Pub Date : 2022-11-11DOI: 10.5194/esd-13-1557-2022
C. Tebaldi, Abigail C. Snyder, K. Dorheim
Abstract. Climate model output emulation has long been attempted to support impact research, mainly to fill in gaps in the scenario space. Given the computational cost of running coupled earth system models (ESMs), which are usually the domain of supercomputers and require on the order of days to weeks to complete a century-long simulation, only a handful of different scenarios are usually chosen to externally force ESM simulations. An effective emulator, able to run on standard computers in times of the order of minutes rather than days could therefore be used to derive climate information under scenarios that were not run by ESMs. Lately, the necessity of accounting for internal variability has also made the availability of initial-condition ensembles, under a specific scenario, important, further increasing the computational demand. At least so far, emulators have been limited to simplified ESM-like output, either seasonal, annual, or decadal averages of basic quantities, like temperature and precipitation, often emulated independently of one another. With this work, we propose a more comprehensive solution to ESM output emulation. Our emulator, STITCHES, uses existing archives of earth system models' (ESMs) scenario experiments to construct ESM-like output under new scenarios or enrich existing initial-condition ensembles, which is what other emulators also aim to do. Importantly, however, STITCHES' output has the same characteristics of the ESM output it sets out to emulate: multivariate, spatially resolved, and high frequency, representing both the forced component and the internal variability around it. STITCHES extends the idea of time sampling – according to which climate outcomes are stratified by the global warming level at which they manifest themselves, irrespective of the scenario and time at which they occur – to the construction of a continuous history of ESM-like output over the whole 21st century, consistent with a 21st-century trajectory of global surface air temperature (GSAT) derived from the scenario that has been chosen as the target of the emulation. STITCHES does so by first splitting the target GSAT trajectory into decade-long windows, then matching each window in turn to a decade-long window within an existing model simulation from the available scenario runs according to its proximity to the target in absolute size of the temperature anomaly and its rate of change. A look-up table is therefore created of a sequence of existing experiment–time-window combinations that, when stitched together, create a GSAT trajectory “similar” to the target. Importantly, we can then stitch together much more than GSAT from these windows, i.e., any output that the ESM has saved for these existing experiment–time-window combinations, at any frequency and spatial scale available in its archive. We show that the stitching does not introduce artifacts in the great majority of cases (we look at temperature and precipitation at monthly frequen
{"title":"STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations","authors":"C. Tebaldi, Abigail C. Snyder, K. Dorheim","doi":"10.5194/esd-13-1557-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1557-2022","url":null,"abstract":"Abstract. Climate model output emulation has long been attempted to support impact research, mainly to fill in gaps in the scenario space. Given the\u0000computational cost of running coupled earth system models (ESMs), which are usually the domain of supercomputers and require on the order of days to weeks to complete a century-long simulation, only a handful of different scenarios are usually chosen to externally force ESM simulations. An effective\u0000emulator, able to run on standard computers in times of the order of minutes rather than days could therefore be used to derive climate\u0000information under scenarios that were not run by ESMs. Lately, the necessity of accounting for internal variability has also made the availability\u0000of initial-condition ensembles, under a specific scenario, important, further increasing the computational demand. At least so far, emulators have\u0000been limited to simplified ESM-like output, either seasonal, annual, or decadal averages of basic quantities, like temperature and precipitation,\u0000often emulated independently of one another. With this work, we propose a more comprehensive solution to ESM output emulation. Our emulator,\u0000STITCHES, uses existing archives of earth system models' (ESMs) scenario experiments to construct ESM-like output under new scenarios or enrich\u0000existing initial-condition ensembles, which is what other emulators also aim to do. Importantly, however, STITCHES' output has the same\u0000characteristics of the ESM output it sets out to emulate: multivariate, spatially resolved, and high frequency, representing both the forced\u0000component and the internal variability around it. STITCHES extends the idea of time sampling – according to which climate outcomes are stratified by\u0000the global warming level at which they manifest themselves, irrespective of the scenario and time at which they occur – to the construction of a\u0000continuous history of ESM-like output over the whole 21st century, consistent with a 21st-century trajectory of global surface air temperature\u0000(GSAT) derived from the scenario that has been chosen as the target of the emulation. STITCHES does so by first splitting the target GSAT trajectory\u0000into decade-long windows, then matching each window in turn to a decade-long window within an existing model simulation from the available scenario\u0000runs according to its proximity to the target in absolute size of the temperature anomaly and its rate of change. A look-up table is therefore\u0000created of a sequence of existing experiment–time-window combinations that, when stitched together, create a GSAT trajectory “similar” to the\u0000target. Importantly, we can then stitch together much more than GSAT from these windows, i.e., any output that the ESM has saved for these existing experiment–time-window combinations, at any frequency and spatial scale available in its archive. We show that the stitching does not introduce artifacts in\u0000the great majority of cases (we look at temperature and precipitation at monthly frequen","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43464533","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 : 2022-11-07DOI: 10.5194/esd-13-1505-2022
Na Li, S. Sippel, Alexander J. Winkler, M. Mahecha, M. Reichstein, A. Bastos
Abstract. One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p≥800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n<60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño–Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30–54 years. For shorter time series (n<30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices.
{"title":"Interannual global carbon cycle variations linked to atmospheric circulation variability","authors":"Na Li, S. Sippel, Alexander J. Winkler, M. Mahecha, M. Reichstein, A. Bastos","doi":"10.5194/esd-13-1505-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1505-2022","url":null,"abstract":"Abstract. One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p≥800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n<60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño–Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30–54 years. For shorter time series (n<30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution.\u0000Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45068453","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 : 2022-11-07DOI: 10.5194/esd-13-1535-2022
S. Fang, C. Timmreck, J. Jungclaus, K. Krüger, H. Schmidt
Abstract. The early 19th century was the coldest period over the past 500 years, when strong tropical volcanic events and a solar minimum coincided. The 1809 unidentified eruption and the 1815 Tambora eruption happened consecutively during the Dalton minimum of solar irradiance; however, the relative role of the two forcing (volcano and solar) agents is still unclear. In this study, we examine the responses from a set of early 19th century simulations with combined and separated volcanic and solar forcing agents, as suggested in the protocol for the past1000 experiment of the Paleoclimate Modelling Intercomparison Project – Phase 4 (PMIP4). From 20-member ensemble simulations with the Max Planck Institute Earth system model (MPI-ESM1.2-LR), we find that the volcano- and solar-induced surface cooling is additive in the global mean/large scale, regardless of combining or separating the forcing agents. The two solar reconstructions (SATIRE (Spectral and Total Irradiance REconstruction-Millennia model) and PMOD (Physikalisch-Meteorologisches Observatorium Davos)) contribute to a cooling before and after 1815 of ∼0.05 and ∼0.15 K monthly average near-surface air cooling, respectively, indicating a limited solar contribution to the early 19th century cold period. The volcanic events provide the main cooling contributions, inducing a surface cooling that peaks at ∼0.82 K for the 1809 event and ∼1.35 K for Tambora. After the Tambora eruption, the temperature in most regions increases toward climatology largely within 5 years, along with the reduction of volcanic forcing. In the northern extratropical oceans, the temperature increases slowly at a constant rate until 1830, which is related to the reduction of seasonality and the concurrent changes in Arctic sea-ice extent. The albedo feedback of Arctic sea ice is found to be the main contributor to the Arctic amplification of the cooling signal. Several non-additive responses to solar and volcanic forcing happen on regional scales. In the atmosphere, the stratospheric polar vortex tends to strengthen when combining both volcano and solar forcing, even though the two forcing agents separately induce opposite-sign changes in stratospheric temperatures and zonal winds. In the ocean, when combining the two forcings, additional surface cold water propagates to the northern extratropics from the additional solar cooling in the tropics, which results in regional cooling along the propagation. Overall, this study not only quantifies the surface responses from combinations of the volcano and solar forcing, but also highlights the components that cannot be simply added from the responses of the individual forcing agents, indicating that a relatively small forcing agent (such as solar in early 19th century) can impact the response from the large forcing (such as the 1815 Tambora eruption) when considering regional climates.
{"title":"On the additivity of climate responses to the volcanic and solar forcing in the early 19th century","authors":"S. Fang, C. Timmreck, J. Jungclaus, K. Krüger, H. Schmidt","doi":"10.5194/esd-13-1535-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1535-2022","url":null,"abstract":"Abstract. The early 19th century was the coldest period over the past 500 years, when strong tropical volcanic events and a solar minimum coincided. The 1809 unidentified eruption and the 1815 Tambora eruption happened consecutively during the Dalton minimum of solar irradiance; however, the relative role of the two forcing (volcano and solar) agents is still unclear. In this study, we examine the responses from a set of early\u000019th century simulations with combined and separated volcanic and solar\u0000forcing agents, as suggested in the protocol for the past1000 experiment of\u0000the Paleoclimate Modelling Intercomparison Project – Phase 4 (PMIP4). From\u000020-member ensemble simulations with the Max Planck Institute Earth system\u0000model (MPI-ESM1.2-LR), we find that the volcano- and solar-induced surface\u0000cooling is additive in the global mean/large scale, regardless of combining\u0000or separating the forcing agents. The two solar reconstructions (SATIRE (Spectral and Total Irradiance\u0000REconstruction-Millennia model) and\u0000PMOD (Physikalisch-Meteorologisches Observatorium Davos)) contribute to a cooling before and after 1815 of ∼0.05 and ∼0.15 K monthly average near-surface air cooling, respectively, indicating a limited solar contribution to the early 19th century cold period. The volcanic events provide the main cooling contributions, inducing a surface cooling that peaks at ∼0.82 K for the 1809 event and ∼1.35 K for Tambora. After the Tambora eruption, the temperature in most regions increases toward climatology largely within 5 years, along with the reduction of volcanic forcing. In the northern extratropical oceans, the temperature increases slowly at a constant rate until 1830, which is related to the reduction of seasonality and the concurrent changes in Arctic sea-ice extent. The albedo feedback of Arctic sea ice is found to be the main contributor to the Arctic amplification of the cooling signal. Several non-additive responses to solar and volcanic forcing happen on regional scales. In the atmosphere, the stratospheric polar vortex tends to strengthen when combining both volcano and solar forcing, even though the two forcing agents separately induce opposite-sign changes in stratospheric temperatures and zonal winds. In the ocean, when combining the two forcings, additional surface cold water propagates to the northern extratropics from the additional solar cooling in the tropics, which results in regional cooling along the propagation. Overall, this study not only quantifies the surface responses from combinations of the volcano and solar forcing, but also highlights the components that cannot be simply added from the responses of the individual forcing agents, indicating that a relatively small forcing agent (such as solar in early 19th century) can impact the response from the large forcing (such as the 1815 Tambora eruption) when considering regional climates.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46653533","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 : 2022-11-02DOI: 10.5194/esd-13-1491-2022
Iason Markantonis, D. Vlachogiannis, A. Sfetsos, I. Kioutsioukis
Abstract. This paper aims to study wet–cold compound events (WCCEs) in Greece for the wet and cold season November–April since these events may affect directly human activities for short or longer periods, as no similar research has been conducted for the country studying the past and future development of these compound events. WCCEs are divided into two different daily compound events, maximum temperature– (TX) accumulated precipitation (RR) and minimum temperature– (TN) accumulated precipitation (RR), using fixed thresholds (RR over 20 mm d−1 and temperature under 0 ∘C). Observational data from the Hellenic National Meteorology Service (HNMS) and simulation data from reanalysis and EURO-CORDEX models were used in the study for the historical period 1980–2004. The ensemble mean of the simulation datasets from projection models was employed for the near future period (2025–2049) to study the impact of climate change on the occurrence of WCCEs under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Following data processing and validation of the models, the potential changes in the distribution of WCCEs in the future were investigated based on the projected and historical simulations. WCCEs determined by fixed thresholds were mostly found over high altitudes with TN–RR events exhibiting a future tendency to reduce particularly under the RCP 8.5 scenario and TX–RR exhibiting similar reduction of probabilities for both scenarios.
摘要本文旨在研究希腊11月至4月湿冷季节的湿冷复合事件(WCCEs)，因为这些事件可能会在短期或较长时间内直接影响人类活动，而该国尚未开展过研究这些复合事件过去和未来发展的类似研究。wcce被分为两个不同的每日复合事件，最高温度累积降水(TX)和最低温度累积降水(RR)，使用固定阈值(RR≥20 mm d - 1和温度≤0°C)。研究使用了1980-2004年历史时期希腊国家气象局(HNMS)的观测资料以及再分析和EURO-CORDEX模式的模拟资料。利用预测模式模拟数据集近未来期(2025-2049年)的集合平均值，研究了代表性浓度路径4.5和8.5情景下气候变化对WCCEs发生的影响。在数据处理和模型验证之后，基于预估和历史模拟，研究了未来WCCEs分布的潜在变化。由固定阈值确定的WCCEs主要在高海拔地区发现，TN-RR事件在未来呈现减少趋势，特别是在RCP 8.5情景下，而TX-RR事件在两种情景下呈现相似的概率减少趋势。
{"title":"Investigation of the extreme wet–cold compound events changes between 2025–2049 and 1980–2004 using regional simulations in Greece","authors":"Iason Markantonis, D. Vlachogiannis, A. Sfetsos, I. Kioutsioukis","doi":"10.5194/esd-13-1491-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1491-2022","url":null,"abstract":"Abstract. This paper aims to study wet–cold compound events (WCCEs) in Greece for the wet and cold season November–April since these events may affect directly human activities for short or longer periods, as no similar research has been conducted for the country studying the past and future development of these compound events. WCCEs are divided into two different daily compound events, maximum temperature– (TX) accumulated precipitation (RR) and minimum temperature– (TN) accumulated precipitation (RR), using\u0000fixed thresholds (RR over 20 mm d−1 and temperature under 0 ∘C). Observational data from the Hellenic National Meteorology Service (HNMS) and simulation data from reanalysis and EURO-CORDEX models were used in the study for the historical period 1980–2004. The ensemble mean of the simulation datasets from projection models was employed for the near future period (2025–2049) to study the impact of climate change on the occurrence of WCCEs under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Following data processing and validation of the models, the potential changes in the distribution of WCCEs in the future were investigated based on the projected and historical simulations. WCCEs determined by fixed thresholds were mostly found over high altitudes with TN–RR events exhibiting a future tendency to reduce particularly under the RCP 8.5 scenario and TX–RR exhibiting similar reduction of probabilities for both scenarios.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45719624","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 : 2022-11-01DOI: 10.5194/esd-13-1473-2022
R. Cremades, Massimo Stella
Abstract. Extreme political populism has been fiercely spreading climate disinformation for years, contributing to a social divide about climate change. In order to profile how both sides of the climate divide communicate climate change, we collected dissemination materials and analysed the mindset of key actors reaching global audiences. We apply network science to textual content in order to reconstruct and analyse the mindsets of key actors across the climate divide. Here, we show that the emerging mindsets support the identification of emotional patterns – such as hypercritical scepticism masking falsehoods under a trustful promotion of change – linked to a quick and pervasive spread of falsehoods, i.e. an infodemic. We find that, in climate change disinformation, the word “climate” represents a fearsome threat linked to inconsistent science. We show that the word “change” represents a reassuring pattern in climate disinformation, characterised by trust and by low anticipation without risk awareness, except for some fear about policy changes. For climate activism, the word “change” is linked to high levels of negative emotions like anger, disgust, and fear related to a perception of existential threats. Furthermore, the word “children” represents an angering concern in climate disinformation, while climate change activism perceives “children” with trust and joy but also sadness for their anticipated future. Mindset reconstruction has the potential to become a relevant tool for identifying and flagging communication materials linked to disinformation, which amplifies the climate divide and facilitates infodemics.
{"title":"Disentangling the climate divide with emotional patterns: a network-based mindset reconstruction approach","authors":"R. Cremades, Massimo Stella","doi":"10.5194/esd-13-1473-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1473-2022","url":null,"abstract":"Abstract. Extreme political populism has been fiercely spreading climate disinformation for years, contributing to a social divide about climate\u0000change. In order to profile how both sides of the climate divide communicate climate change, we collected dissemination materials and analysed the mindset of key actors reaching global audiences. We apply network science to textual content in order to reconstruct and analyse the mindsets of key actors across the climate divide. Here, we show that the emerging mindsets support the identification of emotional patterns – such as hypercritical scepticism masking falsehoods under a trustful promotion of change – linked to a quick and pervasive spread of falsehoods, i.e. an infodemic. We find that, in climate change disinformation, the word “climate” represents a fearsome threat linked to inconsistent science. We show that the word “change” represents a reassuring pattern in climate disinformation, characterised by trust and by low anticipation without risk awareness, except for some fear about policy changes. For climate activism, the word “change” is linked to high levels of negative emotions like anger, disgust, and fear related to a perception of existential threats. Furthermore, the word “children” represents an angering concern in climate disinformation, while climate change activism perceives “children” with trust and joy but also sadness for their anticipated future. Mindset reconstruction has the potential to become a relevant tool for identifying and flagging communication materials linked to disinformation, which amplifies the climate divide and facilitates infodemics.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47421942","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 : 2022-10-28DOI: 10.5194/esd-13-1451-2022
Melissa Ruiz-Vásquez, S. O, A. Brenning, R. Koster, G. Balsamo, U. Weber, G. Arduini, A. Bastos, M. Reichstein, R. Orth
Abstract. Accurate subseasonal weather forecasts, from 2 weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever-growing model complexity, it becomes increasingly difficult to pinpoint weaknesses in the forecast models' process representations which is key to improving forecast accuracy. In this study, we use a comprehensive set of observation-based ecological, hydrological, and meteorological variables to study their potential for explaining temperature forecast errors at the weekly timescale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate-related variables such as surface solar radiation and precipitation, which highlights the model's difficulties in accurately capturing the evolution of the climate-related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation-related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.
{"title":"Exploring the relationship between temperature forecast errors and Earth system variables","authors":"Melissa Ruiz-Vásquez, S. O, A. Brenning, R. Koster, G. Balsamo, U. Weber, G. Arduini, A. Bastos, M. Reichstein, R. Orth","doi":"10.5194/esd-13-1451-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1451-2022","url":null,"abstract":"Abstract. Accurate subseasonal weather forecasts, from 2 weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever-growing model complexity, it becomes increasingly difficult to pinpoint weaknesses in the forecast models' process representations which is key to improving forecast accuracy. In this study, we use a comprehensive set of observation-based ecological, hydrological, and meteorological variables to study their potential for explaining temperature forecast errors at the weekly timescale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate-related variables such as surface solar radiation and precipitation, which highlights the model's difficulties in accurately capturing the evolution of the climate-related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation-related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634708","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 : 2022-10-19DOI: 10.5194/esd-13-1437-2022
R. Mahmood, M. Donat, P. Ortega, F. Doblas-Reyes, C. Delgado-Torres, M. Samsó, P. Bretonnière
Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.
{"title":"Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system","authors":"R. Mahmood, M. Donat, P. Ortega, F. Doblas-Reyes, C. Delgado-Torres, M. Samsó, P. Bretonnière","doi":"10.5194/esd-13-1437-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1437-2022","url":null,"abstract":"Abstract. Near-term projections of climate change are subject to substantial\u0000uncertainty from internal climate variability. Here we present an approach\u0000to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability\u0000immediately prior to a certain start date. This constraint aligns the\u0000observed and simulated variability phases and is conceptually similar to\u0000initialization in seasonal to decadal climate predictions. We apply this\u0000variability constraint to large multi-model projection ensembles from the\u0000Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more\u0000than 200 ensemble members, and evaluate the skill of the constrained\u0000ensemble in predicting the observed near-surface temperature, sea-level\u0000pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47043594","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 : 2022-10-17DOI: 10.5194/esd-13-1417-2022
Nidheesh Gangadharan, H. Goosse, D. Parkes, H. Goelzer, F. Maussion, B. Marzeion
Abstract. Although the global-mean sea level (GMSL) rose over the twentieth century with a positive contribution from thermosteric and barystatic (ice sheets and glaciers) sources, the driving processes of GMSL changes during the pre-industrial Common Era (PCE; 1–1850 CE) are largely unknown. Here, the contributions of glacier and ice sheet mass variations and ocean thermal expansion to GMSL in the Common Era (1–2000 CE) are estimated based on simulations with different physical models. Although the twentieth century global-mean thermosteric sea level (GMTSL) is mainly associated with temperature variations in the upper 700 m (86 % in reconstruction and 74 ± 8 % in model), GMTSL in the PCE is equally controlled by temperature changes below 700 m. The GMTSL does not vary more than ±2 cm during the PCE. GMSL contributions from the Antarctic and Greenland ice sheets tend to cancel each other out during the PCE owing to the differing response of the two ice sheets to atmospheric conditions. The uncertainties of sea-level contribution from land-ice mass variations are large, especially over the first millennium. Despite underestimating the twentieth century model GMSL, there is a general agreement between the model and proxy-based GMSL reconstructions in the CE. Although the uncertainties remain large over the first millennium, model simulations point to glaciers as the dominant source of GMSL changes during the PCE.
{"title":"Process-based estimate of global-mean sea-level changes in the Common Era","authors":"Nidheesh Gangadharan, H. Goosse, D. Parkes, H. Goelzer, F. Maussion, B. Marzeion","doi":"10.5194/esd-13-1417-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1417-2022","url":null,"abstract":"Abstract. Although the global-mean sea level (GMSL) rose over the\u0000twentieth century with a positive contribution from thermosteric and\u0000barystatic (ice sheets and glaciers) sources, the driving processes of GMSL\u0000changes during the pre-industrial Common Era (PCE; 1–1850 CE) are largely\u0000unknown. Here, the contributions of glacier and ice sheet mass variations\u0000and ocean thermal expansion to GMSL in the Common Era (1–2000 CE) are\u0000estimated based on simulations with different physical models. Although the\u0000twentieth century global-mean thermosteric sea level (GMTSL) is mainly\u0000associated with temperature variations in the upper 700 m (86 % in\u0000reconstruction and 74 ± 8 % in model), GMTSL in the PCE is equally\u0000controlled by temperature changes below 700 m. The GMTSL does not vary more\u0000than ±2 cm during the PCE. GMSL contributions from the Antarctic and\u0000Greenland ice sheets tend to cancel each other out during the PCE owing to the\u0000differing response of the two ice sheets to atmospheric conditions. The\u0000uncertainties of sea-level contribution from land-ice mass variations are\u0000large, especially over the first millennium. Despite underestimating the\u0000twentieth century model GMSL, there is a general agreement between the model and proxy-based GMSL reconstructions in the CE. Although the uncertainties remain large over the first millennium, model simulations point to glaciers as the dominant source of GMSL changes during the PCE.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42938915","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 : 2022-10-04DOI: 10.5194/esd-13-1397-2022
A. Ribes, J. Boé, S. Qasmi, B. Dubuisson, H. Douville, L. Terray
Abstract. Building on CMIP6 climate simulations, updated global and regional observations, and recently introduced statistical methods, we provide an updated assessment of past and future warming over France. Following the IPCC AR6 and recent global-scale studies, we combine model results with observations to constrain climate change at the regional scale. Over mainland France, the forced warming in 2020 with respect to 1900–1930 is assessed to be 1.66 [1.41 to 1.90] ∘C, i.e., in the upper range of the CMIP6 estimates, and is almost entirely human-induced. A refined view of the seasonality of this past warming is provided through updated daily climate normals. Projected warming in response to an intermediate emission scenario is assessed to be 3.8 ∘C (2.9 to 4.8 ∘C) in 2100 and rises up to 6.7 [5.2 to 8.2] ∘C in a very high emission scenario, i.e., substantially higher than in previous ensembles of global and regional simulations. Winter warming and summer warming are expected to be about 15 % lower than and 30 % higher than the annual mean warming, respectively, for all scenarios and time periods. This work highlights the importance of combining various lines of evidence, including model and observed data, to deliver the most reliable climate information. This refined regional assessment can feed adaptation planning for a range of activities and provides additional rationale for urgent climate action. Code is made available to facilitate replication over other areas or political entities.
{"title":"An updated assessment of past and future warming over France based on a regional observational constraint","authors":"A. Ribes, J. Boé, S. Qasmi, B. Dubuisson, H. Douville, L. Terray","doi":"10.5194/esd-13-1397-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1397-2022","url":null,"abstract":"Abstract. Building on CMIP6 climate simulations, updated global and regional observations, and recently introduced statistical methods, we provide an updated assessment of past and future warming over France. Following the IPCC AR6 and recent global-scale studies, we combine model results with observations to constrain climate change at the regional scale. Over mainland France, the forced warming in 2020 with respect to 1900–1930 is assessed to be 1.66 [1.41 to 1.90] ∘C, i.e., in the upper range of the CMIP6 estimates, and is almost entirely human-induced. A refined view of the seasonality of this past warming is provided through updated daily climate normals. Projected warming in response to an intermediate emission scenario is assessed to be 3.8 ∘C (2.9 to 4.8 ∘C) in 2100 and rises up to 6.7 [5.2 to 8.2] ∘C in a very high emission scenario, i.e., substantially higher than in previous ensembles of global and regional simulations. Winter warming and summer warming are expected to be about 15 % lower than and 30 % higher than the annual mean warming, respectively, for all scenarios and time periods. This work highlights the importance of combining various lines of evidence, including model and observed data, to deliver the most reliable climate information. This refined regional assessment can feed adaptation planning for a range of activities and provides additional rationale for urgent climate action. Code is made available to facilitate replication over other areas or political entities.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47162366","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 : 2022-09-29DOI: 10.5194/esd-13-1377-2022
L. Slater, C. Huntingford, R. Pywell, J. Redhead, E. Kendon
Abstract. Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is only partially explained by seasonal metrics of temperature and precipitation across key wheat growth stages (foundation, construction and production). We find climate impacts on wheat yields are strongest in years with compound weather extremes across multiple growth stages (e.g. frost and heavy rainfall). To assess how these conditions might evolve in the future, we analyse the latest 2.2 km UK Climate Projections (UKCP Local): on average, the foundation growth stage (broadly 1 October to 9 April) is likely to become warmer and wetter, while the construction (10 April to 10 June) and production (11 June to 26 July) stages are likely to become warmer and slightly drier. Statistical wheat yield projections, obtained by driving the regression model with UKCP Local simulations of precipitation and temperature for the UK's three main wheat-growing regions, indicate continued growth of crop yields in the coming decades. Significantly warmer projected winter night temperatures offset the negative impacts of increasing rainfall during the foundation stage, while warmer day temperatures and drier conditions are generally beneficial to yields in the production stage. This work suggests that on average, at the regional scale, climate change is likely to have more positive impacts on UK wheat yields than previously considered. Against this background of positive change, however, our work illustrates that wheat farming in the UK is likely to move outside of the climatic envelope that it has previously experienced, increasing the risk of unseen weather conditions such as intense local thunderstorms or prolonged droughts, which are beyond the scope of this paper.
{"title":"Resilience of UK crop yields to compound climate change","authors":"L. Slater, C. Huntingford, R. Pywell, J. Redhead, E. Kendon","doi":"10.5194/esd-13-1377-2022","DOIUrl":"https://doi.org/10.5194/esd-13-1377-2022","url":null,"abstract":"Abstract. Recent extreme weather events have had severe impacts on\u0000UK crop yields, and so there is concern that a greater frequency of extremes\u0000could affect crop production in a changing climate. Here we investigate the\u0000impacts of future climate change on wheat, the most widely grown cereal crop\u0000globally, in a temperate country with currently favourable wheat-growing\u0000conditions. Historically, following the plateau of UK wheat yields since the\u00001990s, we find there has been a recent significant increase in wheat yield\u0000volatility, which is only partially explained by seasonal metrics of\u0000temperature and precipitation across key wheat growth stages (foundation,\u0000construction and production). We find climate impacts on wheat yields are\u0000strongest in years with compound weather extremes across multiple growth\u0000stages (e.g. frost and heavy rainfall). To assess how these conditions might\u0000evolve in the future, we analyse the latest 2.2 km UK Climate Projections\u0000(UKCP Local): on average, the foundation growth stage (broadly 1 October\u0000to 9 April) is likely to become warmer and wetter, while the construction\u0000(10 April to 10 June) and production (11 June to 26 July) stages are\u0000likely to become warmer and slightly drier. Statistical wheat yield\u0000projections, obtained by driving the regression model with UKCP Local\u0000simulations of precipitation and temperature for the UK's three main\u0000wheat-growing regions, indicate continued growth of crop yields in the\u0000coming decades. Significantly warmer projected winter night temperatures\u0000offset the negative impacts of increasing rainfall during the foundation\u0000stage, while warmer day temperatures and drier conditions are generally\u0000beneficial to yields in the production stage. This work suggests that on\u0000average, at the regional scale, climate change is likely to have more\u0000positive impacts on UK wheat yields than previously considered. Against this\u0000background of positive change, however, our work illustrates that wheat\u0000farming in the UK is likely to move outside of the climatic envelope that it\u0000has previously experienced, increasing the risk of unseen weather conditions\u0000such as intense local thunderstorms or prolonged droughts, which are beyond\u0000the scope of this paper.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44942520","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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