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2024,
41(9):
1-1.
Abstract:
2024,
41(9):
1661-1679.
doi: 10.1007/s00376-024-3250-7
Abstract:
Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5–10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS.
Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5–10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS.
2024,
41(9):
1680-1690.
doi: 10.1007/s00376-024-3194-y
Abstract:
Arctic sea ice has undergone a significant decline in the Barents–Kara Sea (BKS) since the late 1990s. Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes (mainly referring to La Niña events). The occurrence of multi-year La Niña (MYLA) events has increased significantly in recent decades, and their impact on Arctic sea ice needs to be further explored. In this study, we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña (OTLA) years. The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years. This is because MYLA events tend to be accompanied by a warm Arctic–cold continent pattern with a barotropic high pressure blocked over the Urals region. Consequently, more frequent northward atmospheric rivers intrude into the BKS, intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice. However, in the early winter of OTLA years, a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere, which obstructs the atmospheric rivers to the south of Iceland. We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes. Considering the rapid climate changes in the past, more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.
Arctic sea ice has undergone a significant decline in the Barents–Kara Sea (BKS) since the late 1990s. Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes (mainly referring to La Niña events). The occurrence of multi-year La Niña (MYLA) events has increased significantly in recent decades, and their impact on Arctic sea ice needs to be further explored. In this study, we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña (OTLA) years. The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years. This is because MYLA events tend to be accompanied by a warm Arctic–cold continent pattern with a barotropic high pressure blocked over the Urals region. Consequently, more frequent northward atmospheric rivers intrude into the BKS, intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice. However, in the early winter of OTLA years, a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere, which obstructs the atmospheric rivers to the south of Iceland. We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes. Considering the rapid climate changes in the past, more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.
2024,
41(9):
1691-1703.
doi: 10.1007/s00376-023-3196-1
Abstract:
Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation patterns exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter remarkably changes, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. During the early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entirety of eastern China, thus contributing to more precipitation there. During the late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in differing water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly the opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central-eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China.
Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation patterns exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter remarkably changes, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. During the early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entirety of eastern China, thus contributing to more precipitation there. During the late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in differing water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly the opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central-eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China.
2024,
41(9):
1704-1720.
doi: 10.1007/s00376-023-3096-4
Abstract:
As demonstrated in the first part of this study (Part I), wind-shift boundaries routinely form along the west coast of the Pearl River Delta due to the land–sea contrast of a “trumpet” shape coastline in the summer monsoon season. Through multiple numerical simulations, this article (Part II) aims to examine the roles of the trumpet-shaped coastline in the mesovortex genesis during the 1 June 2020 tornadic event. The modeling reproduced two mesovortices that are in close proximity in time and space to the realistic mesovortices. In addition to the modeled mesovortex over the triple point where strong ambient vertical vorticity was located, another mesovortex originated from an enhanced discrete vortex along an airmass boundary via shear instability. On the fine-scale storm morphology, finger-like echoes preceding hook echoes were also reproduced around the triple point. Results from sensitivity experiments suggest that the unique topography plays an essential role in modifying the vorticity budget during the mesovortex formation. While there is a high likelihood of an upcoming storm evolving into a rotating storm over the triple point, the simulation's accuracy is sensitive to the local environmental details and storm dynamics. The strengths of cold pool surges from upstream storms may influence the stretching of low-level vertically oriented vortex and thus the wrap-up of finger-like echoes. These findings suggest that the trumpet-shaped coastline is an important component of mesovortex production during the active monsoon season. It is hoped that this study will increase the situational awareness for forecasters regarding regional non-mesocyclone tornadic environments.
As demonstrated in the first part of this study (Part I), wind-shift boundaries routinely form along the west coast of the Pearl River Delta due to the land–sea contrast of a “trumpet” shape coastline in the summer monsoon season. Through multiple numerical simulations, this article (Part II) aims to examine the roles of the trumpet-shaped coastline in the mesovortex genesis during the 1 June 2020 tornadic event. The modeling reproduced two mesovortices that are in close proximity in time and space to the realistic mesovortices. In addition to the modeled mesovortex over the triple point where strong ambient vertical vorticity was located, another mesovortex originated from an enhanced discrete vortex along an airmass boundary via shear instability. On the fine-scale storm morphology, finger-like echoes preceding hook echoes were also reproduced around the triple point. Results from sensitivity experiments suggest that the unique topography plays an essential role in modifying the vorticity budget during the mesovortex formation. While there is a high likelihood of an upcoming storm evolving into a rotating storm over the triple point, the simulation's accuracy is sensitive to the local environmental details and storm dynamics. The strengths of cold pool surges from upstream storms may influence the stretching of low-level vertically oriented vortex and thus the wrap-up of finger-like echoes. These findings suggest that the trumpet-shaped coastline is an important component of mesovortex production during the active monsoon season. It is hoped that this study will increase the situational awareness for forecasters regarding regional non-mesocyclone tornadic environments.
2024,
41(9):
1721-1734.
doi: 10.1007/s00376-024-3299-3
Abstract:
Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R<1, mm h–1) accounts for 86% of the total precipitation time, while small raindrops (D<2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in Yangbajain (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relationships between the slope Λ and shape μ,\begin{document}${D_m}$\end{document} ![]()
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Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R<1, mm h–1) accounts for 86% of the total precipitation time, while small raindrops (D<2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in Yangbajain (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relationships between the slope Λ and shape μ,
2024,
41(9):
1735-1750.
doi: 10.1007/s00376-024-3204-0
Abstract:
The Yangtze River basin (YRB) experienced a record-breaking mei-yu season in June‒July 2020. This unique long-lasting extreme event and its origin have attracted considerable attention. Previous studies have suggested that the Indian Ocean (IO) SST forcing and soil moisture anomaly over the Indochina Peninsula (ICP) were responsible for this unexpected event. However, the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event, especially their linkage with atmospheric circulation changes, remain unclear. By using observations and numerical simulations, this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020. Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds. The intensification of the warm condition further magnified the land thermal effects, which in turn facilitated the westward extension of the western North Pacific subtropical high (WNPSH) in June‒July. The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB, thereby contributing to the 2020 mei-yu. In contrast, the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture. The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model. Their synergistic impacts yield a notable 32% increase in YRB precipitation. Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.
The Yangtze River basin (YRB) experienced a record-breaking mei-yu season in June‒July 2020. This unique long-lasting extreme event and its origin have attracted considerable attention. Previous studies have suggested that the Indian Ocean (IO) SST forcing and soil moisture anomaly over the Indochina Peninsula (ICP) were responsible for this unexpected event. However, the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event, especially their linkage with atmospheric circulation changes, remain unclear. By using observations and numerical simulations, this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020. Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds. The intensification of the warm condition further magnified the land thermal effects, which in turn facilitated the westward extension of the western North Pacific subtropical high (WNPSH) in June‒July. The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB, thereby contributing to the 2020 mei-yu. In contrast, the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture. The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model. Their synergistic impacts yield a notable 32% increase in YRB precipitation. Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.
2024,
41(9):
1751-1768.
doi: 10.1007/s00376-023-3220-5
Abstract:
Cold surges (CSs) often occur in the mid-latitude regions of the Northern Hemisphere and have enormous effects on socioeconomic development. We report that the occurrences of CSs and persistent CSs (PCSs) have rebounded since the 1990s, but the trends related to the frequencies of strong CSs (SCSs) and extreme CSs (ECSs) changed from increasing to decreasing after 2000. The highest-ranked model ensemble approach was used to project the occurrences of various CSs under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The frequencies of the total CSs show overall decreasing trends. However, under the SSP1-2.6 scenario, slight increasing trends are noted for SCSs and ECSs in China. Atmospheric circulations that are characterized by an anomalous anticyclonic circulation with a significantly positive 500-hPa geopotential height (Z500) anomaly at high latitudes along with significant negative anomalies in China were favorable for cold air intrusions into China. In addition, the frequencies of all CS types under the SPP5-8.5 scenario greatly decreased in the long term (2071–2100), a finding which is thought to be related to negative SST anomalies in the central and western North Pacific, differences in sea level pressure (SLP) between high- and mid-latitude regions, and a weaker East Asian trough. In terms of ECSs, the decreasing trends observed during the historical period were maintained until 2024 under the SSP1-2.6 scenario. Compared to the SSP1-2.6 scenario, the Z500 pattern showed a trend of strengthened ridges over the Ural region and northern East Asia and weakened troughs over Siberia (60°–90°E) under the SSP2-4.5 and SSP5-8.5 scenarios, contributing to the shift to increasing trends of ECSs after 2014.
Cold surges (CSs) often occur in the mid-latitude regions of the Northern Hemisphere and have enormous effects on socioeconomic development. We report that the occurrences of CSs and persistent CSs (PCSs) have rebounded since the 1990s, but the trends related to the frequencies of strong CSs (SCSs) and extreme CSs (ECSs) changed from increasing to decreasing after 2000. The highest-ranked model ensemble approach was used to project the occurrences of various CSs under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The frequencies of the total CSs show overall decreasing trends. However, under the SSP1-2.6 scenario, slight increasing trends are noted for SCSs and ECSs in China. Atmospheric circulations that are characterized by an anomalous anticyclonic circulation with a significantly positive 500-hPa geopotential height (Z500) anomaly at high latitudes along with significant negative anomalies in China were favorable for cold air intrusions into China. In addition, the frequencies of all CS types under the SPP5-8.5 scenario greatly decreased in the long term (2071–2100), a finding which is thought to be related to negative SST anomalies in the central and western North Pacific, differences in sea level pressure (SLP) between high- and mid-latitude regions, and a weaker East Asian trough. In terms of ECSs, the decreasing trends observed during the historical period were maintained until 2024 under the SSP1-2.6 scenario. Compared to the SSP1-2.6 scenario, the Z500 pattern showed a trend of strengthened ridges over the Ural region and northern East Asia and weakened troughs over Siberia (60°–90°E) under the SSP2-4.5 and SSP5-8.5 scenarios, contributing to the shift to increasing trends of ECSs after 2014.
2024,
41(9):
1769-1786.
doi: 10.1007/s00376-024-3297-5
Abstract:
In order to quantify the influence of external forcings on the predictability limit using observational data, the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent (CNLLE) method. The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent (NLLE) and signal-to-noise ratio methods using a coupled Lorenz model. The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings, therefore, it can quantify the predictability limit induced by the external forcings. On this basis, a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields. The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method. This similarity supports ENSO as the major predictable signal for weather and climate prediction. In addition, a ratio of predictability limit (RPL) calculated by the CNLLE method to that calculated by the NLLE method was proposed. The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit. For instance, ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months, as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean. Moreover, the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.
In order to quantify the influence of external forcings on the predictability limit using observational data, the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent (CNLLE) method. The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent (NLLE) and signal-to-noise ratio methods using a coupled Lorenz model. The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings, therefore, it can quantify the predictability limit induced by the external forcings. On this basis, a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields. The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method. This similarity supports ENSO as the major predictable signal for weather and climate prediction. In addition, a ratio of predictability limit (RPL) calculated by the CNLLE method to that calculated by the NLLE method was proposed. The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit. For instance, ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months, as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean. Moreover, the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.
2024,
41(9):
1787-1803.
doi: 10.1007/s00376-024-3232-9
Abstract:
This study evaluated the simulation performance of mesoscale convective system (MCS)-induced precipitation, focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula. The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analysis data, as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model. Particularly, temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data. Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis, which exhibited a cold and dry bias in the lower levels below 850 hPa. The model, in terms of the precipitation simulations, particularly for high-intensity precipitation over the Yellow Sea, demonstrated higher accuracy when applying ECMWF analysis data as the initial condition. This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features (i.e., warm and humid lower-level atmosphere) over the Yellow Sea. In conclusion, this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data. Furthermore, it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.
This study evaluated the simulation performance of mesoscale convective system (MCS)-induced precipitation, focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula. The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analysis data, as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model. Particularly, temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data. Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis, which exhibited a cold and dry bias in the lower levels below 850 hPa. The model, in terms of the precipitation simulations, particularly for high-intensity precipitation over the Yellow Sea, demonstrated higher accuracy when applying ECMWF analysis data as the initial condition. This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features (i.e., warm and humid lower-level atmosphere) over the Yellow Sea. In conclusion, this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data. Furthermore, it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.
2024,
41(9):
1804-1820.
doi: 10.1007/s00376-024-3234-7
Abstract:
Nitrous oxide (N2O) is a long-lived greenhouse gas that mainly originates from agricultural soils. More and more studies have explored the sources, influencing factors and effective mitigation measures of N2O in recent decades. However, the hierarchy of factors influencing N2O emissions from agricultural soils at the global scale remains unclear. In this study, we carry out correlation and structural equation modeling analysis on a global N2O emission dataset to explore the hierarchy of influencing factors affecting N2O emissions from the nitrogen (N) and non-N fertilized upland farming systems, in terms of climatic factors, soil properties, and agricultural practices. Our results show that the average N2O emission intensity in the N fertilized soils (17.83 g N ha–1 d–1) was significantly greater than that in the non-N fertilized soils (5.34 g N ha−1 d−1) (p< 0.001). Climate factors and agricultural practices are the most important influencing factors on N2O emission in non-N and N fertilized upland soils, respectively. For different climatic zones, without fertilizer, the primary influence factors on soil N2O emissions are soil physical properties in subtropical monsoon zone, whereas climatic factors are key in the temperate zones. With fertilizer, the primary influence factors for subtropical monsoon and temperate continental zones are soil physical properties, while agricultural measures are the main factors in the temperate monsoon zone. Deploying enhanced agricultural practices, such as reduced N fertilizer rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N2O emissions by more than 60% in upland farming systems.
Nitrous oxide (N2O) is a long-lived greenhouse gas that mainly originates from agricultural soils. More and more studies have explored the sources, influencing factors and effective mitigation measures of N2O in recent decades. However, the hierarchy of factors influencing N2O emissions from agricultural soils at the global scale remains unclear. In this study, we carry out correlation and structural equation modeling analysis on a global N2O emission dataset to explore the hierarchy of influencing factors affecting N2O emissions from the nitrogen (N) and non-N fertilized upland farming systems, in terms of climatic factors, soil properties, and agricultural practices. Our results show that the average N2O emission intensity in the N fertilized soils (17.83 g N ha–1 d–1) was significantly greater than that in the non-N fertilized soils (5.34 g N ha−1 d−1) (p< 0.001). Climate factors and agricultural practices are the most important influencing factors on N2O emission in non-N and N fertilized upland soils, respectively. For different climatic zones, without fertilizer, the primary influence factors on soil N2O emissions are soil physical properties in subtropical monsoon zone, whereas climatic factors are key in the temperate zones. With fertilizer, the primary influence factors for subtropical monsoon and temperate continental zones are soil physical properties, while agricultural measures are the main factors in the temperate monsoon zone. Deploying enhanced agricultural practices, such as reduced N fertilizer rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N2O emissions by more than 60% in upland farming systems.
2024,
41(9):
1821-1842.
doi: 10.1007/s00376-024-3167-1
Abstract:
Warming-induced carbon loss via ecosystem respiration (Re) is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of Re (Q10). However, little is known about the patterns and controlling factors of Q10 on the plateau, impeding the comprehension of the intensity of terrestrial carbon–climate feedbacks for these sensitive and vulnerable ecosystems. Here, we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q10 values in diverse climate zones and ecosystems, and further explore the relationships between Q10 and environmental factors. Moreover, structural equation modeling was utilized to identify the direct and indirect factors predicting Q10 values during the annual, growing, and non-growing seasons. The results indicated that the estimated Q10 values were strongly dependent on temperature, generally, with the average Q10 during different time periods increasing with air temperature and soil temperature at different measurement depths (5 cm, 10 cm, 20 cm). The Q10 values differentiated among ecosystems and climatic zones, with warming-induced Q10 declines being stronger in colder regions than elsewhere based on spatial patterns. NDVI was the most cardinal factor in predicting annual Q10 values, significantly and positively correlated with Q10. Soil temperature (Ts) was identified as the other powerful predictor for Q10, and the negative Q10–Ts relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming. Note that the interpretations of the effect of soil moisture on Q10 were complicated, reflected in a significant positive relationship between Q10 and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season. These findings are conducive to improving our understanding of alpine grassland ecosystem carbon–climate feedbacks under warming climates.
Warming-induced carbon loss via ecosystem respiration (Re) is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of Re (Q10). However, little is known about the patterns and controlling factors of Q10 on the plateau, impeding the comprehension of the intensity of terrestrial carbon–climate feedbacks for these sensitive and vulnerable ecosystems. Here, we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q10 values in diverse climate zones and ecosystems, and further explore the relationships between Q10 and environmental factors. Moreover, structural equation modeling was utilized to identify the direct and indirect factors predicting Q10 values during the annual, growing, and non-growing seasons. The results indicated that the estimated Q10 values were strongly dependent on temperature, generally, with the average Q10 during different time periods increasing with air temperature and soil temperature at different measurement depths (5 cm, 10 cm, 20 cm). The Q10 values differentiated among ecosystems and climatic zones, with warming-induced Q10 declines being stronger in colder regions than elsewhere based on spatial patterns. NDVI was the most cardinal factor in predicting annual Q10 values, significantly and positively correlated with Q10. Soil temperature (Ts) was identified as the other powerful predictor for Q10, and the negative Q10–Ts relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming. Note that the interpretations of the effect of soil moisture on Q10 were complicated, reflected in a significant positive relationship between Q10 and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season. These findings are conducive to improving our understanding of alpine grassland ecosystem carbon–climate feedbacks under warming climates.
2024,
41(9):
1843-1852.
doi: 10.1007/s00376-024-3193-z
Abstract:
This study quantified the regional damages resulting from temperature and sea level changes using the Regional Integrated of Climate and Economy (RICE) model, as well as the effects of enabling and disabling the climate impact module on future emission pathways. Results highlight varied damages depending on regional economic development and locations. Specifically, China and Africa could suffer the most serious comprehensive damages caused by temperature change and sea level rise, followed by India, other developing Asian countries (OthAsia), and other high-income countries (OHI). The comprehensive damage fractions for China and Africa are projected to be 15.1% and 12.5% of gross domestic product (GDP) in 2195, with corresponding cumulative damages of 124.0 trillion and 87.3 trillion United States dollars (USD) from 2005 to 2195, respectively. Meanwhile, the comprehensive damage fractions in Japan, Eurasia, and Russia are smaller and projected to be lower than 5.6% of GDP in 2195, with cumulative damages of 6.8 trillion, 4.2 trillion, and 3.3 trillion USD, respectively. Additionally, coastal regions like Africa, the European Union (EU), and OHI show comparable damages for sea level rise and temperature change. In China, however, sea level-induced damages are projected to exceed those from temperature changes. Moreover, this study indicates that switching the damage modules on or off affects the regional and global emission trajectories, but the magnitude is relatively small. By 2195, global emissions under the experiments with all of the damage modules switched off, only the sea level damage module switched on, and only the temperature damage module switched on, were 3.5%, 2.3% and 1.2% higher than those with all of the damage modules switched on, respectively.
This study quantified the regional damages resulting from temperature and sea level changes using the Regional Integrated of Climate and Economy (RICE) model, as well as the effects of enabling and disabling the climate impact module on future emission pathways. Results highlight varied damages depending on regional economic development and locations. Specifically, China and Africa could suffer the most serious comprehensive damages caused by temperature change and sea level rise, followed by India, other developing Asian countries (OthAsia), and other high-income countries (OHI). The comprehensive damage fractions for China and Africa are projected to be 15.1% and 12.5% of gross domestic product (GDP) in 2195, with corresponding cumulative damages of 124.0 trillion and 87.3 trillion United States dollars (USD) from 2005 to 2195, respectively. Meanwhile, the comprehensive damage fractions in Japan, Eurasia, and Russia are smaller and projected to be lower than 5.6% of GDP in 2195, with cumulative damages of 6.8 trillion, 4.2 trillion, and 3.3 trillion USD, respectively. Additionally, coastal regions like Africa, the European Union (EU), and OHI show comparable damages for sea level rise and temperature change. In China, however, sea level-induced damages are projected to exceed those from temperature changes. Moreover, this study indicates that switching the damage modules on or off affects the regional and global emission trajectories, but the magnitude is relatively small. By 2195, global emissions under the experiments with all of the damage modules switched off, only the sea level damage module switched on, and only the temperature damage module switched on, were 3.5%, 2.3% and 1.2% higher than those with all of the damage modules switched on, respectively.
2024,
41(9):
1853-1867.
doi: 10.1007/s00376-024-3278-8
Abstract:
This study assesses the capability of a coarse-resolution ocean model to replicate the response of the Southern Ocean Meridional Overturning Circulation (MOC) to intensified westerlies, focusing on the role of the eddy transfer coefficient (\begin{document}$\kappa $\end{document} ![]()
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\begin{document}$\kappa $\end{document} ![]()
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\begin{document}$\kappa $\end{document} ![]()
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\begin{document}$\kappa $\end{document} ![]()
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\begin{document}$\kappa $\end{document} ![]()
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\begin{document}$\kappa $\end{document} ![]()
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\begin{document}$\kappa $\end{document} ![]()
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This study assesses the capability of a coarse-resolution ocean model to replicate the response of the Southern Ocean Meridional Overturning Circulation (MOC) to intensified westerlies, focusing on the role of the eddy transfer coefficient (
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