Pub Date : 2024-11-20DOI: 10.1038/s41612-024-00836-6
Iravati Ray, Shoumick Mitra, Jariya Kayee, Shufang Yuan, S. M. Shiva Nagendra, Xianfeng Wang, Reshmi Das
India, heavily reliant on coal for power generation, has been a significant emitter of particulate matter (PM) bound lead (Pb) and other heavy metals. It is crucial to understand whether implementation of stricter norms in recent years have effectively reduced emissions from coal combustion. This study aims to investigate and quantify the primary sources of PM2.5 in an area housing a major lignite-fired power plant in South India using Pb isotopic compositions and elemental concentrations. Characteristic ratios such as V/Pb and Cu/Pb demonstrate negligible influence from coal combustion, and indicate that summer aerosols are influenced by open burning. In Pb triple-isotope space the PM2.5 aerosols plot away from coal, overlapping with open burning signatures. These indicate that the atmosphere is predominantly influenced by open burning of solid waste and biomass rather than coal combustion, suggesting a promising decrease in coal emissions. Bayesian mixing model demonstrates that solid waste & biomass burning is the largest anthropogenic contributor towards atmospheric Pb (up to 26%), even in a region of coal combustion and presence of medium and small-scale industries. The dominance of open burning as a pollution source in the vicinity of a lignite fired power plant highlights the necessity for better waste management strategies.
{"title":"Dominance of open burning signatures in PM2.5 near coal plant should redefine pollutant priorities of India","authors":"Iravati Ray, Shoumick Mitra, Jariya Kayee, Shufang Yuan, S. M. Shiva Nagendra, Xianfeng Wang, Reshmi Das","doi":"10.1038/s41612-024-00836-6","DOIUrl":"10.1038/s41612-024-00836-6","url":null,"abstract":"India, heavily reliant on coal for power generation, has been a significant emitter of particulate matter (PM) bound lead (Pb) and other heavy metals. It is crucial to understand whether implementation of stricter norms in recent years have effectively reduced emissions from coal combustion. This study aims to investigate and quantify the primary sources of PM2.5 in an area housing a major lignite-fired power plant in South India using Pb isotopic compositions and elemental concentrations. Characteristic ratios such as V/Pb and Cu/Pb demonstrate negligible influence from coal combustion, and indicate that summer aerosols are influenced by open burning. In Pb triple-isotope space the PM2.5 aerosols plot away from coal, overlapping with open burning signatures. These indicate that the atmosphere is predominantly influenced by open burning of solid waste and biomass rather than coal combustion, suggesting a promising decrease in coal emissions. Bayesian mixing model demonstrates that solid waste & biomass burning is the largest anthropogenic contributor towards atmospheric Pb (up to 26%), even in a region of coal combustion and presence of medium and small-scale industries. The dominance of open burning as a pollution source in the vicinity of a lignite fired power plant highlights the necessity for better waste management strategies.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-17"},"PeriodicalIF":8.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00836-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1038/s41612-024-00832-w
Jonathan D. Beverley, Matthew Newman, Andrew Hoell
Climate models exhibit errors in their simulation of historical trends of variables including sea surface temperature, winds, and precipitation, with important implications for regional and global climate projections. Here, we show that the same trend errors are also present in a suite of initialised seasonal re-forecasts for the years 1993–2016. These re-forecasts are produced by operational models that are similar to Coupled Model Intercomparison Project (CMIP)-class models and share their historical external forcings (e.g. CO2/aerosols). The trend errors, which are often well-developed at very short lead times, represent a roughly linear change in the model mean biases over the 1993–2016 re-forecast record. The similarity of trend errors in both the re-forecasts and historical simulations suggests that climate model trend errors likewise result from evolving mean biases, responding to changing external radiative forcings, instead of being an erroneous long-term response to external forcing. Therefore, these trend errors may be investigated by examining their short-lead development in initialised seasonal forecasts/re-forecasts, which we suggest should also be made by all CMIP models.
{"title":"Climate model trend errors are evident in seasonal forecasts at short leads","authors":"Jonathan D. Beverley, Matthew Newman, Andrew Hoell","doi":"10.1038/s41612-024-00832-w","DOIUrl":"10.1038/s41612-024-00832-w","url":null,"abstract":"Climate models exhibit errors in their simulation of historical trends of variables including sea surface temperature, winds, and precipitation, with important implications for regional and global climate projections. Here, we show that the same trend errors are also present in a suite of initialised seasonal re-forecasts for the years 1993–2016. These re-forecasts are produced by operational models that are similar to Coupled Model Intercomparison Project (CMIP)-class models and share their historical external forcings (e.g. CO2/aerosols). The trend errors, which are often well-developed at very short lead times, represent a roughly linear change in the model mean biases over the 1993–2016 re-forecast record. The similarity of trend errors in both the re-forecasts and historical simulations suggests that climate model trend errors likewise result from evolving mean biases, responding to changing external radiative forcings, instead of being an erroneous long-term response to external forcing. Therefore, these trend errors may be investigated by examining their short-lead development in initialised seasonal forecasts/re-forecasts, which we suggest should also be made by all CMIP models.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-13"},"PeriodicalIF":8.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00832-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1038/s41612-024-00777-0
Pankaj Upadhyaya, Saroj K. Mishra, John T. Fasullo, In-Sik Kang
Over the last four decades (1980–2020), the summer westerlies that prevail in South Asia along the monsoon trough region have weakened by about 25% based on multiple reanalysis datasets. Trends in a range of climate model simulations suggest that the weakening is driven by multiple anthropogenic forcings. Over the period, sea-level pressure has increased by 0.6–1.0 hPa over South Asia’s northwestern regions, induced by cooling due to aerosol emission and changes in land use and land cover, and has decreased over the Arabian Peninsula mainly due to warming by greenhouse gases. These changes in temperature and pressure act to weaken the regional pressure gradient, deflecting the subtropical westerlies from South Asia toward the Arabian Peninsula and weakening the winds in the monsoon trough and its adjacent region. The slowing down of winds appears to result in an anomalous moisture loading and increase in rainfall over the semi-arid northwestern South Asia. This weakening and its associated changes in regional climate are highly relevant to policymaking across South Asia.
{"title":"Attributing the recent weakening of the South Asian subtropical westerlies","authors":"Pankaj Upadhyaya, Saroj K. Mishra, John T. Fasullo, In-Sik Kang","doi":"10.1038/s41612-024-00777-0","DOIUrl":"10.1038/s41612-024-00777-0","url":null,"abstract":"Over the last four decades (1980–2020), the summer westerlies that prevail in South Asia along the monsoon trough region have weakened by about 25% based on multiple reanalysis datasets. Trends in a range of climate model simulations suggest that the weakening is driven by multiple anthropogenic forcings. Over the period, sea-level pressure has increased by 0.6–1.0 hPa over South Asia’s northwestern regions, induced by cooling due to aerosol emission and changes in land use and land cover, and has decreased over the Arabian Peninsula mainly due to warming by greenhouse gases. These changes in temperature and pressure act to weaken the regional pressure gradient, deflecting the subtropical westerlies from South Asia toward the Arabian Peninsula and weakening the winds in the monsoon trough and its adjacent region. The slowing down of winds appears to result in an anomalous moisture loading and increase in rainfall over the semi-arid northwestern South Asia. This weakening and its associated changes in regional climate are highly relevant to policymaking across South Asia.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-11"},"PeriodicalIF":8.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00777-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1038/s41612-024-00834-8
Puja Das, August Posch, Nathan Barber, Michael Hicks, Kate Duffy, Thomas Vandal, Debjani Singh, Katie van Werkhoven, Auroop R. Ganguly
Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists’ judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management.
{"title":"Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting","authors":"Puja Das, August Posch, Nathan Barber, Michael Hicks, Kate Duffy, Thomas Vandal, Debjani Singh, Katie van Werkhoven, Auroop R. Ganguly","doi":"10.1038/s41612-024-00834-8","DOIUrl":"10.1038/s41612-024-00834-8","url":null,"abstract":"Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists’ judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-15"},"PeriodicalIF":8.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00834-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-17DOI: 10.1038/s41612-024-00826-8
Chongyang Zhang, Jiankai Zhang, Amanda C. Maycock, Wenshou Tian
Different tropospheric precursor anomalies leading to sudden stratospheric warmings (SSWs) may result in different circulation evolution. This study finds that there are distinct differences in tropospheric circulation evolutions during SSWs following anomalously strong- (SUR-SSWs) and weak- (WUR-SSWs) Ural ridge. SUR-SSWs exhibit enhanced East Asian trough in the following week, while enhanced Greenland ridge and negative tropospheric annular mode anomalies can persist for 1 month. In contrast, WUR-SSWs exhibit surface cooling over northern Eurasia without notable tropospheric annular mode anomalies. During SUR-SSWs, waves induced by the enhanced Ural wave source tend to propagate below the tropopause, amplifying the East Asian trough. Additionally, due to decreased wave phase speed, the preexisting Ural ridge anomalies migrate westward and amplify the Greenland ridge. Before WUR-SSWs, preexisting cooling over Northeast Asia migrates westward and amplifies northern Eurasia cooling. Thus, the Ural ridge anomalies prior to SSWs significantly influence post-SSW tropospheric circulation.
{"title":"Distinct tropospheric anomalies during sudden stratospheric warming events accompanied by strong and weak Ural Ridge","authors":"Chongyang Zhang, Jiankai Zhang, Amanda C. Maycock, Wenshou Tian","doi":"10.1038/s41612-024-00826-8","DOIUrl":"10.1038/s41612-024-00826-8","url":null,"abstract":"Different tropospheric precursor anomalies leading to sudden stratospheric warmings (SSWs) may result in different circulation evolution. This study finds that there are distinct differences in tropospheric circulation evolutions during SSWs following anomalously strong- (SUR-SSWs) and weak- (WUR-SSWs) Ural ridge. SUR-SSWs exhibit enhanced East Asian trough in the following week, while enhanced Greenland ridge and negative tropospheric annular mode anomalies can persist for 1 month. In contrast, WUR-SSWs exhibit surface cooling over northern Eurasia without notable tropospheric annular mode anomalies. During SUR-SSWs, waves induced by the enhanced Ural wave source tend to propagate below the tropopause, amplifying the East Asian trough. Additionally, due to decreased wave phase speed, the preexisting Ural ridge anomalies migrate westward and amplify the Greenland ridge. Before WUR-SSWs, preexisting cooling over Northeast Asia migrates westward and amplifies northern Eurasia cooling. Thus, the Ural ridge anomalies prior to SSWs significantly influence post-SSW tropospheric circulation.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-14"},"PeriodicalIF":8.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00826-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-17DOI: 10.1038/s41612-024-00831-x
Jilan Jiang, Yimin Liu, Jun Meng, Guoxiong Wu, Bian He, Tingting Ma, Wen Bao, Jingfang Fan
Recently, extreme heatwaves have frequently concurrently swept across Europe and East Asia, causing severe cascading socioeconomic consequences. However, the nonlinear synchronization relationship between these heatwaves and their underlying physical mechanisms remains poorly understood. Utilizing the event synchronization climate network method, atmospheric dynamic diagnostics, and numerical experiments, we revealed robust synchronization between heatwaves over Europe and East Asia, strongly associated with dry soil moisture conditions over the Tibetan Plateau from the preceding winter to summer. Dry soil moisture triggers an equivalent barotropic anticyclone north of the Tibetan Plateau, coinciding with the subtropical westerly jet waveguide and initiating circumglobal atmospheric Rossby waves propagating westward and eastward. Consequently, an equivalent barotropic anticyclone develops over Europe. These anticyclones induce simultaneous heatwaves across Europe and East Asia by increasing downward solar radiation and adiabatic sinking, amplified by positive land-atmosphere feedback. Our findings significantly enhance the understanding and predictive capabilities of these synchronous heatwaves across Eurasia.
{"title":"Dry soil moisture on the Tibetan plateau drives synchronous extreme heatwaves in Europe and East Asia","authors":"Jilan Jiang, Yimin Liu, Jun Meng, Guoxiong Wu, Bian He, Tingting Ma, Wen Bao, Jingfang Fan","doi":"10.1038/s41612-024-00831-x","DOIUrl":"10.1038/s41612-024-00831-x","url":null,"abstract":"Recently, extreme heatwaves have frequently concurrently swept across Europe and East Asia, causing severe cascading socioeconomic consequences. However, the nonlinear synchronization relationship between these heatwaves and their underlying physical mechanisms remains poorly understood. Utilizing the event synchronization climate network method, atmospheric dynamic diagnostics, and numerical experiments, we revealed robust synchronization between heatwaves over Europe and East Asia, strongly associated with dry soil moisture conditions over the Tibetan Plateau from the preceding winter to summer. Dry soil moisture triggers an equivalent barotropic anticyclone north of the Tibetan Plateau, coinciding with the subtropical westerly jet waveguide and initiating circumglobal atmospheric Rossby waves propagating westward and eastward. Consequently, an equivalent barotropic anticyclone develops over Europe. These anticyclones induce simultaneous heatwaves across Europe and East Asia by increasing downward solar radiation and adiabatic sinking, amplified by positive land-atmosphere feedback. Our findings significantly enhance the understanding and predictive capabilities of these synchronous heatwaves across Eurasia.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-10"},"PeriodicalIF":8.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00831-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1038/s41612-024-00813-z
Seol-Hee Oh, Yoo-Geun Ham
To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.
{"title":"Few shot learning for Korean winter temperature forecasts","authors":"Seol-Hee Oh, Yoo-Geun Ham","doi":"10.1038/s41612-024-00813-z","DOIUrl":"10.1038/s41612-024-00813-z","url":null,"abstract":"To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-11"},"PeriodicalIF":8.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00813-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-10DOI: 10.1038/s41612-024-00829-5
Manuel Tobias Blau, Pratik Kad, Jenny V. Turton, Kyung-Ja Ha
The warming of mountains has become evident in recent years, with a mean global warming rate of 1.19 °C from 1979 to 2022. However, unveiling the global divergent decline of persistent mountain snow cover in the face of climate shifts remains unexplored. However, the global decline of persistent mountain snow cover due to climate change is not well understood. This study uses reanalysis and satellite data to examine changes in snow cover lasting over six months across our global mountain regions. We reveal a significant global mean decline of 7.79% in persistent snow cover over the past 44 years. The regional snow cover trends exhibit a heterogeneous and non-linear response to its regional warming rate. Our findings highlight the interplay between global warming and snow cover, emphasizing the need for sustainable development strategies to address the potential impacts of diminishing mountain snow.
{"title":"Uneven global retreat of persistent mountain snow cover alongside mountain warming from ERA5-land","authors":"Manuel Tobias Blau, Pratik Kad, Jenny V. Turton, Kyung-Ja Ha","doi":"10.1038/s41612-024-00829-5","DOIUrl":"10.1038/s41612-024-00829-5","url":null,"abstract":"The warming of mountains has become evident in recent years, with a mean global warming rate of 1.19 °C from 1979 to 2022. However, unveiling the global divergent decline of persistent mountain snow cover in the face of climate shifts remains unexplored. However, the global decline of persistent mountain snow cover due to climate change is not well understood. This study uses reanalysis and satellite data to examine changes in snow cover lasting over six months across our global mountain regions. We reveal a significant global mean decline of 7.79% in persistent snow cover over the past 44 years. The regional snow cover trends exhibit a heterogeneous and non-linear response to its regional warming rate. Our findings highlight the interplay between global warming and snow cover, emphasizing the need for sustainable development strategies to address the potential impacts of diminishing mountain snow.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-14"},"PeriodicalIF":8.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00829-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1038/s41612-024-00830-y
Karam Mansour, Stefano Decesari, Marco Paglione, Silvia Becagli, Matteo Rinaldi
The study proposes an approach to elucidate spatiotemporal mesoscale variations of seawater Dimethylsulfide (DMS) concentrations, the largest natural source of atmospheric sulfur aerosol, based on the Gaussian Process Regression (GPR) machine learning model. Presently, the GPR was trained and evaluated by nested cross-validation across the warm-oligotrophic Mediterranean Sea, a climate hot spot region, leveraging the high-resolution satellite measurements and Mediterranean physical reanalysis together with in-situ DMS observations. The end product is daily gridded fields with a spatial resolution of 0.083° × 0.083° (~9 km) that spans 23 years (1998–2020). Extensive observations of atmospheric methanesulfonic acid (MSA), a typical biogenic secondary aerosol component from DMS oxidation, are consistent with the parameterized high-resolution estimates of sea-to-air DMS flux (FDMS). This represents substantial progress over existing coarse-resolution DMS global maps which do not accurately depict the seasonal patterns of MSA in the Mediterranean atmospheric boundary layer.
{"title":"Nested cross-validation Gaussian process to model dimethylsulfide mesoscale variations in warm oligotrophic Mediterranean seawater","authors":"Karam Mansour, Stefano Decesari, Marco Paglione, Silvia Becagli, Matteo Rinaldi","doi":"10.1038/s41612-024-00830-y","DOIUrl":"10.1038/s41612-024-00830-y","url":null,"abstract":"The study proposes an approach to elucidate spatiotemporal mesoscale variations of seawater Dimethylsulfide (DMS) concentrations, the largest natural source of atmospheric sulfur aerosol, based on the Gaussian Process Regression (GPR) machine learning model. Presently, the GPR was trained and evaluated by nested cross-validation across the warm-oligotrophic Mediterranean Sea, a climate hot spot region, leveraging the high-resolution satellite measurements and Mediterranean physical reanalysis together with in-situ DMS observations. The end product is daily gridded fields with a spatial resolution of 0.083° × 0.083° (~9 km) that spans 23 years (1998–2020). Extensive observations of atmospheric methanesulfonic acid (MSA), a typical biogenic secondary aerosol component from DMS oxidation, are consistent with the parameterized high-resolution estimates of sea-to-air DMS flux (FDMS). This represents substantial progress over existing coarse-resolution DMS global maps which do not accurately depict the seasonal patterns of MSA in the Mediterranean atmospheric boundary layer.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-12"},"PeriodicalIF":8.5,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00830-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1038/s41612-024-00822-y
Thomas J. Bracegirdle, Thomas Caton Harrison, Caroline R. Holmes, Hua Lu, Patrick Martineau, Tony Phillips
In this study, available large ensemble datasets in the Coupled Model Intercomparison Phase 6 (CMIP6) archive were used to provide the first multi-variate overview of the evolution of extreme seasons over Antarctica and the Southern Ocean during the 20th and 21st centuries following medium-to-high radiative forcing scenarios. The results show significant differences between simulated changes in background mean climate and changes in low (10th percentile) and high (90th percentile) extreme seasons. Regional winter warming is most pronounced for cold extremes. In summer, there are more pronounced increases in high extremes in precipitation and westerly wind during the ozone hole formation period (late 20th century), affecting coastal regions and, in particular, the Antarctic Peninsula. At midlatitudes, there is a reduction in the range of summer season wind extremes. Suggested mechanisms for these differences are provided relating to sea ice retreat and westerly jet position.
{"title":"Antarctic extreme seasons under 20th and 21st century climate change","authors":"Thomas J. Bracegirdle, Thomas Caton Harrison, Caroline R. Holmes, Hua Lu, Patrick Martineau, Tony Phillips","doi":"10.1038/s41612-024-00822-y","DOIUrl":"10.1038/s41612-024-00822-y","url":null,"abstract":"In this study, available large ensemble datasets in the Coupled Model Intercomparison Phase 6 (CMIP6) archive were used to provide the first multi-variate overview of the evolution of extreme seasons over Antarctica and the Southern Ocean during the 20th and 21st centuries following medium-to-high radiative forcing scenarios. The results show significant differences between simulated changes in background mean climate and changes in low (10th percentile) and high (90th percentile) extreme seasons. Regional winter warming is most pronounced for cold extremes. In summer, there are more pronounced increases in high extremes in precipitation and westerly wind during the ozone hole formation period (late 20th century), affecting coastal regions and, in particular, the Antarctic Peninsula. At midlatitudes, there is a reduction in the range of summer season wind extremes. Suggested mechanisms for these differences are provided relating to sea ice retreat and westerly jet position.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-12"},"PeriodicalIF":8.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00822-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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