René M. van Westen, Elian Y. P. Vanderborght, Michael Kliphuis, Henk A. Dijkstra
The Atlantic Meridional Overturning Circulation (AMOC) is a key component of the climate system and considered to be a tipping element. There is still a large uncertainty on the critical global warming level at which the AMOC will start to collapse. Here we analyse targeted climate model simulations, together with observations, reanalysis products and a suite of state-of-the-art climate model results to reassess this critical global warming level. We find a critical threshold of +3C global mean surface temperature increase compared to pre-industrial with a lower bound of +2.2C (10%-Cl). Such global mean surface temperature anomalies are expected to be reached after 2050. This means that the AMOC is more likely than not (> 50%) to tip within the 21st century under a middle-of-the-road climate change scenario and very likely (> 90%) to tip under a high emissions scenario. The AMOC collapse induced cooling is shown to be offset by the regional warming over Northwestern Europe during the 21st century, but will still induce severe impacts on society.
{"title":"Substantial Risk of 21st Century AMOC Tipping even under Moderate Climate Change","authors":"René M. van Westen, Elian Y. P. Vanderborght, Michael Kliphuis, Henk A. Dijkstra","doi":"arxiv-2407.19909","DOIUrl":"https://doi.org/arxiv-2407.19909","url":null,"abstract":"The Atlantic Meridional Overturning Circulation (AMOC) is a key component of\u0000the climate system and considered to be a tipping element. There is still a\u0000large uncertainty on the critical global warming level at which the AMOC will\u0000start to collapse. Here we analyse targeted climate model simulations, together\u0000with observations, reanalysis products and a suite of state-of-the-art climate\u0000model results to reassess this critical global warming level. We find a\u0000critical threshold of +3C global mean surface temperature increase compared to\u0000pre-industrial with a lower bound of +2.2C (10%-Cl). Such global mean surface\u0000temperature anomalies are expected to be reached after 2050. This means that\u0000the AMOC is more likely than not (> 50%) to tip within the 21st century under a\u0000middle-of-the-road climate change scenario and very likely (> 90%) to tip under\u0000a high emissions scenario. The AMOC collapse induced cooling is shown to be\u0000offset by the regional warming over Northwestern Europe during the 21st\u0000century, but will still induce severe impacts on society.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866666","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}
Tao Li, Lixing Wang, Zihan Qiu, Philippe Ciais, Taochun Sun, Matthew W. Jones, Robbie M. Andrew, Glen P. Peters, Piyu ke, Xiaoting Huang, Robert B. Jackson, Zhu Liu
High temporal resolution CO2 emission data are crucial for understanding the drivers of emission changes, however, current emission dataset is only available on a yearly basis. Here, we extended a global daily CO2 emissions dataset backwards in time to 1970 using machine learning algorithm, which was trained to predict historical daily emissions on national scales based on relationships between daily emission variations and predictors established for the period since 2019. Variation in daily CO2 emissions far exceeded the smoothed seasonal variations. For example, the range of daily CO2 emissions equivalent to 31% of the year average daily emissions in China and 46% of that in India in 2022, respectively. We identified the critical emission-climate temperature (Tc) is 16.5 degree celsius for global average (18.7 degree celsius for China, 14.9 degree celsius for U.S., and 18.4 degree celsius for Japan), in which negative correlation observed between daily CO2 emission and ambient temperature below Tc and a positive correlation above it, demonstrating increased emissions associated with higher ambient temperature. The long-term time series spanning over fifty years of global daily CO2 emissions reveals an increasing trend in emissions due to extreme temperature events, driven by the rising frequency of these occurrences. This work suggests that, due to climate change, greater efforts may be needed to reduce CO2 emissions.
{"title":"Reconstructing Global Daily CO2 Emissions via Machine Learning","authors":"Tao Li, Lixing Wang, Zihan Qiu, Philippe Ciais, Taochun Sun, Matthew W. Jones, Robbie M. Andrew, Glen P. Peters, Piyu ke, Xiaoting Huang, Robert B. Jackson, Zhu Liu","doi":"arxiv-2407.20057","DOIUrl":"https://doi.org/arxiv-2407.20057","url":null,"abstract":"High temporal resolution CO2 emission data are crucial for understanding the\u0000drivers of emission changes, however, current emission dataset is only\u0000available on a yearly basis. Here, we extended a global daily CO2 emissions\u0000dataset backwards in time to 1970 using machine learning algorithm, which was\u0000trained to predict historical daily emissions on national scales based on\u0000relationships between daily emission variations and predictors established for\u0000the period since 2019. Variation in daily CO2 emissions far exceeded the\u0000smoothed seasonal variations. For example, the range of daily CO2 emissions\u0000equivalent to 31% of the year average daily emissions in China and 46% of that\u0000in India in 2022, respectively. We identified the critical emission-climate\u0000temperature (Tc) is 16.5 degree celsius for global average (18.7 degree celsius\u0000for China, 14.9 degree celsius for U.S., and 18.4 degree celsius for Japan), in\u0000which negative correlation observed between daily CO2 emission and ambient\u0000temperature below Tc and a positive correlation above it, demonstrating\u0000increased emissions associated with higher ambient temperature. The long-term\u0000time series spanning over fifty years of global daily CO2 emissions reveals an\u0000increasing trend in emissions due to extreme temperature events, driven by the\u0000rising frequency of these occurrences. This work suggests that, due to climate\u0000change, greater efforts may be needed to reduce CO2 emissions.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866665","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}
Weather forecasting refers to learning evolutionary patterns of some key upper-air and surface variables which is of great significance. Recently, deep learning-based methods have been increasingly applied in the field of weather forecasting due to their powerful feature learning capabilities. However, prediction methods based on the original space iteration struggle to effectively and efficiently utilize large number of weather variables. Therefore, we propose an 'encoding-prediction-decoding' prediction network. This network can efficiently benefit to more related input variables with key variables, that is, it can adaptively extract key variable-related low-dimensional latent feature from much more input atmospheric variables for iterative prediction. And we construct a loss function to guide the iteration of latent feature by utilizing multiple atmospheric variables in corresponding lead times. The obtained latent features through iterative prediction are then decoded to obtain the predicted values of key variables in multiple lead times. In addition, we improve the HTA algorithm in cite{bi2023accurate} by inputting more time steps to enhance the temporal correlation between the prediction results and input variables. Both qualitative and quantitative prediction results on ERA5 dataset validate the superiority of our method over other methods. (The code will be available at https://github.com/rs-lsl/Kvp-lsi)
{"title":"Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space","authors":"Shuangliang Li, Siwei Li","doi":"arxiv-2407.19187","DOIUrl":"https://doi.org/arxiv-2407.19187","url":null,"abstract":"Weather forecasting refers to learning evolutionary patterns of some key\u0000upper-air and surface variables which is of great significance. Recently, deep\u0000learning-based methods have been increasingly applied in the field of weather\u0000forecasting due to their powerful feature learning capabilities. However,\u0000prediction methods based on the original space iteration struggle to\u0000effectively and efficiently utilize large number of weather variables.\u0000Therefore, we propose an 'encoding-prediction-decoding' prediction network.\u0000This network can efficiently benefit to more related input variables with key\u0000variables, that is, it can adaptively extract key variable-related\u0000low-dimensional latent feature from much more input atmospheric variables for\u0000iterative prediction. And we construct a loss function to guide the iteration\u0000of latent feature by utilizing multiple atmospheric variables in corresponding\u0000lead times. The obtained latent features through iterative prediction are then\u0000decoded to obtain the predicted values of key variables in multiple lead times.\u0000In addition, we improve the HTA algorithm in cite{bi2023accurate} by inputting\u0000more time steps to enhance the temporal correlation between the prediction\u0000results and input variables. Both qualitative and quantitative prediction\u0000results on ERA5 dataset validate the superiority of our method over other\u0000methods. (The code will be available at https://github.com/rs-lsl/Kvp-lsi)","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866669","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 wind speed and direction forecasting is paramount across many sectors, spanning agriculture, renewable energy generation, and bushfire management. However, conventional forecasting models encounter significant challenges in precisely predicting wind conditions at high spatial resolutions for individual locations or small geographical areas (< 20 km2) and capturing medium to long-range temporal trends and comprehensive spatio-temporal patterns. This study focuses on a spatial temporal approach for high-resolution gridded wind forecasting at the height of 3 and 10 metres across large areas of the Southwest of Western Australia to overcome these challenges. The model utilises the data that covers a broad geographic area and harnesses a diverse array of meteorological factors, including terrain characteristics, air pressure, 10-metre wind forecasts from the European Centre for Medium-Range Weather Forecasts, and limited observation data from sparsely distributed weather stations (such as 3-metre wind profiles, humidity, and temperature), the model demonstrates promising advancements in wind forecasting accuracy and reliability across the entire region of interest. This paper shows the potential of our machine learning model for wind forecasts across various prediction horizons and spatial coverage. It can help facilitate more informed decision-making and enhance resilience across critical sectors.
{"title":"Spatial Temporal Approach for High-Resolution Gridded Wind Forecasting across Southwest Western Australia","authors":"Fuling Chen, Kevin Vinsen, Arthur Filoche","doi":"arxiv-2407.20283","DOIUrl":"https://doi.org/arxiv-2407.20283","url":null,"abstract":"Accurate wind speed and direction forecasting is paramount across many\u0000sectors, spanning agriculture, renewable energy generation, and bushfire\u0000management. However, conventional forecasting models encounter significant\u0000challenges in precisely predicting wind conditions at high spatial resolutions\u0000for individual locations or small geographical areas (< 20 km2) and capturing\u0000medium to long-range temporal trends and comprehensive spatio-temporal\u0000patterns. This study focuses on a spatial temporal approach for high-resolution\u0000gridded wind forecasting at the height of 3 and 10 metres across large areas of\u0000the Southwest of Western Australia to overcome these challenges. The model\u0000utilises the data that covers a broad geographic area and harnesses a diverse\u0000array of meteorological factors, including terrain characteristics, air\u0000pressure, 10-metre wind forecasts from the European Centre for Medium-Range\u0000Weather Forecasts, and limited observation data from sparsely distributed\u0000weather stations (such as 3-metre wind profiles, humidity, and temperature),\u0000the model demonstrates promising advancements in wind forecasting accuracy and\u0000reliability across the entire region of interest. This paper shows the\u0000potential of our machine learning model for wind forecasts across various\u0000prediction horizons and spatial coverage. It can help facilitate more informed\u0000decision-making and enhance resilience across critical sectors.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866664","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}
In August 2023, the long-planned discharging of radioactive wastewater from the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) started after the confirmation of its feasibility and safety. As this water contains elevated amounts of tritium even after being diluted, a lot of resources have been invested in the monitoring of the Fukushima coastal region where the discharge outlet is located. We compare the first $^3$H surface activity concentrations from these measurements (up to the end of November 2023) with the available background values to evaluate a possible impact of the long-term discharging on humans and environmental levels of the radionuclide of interest in the same or nearby area. From our results, we can conclude that the joint effect of horizontal and vertical mixing has been significant enough to reduce tritium concentrations at the monitored locations in the region close to the FDNPP port two days after the end of the respective phase of the discharging beyond the detection limit of the applied analytical methods (~ 0.3 Bq L$^{-1}$) which is by five orders of magnitude lower than safety limit for drinking water set by the World Health Organization (WHO). Moreover, the distant correlation analysis showed that tritium concentrations at stations located further than 1.4 km were very close to pre-discharge levels (~ 0.4 Bq L$^{-1}$). We also estimated that the $^3$H activity concentration in the offshore Fukushima region would be elevated by 0.01 Bq L$^{-1}$ at maximum over a year of continuous discharging, which is in concordance with the already published modelling papers and much less than the impact of the FDNPP accident in 2011.
{"title":"Assessment of environmental impacts from authorized discharges of tritiated water from the Fukushima site to coastal and offshore regions","authors":"Jakub Kaizer, Katsumi Hirose, Pavel P. Povinec","doi":"arxiv-2407.18664","DOIUrl":"https://doi.org/arxiv-2407.18664","url":null,"abstract":"In August 2023, the long-planned discharging of radioactive wastewater from\u0000the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) started after the\u0000confirmation of its feasibility and safety. As this water contains elevated\u0000amounts of tritium even after being diluted, a lot of resources have been\u0000invested in the monitoring of the Fukushima coastal region where the discharge\u0000outlet is located. We compare the first $^3$H surface activity concentrations\u0000from these measurements (up to the end of November 2023) with the available\u0000background values to evaluate a possible impact of the long-term discharging on\u0000humans and environmental levels of the radionuclide of interest in the same or\u0000nearby area. From our results, we can conclude that the joint effect of\u0000horizontal and vertical mixing has been significant enough to reduce tritium\u0000concentrations at the monitored locations in the region close to the FDNPP port\u0000two days after the end of the respective phase of the discharging beyond the\u0000detection limit of the applied analytical methods (~ 0.3 Bq L$^{-1}$) which is\u0000by five orders of magnitude lower than safety limit for drinking water set by\u0000the World Health Organization (WHO). Moreover, the distant correlation analysis\u0000showed that tritium concentrations at stations located further than 1.4 km were\u0000very close to pre-discharge levels (~ 0.4 Bq L$^{-1}$). We also estimated that\u0000the $^3$H activity concentration in the offshore Fukushima region would be\u0000elevated by 0.01 Bq L$^{-1}$ at maximum over a year of continuous discharging,\u0000which is in concordance with the already published modelling papers and much\u0000less than the impact of the FDNPP accident in 2011.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866670","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}
Brandon N. Benton, Grant Buster, Pavlo Pinchuk, Andrew Glaws, Ryan N. King, Galen Maclaurin, Ilya Chernyakhovskiy
With an increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate high-resolution wind data. Conventional downscaling methods for generating these data have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method, using generative adversarial networks (GANs), for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). We achieve results comparable in historical accuracy and spatiotemporal variability to conventional downscaling by training a GAN model with ERA5 low-resolution input and high-resolution targets from the Wind Integration National Dataset, while reducing computational costs over dynamical downscaling by two orders of magnitude. Spatiotemporal cross-validation shows low error and high correlations with observations and excellent agreement with holdout data across distributions of physical metrics. We apply this approach to downscale 30-km hourly ERA5 data to 2-km 5-minute wind data for January 2000 through December 2023 at multiple hub heights over Eastern Europe. Uncertainty is estimated over the period with observational data by additionally downscaling the members of the European Centre for Medium-Range Weather Forecasting Ensemble of Data Assimilations. Comparisons against observational data from the Meteorological Assimilation Data Ingest System and multiple wind farms show comparable performance to the CONUS validation. This 24-year data record is the first member of the super resolution for renewable energy resource data with wind from reanalysis data dataset (Sup3rWind).
{"title":"Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine","authors":"Brandon N. Benton, Grant Buster, Pavlo Pinchuk, Andrew Glaws, Ryan N. King, Galen Maclaurin, Ilya Chernyakhovskiy","doi":"arxiv-2407.19086","DOIUrl":"https://doi.org/arxiv-2407.19086","url":null,"abstract":"With an increasing share of the electricity grid relying on wind to provide\u0000generating capacity and energy, there is an expanding global need for\u0000historically accurate high-resolution wind data. Conventional downscaling\u0000methods for generating these data have a high computational burden and require\u0000extensive tuning for historical accuracy. In this work, we present a novel deep\u0000learning-based spatiotemporal downscaling method, using generative adversarial\u0000networks (GANs), for generating historically accurate high-resolution wind\u0000resource data from the European Centre for Medium-Range Weather Forecasting\u0000Reanalysis version 5 data (ERA5). We achieve results comparable in historical\u0000accuracy and spatiotemporal variability to conventional downscaling by training\u0000a GAN model with ERA5 low-resolution input and high-resolution targets from the\u0000Wind Integration National Dataset, while reducing computational costs over\u0000dynamical downscaling by two orders of magnitude. Spatiotemporal\u0000cross-validation shows low error and high correlations with observations and\u0000excellent agreement with holdout data across distributions of physical metrics.\u0000We apply this approach to downscale 30-km hourly ERA5 data to 2-km 5-minute\u0000wind data for January 2000 through December 2023 at multiple hub heights over\u0000Eastern Europe. Uncertainty is estimated over the period with observational\u0000data by additionally downscaling the members of the European Centre for\u0000Medium-Range Weather Forecasting Ensemble of Data Assimilations. Comparisons\u0000against observational data from the Meteorological Assimilation Data Ingest\u0000System and multiple wind farms show comparable performance to the CONUS\u0000validation. This 24-year data record is the first member of the super\u0000resolution for renewable energy resource data with wind from reanalysis data\u0000dataset (Sup3rWind).","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866667","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}
Parvathi Kooloth, Jian Lu, Craig Bakker, Derek DeSantis, Adam Rupe
Several Earth system components are at a high risk of undergoing rapid and irreversible qualitative changes or `tipping', due to increasing climate warming. Potential tipping elements include Arctic sea-ice, Atlantic meridional overturning circulation, and tropical coral reefs. Amidst such immediate concerns, it has become necessary to investigate the feasibility of arresting or even reversing the crossing of tipping thresholds using feedback control. In this paper, we study the control of an idealized diffusive energy balance model (EBM) for the Earth's climate; this model has two tipping points due to strong co-albedo feedback. One of these tipping points is a `small icecap' instability responsible for a rapid transition to an ice-free climate state under increasing greenhouse gas (GHG) forcing. We develop an optimal control strategy for the EBM under different climate forcing scenarios with the goal of reversing sea ice loss while minimizing costs. We find that effective control is achievable for such a system, but the cost of reversing sea-ice loss nearly quadruples for an initial state that has just tipped as compared to a state before reaching the tipping point. We also show that thermal inertia may delay tipping leading to an overshoot of the critical GHG forcing threshold. This may offer a short intervention window (overshoot window) during which the control required to reverse sea-ice loss only scales linearly with intervention time. While systems with larger system inertia may have longer overshoot windows, this increased elbow room comes with a steeper rise in the requisite control once the intervention is delayed past this window. Additionally, we find that the requisite control to restore sea-ice is localized in the polar region.
{"title":"How optimal control of polar sea-ice depends on its tipping points","authors":"Parvathi Kooloth, Jian Lu, Craig Bakker, Derek DeSantis, Adam Rupe","doi":"arxiv-2407.17357","DOIUrl":"https://doi.org/arxiv-2407.17357","url":null,"abstract":"Several Earth system components are at a high risk of undergoing rapid and\u0000irreversible qualitative changes or `tipping', due to increasing climate\u0000warming. Potential tipping elements include Arctic sea-ice, Atlantic meridional\u0000overturning circulation, and tropical coral reefs. Amidst such immediate\u0000concerns, it has become necessary to investigate the feasibility of arresting\u0000or even reversing the crossing of tipping thresholds using feedback control. In\u0000this paper, we study the control of an idealized diffusive energy balance model\u0000(EBM) for the Earth's climate; this model has two tipping points due to strong\u0000co-albedo feedback. One of these tipping points is a `small icecap' instability\u0000responsible for a rapid transition to an ice-free climate state under\u0000increasing greenhouse gas (GHG) forcing. We develop an optimal control strategy\u0000for the EBM under different climate forcing scenarios with the goal of\u0000reversing sea ice loss while minimizing costs. We find that effective control\u0000is achievable for such a system, but the cost of reversing sea-ice loss nearly\u0000quadruples for an initial state that has just tipped as compared to a state\u0000before reaching the tipping point. We also show that thermal inertia may delay\u0000tipping leading to an overshoot of the critical GHG forcing threshold. This may\u0000offer a short intervention window (overshoot window) during which the control\u0000required to reverse sea-ice loss only scales linearly with intervention time.\u0000While systems with larger system inertia may have longer overshoot windows,\u0000this increased elbow room comes with a steeper rise in the requisite control\u0000once the intervention is delayed past this window. Additionally, we find that\u0000the requisite control to restore sea-ice is localized in the polar region.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783447","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}
The Quasi-Biennial Oscillation (QBO) is the dominant mode of variability in the equatorial stratosphere. It is characterized by alternating descending easterly and westerly jets over a period of approximately 28 months. It has long been known that the QBO interactions with the annual cycle, e.g., through variation in tropical upwelling, leading to variations in the descent rate of the jets and, resultingly, the QBO period. Understanding these interactions, however, has been hindered by the fact that conventional measures of the QBO convolve these interactions. Koopman formalism, derived from dynamical systems, allows one to decompose spatio-temporal datasets (or nonlinear systems) into spatial modes that evolve coherently with distinct frequencies. We use a data-driven approximation of the Koopman operator on zonal-mean zonal-wind to find modes that correspond to the annual cycle, the QBO, and the nonlinear interactions between the two. From these modes, we establish a data-driven index for a "pure" QBO that is independent of the annual cycle and investigate how the annual cycle modulates the QBO. We begin with what is already known, quantifying the Holton-Tan effect, a nonlinear interaction between the QBO and the annual cycle of the polar stratospheric vortex. We then use the pure QBO to do something new, quantifying how the annual cycle changes the descent rate of the QBO, revealing annual variations with amplitudes comparable to the $30 , mathrm{m} , mathrm{day}^{-1}$ mean descent rate. We compare these results to the annual variation in tropical upwelling and interpret then with a simple model.
{"title":"The QBO, the annual cycle, and their interactions: Isolating periodic modes with Koopman analysis","authors":"Claire Valva, Edwin P. Gerber","doi":"arxiv-2407.17422","DOIUrl":"https://doi.org/arxiv-2407.17422","url":null,"abstract":"The Quasi-Biennial Oscillation (QBO) is the dominant mode of variability in\u0000the equatorial stratosphere. It is characterized by alternating descending\u0000easterly and westerly jets over a period of approximately 28 months. It has\u0000long been known that the QBO interactions with the annual cycle, e.g., through\u0000variation in tropical upwelling, leading to variations in the descent rate of\u0000the jets and, resultingly, the QBO period. Understanding these interactions,\u0000however, has been hindered by the fact that conventional measures of the QBO\u0000convolve these interactions. Koopman formalism, derived from dynamical systems,\u0000allows one to decompose spatio-temporal datasets (or nonlinear systems) into\u0000spatial modes that evolve coherently with distinct frequencies. We use a\u0000data-driven approximation of the Koopman operator on zonal-mean zonal-wind to\u0000find modes that correspond to the annual cycle, the QBO, and the nonlinear\u0000interactions between the two. From these modes, we establish a data-driven\u0000index for a \"pure\" QBO that is independent of the annual cycle and investigate\u0000how the annual cycle modulates the QBO. We begin with what is already known,\u0000quantifying the Holton-Tan effect, a nonlinear interaction between the QBO and\u0000the annual cycle of the polar stratospheric vortex. We then use the pure QBO to\u0000do something new, quantifying how the annual cycle changes the descent rate of\u0000the QBO, revealing annual variations with amplitudes comparable to the $30 ,\u0000mathrm{m} , mathrm{day}^{-1}$ mean descent rate. We compare these results to\u0000the annual variation in tropical upwelling and interpret then with a simple\u0000model.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783446","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}
The upper atmosphere at the altitude of 60-110 km, the mesosphere and lower thermosphere (MLT), has the least observational data of all atmospheres due to the difficulties of in-situ observations. Previous studies demonstrated that atmospheric occultation of cosmic X-ray sources is an effective technique to investigate the MLT. Aiming to measure the atmospheric density of the MLT continuously, we are developing an X-ray camera, "Soipix for observing Upper atmosphere as Iss experiment Mission (SUIM)", dedicated to atmospheric observations. SUIM will be installed on the exposed area of the International Space Station (ISS) and face the ram direction of the ISS to point toward the Earth rim. Observing the cosmic X-ray background (CXB) transmitted through the atmosphere, we will measure the absorption column density via spectroscopy and thus obtain the density of the upper atmosphere. The X-ray camera is composed of a slit collimator and two X-ray SOI-CMOS pixel sensors (SOIPIX), and will stand on its own and make observations, controlled by a CPU-embedded FPGA "Zynq". We plan to install the SUIM payload on the ISS in 2025 during the solar maximum. In this paper, we report the overview and the development status of this project.
在所有大气层中，由于现场观测的困难，高度在60-110千米的高层大气，即中间层和低温层（MLT）的观测数据最少。以往的研究表明，对宇宙X射线源进行大气层掩星是研究中间层和低温层的一种有效技术。为了持续测量多层大气层的大气密度，我们正在开发一种专门用于大气观测的 X 射线照相机 "观测高层大气的 Soipix 作为 Iss 实验任务（SUIM）"。SUIM 将安装在国际空间站（ISS）的暴露区域，面向国际空间站的冲压方向，指向地球边缘。通过观测穿过大气层的宇宙 X 射线背景（CXB），我们将通过光谱法测量吸收柱密度，从而获得高层大气的密度。X 射线相机由一个狭缝准直器和两个 X 射线 SOI-CMOS 像素传感器（SOIPIX）组成，将独立运行并进行观测，由嵌入 CPU 的 FPGA "Zynq "控制。我们计划在 2025 年太阳活动高峰期将 SUIM 有效载荷安装到国际空间站上。在本文中，我们将报告该项目的概况和开发状况。
{"title":"SUIM project: measuring the upper atmosphere from the ISS by observations of the CXB transmitted through the Earth rim","authors":"Kumiko K. Nobukawa, Ayaki Takeda, Satoru Katsuda, Takeshi G. Tsuru, Kazuhiro Nakazawa, Koji Mori, Hiroyuki Uchida, Masayoshi Nobukawa, Eisuke Kurogi, Takumi Kishimoto, Reo Matsui, Yuma Aoki, Yamato Ito, Satoru Kuwano, Tomitaka Tanaka, Mizuki Uenomachi, Masamune Matsuda, Takaya Yamawaki, Takayoshi Kohmura","doi":"arxiv-2407.16922","DOIUrl":"https://doi.org/arxiv-2407.16922","url":null,"abstract":"The upper atmosphere at the altitude of 60-110 km, the mesosphere and lower\u0000thermosphere (MLT), has the least observational data of all atmospheres due to\u0000the difficulties of in-situ observations. Previous studies demonstrated that\u0000atmospheric occultation of cosmic X-ray sources is an effective technique to\u0000investigate the MLT. Aiming to measure the atmospheric density of the MLT\u0000continuously, we are developing an X-ray camera, \"Soipix for observing Upper\u0000atmosphere as Iss experiment Mission (SUIM)\", dedicated to atmospheric\u0000observations. SUIM will be installed on the exposed area of the International\u0000Space Station (ISS) and face the ram direction of the ISS to point toward the\u0000Earth rim. Observing the cosmic X-ray background (CXB) transmitted through the\u0000atmosphere, we will measure the absorption column density via spectroscopy and\u0000thus obtain the density of the upper atmosphere. The X-ray camera is composed\u0000of a slit collimator and two X-ray SOI-CMOS pixel sensors (SOIPIX), and will\u0000stand on its own and make observations, controlled by a CPU-embedded FPGA\u0000\"Zynq\". We plan to install the SUIM payload on the ISS in 2025 during the solar\u0000maximum. In this paper, we report the overview and the development status of\u0000this project.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141786051","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}
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Boedecker, Carsten F. Dormann, Florian Pappenberger, Gianpaolo Balsamo
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with surface heterogeneity and forecast water, carbon and energy fluxes, and coupled with an atmospheric model provide boundary and initial conditions. This numerical parametrization of atmospheric boundaries being computationally expensive, statistical surrogate models are increasingly used to accelerated progress in experimental research. We evaluated the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes, which are integral to forecasting water, carbon, and energy fluxes in coupled atmospheric models. Specifically, we compared the performance of a Long-Short Term Memory (LSTM) encoder-decoder network, extreme gradient boosting, and a feed-forward neural network within a physics-informed multi-objective framework. This framework emulates key states of the ECMWF's Integrated Forecasting System (IFS) land surface scheme, ECLand, across continental and global scales. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned, the XGB scores consistently high across tasks and the MLP provides an excellent implementation-time-accuracy trade-off. The runtime reduction achieved by the emulators in comparison to the full numerical models are significant, offering a faster, yet reliable alternative for conducting numerical experiments on land surfaces.
{"title":"Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators","authors":"Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Boedecker, Carsten F. Dormann, Florian Pappenberger, Gianpaolo Balsamo","doi":"arxiv-2407.16463","DOIUrl":"https://doi.org/arxiv-2407.16463","url":null,"abstract":"Most useful weather prediction for the public is near the surface. The\u0000processes that are most relevant for near-surface weather prediction are also\u0000those that are most interactive and exhibit positive feedback or have key role\u0000in energy partitioning. Land surface models (LSMs) consider these processes\u0000together with surface heterogeneity and forecast water, carbon and energy\u0000fluxes, and coupled with an atmospheric model provide boundary and initial\u0000conditions. This numerical parametrization of atmospheric boundaries being\u0000computationally expensive, statistical surrogate models are increasingly used\u0000to accelerated progress in experimental research. We evaluated the efficiency\u0000of three surrogate models in speeding up experimental research by simulating\u0000land surface processes, which are integral to forecasting water, carbon, and\u0000energy fluxes in coupled atmospheric models. Specifically, we compared the\u0000performance of a Long-Short Term Memory (LSTM) encoder-decoder network, extreme\u0000gradient boosting, and a feed-forward neural network within a physics-informed\u0000multi-objective framework. This framework emulates key states of the ECMWF's\u0000Integrated Forecasting System (IFS) land surface scheme, ECLand, across\u0000continental and global scales. Our findings indicate that while all models on\u0000average demonstrate high accuracy over the forecast period, the LSTM network\u0000excels in continental long-range predictions when carefully tuned, the XGB\u0000scores consistently high across tasks and the MLP provides an excellent\u0000implementation-time-accuracy trade-off. The runtime reduction achieved by the\u0000emulators in comparison to the full numerical models are significant, offering\u0000a faster, yet reliable alternative for conducting numerical experiments on land\u0000surfaces.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783449","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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