Abstract. Along with 400 kyr periodicities, multi-million-year cycles have been found in δ13C records over different time periods. An ∼ 8–9 Myr periodicity is found throughout the Cenozoic and part of the Mesozoic. The robust presence of this periodicity in δ13C records suggests an astronomical origin. However, this periodicity is barely visible in the astronomical forcing. Due to the large fractionation factor of organic matter, its burial or oxidation produces large δ13C variations for moderate carbon variations. Therefore, astronomical forcing of organic matter fluxes is a plausible candidate to explain the oscillations observed in the δ13C records. So far, modelling studies forcing astronomically the organic matter burial have been able to produce 400 kyr and 2.4 Myr cycles in δ13C but were not able to produce longer cycles, such as 8–9 Myr cycles. Here, we propose a mathematical mechanism compatible with the biogeochemistry that could explain the presence of multi-million-year cycles in the δ13C records and their stability over time: a preferential phase locking to multiples of the 2.4 Myr eccentricity period. With a simple non-linear conceptual model for the carbon cycle that has multiple equilibria, we are able to extract longer periods than with a simple linear model – more specifically, multi-million-year periods.
{"title":"Multi-million-year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes","authors":"Gaëlle Leloup, D. Paillard","doi":"10.5194/esd-14-291-2023","DOIUrl":"https://doi.org/10.5194/esd-14-291-2023","url":null,"abstract":"Abstract. Along with 400 kyr periodicities, multi-million-year cycles have been found in δ13C records over different time periods. An ∼ 8–9 Myr periodicity is found throughout the Cenozoic and part of the Mesozoic. The robust presence of this periodicity in δ13C records suggests an astronomical origin. However, this periodicity is barely visible in the astronomical forcing. Due to the large fractionation factor of organic matter, its burial or oxidation produces large δ13C variations for moderate carbon variations. Therefore, astronomical forcing of organic matter fluxes is a plausible candidate to explain the oscillations observed in the δ13C records. So far, modelling studies forcing astronomically the organic matter burial have been able to produce 400 kyr and 2.4 Myr cycles in δ13C but were not able to produce longer cycles, such as 8–9 Myr cycles. Here, we propose a mathematical mechanism compatible with the biogeochemistry that could explain the presence of multi-million-year cycles in the δ13C records and their stability over time: a preferential phase locking to multiples of the 2.4 Myr eccentricity period. With a simple non-linear conceptual model for the carbon cycle that has multiple equilibria, we are able to extract longer periods than with a simple linear model – more specifically, multi-million-year periods.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45101167","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}
Abstract. The Madden–Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the tropical region. The MJO affects precipitation over highly populated areas, especially around southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact. Indices of the MJO can be derived from the first principal components of zonal wind and outgoing longwave radiation (OLR) in the tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our goal is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). We generate an ensemble of 100 members for the MJO amplitude for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using probabilistic scores and deterministic skill scores. According to score-based criteria, we find that a reasonable forecast of the MJO index could be achieved within 40 d lead times for the different seasons. We compare our SWG forecast with other forecasts of the MJO. The comparison shows that the SWG forecast has skill compared to ECMWF forecasts for lead times above 20 d and better skill compared to machine learning forecasts for small lead times.
{"title":"Ensemble forecast of an index of the Madden–Julian Oscillation using a stochastic weather generator based on circulation analogs","authors":"Meriem Krouma, Riccardo Silini, P. Yiou","doi":"10.5194/esd-14-273-2023","DOIUrl":"https://doi.org/10.5194/esd-14-273-2023","url":null,"abstract":"Abstract. The Madden–Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the tropical region. The MJO affects precipitation over highly populated areas, especially around southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact.\u0000Indices of the MJO can be derived from the first principal components of zonal wind and outgoing longwave radiation (OLR) in the tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our goal is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG).\u0000We generate an ensemble of 100 members for the MJO amplitude for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using probabilistic scores\u0000and deterministic skill scores.\u0000According to score-based criteria, we find that a reasonable forecast of the MJO index could be achieved within 40 d lead times for the different seasons. We compare our SWG forecast with other forecasts of the MJO.\u0000The comparison shows that the SWG forecast has skill compared to ECMWF forecasts for lead times above 20 d and better skill compared to machine learning forecasts for small lead times.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46107972","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}
Raed Hamed, S. Vijverberg, A. V. van Loon, J. Aerts, D. Coumou
Abstract. Around 80 % of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean variability, with soybeans being especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño–Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas, presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on soybean yields via mediating variables like local weather conditions and extratropical sea surface temperatures (SSTs). We show that soybean yields are predominately driven by soil moisture conditions in summer, explaining ∼50 %, 18 % and 40 % of yield variability in SESA, CB and the US respectively. Summer soil moisture is strongly driven by spring soil moisture, as well as by remote extratropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of −1.5 standard deviation (SD) lead to a −0.4 SD soybean reduction in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (−1.5 SD), then estimated soybean losses increase to −0.9 SD for the US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niñas can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate-related global soybean supply shocks.
{"title":"Persistent La Niñas drive joint soybean harvest failures in North and South America","authors":"Raed Hamed, S. Vijverberg, A. V. van Loon, J. Aerts, D. Coumou","doi":"10.5194/esd-14-255-2023","DOIUrl":"https://doi.org/10.5194/esd-14-255-2023","url":null,"abstract":"Abstract. Around 80 % of global soybean supply is produced in southeast\u0000South America (SESA), central Brazil (CB) and the United States (US) alone.\u0000This concentration of production in few regions makes global soybean supply\u0000sensitive to spatially compounding harvest failures. Weather variability is\u0000a key driver of soybean variability, with soybeans being especially vulnerable to\u0000hot and dry conditions during the reproductive growth stage in summer. El\u0000Niño–Southern Oscillation (ENSO) teleconnections can influence summer\u0000weather conditions across the Americas, presenting potential risks for\u0000spatially compounding harvest failures. Here, we develop causal structural\u0000models to quantify the influence of ENSO on soybean yields via mediating\u0000variables like local weather conditions and extratropical sea surface\u0000temperatures (SSTs). We show that soybean yields are predominately driven by\u0000soil moisture conditions in summer, explaining ∼50 %, 18 %\u0000and 40 % of yield variability in SESA, CB and the US respectively. Summer soil\u0000moisture is strongly driven by spring soil moisture, as well as by remote\u0000extratropical SST patterns in both hemispheres. Both of these soil moisture\u0000drivers are again influenced by ENSO. Our causal models show that persistent\u0000negative ENSO anomalies of −1.5 standard deviation (SD) lead to a −0.4 SD\u0000soybean reduction in the US and SESA. When spring soil moisture and\u0000extratropical SST precursors are pronouncedly negative (−1.5 SD), then\u0000estimated soybean losses increase to −0.9 SD for the US and SESA. Thus, by\u0000influencing extratropical SSTs and spring soil moisture, persistent La\u0000Niñas can trigger substantial soybean losses in both the US and SESA,\u0000with only minor potential gains in CB. Our findings highlight the physical\u0000pathways by which ENSO conditions can drive spatially compounding events.\u0000Such information may increase preparedness against climate-related global\u0000soybean supply shocks.","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45235292","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}
Abstract. Afforestation is an important mitigation strategy for climate change due to its carbon sequestration potential. Besides this favorable biogeochemical effect on global CO2 concentrations, afforestation also affects the regional climate by changing the biogeophysical land surface characteristics. In this study, we investigate the effects of an idealized global CO2 reduction to pre-industrial conditions by a Europe-wide afforestation experiment on the regional longwave radiation balance, starting in the year 1986 on a continent entirely covered with grassland. Results show that the impact of biogeophysical processes on the surface temperatures is much stronger than that of biogeochemical processes. Furthermore, biogeophysically induced changes of the surface temperatures, atmospheric temperatures, and moisture concentrations are as important for the regional longwave radiation balance as the global CO2 reduction. While the outgoing longwave radiation is increased in winter, it is reduced in summer. In terms of annual total, a Europe-wide afforestation has a regional warming effect despite reduced CO2 concentrations. Thus, even for an idealized reduction of the global CO2 concentrations to pre-industrial levels, the European climate response to afforestation would still be dominated by its biogeophysical effects.
{"title":"The response of the regional longwave radiation balance and climate system in Europe to an idealized afforestation experiment","authors":"M. Breil, Felix Krawczyk, J. Pinto","doi":"10.5194/esd-14-243-2023","DOIUrl":"https://doi.org/10.5194/esd-14-243-2023","url":null,"abstract":"Abstract. Afforestation is an important mitigation strategy for climate change due to\u0000its carbon sequestration potential. Besides this favorable biogeochemical\u0000effect on global CO2 concentrations, afforestation also affects the\u0000regional climate by changing the biogeophysical land surface\u0000characteristics. In this study, we investigate the effects of an idealized\u0000global CO2 reduction to pre-industrial conditions by a Europe-wide\u0000afforestation experiment on the regional longwave radiation balance,\u0000starting in the year 1986 on a continent entirely covered with grassland.\u0000Results show that the impact of biogeophysical processes on the surface\u0000temperatures is much stronger than that of biogeochemical processes. Furthermore,\u0000biogeophysically induced changes of the surface temperatures, atmospheric\u0000temperatures, and moisture concentrations are as important for the regional\u0000longwave radiation balance as the global CO2 reduction. While the\u0000outgoing longwave radiation is increased in winter, it is reduced in summer.\u0000In terms of annual total, a Europe-wide afforestation has a regional warming effect\u0000despite reduced CO2 concentrations. Thus, even for an idealized\u0000reduction of the global CO2 concentrations to pre-industrial levels,\u0000the European climate response to afforestation would still be dominated by\u0000its biogeophysical effects.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49482735","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}
A. Kleidon, G. Messori, Somnath Baidya Roy, Ira Didenkulova, N. Zeng
{"title":"Editorial: Global warming is due to an enhanced greenhouse effect, and anthropogenic heat emissions currently play a negligible role at the global scale","authors":"A. Kleidon, G. Messori, Somnath Baidya Roy, Ira Didenkulova, N. Zeng","doi":"10.5194/esd-14-241-2023","DOIUrl":"https://doi.org/10.5194/esd-14-241-2023","url":null,"abstract":"<jats:p>\u0000 </jats:p>","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44454993","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}
Abstract. Climate change has been proven to be an indisputable fact and to be occurring at a faster rate (compared to the other regions at the same latitude of the world) in boreal forest areas. Climate change has been observed to have a strong influence on forests; however, until now, the amount of quantitative information on the climate drivers that are producing changes in boreal forest has been limited. The objectives of this work were to quantify the spatiotemporal characteristics of boreal forest and forest types and to find the significant climate drivers that are producing changes in boreal forest. The boreal forest in Krasnoyarsk Krai, Siberia, Russia, which lies within the latitude range 51–69∘ N, was selected as the study area. The distribution of the boreal forest and forest types in the years 1985, 1995, 2005 and 2015 were derived from a series of Landsat data. The spatiotemporal changes in the boreal forest and forest types that occurred over each 10-year period within each 2∘ latitudinal zone between 51 and 69∘ N from 1985 to 2015 were then comprehensively analyzed. The results show that the total area of forest increased over the study period and that the increase was fastest in the high-latitude zone between 63 and 69∘ N. The increases in the areas of broad-leaved and coniferous forests were found to have different characteristics. In the medium-latitude zone between 57 and 63∘ N in particular, the area of broad-leaved forest grew faster than that of coniferous forest. Finally, the influence of the climate factors of temperature and precipitation on changes in the forests was analyzed. The results indicate that temperature rather than precipitation is the main climate factor that is driving change.
{"title":"Spatiotemporal changes in the boreal forest in Siberia over the period 1985–2015 against the background of climate change","authors":"W. Fu, L. Tian, Y. Tao, Mingyang Li, Huadong Guo","doi":"10.5194/esd-14-223-2023","DOIUrl":"https://doi.org/10.5194/esd-14-223-2023","url":null,"abstract":"Abstract. Climate change has been proven to be an indisputable fact\u0000and to be occurring at a faster rate (compared to the other regions at the\u0000same latitude of the world) in boreal forest areas. Climate change has been\u0000observed to have a strong influence on forests; however, until now, the\u0000amount of quantitative information on the climate drivers that are producing\u0000changes in boreal forest has been limited. The objectives of this work were to\u0000quantify the spatiotemporal characteristics of boreal forest and forest\u0000types and to find the significant climate drivers that are producing changes\u0000in boreal forest. The boreal forest in Krasnoyarsk Krai, Siberia, Russia,\u0000which lies within the latitude range 51–69∘ N, was\u0000selected as the study area. The distribution of the boreal forest and forest\u0000types in the years 1985, 1995, 2005 and 2015 were derived from a series of\u0000Landsat data. The spatiotemporal changes in the boreal forest and forest\u0000types that occurred over each 10-year period within each 2∘\u0000latitudinal zone between 51 and 69∘ N from 1985 to\u00002015 were then comprehensively analyzed. The results show that the total\u0000area of forest increased over the study period and that the increase was\u0000fastest in the high-latitude zone between 63 and 69∘ N. The increases in the areas of broad-leaved and coniferous forests were\u0000found to have different characteristics. In the medium-latitude zone between\u000057 and 63∘ N in particular, the area of broad-leaved\u0000forest grew faster than that of coniferous forest. Finally, the\u0000influence of the climate factors of temperature and precipitation on changes\u0000in the forests was analyzed. The results indicate that temperature rather\u0000than precipitation is the main climate factor that is driving change.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44511678","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}
Taylor Smith, Ruxandra-Maria Zotta, C. Boulton, T. Lenton, W. Dorigo, N. Boers
Abstract. Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.
{"title":"Reliability of resilience estimation based on multi-instrument time series","authors":"Taylor Smith, Ruxandra-Maria Zotta, C. Boulton, T. Lenton, W. Dorigo, N. Boers","doi":"10.5194/esd-14-173-2023","DOIUrl":"https://doi.org/10.5194/esd-14-173-2023","url":null,"abstract":"Abstract. Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44474206","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}
E. Massoud, L. Andrews, R. Reichle, A. Molod, Jongmin Park, S. Ruehr, M. Girotto
Abstract. Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of Ranom=0.18 for precipitation to Ranom=0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from Ranom=0.13 for snow water equivalent to Ranom=0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.
{"title":"Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System","authors":"E. Massoud, L. Andrews, R. Reichle, A. Molod, Jongmin Park, S. Ruehr, M. Girotto","doi":"10.5194/esd-14-147-2023","DOIUrl":"https://doi.org/10.5194/esd-14-147-2023","url":null,"abstract":"Abstract. Seasonal variability of the global hydrologic cycle\u0000directly impacts human activities, including hazard assessment and\u0000mitigation, agricultural decisions, and water resources management. This is\u0000particularly true across the High Mountain Asia (HMA) region, where\u0000availability of water resources can change depending on local seasonality of\u0000the hydrologic cycle. Forecasting the atmospheric states and surface\u0000conditions, including hydrometeorologically relevant variables, at\u0000subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of\u0000active research and development. NASA's Goddard Earth Observing System\u0000(GEOS) S2S prediction system has been developed with this research goal in\u0000mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2)\u0000hydrometeorological forecasts at 1–3-month lead times in the HMA region,\u0000including a portion of the Indian subcontinent, during the retrospective\u0000forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air\u0000temperature, total precipitation, fractional snow cover, snow water\u0000equivalent, surface soil moisture, and terrestrial water storage forecasts\u0000against the Modern-Era Retrospective analysis for Research and Applications,\u0000Version 2 (MERRA-2) and independent reanalysis data, satellite observations,\u0000and data fusion products. Anomaly correlation is highest when the forecasts\u0000are evaluated against MERRA-2 and particularly in variables with long memory\u0000in the climate system, likely due to the similar initial conditions and model\u0000architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results\u0000for the 1-month forecast skill range from an anomaly correlation of\u0000Ranom=0.18 for precipitation to Ranom=0.62 for soil moisture.\u0000Anomaly correlations are consistently lower when forecasts are evaluated\u0000against independent observations; results for the 1-month forecast skill\u0000range from Ranom=0.13 for snow water equivalent to Ranom=0.24\u0000for fractional snow cover. We find that, generally, hydrometeorological\u0000forecast skill is dependent on the forecast lead time, the memory of the\u0000variable within the physical system, and the validation dataset used.\u0000Overall, these results benchmark the GEOS-S2S system's ability to forecast\u0000HMA hydrometeorology.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44318886","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}
Abstract. Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.
{"title":"Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States","authors":"A. Wootten, E. Massoud, D. Waliser, Huikyo Lee","doi":"10.5194/esd-14-121-2023","DOIUrl":"https://doi.org/10.5194/esd-14-121-2023","url":null,"abstract":"Abstract. Given the increasing use of climate projections and multi-model\u0000ensemble weighting for a diverse array of applications, this project\u0000assesses the sensitivities of climate model weighting strategies and their\u0000resulting ensemble means to multiple components, such as the weighting\u0000schemes, climate variables, or spatial domains of interest. The purpose of\u0000this study is to assess the sensitivities associated with multi-model\u0000weighting strategies. The analysis makes use of global climate models from\u0000the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their\u0000statistically downscaled counterparts created with the localized constructed\u0000analogs (LOCA) method. This work focuses on historical and projected future\u0000mean precipitation and daily high temperatures of the south-central United\u0000States. Results suggest that the model weights and the corresponding\u0000weighted model means can be sensitive to the weighting strategy that is\u0000applied. For instance, when estimating model weights based on Louisiana\u0000precipitation, the weighted projections show a wetter and cooler\u0000south-central domain in the future compared to other weighting strategies.\u0000Alternatively, for example, when estimating model weights based on New\u0000Mexico temperature, the weighted projections show a drier and warmer\u0000south-central domain in the future. However, when considering the entire\u0000south-central domain in estimating the model weights, the weighted future\u0000projections show a compromise in the precipitation and temperature\u0000estimates. As for uncertainty, our matrix of results provided a more certain\u0000picture of future climate compared to the spread in the original model\u0000ensemble. If future impact assessments utilize weighting strategies, then\u0000our findings suggest that how the specific weighting strategy is used with\u0000climate projections may depend on the needs of an impact assessment or\u0000adaptation plan.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46734819","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}
I. E. de Vries, S. Sippel, A. Pendergrass, R. Knutti
Abstract. Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.
{"title":"Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change","authors":"I. E. de Vries, S. Sippel, A. Pendergrass, R. Knutti","doi":"10.5194/esd-14-81-2023","DOIUrl":"https://doi.org/10.5194/esd-14-81-2023","url":null,"abstract":"Abstract. Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.\u0000","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48708448","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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