Pub Date : 2024-06-04DOI: 10.1088/2752-5295/ad53f7
Paula Romanovska, Sabine Undorf, Bernhard Schauberger, A. Duisenbekova, C. Gornott
Northern Kazakhstan is a major wheat exporter, contributing to food security in Central Asia and beyond. However, wheat yields fluctuate and low-producing years occur frequently. It is currently unclear to what extent human-induced climate change contributes to this. The most severe low-producing year in this century was in 2010, which had severe consequences for the food security of wheat-importing countries. Here, we present a climate impact attribution study that quantifies the impact of human-induced climate change on the average wheat production and associated economic revenues in northern Kazakhstan in the 21st century and on the likelihood of a low-production year like 2010. The study uses bias-adjusted counterfactual and factual climate model data from two large ensembles of latest-generation climate models as input to a statistical subnational yield model. We consider the climate data and the yield model as fit for purpose as first, the factual climate simulations represent the observations, second, the out-of-sample validation of the yield model performs reasonably well with a mean R2 of 0.54, and third, the results are robust under the performed sensitivity tests. Human-induced climate change has had a critical impact on wheat production, specifically through increases in daily-minimum temperatures and extreme heat. This has resulted in a decrease in yields during 2000-2019 by approximately 6.2% to 8.2% (uncertainty range of two climate models) and an increased likelihood of the 2010 low-production event by 1.5 to 4.7 times (10th to 90th percentile uncertainty range covering both climate models). During 2000-2019, human-induced climate change caused economic losses estimated at between 96 and 180 million USD per year (10th to 90th percentile uncertainty range covering both climate models). These results highlight the necessity for ambitious global mitigation efforts and measures to adapt wheat production to increasing temperatures, ensuring regional and global food security.
{"title":"Human-induced climate change has decreased wheat production in northern Kazakhstan","authors":"Paula Romanovska, Sabine Undorf, Bernhard Schauberger, A. Duisenbekova, C. Gornott","doi":"10.1088/2752-5295/ad53f7","DOIUrl":"https://doi.org/10.1088/2752-5295/ad53f7","url":null,"abstract":"\u0000 Northern Kazakhstan is a major wheat exporter, contributing to food security in Central Asia and beyond. However, wheat yields fluctuate and low-producing years occur frequently. It is currently unclear to what extent human-induced climate change contributes to this. The most severe low-producing year in this century was in 2010, which had severe consequences for the food security of wheat-importing countries. Here, we present a climate impact attribution study that quantifies the impact of human-induced climate change on the average wheat production and associated economic revenues in northern Kazakhstan in the 21st century and on the likelihood of a low-production year like 2010. The study uses bias-adjusted counterfactual and factual climate model data from two large ensembles of latest-generation climate models as input to a statistical subnational yield model. We consider the climate data and the yield model as fit for purpose as first, the factual climate simulations represent the observations, second, the out-of-sample validation of the yield model performs reasonably well with a mean R2 of 0.54, and third, the results are robust under the performed sensitivity tests. Human-induced climate change has had a critical impact on wheat production, specifically through increases in daily-minimum temperatures and extreme heat. This has resulted in a decrease in yields during 2000-2019 by approximately 6.2% to 8.2% (uncertainty range of two climate models) and an increased likelihood of the 2010 low-production event by 1.5 to 4.7 times (10th to 90th percentile uncertainty range covering both climate models). During 2000-2019, human-induced climate change caused economic losses estimated at between 96 and 180 million USD per year (10th to 90th percentile uncertainty range covering both climate models). These results highlight the necessity for ambitious global mitigation efforts and measures to adapt wheat production to increasing temperatures, ensuring regional and global food security.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268024","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}
Pub Date : 2024-06-04DOI: 10.1088/2752-5295/ad53f6
Xiuyuan Ding, Gang Chen, Gudrun Magnusdottir
Extreme stratospheric wave activity has been linked to surface cold extremes over North America, but little is known whether the Quasi-biennial Oscillation (QBO) plays a role in this linkage. Here, by comparing strong stratospheric wave events during the westerly phase (wQBO) with those during the easterly phase (eQBO), we show that the cooling signature following strong wave events depends on the QBO phase in observations. During wQBO, strong wave events are followed by an increased risk of North American cold extremes and a vertical structure shift from a westward phase tilt to an eastward tilt. However, strong wave events under eQBO do not change the cold risk nor alter the vertical tilt. We further examine this dependence on QBO in QBO-resolving climate models, finding that the cooling signature of strong wave events in models is largely insensitive to QBO phases. This insensitivity is suggested to be linked to model biases in the stratospheric wave representation.
{"title":"North American cooling signature of strong stratospheric wave events depends on the QBO phase","authors":"Xiuyuan Ding, Gang Chen, Gudrun Magnusdottir","doi":"10.1088/2752-5295/ad53f6","DOIUrl":"https://doi.org/10.1088/2752-5295/ad53f6","url":null,"abstract":"\u0000 Extreme stratospheric wave activity has been linked to surface cold extremes over North America, but little is known whether the Quasi-biennial Oscillation (QBO) plays a role in this linkage. Here, by comparing strong stratospheric wave events during the westerly phase (wQBO) with those during the easterly phase (eQBO), we show that the cooling signature following strong wave events depends on the QBO phase in observations. During wQBO, strong wave events are followed by an increased risk of North American cold extremes and a vertical structure shift from a westward phase tilt to an eastward tilt. However, strong wave events under eQBO do not change the cold risk nor alter the vertical tilt. We further examine this dependence on QBO in QBO-resolving climate models, finding that the cooling signature of strong wave events in models is largely insensitive to QBO phases. This insensitivity is suggested to be linked to model biases in the stratospheric wave representation.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"80 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268434","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}
Pub Date : 2024-06-04DOI: 10.1088/2752-5295/ad53f4
Daniel B Henrique, Xuesong Zhang, An Wang, E. Lagacé, Kyup Lee, Paul Kushner, I. D. Posen
Battery electric vehicles (BEVs) influence total and peak electricity demand, but few studies account for climate when studying these effects. This study quantifies BEV charging demand in the Greater Toronto and Hamilton Area using a detailed trip level approach, accounting for the effect of present and future temperatures on BEV energy consumption. The impact of temperature on charging demand was largest in winter. In 2019, charging demand increases by 52% on an average January day, and up to 82% on extreme days (relative to mild weather conditions). At 30% penetration, BEVs increase peak demand on January’s coldest day by 600-3600 MW (3-5%), of which 300-700 MW is driven by temperature, depending on the charging scenario. Climate change introduces small changes, increasing summer and decreasing winter charging demand. These results highlight the importance of adjusting for regional climate variation and temperature extremes when analyzing the impact of BEVs on the grid.
{"title":"The effects of climate and climate change on electric vehicle charging demand in Toronto, Canada","authors":"Daniel B Henrique, Xuesong Zhang, An Wang, E. Lagacé, Kyup Lee, Paul Kushner, I. D. Posen","doi":"10.1088/2752-5295/ad53f4","DOIUrl":"https://doi.org/10.1088/2752-5295/ad53f4","url":null,"abstract":"\u0000 Battery electric vehicles (BEVs) influence total and peak electricity demand, but few studies account for climate when studying these effects. This study quantifies BEV charging demand in the Greater Toronto and Hamilton Area using a detailed trip level approach, accounting for the effect of present and future temperatures on BEV energy consumption. The impact of temperature on charging demand was largest in winter. In 2019, charging demand increases by 52% on an average January day, and up to 82% on extreme days (relative to mild weather conditions). At 30% penetration, BEVs increase peak demand on January’s coldest day by 600-3600 MW (3-5%), of which 300-700 MW is driven by temperature, depending on the charging scenario. Climate change introduces small changes, increasing summer and decreasing winter charging demand. These results highlight the importance of adjusting for regional climate variation and temperature extremes when analyzing the impact of BEVs on the grid.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"72 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268205","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}
Pub Date : 2024-05-16DOI: 10.1088/2752-5295/ad3fdc
J. Ono, Y. Komuro, H. Tatebe, N. Kimura
Initialization of sea ice and the upper halocline in the Arctic Ocean is crucial for sea-ice prediction, but their representation in climate models still remains biased. Here, using historical and four different simulations by a single climate model, we find that constraining the sea-ice momentum by surface wind stress contributes to a better representation of the sea-ice velocity, area, and concentration. Moreover, the wind-constrained sea-ice drift modifies the underlying ocean structure via ice-ocean stress, leading to an improved climatological halocline’s vertical structure in the Canada Basin. This is because the excessively represented negative wind and ice-ocean stress curl in the climate model is weakened when constraining the sea-ice momentum and consequently the downward vertical speed, including the Ekman pumping, is also weakened at depths of 0–500 m, alleviating the deepening of isohalines. From these results, the improvement of sea-ice and ocean states by constraining sea-ice momentum is expected to make sea-ice prediction more accurate.
{"title":"Effectiveness of wind-constrained sea-ice momentum on formation of sea-ice distribution and upper halocline of Arctic Ocean in climate model","authors":"J. Ono, Y. Komuro, H. Tatebe, N. Kimura","doi":"10.1088/2752-5295/ad3fdc","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3fdc","url":null,"abstract":"\u0000 Initialization of sea ice and the upper halocline in the Arctic Ocean is crucial for sea-ice prediction, but their representation in climate models still remains biased. Here, using historical and four different simulations by a single climate model, we find that constraining the sea-ice momentum by surface wind stress contributes to a better representation of the sea-ice velocity, area, and concentration. Moreover, the wind-constrained sea-ice drift modifies the underlying ocean structure via ice-ocean stress, leading to an improved climatological halocline’s vertical structure in the Canada Basin. This is because the excessively represented negative wind and ice-ocean stress curl in the climate model is weakened when constraining the sea-ice momentum and consequently the downward vertical speed, including the Ekman pumping, is also weakened at depths of 0–500 m, alleviating the deepening of isohalines. From these results, the improvement of sea-ice and ocean states by constraining sea-ice momentum is expected to make sea-ice prediction more accurate.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"54 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970673","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}
Pub Date : 2024-05-07DOI: 10.1088/2752-5295/ad4815
Joseph Giguere, D. Gilford, Andrew Pershing
Ocean temperatures around the world are rising and hit record levels around the world in 2023. While trends are clear and likely strongly connected to human-caused climate change, the oceans also exhibit variability on the daily level, leading to local extremes such as marine heatwaves. We present an operational system to estimate the impact of human-caused climate change on daily sea surface temperatures anywhere in the ocean. This system uses a multi-method approach combining observed trends and paired control/forced climate model runs from CMIP6. Our approach is novel in its flexibility and ease of application for global, daily use for any day since the beginning of the satellite era (1982--2023). The system allows for rapid evaluation for further study of attributable ocean temperatures and real-time communications of attributable ongoing events. We apply the system to well-documented heatwaves in the Tasman Sea, Gulf of Maine, and Mediterranean Sea over the past decade, as well as global conditions in July 2023, to confirm that the system produces estimates consistent with other attribution methods, and to simulate how our system handles interesting events as they are occurring. Each of these events strongly reflected impacts of climate change: their temperatures were consistently made at least four times as likely to occur in our human-influenced climate than in a world without climate change. Meanwhile, in July 2023, almost all (>70%) of the ocean's temperatures were made at least twice as likely to occur on any given day. Rapid attribution of daily ocean temperatures provides a pathway for quantifying the influence of climate change on ecological impacts like coral bleaching and on ocean-generated/influenced storms like tropical cyclones.
{"title":"Attributing daily ocean temperatures to anthropogenic climate change","authors":"Joseph Giguere, D. Gilford, Andrew Pershing","doi":"10.1088/2752-5295/ad4815","DOIUrl":"https://doi.org/10.1088/2752-5295/ad4815","url":null,"abstract":"\u0000 Ocean temperatures around the world are rising and hit record levels around the world in 2023. While trends are clear and likely strongly connected to human-caused climate change, the oceans also exhibit variability on the daily level, leading to local extremes such as marine heatwaves. We present an operational system to estimate the impact of human-caused climate change on daily sea surface temperatures anywhere in the ocean. This system uses a multi-method approach combining observed trends and paired control/forced climate model runs from CMIP6. Our approach is novel in its flexibility and ease of application for global, daily use for any day since the beginning of the satellite era (1982--2023). The system allows for rapid evaluation for further study of attributable ocean temperatures and real-time communications of attributable ongoing events. We apply the system to well-documented heatwaves in the Tasman Sea, Gulf of Maine, and Mediterranean Sea over the past decade, as well as global conditions in July 2023, to confirm that the system produces estimates consistent with other attribution methods, and to simulate how our system handles interesting events as they are occurring. Each of these events strongly reflected impacts of climate change: their temperatures were consistently made at least four times as likely to occur in our human-influenced climate than in a world without climate change. Meanwhile, in July 2023, almost all (>70%) of the ocean's temperatures were made at least twice as likely to occur on any given day. Rapid attribution of daily ocean temperatures provides a pathway for quantifying the influence of climate change on ecological impacts like coral bleaching and on ocean-generated/influenced storms like tropical cyclones.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"65 s255","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002520","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}
Pub Date : 2024-04-23DOI: 10.1088/2752-5295/ad4201
Stephanie Hay, J. Screen, J. Catto
We examine sources of uncertainty in projections of Arctic Amplification (AA) using the CMIP6 multi-model ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 multi-model mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.
我们利用 CMIP6 多模式集合和单一模式初始条件大型历史和未来情景模拟集合，研究了北极增温（AA）预测的不确定性来源。在 CMIP6 多模式平均值中，年平均 AA 比率在不同时间和不同情景下都稳定在 2.5 左右，因此 AA 幅值的情景不确定性极小，可以忽略不计。在低排放和高排放情景下，由于不同的根本原因，会出现与稳定值的偏差，后者主要体现在夏季和秋季。对模型不确定性的最佳估计至少比 CMIP6 中情景的不确定性大一个数量级。大型集合显示，在 21 世纪的大部分时间里，不可还原的内部变率与模式不确定性的大小相似，但在 21 世纪末的最低排放情景下除外，因为在该情景下，不可还原的内部变率可能是模式不确定性的两倍。
{"title":"Steady but model dependent Arctic amplification of the forced temperature response in 21st century CMIP6 projections","authors":"Stephanie Hay, J. Screen, J. Catto","doi":"10.1088/2752-5295/ad4201","DOIUrl":"https://doi.org/10.1088/2752-5295/ad4201","url":null,"abstract":"\u0000 We examine sources of uncertainty in projections of Arctic Amplification (AA) using the CMIP6 multi-model ensemble and single model initial-condition large ensembles of historical and future scenario simulations. In the CMIP6 multi-model mean, the annual mean AA ratio is steady at approximately 2.5, both in time and across scenarios, resulting in negligibly small scenario uncertainty in the magnitude of AA. Deviations from the steady value can be found at the low and high emission scenarios due to different root causes, with the latter being mostly evident in the summer and autumn seasons. Best estimates of model uncertainty are at least an order of magnitude larger than scenario uncertainty in CMIP6. The large ensembles reveal that irreducible internal variability has a similar magnitude to model uncertainty for most of the 21st century, except in the lowest emission scenario at the end of the 21st century when it could be twice as large.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"136 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668657","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}
Pub Date : 2024-04-23DOI: 10.1088/2752-5295/ad4200
Shirin Ermis, Nicholas J Leach, F. Lott, S. Sparrow, Antje Weisheimer
The widespread destruction and insurance losses incurred by midlatitude storms every year makes it an imperative to study how storms change with climate change. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small climate change signal in variables such as windspeed compared to the noise of weather, as well as the high resolutions required to represent the dynamic processes in the storms. The midlatitude cyclone Eunice hit the South of the UK on February 18, 2022. Here, we assess how Eunice was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, thus significantly increasing our confidence in its ability to model the key physical underlying processes and how they repsond to climate change. Using modified boundary conditions for the greenhouse gas concentrations and changed initial conditions for the 3D ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times of eight, four and two days, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution of a single event enables combining event specificity and a focus on dynamic changes with the assessment of changes in risks from strong winds. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.
{"title":"Event attribution of a midlatitude windstorm using ensemble weather forecasts","authors":"Shirin Ermis, Nicholas J Leach, F. Lott, S. Sparrow, Antje Weisheimer","doi":"10.1088/2752-5295/ad4200","DOIUrl":"https://doi.org/10.1088/2752-5295/ad4200","url":null,"abstract":"\u0000 The widespread destruction and insurance losses incurred by midlatitude storms every year makes it an imperative to study how storms change with climate change. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small climate change signal in variables such as windspeed compared to the noise of weather, as well as the high resolutions required to represent the dynamic processes in the storms. The midlatitude cyclone Eunice hit the South of the UK on February 18, 2022. Here, we assess how Eunice was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, thus significantly increasing our confidence in its ability to model the key physical underlying processes and how they repsond to climate change. Using modified boundary conditions for the greenhouse gas concentrations and changed initial conditions for the 3D ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times of eight, four and two days, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution of a single event enables combining event specificity and a focus on dynamic changes with the assessment of changes in risks from strong winds. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"110 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669906","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}
Pub Date : 2024-04-17DOI: 10.1088/2752-5295/ad3fdb
M. Reboita, Glauber Willian de Souza Ferreira, João Gabriel Martins Ribeiro, Shaukat Ali
This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995-2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation (CMAP), the Global Precipitation Climatology Project (GPCP), and the ERA5 reanalysis. The models’ performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.
{"title":"Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America","authors":"M. Reboita, Glauber Willian de Souza Ferreira, João Gabriel Martins Ribeiro, Shaukat Ali","doi":"10.1088/2752-5295/ad3fdb","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3fdb","url":null,"abstract":"\u0000 This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995-2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation (CMAP), the Global Precipitation Climatology Project (GPCP), and the ERA5 reanalysis. The models’ performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691280","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}
Pub Date : 2024-04-17DOI: 10.1088/2752-5295/ad3fda
Claude-Michel Nzotungicimpaye, H. D. Matthews
Anthropogenic CO2 emissions are causing climate change, and impacts of climate change are already affecting every region on Earth. The purpose of this review is to investigate climate impacts that can be linked quantitatively to cumulative CO2 emissions (CE), with a focus on impacts scaling linearly with CE. The reviewed studies indicate a proportionality between CE and various observable climate impacts such as regional warming, extreme daily temperatures, heavy precipitation events, seasonal changes in temperature and precipitation, global mean precipitation increase over ocean, sea ice decline in September across the Arctic Ocean, surface ocean acidification, global mean sea level rise, different marine heatwave characteristics, changes in habitat viability for non-human primates, as well as labour productivity loss due to extreme heat exposure. From the reviewed literature, we report estimates of these climate impacts resulting from one trillion tonne of CE (1 Tt C). These estimates are highly relevant for climate policy as they provide a way for assessing climate impacts associated with every amount of CO2 emitted by human activities. With the goal of expanding the number of climate impacts that could be linked quantitatively to CE, we propose a framework for estimating additional climate impacts resulting from CE. This framework builds on the transient climate response to cumulative emissions (TCRE), and it is applicable to climate impacts that scale linearly with global warming. We illustrate how the framework can be applied to quantify physical, biological, and societal climate impacts resulting from CE. With this review, we highlight that each tonne of CO2 emissions matters in terms of resulting impacts on natural and human systems.
人为二氧化碳排放正在引起气候变化，而气候变化的影响已经影响到地球上的每一个地区。本综述的目的是研究可与累积二氧化碳排放量（CE）定量联系起来的气候影响，重点是与 CE 成线性比例的影响。综述的研究表明，CE 与各种可观测到的气候影响之间存在比例关系，如区域变暖、极端日气温、强降水事件、气温和降水的季节性变化、全球海洋平均降水量增加、北冰洋九月海冰减少、表层海洋酸化、全球平均海平面上升、不同的海洋热浪特征、非人灵长类动物栖息地生存能力的变化，以及极端高温暴露造成的劳动生产率损失。根据所查阅的文献，我们报告了一万亿吨二氧化碳当量（1 Tt C）对这些气候影响的估计值。这些估计值与气候政策高度相关，因为它们为评估与人类活动排放的每一二氧化碳量相关的气候影响提供了一种方法。为了扩大可与二氧化碳排放量定量联系起来的气候影响的数量，我们提出了一个估算二氧化碳排放量造成的额外气候影响的框架。该框架以累积排放的瞬态气候响应（TCRE）为基础，适用于与全球变暖成线性比例的气候影响。我们说明了如何将该框架应用于量化气候变化对物理、生物和社会造成的气候影响。通过这一回顾，我们强调每吨二氧化碳的排放都会对自然和人类系统产生影响。
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Pub Date : 2024-04-17DOI: 10.1088/2752-5295/ad3fdd
Miranda Yeen Morgan, Elizabeth Bryan, Marlène Elias
Women’s leadership is increasingly considered critical for achieving climate-resilient agrifood systems. Numerous initiatives and policies highlighting the business case for women’s leadership to deliver a range of positive social, economic and environmental outcomes. In this Perspective, we examine the business case, finding uneven evidence linking women’s leadership to increased resilience to climate change. We problematize the ways women’s leadership is typically understood in this area and argue that, despite the value and utility of understanding the pathways through which women’s leadership can strengthen climate-resilient agrifood systems, support for increasing women’s leadership should not be contingent on proving the business case or its instrumental value. Rather, increasing the leadership of women in all their diversity in climate action is a moral imperative and non-negotiable due to women’s human right to have meaningful influence in the decisions that affect their lives. Finally, we propose ways to reframe the debate on women’s leadership in climate and agrifood systems and suggest priorities for future research in this area.
{"title":"Women’s leadership in climate-resilient agrifood systems: defining a future research agenda","authors":"Miranda Yeen Morgan, Elizabeth Bryan, Marlène Elias","doi":"10.1088/2752-5295/ad3fdd","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3fdd","url":null,"abstract":"\u0000 Women’s leadership is increasingly considered critical for achieving climate-resilient agrifood systems. Numerous initiatives and policies highlighting the business case for women’s leadership to deliver a range of positive social, economic and environmental outcomes. In this Perspective, we examine the business case, finding uneven evidence linking women’s leadership to increased resilience to climate change. We problematize the ways women’s leadership is typically understood in this area and argue that, despite the value and utility of understanding the pathways through which women’s leadership can strengthen climate-resilient agrifood systems, support for increasing women’s leadership should not be contingent on proving the business case or its instrumental value. Rather, increasing the leadership of women in all their diversity in climate action is a moral imperative and non-negotiable due to women’s human right to have meaningful influence in the decisions that affect their lives. Finally, we propose ways to reframe the debate on women’s leadership in climate and agrifood systems and suggest priorities for future research in this area.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":" 87","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692334","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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