Pub Date : 2023-12-08DOI: 10.1088/2752-5295/ad13ac
Xianglin Ren, Wei Liu, Robert J. Allen, Se-Yong Song
In the era of escalating climate change, understanding human impacts on marine heatwaves (MHWs) becomes essential. This study harnesses climate model historical and single forcing simulations to delve into the individual roles of anthropogenic greenhouse gases and aerosols in shaping the characteristics of global MHWs over the past several decades. The results suggest that greenhouse gas variations lead to longer-lasting, more frequent, and intense MHWs. In contrast, anthropogenic aerosols markedly curbs the intensity and growth of MHWs. Further analysis of the sea surface temperature (SST) probability distribution reveals that anthropogenic greenhouse gases and aerosols have opposing effects on the tails of the SST probability distribution, causing the tails to expand and contract, respectively. Climate extremes such as MHWs are accordingly promoted and reduced. Our study underscores the significant impacts of anthropogenic greenhouse gases and aerosols on MHWs, which go far beyond the customary concept that these anthropogenic forcings modulate climate extremes by shifting global SST probabilities via modifying the mean-state SST.
{"title":"Distinct anthropogenic greenhouse gas and aerosol induced marine heatwaves","authors":"Xianglin Ren, Wei Liu, Robert J. Allen, Se-Yong Song","doi":"10.1088/2752-5295/ad13ac","DOIUrl":"https://doi.org/10.1088/2752-5295/ad13ac","url":null,"abstract":"In the era of escalating climate change, understanding human impacts on marine heatwaves (MHWs) becomes essential. This study harnesses climate model historical and single forcing simulations to delve into the individual roles of anthropogenic greenhouse gases and aerosols in shaping the characteristics of global MHWs over the past several decades. The results suggest that greenhouse gas variations lead to longer-lasting, more frequent, and intense MHWs. In contrast, anthropogenic aerosols markedly curbs the intensity and growth of MHWs. Further analysis of the sea surface temperature (SST) probability distribution reveals that anthropogenic greenhouse gases and aerosols have opposing effects on the tails of the SST probability distribution, causing the tails to expand and contract, respectively. Climate extremes such as MHWs are accordingly promoted and reduced. Our study underscores the significant impacts of anthropogenic greenhouse gases and aerosols on MHWs, which go far beyond the customary concept that these anthropogenic forcings modulate climate extremes by shifting global SST probabilities via modifying the mean-state SST.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"59 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138587901","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 : 2023-11-24DOI: 10.1088/2752-5295/ad0f9d
Sisay Guta Alemu, C. H. Sime, T. A. Demissie
Rising global temperatures and shifting precipitation patterns have significant socio-economic consequences if not properly studied and predicted. Regional climate models (RCMs) are utilized to assess local-scale climate change. However, the reliability of individual models must be validated due to inherent limitations and methodological constraints. This study evaluates the performance of CORDEX Africa RCMs using observed rainfall and air temperature data from 1986 to 2005. Model performance was evaluated using statistical indicators such as bias, RMSE, r, MAE, and a concise plot of the statistical indicators which is Taylor’s diagram. In rainfall simulation, the RACMO22T performed admirably in the upper parts of the basin (region of high rainfall and cold temperature) and lower regions of the basin (region of low rainfall and hot temperature) with bias −8.64% and 6.19% respectively. HIRHAM5 and CCLM4-8 simulate well the maximum temperature in the upper parts with biases of (0.14 °C and −0.14 °C respectively), whereas RCA4 is well performed in the lower parts of the basin. CCLM4-8 is good for minimum temperature simulation in the upper parts, but HIRHAM5 and RCA4 are good in the lower parts of the basin. In rainfall simulation, all models are slightly good in dry months than in wet. All models underestimated the maximum temperature and overestimated the minimum temperature in the study area as compared to the observed.
{"title":"Historical simulations of temperature and precipitation from the CORDEX Africa model in the Wabi Shebele Basin","authors":"Sisay Guta Alemu, C. H. Sime, T. A. Demissie","doi":"10.1088/2752-5295/ad0f9d","DOIUrl":"https://doi.org/10.1088/2752-5295/ad0f9d","url":null,"abstract":"Rising global temperatures and shifting precipitation patterns have significant socio-economic consequences if not properly studied and predicted. Regional climate models (RCMs) are utilized to assess local-scale climate change. However, the reliability of individual models must be validated due to inherent limitations and methodological constraints. This study evaluates the performance of CORDEX Africa RCMs using observed rainfall and air temperature data from 1986 to 2005. Model performance was evaluated using statistical indicators such as bias, RMSE, r, MAE, and a concise plot of the statistical indicators which is Taylor’s diagram. In rainfall simulation, the RACMO22T performed admirably in the upper parts of the basin (region of high rainfall and cold temperature) and lower regions of the basin (region of low rainfall and hot temperature) with bias −8.64% and 6.19% respectively. HIRHAM5 and CCLM4-8 simulate well the maximum temperature in the upper parts with biases of (0.14 °C and −0.14 °C respectively), whereas RCA4 is well performed in the lower parts of the basin. CCLM4-8 is good for minimum temperature simulation in the upper parts, but HIRHAM5 and RCA4 are good in the lower parts of the basin. In rainfall simulation, all models are slightly good in dry months than in wet. All models underestimated the maximum temperature and overestimated the minimum temperature in the study area as compared to the observed.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"101 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139238948","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 : 2023-11-24DOI: 10.1088/2752-5295/ad0f9e
Akinsanola A A, Kooperman G J, Hannah W M, Reed K A, Pendergrass A G, Hsu Wei-Ching
Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.
{"title":"Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1","authors":"Akinsanola A A, Kooperman G J, Hannah W M, Reed K A, Pendergrass A G, Hsu Wei-Ching","doi":"10.1088/2752-5295/ad0f9e","DOIUrl":"https://doi.org/10.1088/2752-5295/ad0f9e","url":null,"abstract":"Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"35 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139241331","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 : 2023-11-21DOI: 10.1088/2752-5295/ad0e86
Chibuike Chiedozie Ibebuchi
The subtropical Indian Ocean Dipole (SIOD) significantly influences climate variability, predominantly within parts of the Southern Hemisphere. This study applies an autoencoder—a type of artificial neural network (ANN)—known for its ability to capture intricate non-linear relationships in data through the process of encoding and decoding—to analyze the spatiotemporal characteristics of the SIOD. The encoded SIOD pattern(s) is compared to the conventional definition of the SIOD, calculated as the sea surface temperature (SST) anomaly difference between the western and eastern subtropical Indian Ocean. The analysis reveals two encoded patterns consistent with the conventional SIOD structure, predominantly represented by the SST dipole pattern south of Madagascar and off Australia’s west coast. During different analysis periods, distinct variability in the global SST patterns associated with the SIOD was observed. This variability underscores the SIOD’s dynamic nature and the challenges of accurately defining modes of variability with limited records. One of the ANN patterns has a substantial congruence match of 0.92 with the conventional SIOD pattern, while the other represents an alternate non-linear pattern within the SIOD. This implies the potential existence of additional non-linear SIOD patterns in the subtropical Indian Ocean, complementing the traditional model. When global temperature and precipitation are regressed onto the ANN temporal patterns and the conventional SIOD index, both appear to be associated with anomalous climate conditions over parts of Australia, with several other consistent global impacts. Nevertheless, due to the non-linear nature of the ANN patterns, their effects on local temperature and precipitation vary across different regions as compared to the conventional SIOD index. This study highlights that while the conventional SIOD pattern is consistent with the ANN-derived SIOD pattern, the climate system’s complexity and non-linearity might require ANN modeling to advance our comprehension of climatic modes.
亚热带印度洋偶极子（SIOD）对气候变异有重大影响，主要是在南半球的部分地区。本研究采用自动编码器--一种人工神经网络（ANN），因其能够通过编码和解码过程捕捉数据中错综复杂的非线性关系而闻名--来分析 SIOD 的时空特征。编码后的 SIOD 模式与 SIOD 的传统定义进行了比较，SIOD 的计算方法是亚热带印度洋西部和东部之间的海面温度（SST）异常差。分析表明，有两种编码模式与传统的 SIOD 结构一致，主要表现为马达加斯加以南和澳大利亚西海岸附近的海面温度偶极模式。在不同的分析时段，观察到与 SIOD 相关的全球海温模式有明显的变化。这种变异性突显了 SIOD 的动态性质，以及在记录有限的情况下准确定义变异模式所面临的挑战。其中一个 ANN 模式与传统 SIOD 模式的吻合度高达 0.92，而另一个则代表了 SIOD 中的另一种非线性模式。这意味着亚热带印度洋可能存在其他非线性 SIOD 模式，对传统模式进行了补充。当将全球温度和降水量回归到 ANN 时间模式和传统 SIOD 指数时，两者似乎都与澳大利亚部分地区的异常气候条件有关，并有其他一些一致的全球影响。然而，由于 ANN 模式的非线性性质，与传统 SIOD 指数相比，它们对不同地区的当地气温和降水的影响各不相同。这项研究强调，虽然传统的 SIOD 模式与 ANN 导出的 SIOD 模式一致，但气候系统的复杂性和非线性可能需要 ANN 建模来推进我们对气候模式的理解。
{"title":"Exploring non-linear modes of the subtropical Indian Ocean Dipole using autoencoder neural networks","authors":"Chibuike Chiedozie Ibebuchi","doi":"10.1088/2752-5295/ad0e86","DOIUrl":"https://doi.org/10.1088/2752-5295/ad0e86","url":null,"abstract":"The subtropical Indian Ocean Dipole (SIOD) significantly influences climate variability, predominantly within parts of the Southern Hemisphere. This study applies an autoencoder—a type of artificial neural network (ANN)—known for its ability to capture intricate non-linear relationships in data through the process of encoding and decoding—to analyze the spatiotemporal characteristics of the SIOD. The encoded SIOD pattern(s) is compared to the conventional definition of the SIOD, calculated as the sea surface temperature (SST) anomaly difference between the western and eastern subtropical Indian Ocean. The analysis reveals two encoded patterns consistent with the conventional SIOD structure, predominantly represented by the SST dipole pattern south of Madagascar and off Australia’s west coast. During different analysis periods, distinct variability in the global SST patterns associated with the SIOD was observed. This variability underscores the SIOD’s dynamic nature and the challenges of accurately defining modes of variability with limited records. One of the ANN patterns has a substantial congruence match of 0.92 with the conventional SIOD pattern, while the other represents an alternate non-linear pattern within the SIOD. This implies the potential existence of additional non-linear SIOD patterns in the subtropical Indian Ocean, complementing the traditional model. When global temperature and precipitation are regressed onto the ANN temporal patterns and the conventional SIOD index, both appear to be associated with anomalous climate conditions over parts of Australia, with several other consistent global impacts. Nevertheless, due to the non-linear nature of the ANN patterns, their effects on local temperature and precipitation vary across different regions as compared to the conventional SIOD index. This study highlights that while the conventional SIOD pattern is consistent with the ANN-derived SIOD pattern, the climate system’s complexity and non-linearity might require ANN modeling to advance our comprehension of climatic modes.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254124","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}
Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO2), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.
{"title":"Climate change impacts on global potato yields: a review","authors":"Toyin Adekanmbi, Xiuquan Wang, S. Basheer, Suqi Liu, Aili Yang, Huiyan Cheng","doi":"10.1088/2752-5295/ad0e13","DOIUrl":"https://doi.org/10.1088/2752-5295/ad0e13","url":null,"abstract":"Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO2), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139259156","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 : 2023-09-01DOI: 10.1088/2752-5295/acf606
F. Song, Ruby Leung, Jian Lu, Tianjun Zhou, Ping Huang
Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, pattern shift, phase and wet/dry season length changes. In a warming climate, the amplitude of tropical rainfall annual cycle is enhanced, more evidently over ocean, while the phase of tropical rainfall annual cycle is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The pattern shift under warming is marked by two features: equatorward shift of the inter-tropical convergence zone throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter is suggested to be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress of the last decade, many gaps remain in understanding the mechanism, quantifying and attributing the emergence, narrowing the inter-model uncertainty, and evaluating the impact of tropical rainfall annual cycle changes, motivating future work guided by some directions proposed in this review.
{"title":"Advances in understanding the changes of tropical rainfall annual cycle: a review","authors":"F. Song, Ruby Leung, Jian Lu, Tianjun Zhou, Ping Huang","doi":"10.1088/2752-5295/acf606","DOIUrl":"https://doi.org/10.1088/2752-5295/acf606","url":null,"abstract":"Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, pattern shift, phase and wet/dry season length changes. In a warming climate, the amplitude of tropical rainfall annual cycle is enhanced, more evidently over ocean, while the phase of tropical rainfall annual cycle is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The pattern shift under warming is marked by two features: equatorward shift of the inter-tropical convergence zone throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter is suggested to be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress of the last decade, many gaps remain in understanding the mechanism, quantifying and attributing the emergence, narrowing the inter-model uncertainty, and evaluating the impact of tropical rainfall annual cycle changes, motivating future work guided by some directions proposed in this review.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134406823","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 : 2023-08-29DOI: 10.1088/2752-5295/acf4b5
M. Hauer, V. Mueller, G. Sheriff
Although the most dire societal impacts of sea-level rise (SLR) typically manifest toward the end of the 21st century, many coastal communities face challenges in the present due to recurrent tidal flooding. Few studies have documented transportation disruptions due to tidal flooding in the recent past. Here, we address this issue by combining home and work locations for approximately 500 million commuters in coastal US counties from 2002 to 2017. We find tidal flooding delays coastal commuters by approximately 22 min per year in 2015–2017, increasing to between 200 and 650 min by 2060 under various SLR scenarios. Adjustments in residential and work locations reduce the growth in commuting delays for approximately 40% of US counties. For residents in coastal counties, SLR is not a distant threat—it is already lapping at their toes.
{"title":"Sea level rise already delays coastal commuters","authors":"M. Hauer, V. Mueller, G. Sheriff","doi":"10.1088/2752-5295/acf4b5","DOIUrl":"https://doi.org/10.1088/2752-5295/acf4b5","url":null,"abstract":"Although the most dire societal impacts of sea-level rise (SLR) typically manifest toward the end of the 21st century, many coastal communities face challenges in the present due to recurrent tidal flooding. Few studies have documented transportation disruptions due to tidal flooding in the recent past. Here, we address this issue by combining home and work locations for approximately 500 million commuters in coastal US counties from 2002 to 2017. We find tidal flooding delays coastal commuters by approximately 22 min per year in 2015–2017, increasing to between 200 and 650 min by 2060 under various SLR scenarios. Adjustments in residential and work locations reduce the growth in commuting delays for approximately 40% of US counties. For residents in coastal counties, SLR is not a distant threat—it is already lapping at their toes.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294351","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 : 2023-08-29DOI: 10.1088/2752-5295/acf4b7
Olivia Linke, N. Feldl, J. Quaas
The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of uncertainty in model projections of Arctic climate change. Previous work has shown that the spread in model predictions of future Arctic amplification (AA) can be traced back to the inter-model spread in simulated long-term sea ice loss. We demonstrate that the strength of future AA is further linked to the current climate’s, observable sea ice state across the multi-model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). The implication is that the sea-ice climatology sets the stage for long-term changes through the 21st century, which mediate the degree by which Arctic warming is amplified with respect to global warming. We determine that a lower base-climate sea ice extent and sea ice concentration (SIC) in CMIP6 models enable stronger ice melt in both future climate and during the seasonal cycle. In particular, models with lower Arctic-mean SIC project stronger future ice loss and a more intense seasonal cycle in ice melt and growth. Both processes systemically link to a larger future AA across climate models. These results are manifested by the role of climate feedbacks that have been widely identified as major drivers of AA. We show in particular that models with low base-climate SIC predict a systematically stronger warming contribution through both sea-ice albedo feedback and temperature feedbacks in the future, as compared to models with high SIC. From our derived linear regressions in conjunction with observations, we estimate a 21st-century AA over sea ice of 2.47–3.34 with respect to global warming. Lastly, from the tight relationship between base-climate SIC and the projected timing of an ice-free September, we predict a seasonally ice-free Arctic by mid-century under a high-emission scenario.
{"title":"Current-climate sea ice amount and seasonality as constraints for future Arctic amplification","authors":"Olivia Linke, N. Feldl, J. Quaas","doi":"10.1088/2752-5295/acf4b7","DOIUrl":"https://doi.org/10.1088/2752-5295/acf4b7","url":null,"abstract":"The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of uncertainty in model projections of Arctic climate change. Previous work has shown that the spread in model predictions of future Arctic amplification (AA) can be traced back to the inter-model spread in simulated long-term sea ice loss. We demonstrate that the strength of future AA is further linked to the current climate’s, observable sea ice state across the multi-model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). The implication is that the sea-ice climatology sets the stage for long-term changes through the 21st century, which mediate the degree by which Arctic warming is amplified with respect to global warming. We determine that a lower base-climate sea ice extent and sea ice concentration (SIC) in CMIP6 models enable stronger ice melt in both future climate and during the seasonal cycle. In particular, models with lower Arctic-mean SIC project stronger future ice loss and a more intense seasonal cycle in ice melt and growth. Both processes systemically link to a larger future AA across climate models. These results are manifested by the role of climate feedbacks that have been widely identified as major drivers of AA. We show in particular that models with low base-climate SIC predict a systematically stronger warming contribution through both sea-ice albedo feedback and temperature feedbacks in the future, as compared to models with high SIC. From our derived linear regressions in conjunction with observations, we estimate a 21st-century AA over sea ice of 2.47–3.34 with respect to global warming. Lastly, from the tight relationship between base-climate SIC and the projected timing of an ice-free September, we predict a seasonally ice-free Arctic by mid-century under a high-emission scenario.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127101514","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 : 2023-08-29DOI: 10.1088/2752-5295/acf4b4
Jordis S. Tradowsky, G. Bodeker, Christopher John Noble, D. Stone, G. Rye, Leroy Bird, William Herewini, Sapna Rana, Johannes Rausch, I. Soltanzadeh
A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.
{"title":"A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand","authors":"Jordis S. Tradowsky, G. Bodeker, Christopher John Noble, D. Stone, G. Rye, Leroy Bird, William Herewini, Sapna Rana, Johannes Rausch, I. Soltanzadeh","doi":"10.1088/2752-5295/acf4b4","DOIUrl":"https://doi.org/10.1088/2752-5295/acf4b4","url":null,"abstract":"A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133438972","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 : 2023-08-29DOI: 10.1088/2752-5295/acf4b6
M. Zachariah, Arulalan T, K. AchutaRao, F. Saeed, Roshan Jha, M. Dhasmana, A. Mondal, R. Bonnet, R. Vautard, S. Philip, S. Kew, Maja Vahlberg, Roop K. Singh, J. Arrighi, Dorothy Heinrich, L. Thalheimer, Carolina Pereira Marghidan, Aditi Kapoor, M. V. van Aalst, E. Raju, Sihan Li, Jingru Sun, G. Vecchi, Wenchang Yang, M. Hauser, D. Schumacher, S. Seneviratne, L. Harrington, F. Otto
In March 2022, large parts over the north Indian plains including the breadbasket region, and southern Pakistan began experiencing prolonged heat, which continued into May. The event was exacerbated due to prevailing dry conditions in the region, resulting in devastating consequences for public health and agriculture. Using event attribution methods, we analyse the role of human-induced climate change in altering the chances of such an event. To capture the extent of the impacts, we choose March–April average of daily maximum temperature over the most affected region in India and Pakistan as the variable. In observations, the 2022 event has a return period of ∼1-in-100 years. For each of the climate models, we then calculate the change in probability and intensity of a 1-in-100 year event between the actual and counterfactual worlds for quantifying the role of climate change. We estimate that human-caused climate change made this heatwave about 1 °C hotter and 30 times more likely in the current, 2022 climate, as compared to the 1.2 °C cooler, pre-industrial climate. Under a future global warming of 2 °C above pre-industrial levels, heatwaves like this are expected to become even more common (2–20 times more likely) and hotter (by 0 °C–1.5 °C) compared to now. Stronger and frequent heat waves in the future will impact vulnerable groups as conditions in some regions exceed limits for human survivability. Therefore, mitigation is essential for avoiding loss of lives and livelihood. Heat Action Plans have proved effective to help reduce heat-related mortality in both countries.
{"title":"Attribution of 2022 early-spring heatwave in India and Pakistan to climate change: lessons in assessing vulnerability and preparedness in reducing impacts","authors":"M. Zachariah, Arulalan T, K. AchutaRao, F. Saeed, Roshan Jha, M. Dhasmana, A. Mondal, R. Bonnet, R. Vautard, S. Philip, S. Kew, Maja Vahlberg, Roop K. Singh, J. Arrighi, Dorothy Heinrich, L. Thalheimer, Carolina Pereira Marghidan, Aditi Kapoor, M. V. van Aalst, E. Raju, Sihan Li, Jingru Sun, G. Vecchi, Wenchang Yang, M. Hauser, D. Schumacher, S. Seneviratne, L. Harrington, F. Otto","doi":"10.1088/2752-5295/acf4b6","DOIUrl":"https://doi.org/10.1088/2752-5295/acf4b6","url":null,"abstract":"In March 2022, large parts over the north Indian plains including the breadbasket region, and southern Pakistan began experiencing prolonged heat, which continued into May. The event was exacerbated due to prevailing dry conditions in the region, resulting in devastating consequences for public health and agriculture. Using event attribution methods, we analyse the role of human-induced climate change in altering the chances of such an event. To capture the extent of the impacts, we choose March–April average of daily maximum temperature over the most affected region in India and Pakistan as the variable. In observations, the 2022 event has a return period of ∼1-in-100 years. For each of the climate models, we then calculate the change in probability and intensity of a 1-in-100 year event between the actual and counterfactual worlds for quantifying the role of climate change. We estimate that human-caused climate change made this heatwave about 1 °C hotter and 30 times more likely in the current, 2022 climate, as compared to the 1.2 °C cooler, pre-industrial climate. Under a future global warming of 2 °C above pre-industrial levels, heatwaves like this are expected to become even more common (2–20 times more likely) and hotter (by 0 °C–1.5 °C) compared to now. Stronger and frequent heat waves in the future will impact vulnerable groups as conditions in some regions exceed limits for human survivability. Therefore, mitigation is essential for avoiding loss of lives and livelihood. Heat Action Plans have proved effective to help reduce heat-related mortality in both countries.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129009578","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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