Pub Date : 2024-04-16DOI: 10.1088/2752-5295/ad3f3c
Mark T. Richardson
Satellite land surface temperature (Ts) records have now reached 20+ year length, but their trends may differ from historical records built from in-situ measurements of near-surface air temperature (Tas). In the ERA5 reanalysis, 60°S—60°N land Ts and Tas trends can differ by up to ±0.06 °C decade-1 over 20 years, depending on the period, or more on smaller spatial scales. Here I use 1979—1998 outputs from ACCESS1-0 climate model simulations with prescribed land Ts to understand changes in Ts and Tas. CO2’s effective radiative forcing (ERF) causes adjustments that warm Tas relative to Ts. In ACCESS1-0, vegetation enhances the adjustments to CO2 over land. Meanwhile, feedbacks in ACCESS1-0 oppose the adjustments, resulting in small long-term net effects on global temperature estimates. In coupled simulations from other models, there is no agreement on whether Ts or Tas warms more and the most extreme case shows global long-term differences of just 5 % between land Ts or land Tas trends. The results contrast with over-ocean behaviour where adjustments and feedbacks reinforce each other, and drive larger long-term Tas warming relative to Ts across all models.
卫星陆地表面温度（Ts）记录现已达到 20 多年的长度，但其趋势可能与近地面气温（Tas）原位测量建立的历史记录不同。在ERA5再分析中，南纬60°-北纬60°陆地Ts和Tas在20年内的趋势差异可达±0.06°C decade-1（取决于时期），在较小的空间尺度上差异更大。在此，我利用 1979-1998 年 ACCESS1-0 气候模式模拟的陆地 Ts 输出结果来了解 Ts 和 Tas 的变化。二氧化碳的有效辐射强迫（ERF）会导致相对于Ts的Tas变暖。在 ACCESS1-0 中，植被增强了陆地对 CO2 的调节。与此同时，ACCESS1-0 中的反馈作用反对这种调整，导致对全球温度估计的长期净影响很小。在其他模式的耦合模拟中，Ts 或 Tas 的升温幅度并不一致，最极端的情况是陆地 Ts 或陆地 Tas 趋势的全球长期差异仅为 5%。这些结果与大洋行为形成了鲜明对比，在大洋行为中，调整和反馈相互加强，在所有模式中，大洋的长期升温幅度大于陆地的升温幅度。
{"title":"Interchangeability of multi-decade skin and surface air temperature trends over land in models","authors":"Mark T. Richardson","doi":"10.1088/2752-5295/ad3f3c","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3f3c","url":null,"abstract":"\u0000 Satellite land surface temperature (Ts) records have now reached 20+ year length, but their trends may differ from historical records built from in-situ measurements of near-surface air temperature (Tas). In the ERA5 reanalysis, 60°S—60°N land Ts and Tas trends can differ by up to ±0.06 °C decade-1 over 20 years, depending on the period, or more on smaller spatial scales. Here I use 1979—1998 outputs from ACCESS1-0 climate model simulations with prescribed land Ts to understand changes in Ts and Tas. CO2’s effective radiative forcing (ERF) causes adjustments that warm Tas relative to Ts. In ACCESS1-0, vegetation enhances the adjustments to CO2 over land. Meanwhile, feedbacks in ACCESS1-0 oppose the adjustments, resulting in small long-term net effects on global temperature estimates. In coupled simulations from other models, there is no agreement on whether Ts or Tas warms more and the most extreme case shows global long-term differences of just 5 % between land Ts or land Tas trends. The results contrast with over-ocean behaviour where adjustments and feedbacks reinforce each other, and drive larger long-term Tas warming relative to Ts across all models.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"27 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696058","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 : 2024-04-03DOI: 10.1088/2752-5295/ad3a0d
Yi-Chang Chen, Yu‐Chiao Liang, Chien-Ming Wu, Jin-De Huang, Simon H Lee, Yih Wang, Yi-Jhen Zeng
Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder – a generative deep learning model – to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the variational auto-encoder gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.
{"title":"Exploiting a variational auto-encoder to represent the evolution of sudden stratospheric warmings","authors":"Yi-Chang Chen, Yu‐Chiao Liang, Chien-Ming Wu, Jin-De Huang, Simon H Lee, Yih Wang, Yi-Jhen Zeng","doi":"10.1088/2752-5295/ad3a0d","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3a0d","url":null,"abstract":"\u0000 Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder – a generative deep learning model – to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the variational auto-encoder gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748370","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 : 2024-04-03DOI: 10.1088/2752-5295/ad3a0c
Yanan Duan, Sanjiv Kumar
The signal-to-noise ratio paradox is interpreted as the climate model’s ability to predict observations better than the model itself. This view is counterintuitive, given that climate models are simplified numerical representations of complex earth system dynamics. A revised interpretation is provided here: the signal-to-noise ratio paradox represents excessive noise in climate predictions and projections. Noise is potentially reducible, providing a scientific basis for improving the signal in regional climate projections. The signal-to-noise ratio paradox was assessed in long-term climate projections using Single-model and Multi-model Large Ensemble climate data. A null hypothesis was constructed by performing bootstrap resampling of climate model ensembles to test its ability to predict the 20th-century temperature and precipitation trends locally and compare it with the observations. The rejection of the null hypothesis indicates the existence of a paradox. The multi-model large ensemble does not reject the null hypothesis in most places globally. The rejection rate in the single-model large ensemble is related to the model's fidelity to simulate internal climate variability rather than its ensemble size. For regions where the null hypothesis is rejected in the multi-model large ensemble, for example, India, the paradox is caused by a smaller signal strength in the climate model's ensemble. The signal strength was improved by 100% through ensemble selection and based on past performance, which reduced uncertainty in India's 30-year temperature projections by 25%. Consistent with previous studies, precipitation projections are noisier, leading to a paradox metric value 2-3 times higher than that of the temperature projections. The application of ensemble selection methodology significantly decreased uncertainty in precipitation projections for the United Kingdom, Western Australia, and Northeastern America by 47%, 36%, and 20%, respectively. Overall, this study makes a unique contribution by reducing uncertainty at the temporal scale, specifically in estimating trends using the signal-to-noise ratio paradox metric.
{"title":"A revised interpretation of signal-to-noise ratio paradox and its application to constrain regional climate projections","authors":"Yanan Duan, Sanjiv Kumar","doi":"10.1088/2752-5295/ad3a0c","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3a0c","url":null,"abstract":"\u0000 The signal-to-noise ratio paradox is interpreted as the climate model’s ability to predict observations better than the model itself. This view is counterintuitive, given that climate models are simplified numerical representations of complex earth system dynamics. A revised interpretation is provided here: the signal-to-noise ratio paradox represents excessive noise in climate predictions and projections. Noise is potentially reducible, providing a scientific basis for improving the signal in regional climate projections. The signal-to-noise ratio paradox was assessed in long-term climate projections using Single-model and Multi-model Large Ensemble climate data. A null hypothesis was constructed by performing bootstrap resampling of climate model ensembles to test its ability to predict the 20th-century temperature and precipitation trends locally and compare it with the observations. The rejection of the null hypothesis indicates the existence of a paradox. The multi-model large ensemble does not reject the null hypothesis in most places globally. The rejection rate in the single-model large ensemble is related to the model's fidelity to simulate internal climate variability rather than its ensemble size. For regions where the null hypothesis is rejected in the multi-model large ensemble, for example, India, the paradox is caused by a smaller signal strength in the climate model's ensemble. The signal strength was improved by 100% through ensemble selection and based on past performance, which reduced uncertainty in India's 30-year temperature projections by 25%. Consistent with previous studies, precipitation projections are noisier, leading to a paradox metric value 2-3 times higher than that of the temperature projections. The application of ensemble selection methodology significantly decreased uncertainty in precipitation projections for the United Kingdom, Western Australia, and Northeastern America by 47%, 36%, and 20%, respectively. Overall, this study makes a unique contribution by reducing uncertainty at the temporal scale, specifically in estimating trends using the signal-to-noise ratio paradox metric.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"102 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140750541","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 : 2024-04-03DOI: 10.1088/2752-5295/ad3a0e
Uzoma Chukwuemeka Nworgu, H. Nnamchi, Nilton Manuel Évora Do Rosário
The South Atlantic Ocean Dipole (SAOD) exerts strong influence on climate variability in parts of Africa and South America. Here we assess the ability of an ensemble of 35 state-of-the-art coupled global climate models to simulate the SAOD impacts on regional rainfall for the historical period (1950 to 2014) and future projections (2015 - 2079). For both periods we consider the peak phase of the dipole which is in austral winter. Observational analysis reveals four regions with spatially coherent SAOD impacts on rainfall; Northern Amazon, Guinea Coast, Central Africa, and Southeast Brazil. The observed rainfall response to the SAOD over Northern Amazon (0.31 mm/day), Guinea Coast (0.38 mm/day), and Southeast Brazil (0.12 mm/day) are significantly underestimated by the modeled ensemble-mean response of 0.10±0.15 mm/day, 0.05±0.15 mm/day, -0.01±0.04 mm/day, respectively. A too southerly rain belt in the ensemble, associated with warmer-than-observed Atlantic cold tongue, leads to better performance of models over Central Africa (46% simulate observations-consistent SAOD-rainfall correlations) and poor performance over the Guinea Coast (only 5.7% simulate observations-consistent SAOD-rainfall correlations). We found divergent responses among the projections of ensemble members precluding a categorical statement on the future strength of the SAOD-rainfall relationship in a high-emissions scenario. Our findings highlight key uncertainties that must be addressed to enhance the value of SAOD-rainfall projections for the affected African and South American countries.
{"title":"Divergent future change in South Atlantic Ocean Dipole impacts on regional rainfall in CMIP6 models","authors":"Uzoma Chukwuemeka Nworgu, H. Nnamchi, Nilton Manuel Évora Do Rosário","doi":"10.1088/2752-5295/ad3a0e","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3a0e","url":null,"abstract":"\u0000 The South Atlantic Ocean Dipole (SAOD) exerts strong influence on climate variability in parts of Africa and South America. Here we assess the ability of an ensemble of 35 state-of-the-art coupled global climate models to simulate the SAOD impacts on regional rainfall for the historical period (1950 to 2014) and future projections (2015 - 2079). For both periods we consider the peak phase of the dipole which is in austral winter. Observational analysis reveals four regions with spatially coherent SAOD impacts on rainfall; Northern Amazon, Guinea Coast, Central Africa, and Southeast Brazil. The observed rainfall response to the SAOD over Northern Amazon (0.31 mm/day), Guinea Coast (0.38 mm/day), and Southeast Brazil (0.12 mm/day) are significantly underestimated by the modeled ensemble-mean response of 0.10±0.15 mm/day, 0.05±0.15 mm/day, -0.01±0.04 mm/day, respectively. A too southerly rain belt in the ensemble, associated with warmer-than-observed Atlantic cold tongue, leads to better performance of models over Central Africa (46% simulate observations-consistent SAOD-rainfall correlations) and poor performance over the Guinea Coast (only 5.7% simulate observations-consistent SAOD-rainfall correlations). We found divergent responses among the projections of ensemble members precluding a categorical statement on the future strength of the SAOD-rainfall relationship in a high-emissions scenario. Our findings highlight key uncertainties that must be addressed to enhance the value of SAOD-rainfall projections for the affected African and South American countries.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"316 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749878","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 : 2024-04-03DOI: 10.1088/2752-5295/ad3a0b
B. Mezzina, H. Goosse, P. Huot, Sylvain Marchi, N. V. van Lipzig
The 2016 Antarctic sea ice extent (SIE) drop was a rapid decrease that led to persistent low sea ice conditions. The event was triggered by atmospheric anomalies, but the potential preconditioning role of the ocean is unsettled. Here, we use sensitivity experiments with a fully-coupled regional climate model to elucidate the impact of the ocean conditions on the drop and on the persistence of the negative SIE anomalies during 2017. In particular, we re-initialize the model in January 2016 using different ocean and sea ice conditions, keeping lateral boundary forcings in the atmosphere and ocean unchanged. We find that the state of the Southern Ocean in early 2016 does not determine whether the drop occurs or not, but indeed has an impact on its amplitude and regional characteristics. Our results also indicate that the ocean initialization affects the sea ice recovery after the drop in the short term (one year), especially in the Weddell sector. The ocean’s influence appears not to be linked to the ocean surface and sea-ice initialization, but rather to the sub-surface conditions (between 50 m and 150 m) and heat exchange fluctuations at the regional scale, while the atmospheric forcing triggering the drop is driven by the large-scale circulation.
{"title":"Contributions of atmospheric forcing and ocean preconditioning in the 2016 Antarctic sea ice extent drop","authors":"B. Mezzina, H. Goosse, P. Huot, Sylvain Marchi, N. V. van Lipzig","doi":"10.1088/2752-5295/ad3a0b","DOIUrl":"https://doi.org/10.1088/2752-5295/ad3a0b","url":null,"abstract":"\u0000 The 2016 Antarctic sea ice extent (SIE) drop was a rapid decrease that led to persistent low sea ice conditions. The event was triggered by atmospheric anomalies, but the potential preconditioning role of the ocean is unsettled. Here, we use sensitivity experiments with a fully-coupled regional climate model to elucidate the impact of the ocean conditions on the drop and on the persistence of the negative SIE anomalies during 2017. In particular, we re-initialize the model in January 2016 using different ocean and sea ice conditions, keeping lateral boundary forcings in the atmosphere and ocean unchanged. We find that the state of the Southern Ocean in early 2016 does not determine whether the drop occurs or not, but indeed has an impact on its amplitude and regional characteristics. Our results also indicate that the ocean initialization affects the sea ice recovery after the drop in the short term (one year), especially in the Weddell sector. The ocean’s influence appears not to be linked to the ocean surface and sea-ice initialization, but rather to the sub-surface conditions (between 50 m and 150 m) and heat exchange fluctuations at the regional scale, while the atmospheric forcing triggering the drop is driven by the large-scale circulation.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"212 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746560","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 : 2024-03-15DOI: 10.1088/2752-5295/ad34a7
Zitong Li, Weihang Liu, Tao Ye, Shuo Chen, Yiqing Liu, Ran Sun, Ning Zhan
Compound climate events are major threats to crop production under climate change. However, the heterogeneity in the impact of compound events on crop yield and its drivers remain poorly understood. Herein, we used empirical approach to evaluate the impact of compound hot–dry and cold–wet events on maize yield in China at the county level from 1990 to 2016, with a special focus on the spatial heterogeneity. Our findings indicate comparable impact of extremely compound cold–wet events (−12.8% ± 3.6%) on maize yield loss to extremely compound hot–dry events (−11.3% ± 2.1%). The spatial pattern of compound hot–dry and cold–wet events impacts on maize yield was dominantly associated with moisture regime, followed by management practices and soil properties. Specifically, drier counties and counties with less fraction of clay soil and organic carbon tend to experience greater yield loss due to compound hot–dry events, and wet condition, excessive fertilizer, clay soil and rich organic carbon aggravate the maize yield loss due to compound cold–wet events. Moreover, the land–atmosphere coupling exacerbated the heterogeneous yield impact through divergent heat transfer. In drier regions, the greater proportion of sensible heat creates a positive feedback between drier land and hotter atmosphere. In contrast, the greater proportion of latent heat in wetter regions results in a positive feedback between wetter land and colder atmosphere. Our results highlighted a critical element to explore in further studies focused on the land-atmosphere coupling in agricultural risk under climate change.
{"title":"Land-atmosphere coupling exacerbates the moisture-associated heterogeneous impacts of compound extreme events on maize yield in China","authors":"Zitong Li, Weihang Liu, Tao Ye, Shuo Chen, Yiqing Liu, Ran Sun, Ning Zhan","doi":"10.1088/2752-5295/ad34a7","DOIUrl":"https://doi.org/10.1088/2752-5295/ad34a7","url":null,"abstract":"\u0000 Compound climate events are major threats to crop production under climate change. However, the heterogeneity in the impact of compound events on crop yield and its drivers remain poorly understood. Herein, we used empirical approach to evaluate the impact of compound hot–dry and cold–wet events on maize yield in China at the county level from 1990 to 2016, with a special focus on the spatial heterogeneity. Our findings indicate comparable impact of extremely compound cold–wet events (−12.8% ± 3.6%) on maize yield loss to extremely compound hot–dry events (−11.3% ± 2.1%). The spatial pattern of compound hot–dry and cold–wet events impacts on maize yield was dominantly associated with moisture regime, followed by management practices and soil properties. Specifically, drier counties and counties with less fraction of clay soil and organic carbon tend to experience greater yield loss due to compound hot–dry events, and wet condition, excessive fertilizer, clay soil and rich organic carbon aggravate the maize yield loss due to compound cold–wet events. Moreover, the land–atmosphere coupling exacerbated the heterogeneous yield impact through divergent heat transfer. In drier regions, the greater proportion of sensible heat creates a positive feedback between drier land and hotter atmosphere. In contrast, the greater proportion of latent heat in wetter regions results in a positive feedback between wetter land and colder atmosphere. Our results highlighted a critical element to explore in further studies focused on the land-atmosphere coupling in agricultural risk under climate change.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237527","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 : 2024-03-15DOI: 10.1088/2752-5295/ad34a8
Thomas Bossy, T. Gasser, Franck Lecocq, Johannes Bednar, Katsumasa Tanaka, P. Ciais
Each run of an Integrated Assessment Models produces a single mitigation pathway consistent with stated objectives (e.g. maximum temperature) and optimizing some objective function (e.g., minimizing total discounted costs of mitigation). Even though models can be run thousands of times, it is unclear how built-in assumptions constrain the final set of pathways. Here we aim at broadly exploring the space of possible mitigation scenarios for a given mitigation target, and at characterizing the sets of pathways that are (near-)optimal, taking uncertainties into account. We produce an extensive set of CO2 emission pathways that stay below 2°C of warming using a reduced-form climate-carbon model with a thousand different physical states. We then identify 18 sets of quasi “least-cost” mitigation pathways, under six assumptions about cost functions and three different cost minimization functions embarking different visions of intergenerational cost distribution. A first key outcome is that the absence or presence of inertia in the cost function plays a pivotal role in the resulting set of least-cost pathways. Second, despite inherent structural differences, we find common pathways across the 18 combinations in 96% of the physical states studied. Interpreting these common pathways as robust economically and in terms of intergenerational distribution, we shed light on some of their characteristics, even though these robust pathways differ for each physical state.
{"title":"Least-cost and 2°C-compliant mitigation pathways robust to physical uncertainty, economic paradigms, and intergenerational cost distribution","authors":"Thomas Bossy, T. Gasser, Franck Lecocq, Johannes Bednar, Katsumasa Tanaka, P. Ciais","doi":"10.1088/2752-5295/ad34a8","DOIUrl":"https://doi.org/10.1088/2752-5295/ad34a8","url":null,"abstract":"\u0000 Each run of an Integrated Assessment Models produces a single mitigation pathway consistent with stated objectives (e.g. maximum temperature) and optimizing some objective function (e.g., minimizing total discounted costs of mitigation). Even though models can be run thousands of times, it is unclear how built-in assumptions constrain the final set of pathways. Here we aim at broadly exploring the space of possible mitigation scenarios for a given mitigation target, and at characterizing the sets of pathways that are (near-)optimal, taking uncertainties into account. We produce an extensive set of CO2 emission pathways that stay below 2°C of warming using a reduced-form climate-carbon model with a thousand different physical states. We then identify 18 sets of quasi “least-cost” mitigation pathways, under six assumptions about cost functions and three different cost minimization functions embarking different visions of intergenerational cost distribution. A first key outcome is that the absence or presence of inertia in the cost function plays a pivotal role in the resulting set of least-cost pathways. Second, despite inherent structural differences, we find common pathways across the 18 combinations in 96% of the physical states studied. Interpreting these common pathways as robust economically and in terms of intergenerational distribution, we shed light on some of their characteristics, even though these robust pathways differ for each physical state.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"41 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238747","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 : 2024-03-15DOI: 10.1088/2752-5295/ad34a9
Amit Kumar Maurya, Somil Swarnkar, Shivendra Prakash
The Indian Ganga Basin (IGB) is one of the most valuable socioeconomic regions in the Indian subcontinent. The IGB supports more than half a billion people due to an abundant supply of freshwater for agro-industrial purposes, primarily through Indian Summer Monsoon (ISM) rainfall contributions (~85%). Any alterations in ISM characteristics would significantly affect freshwater availability, and as a result, socioeconomic activities would be affected. Therefore, in this study, we have attempted to assess how the monsoon rain spell characteristics, i.e., peak, volume, and duration, altered historically between 1901 to 2019. We further analyzed the specific IGB regions where monsoon rain spell changes are more prominent and their hydrological implications. Our estimates reveal that short-duration high-magnitude rain spells have significantly increased across the major regions of the IGB after 1960, which implies the increased probabilities of flash flood hazards. At the same time, the rain spell volumes have been depleted across the IGB after 1960, especially in the eastern Indo-Gangetic plains and southern IGB regions, indicating increased drought frequencies. Further, Himalayan regions, i.e., upper Ganga, upper Yamuna, and upper Ghaghra, have demonstrated increasing magnitudes of rain spell peaks, volume, and duration post-1960. In addition, the continuous warming and anthropogenic alterations might further exaggerate the current situation. Thus, these inferences are helpful for river basin management strategies to deal with the extreme hydrological disasters in the IGB.
{"title":"Hydrological impacts of altered monsoon rain spells in the Indian Ganga basin: a century-long perspective","authors":"Amit Kumar Maurya, Somil Swarnkar, Shivendra Prakash","doi":"10.1088/2752-5295/ad34a9","DOIUrl":"https://doi.org/10.1088/2752-5295/ad34a9","url":null,"abstract":"\u0000 The Indian Ganga Basin (IGB) is one of the most valuable socioeconomic regions in the Indian subcontinent. The IGB supports more than half a billion people due to an abundant supply of freshwater for agro-industrial purposes, primarily through Indian Summer Monsoon (ISM) rainfall contributions (~85%). Any alterations in ISM characteristics would significantly affect freshwater availability, and as a result, socioeconomic activities would be affected. Therefore, in this study, we have attempted to assess how the monsoon rain spell characteristics, i.e., peak, volume, and duration, altered historically between 1901 to 2019. We further analyzed the specific IGB regions where monsoon rain spell changes are more prominent and their hydrological implications. Our estimates reveal that short-duration high-magnitude rain spells have significantly increased across the major regions of the IGB after 1960, which implies the increased probabilities of flash flood hazards. At the same time, the rain spell volumes have been depleted across the IGB after 1960, especially in the eastern Indo-Gangetic plains and southern IGB regions, indicating increased drought frequencies. Further, Himalayan regions, i.e., upper Ganga, upper Yamuna, and upper Ghaghra, have demonstrated increasing magnitudes of rain spell peaks, volume, and duration post-1960. In addition, the continuous warming and anthropogenic alterations might further exaggerate the current situation. Thus, these inferences are helpful for river basin management strategies to deal with the extreme hydrological disasters in the IGB.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"41 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237514","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 : 2024-03-06DOI: 10.1088/2752-5295/ad3086
Mriga Bansal, Natalia D'Agosti
Do female policymakers encourage the production of renewable energy compared to their male counterparts? Using instrumental variables, we conduct a cross-country analysis of 39 high-income countries for the years 1997-2020 using quota laws and women’s suffrage as instruments for women’s participation in the parliament. We find that a 1 percentage point increase in the proportion of women in the legislature increases renewable energy production by 1.54 percentage points. This study suggests that fostering policies that boost women's participation in policy-making positions is beneficial, especially when considering the positive spillover to other countries.
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Pub Date : 2024-03-02DOI: 10.1088/2752-5295/ad2f5c
Biswajit Jena, S. Pattnaik
The low-pressure systems intensified to cyclones prior to the onset of the Indian summer monsoon season over the North Indian Ocean (NIO) are referred to as pre-monsoon season (PMS) cyclones. Climate change is amplifying the pre-monsoon cyclone landscape, fostering more frequent and intense storms with altered tracks, resulting in heightened risks for coastal communities and economies. This study investigates the interdecadal variations in tropical cyclone (TC) and key large-scale atmospheric parameters that influence the characteristics of cyclones, including track, frequency over the Bay of Bengal (BoB) during the pre-monsoon season from 60 years of data. The large-scale atmospheric parameters are analysed by calculating climatological anomalies. It is noted that the frequency of cyclones making landfall over the eastern Indian coastal landmass has increased in the recent decade compared to the past five decades. Compared to the past fifty years, the percentage frequency has increased in the recent ten years by 50%. At the low level, stronger easterlies are dominant and upper-level jet streams shift to lower latitudes, indicating that the path of cyclones has shifted from the north (N)-northeast (NE) to the northwest (NW), i.e., towards the east Indian coastal landmass, over the recent decade. In contrast to the previous five decades, an unusual low-pressure region has emerged over the NW India and Pakistan regions, creating a favourable path for cyclones moving towards the Indian region in recent decade. Cyclones have been more intense in the recent decade than they were in the previous five decades, according to the rise in low- and mid-level specific humidity (SPH) and temperature over the BoB.
{"title":"Interdecadal variability of the pre-monsoon cyclone characteristics over the Bay of Bengal","authors":"Biswajit Jena, S. Pattnaik","doi":"10.1088/2752-5295/ad2f5c","DOIUrl":"https://doi.org/10.1088/2752-5295/ad2f5c","url":null,"abstract":"\u0000 The low-pressure systems intensified to cyclones prior to the onset of the Indian summer monsoon season over the North Indian Ocean (NIO) are referred to as pre-monsoon season (PMS) cyclones. Climate change is amplifying the pre-monsoon cyclone landscape, fostering more frequent and intense storms with altered tracks, resulting in heightened risks for coastal communities and economies. This study investigates the interdecadal variations in tropical cyclone (TC) and key large-scale atmospheric parameters that influence the characteristics of cyclones, including track, frequency over the Bay of Bengal (BoB) during the pre-monsoon season from 60 years of data. The large-scale atmospheric parameters are analysed by calculating climatological anomalies. It is noted that the frequency of cyclones making landfall over the eastern Indian coastal landmass has increased in the recent decade compared to the past five decades. Compared to the past fifty years, the percentage frequency has increased in the recent ten years by 50%. At the low level, stronger easterlies are dominant and upper-level jet streams shift to lower latitudes, indicating that the path of cyclones has shifted from the north (N)-northeast (NE) to the northwest (NW), i.e., towards the east Indian coastal landmass, over the recent decade. In contrast to the previous five decades, an unusual low-pressure region has emerged over the NW India and Pakistan regions, creating a favourable path for cyclones moving towards the Indian region in recent decade. Cyclones have been more intense in the recent decade than they were in the previous five decades, according to the rise in low- and mid-level specific humidity (SPH) and temperature over the BoB.","PeriodicalId":432508,"journal":{"name":"Environmental Research: Climate","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081379","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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