000and the state of the art of the project. FATE is supposed to draw the roadmap\u0000towards the optical turbulence forecast for the ELT.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diego G. Miralles, Jordi Vila-Guerau de Arellano, Tim R. McVicar, Miguel D. Mahecha
Vegetation often understood merely as the result of long-term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by altering surface roughness, consuming significant water resources through transpiration and interception, lowering atmospheric CO2 concentration, and controlling net radiation and its partitioning into sensible and latent heat fluxes. This influence propagates through the atmosphere, from microclimate scales to the entire atmospheric boundary layer, subsequently impacting large-scale circulation and the global transport of heat and moisture. Understanding the feedbacks between vegetation and atmosphere across multiple scales is crucial for predicting the influence of land use and cover changes and for accurately representing these processes in climate models. This short review aims to discuss the mechanisms through which vegetation modulates climate across spatial and temporal scales. Particularly, we evaluate the influence of vegetation on circulation patterns, precipitation and temperature, both in terms of trends and extreme events, such as droughts and heatwaves. The main goal is to highlight the state of science and review recent studies that may help advance our collective understanding of vegetation feedbacks and the role they play in climate.
{"title":"Vegetation-climate feedbacks across scales","authors":"Diego G. Miralles, Jordi Vila-Guerau de Arellano, Tim R. McVicar, Miguel D. Mahecha","doi":"arxiv-2409.04872","DOIUrl":"https://doi.org/arxiv-2409.04872","url":null,"abstract":"Vegetation often understood merely as the result of long-term climate\u0000conditions. However, vegetation itself plays a fundamental role in shaping\u0000Earth's climate by regulating the energy, water, and biogeochemical cycles\u0000across terrestrial landscapes. It exerts influence by altering surface\u0000roughness, consuming significant water resources through transpiration and\u0000interception, lowering atmospheric CO2 concentration, and controlling net\u0000radiation and its partitioning into sensible and latent heat fluxes. This\u0000influence propagates through the atmosphere, from microclimate scales to the\u0000entire atmospheric boundary layer, subsequently impacting large-scale\u0000circulation and the global transport of heat and moisture. Understanding the\u0000feedbacks between vegetation and atmosphere across multiple scales is crucial\u0000for predicting the influence of land use and cover changes and for accurately\u0000representing these processes in climate models. This short review aims to\u0000discuss the mechanisms through which vegetation modulates climate across\u0000spatial and temporal scales. Particularly, we evaluate the influence of\u0000vegetation on circulation patterns, precipitation and temperature, both in\u0000terms of trends and extreme events, such as droughts and heatwaves. The main\u0000goal is to highlight the state of science and review recent studies that may\u0000help advance our collective understanding of vegetation feedbacks and the role\u0000they play in climate.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Pals, Sebastian Bathiany, Richard Wood, Niklas Boers
Numerical models of complex systems like the Earth system are expensive to run and involve many uncertain and typically hand-tuned parameters. In the context of anthropogenic climate change, there is particular concern that specific tipping elements, like the Atlantic Meridional Overturning Circulation, might be overly stable in models due to imperfect parameter choices. However, estimates of the critical forcing thresholds are highly uncertain because the parameter spaces can practically not be explored. Here, we introduce a method for efficient, systematic, and objective calibration of process-based models. Our method drives the system toward parameter configurations where it loses or gains stability, and scales much more efficiently than a brute force approach. We successfully apply the method to a simple bistable model and a conceptual but physically plausible model of the global ocean circulation, demonstrating that our method can help find hidden tipping points, and can calibrate complex models under user-defined constraints.
{"title":"Targeted calibration to adjust stability biases in non-differentiable complex system models","authors":"Daniel Pals, Sebastian Bathiany, Richard Wood, Niklas Boers","doi":"arxiv-2409.04063","DOIUrl":"https://doi.org/arxiv-2409.04063","url":null,"abstract":"Numerical models of complex systems like the Earth system are expensive to\u0000run and involve many uncertain and typically hand-tuned parameters. In the\u0000context of anthropogenic climate change, there is particular concern that\u0000specific tipping elements, like the Atlantic Meridional Overturning\u0000Circulation, might be overly stable in models due to imperfect parameter\u0000choices. However, estimates of the critical forcing thresholds are highly\u0000uncertain because the parameter spaces can practically not be explored. Here,\u0000we introduce a method for efficient, systematic, and objective calibration of\u0000process-based models. Our method drives the system toward parameter\u0000configurations where it loses or gains stability, and scales much more\u0000efficiently than a brute force approach. We successfully apply the method to a\u0000simple bistable model and a conceptual but physically plausible model of the\u0000global ocean circulation, demonstrating that our method can help find hidden\u0000tipping points, and can calibrate complex models under user-defined\u0000constraints.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean Rabault, Catherine Taelman, Martina Idžanović, Gaute Hope, Takehiko Nose, Yngve Kristoffersen, Atle Jensen, Øyvind Breivik, Helge Thomas Bryhni, Mario Hoppmann, Denis Demchev, Anton Korosov, Malin Johansson, Torbjørn Eltoft, Knut-Frode Dagestad, Johannes Röhrs, Leif Eriksson, Marina Durán Moro, Edel S. U. Rikardsen, Takuji Waseda, Tsubasa Kodaira, Johannes Lohse, Thibault Desjonquères, Sveinung Olsen, Olav Gundersen, Victor Cesar Martins de Aguiar, Truls Karlsen, Alexander Babanin, Joey Voermans, Jeong-Won Park, Malte Müller
Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.
{"title":"An OpenMetBuoy dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023","authors":"Jean Rabault, Catherine Taelman, Martina Idžanović, Gaute Hope, Takehiko Nose, Yngve Kristoffersen, Atle Jensen, Øyvind Breivik, Helge Thomas Bryhni, Mario Hoppmann, Denis Demchev, Anton Korosov, Malin Johansson, Torbjørn Eltoft, Knut-Frode Dagestad, Johannes Röhrs, Leif Eriksson, Marina Durán Moro, Edel S. U. Rikardsen, Takuji Waseda, Tsubasa Kodaira, Johannes Lohse, Thibault Desjonquères, Sveinung Olsen, Olav Gundersen, Victor Cesar Martins de Aguiar, Truls Karlsen, Alexander Babanin, Joey Voermans, Jeong-Won Park, Malte Müller","doi":"arxiv-2409.04151","DOIUrl":"https://doi.org/arxiv-2409.04151","url":null,"abstract":"Sea ice is a key element of the global Earth system, with a major impact on\u0000global climate and regional weather. Unfortunately, accurate sea ice modeling\u0000is challenging due to the diversity and complexity of underlying physics\u0000happening there, and a relative lack of ground truth observations. This is\u0000especially true for the Marginal Ice Zone (MIZ), which is the area where sea\u0000ice is affected by incoming ocean waves. Waves contribute to making the area\u0000dynamic, and due to the low survival time of the buoys deployed there, the MIZ\u0000is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs)\u0000around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs\u0000are affordable enough to be deployed in large number, and gather information\u0000about drift (GPS position) and waves (1-dimensional elevation spectrum). This\u0000provides data focusing on the area around Svalbard with unprecedented spatial\u0000and temporal resolution. We expect that this will allow to perform validation\u0000and calibration of ice models and remote sensing algorithms.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142215403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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