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- Author or Editor: Cheng Zhou x
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Abstract
North China has undergone a severe drying trend since the 1950s, but whether this trend is natural variability or anthropogenic change remains unknown due to the short data length. This study extends the analysis of dry–wet changes in north China to 1900–2010 on the basis of self-calibrated Palmer drought severity index (PDSI) data. The ensemble empirical mode decomposition method is used to detect multidecadal variability. A transition from significant wetting to significant drying is detected around 1959/60. Approximately 70% of the drying trend during 1960–90 originates from 50–70-yr multidecadal variability related to Pacific decadal oscillation (PDO) phase changes. The PDSI in north China is significantly negatively correlated with the PDO index, particularly at the 50–70-yr time scale, and is also stable during 1900–2010. Composite differences between two positive PDO phases (1922–45 and 1977–2002) and one negative PDO phase (1946–76) for summer exhibit an anomalous Pacific–Japan/East Asian–Pacific patternlike teleconnection, which may develop locally in response to the PDO-associated warm sea surface temperature anomalies in the tropical Indo-Pacific Ocean and meridionally extends from the tropical western Pacific to north China along the East Asian coast. North China is dominated by an anomalous high pressure system at mid–low levels and an anticyclone at 850 hPa, which are favorable for dry conditions. In addition, a weakened land–sea thermal contrast in East Asia from a negative to a positive PDO phase also plays a role in the dry conditions in north China by weakening the East Asian summer monsoon.
Abstract
North China has undergone a severe drying trend since the 1950s, but whether this trend is natural variability or anthropogenic change remains unknown due to the short data length. This study extends the analysis of dry–wet changes in north China to 1900–2010 on the basis of self-calibrated Palmer drought severity index (PDSI) data. The ensemble empirical mode decomposition method is used to detect multidecadal variability. A transition from significant wetting to significant drying is detected around 1959/60. Approximately 70% of the drying trend during 1960–90 originates from 50–70-yr multidecadal variability related to Pacific decadal oscillation (PDO) phase changes. The PDSI in north China is significantly negatively correlated with the PDO index, particularly at the 50–70-yr time scale, and is also stable during 1900–2010. Composite differences between two positive PDO phases (1922–45 and 1977–2002) and one negative PDO phase (1946–76) for summer exhibit an anomalous Pacific–Japan/East Asian–Pacific patternlike teleconnection, which may develop locally in response to the PDO-associated warm sea surface temperature anomalies in the tropical Indo-Pacific Ocean and meridionally extends from the tropical western Pacific to north China along the East Asian coast. North China is dominated by an anomalous high pressure system at mid–low levels and an anticyclone at 850 hPa, which are favorable for dry conditions. In addition, a weakened land–sea thermal contrast in East Asia from a negative to a positive PDO phase also plays a role in the dry conditions in north China by weakening the East Asian summer monsoon.
Abstract
The Kelvin wave is the gravest eigenmode of Laplace’s tidal equation. It is widely observed in both the ocean and the atmosphere. In the absence of mean currents, the Kelvin wave depends on two parameters: the zonal wavenumber s (always an integer) and Lamb’s parameter ϵ. An asymptotic approximation valid in the limit
Abstract
The Kelvin wave is the gravest eigenmode of Laplace’s tidal equation. It is widely observed in both the ocean and the atmosphere. In the absence of mean currents, the Kelvin wave depends on two parameters: the zonal wavenumber s (always an integer) and Lamb’s parameter ϵ. An asymptotic approximation valid in the limit
Abstract
The Gaussian assumption has been widely used without testing in many previous studies on climate variability and change that have used traditional statistical methods to estimate linear trends, diagnose physical mechanisms, or construct statistical prediction/downscaling models. In this study, the authors carefully test the normality of two hot extreme indices in Macao, China, during the last 100 years based on consecutive daily temperature observational data and find that the occurrences of both hot day and hot night indices are non-Gaussian. Simple least squares fitting is shown to overestimate the linear trend when the Gaussian assumption is violated. Two approaches are further proposed to statistically predict non-Gaussian temperature extremes: one uses a multiple linear regression model after transforming the non-Gaussian predictant to a quasi-Gaussian variable and uses Pearson’s correlation test to identify potential predictors, and the other uses a generalized linear model when the transformation is difficult and uses a nonparametric Spearman’s correlation test to identify potential predictors. The annual occurrences of hot days and hot nights in Macao are used as examples of these two approaches, respectively. The physical mechanisms for these two hot extremes in Macao are also investigated, and the results show that both are related to the interannual and interdecadal variability of a coupled El Niño–Southern Oscillation (ENSO)–East Asian summer monsoon system. Finally, the authors caution other researchers to test the assumed distribution of climate extremes and to apply appropriate statistical approaches.
Abstract
The Gaussian assumption has been widely used without testing in many previous studies on climate variability and change that have used traditional statistical methods to estimate linear trends, diagnose physical mechanisms, or construct statistical prediction/downscaling models. In this study, the authors carefully test the normality of two hot extreme indices in Macao, China, during the last 100 years based on consecutive daily temperature observational data and find that the occurrences of both hot day and hot night indices are non-Gaussian. Simple least squares fitting is shown to overestimate the linear trend when the Gaussian assumption is violated. Two approaches are further proposed to statistically predict non-Gaussian temperature extremes: one uses a multiple linear regression model after transforming the non-Gaussian predictant to a quasi-Gaussian variable and uses Pearson’s correlation test to identify potential predictors, and the other uses a generalized linear model when the transformation is difficult and uses a nonparametric Spearman’s correlation test to identify potential predictors. The annual occurrences of hot days and hot nights in Macao are used as examples of these two approaches, respectively. The physical mechanisms for these two hot extremes in Macao are also investigated, and the results show that both are related to the interannual and interdecadal variability of a coupled El Niño–Southern Oscillation (ENSO)–East Asian summer monsoon system. Finally, the authors caution other researchers to test the assumed distribution of climate extremes and to apply appropriate statistical approaches.
Abstract
In this study, a method based on singular vector analysis is proposed to improve El Niño–Southern Oscillation (ENSO) predictions. Its essential idea is that the initial errors are projected onto their optimal growth patterns, which are propagated by the tangent linear model (TLM) of the original prediction model. The forecast errors at a given lead time of predictions are obtained, and then removed from the raw predictions. This method is applied to a realistic ENSO prediction model for improving prediction skill for the period from 1980 to 1999. This correction method considerably improves the ENSO prediction skill, compared with the original predictions without the correction.
Abstract
In this study, a method based on singular vector analysis is proposed to improve El Niño–Southern Oscillation (ENSO) predictions. Its essential idea is that the initial errors are projected onto their optimal growth patterns, which are propagated by the tangent linear model (TLM) of the original prediction model. The forecast errors at a given lead time of predictions are obtained, and then removed from the raw predictions. This method is applied to a realistic ENSO prediction model for improving prediction skill for the period from 1980 to 1999. This correction method considerably improves the ENSO prediction skill, compared with the original predictions without the correction.
Abstract
This study investigates simulated fair-weather shallow cumulus-topped boundary layer (SCTBL) on kilometer- and subkilometer-scale horizontal resolutions, also known as the numerical gray zone of boundary layer turbulence. Based on a priori analysis of a simulated classic SCTBL with large-eddy simulation, its gray zone scale is determined. The dominant length scale of the cloud layer (CL) is found to be the effective cloud diameter, while that of the underlying mixed layer (ML) is the size of organized convection. The two scales are linked by a simple geometric argument based on vertically coherent updrafts, and are quantified through spectral analysis. Comparison to a simulated dry convective boundary layer (CBL) further reveals that the ML gray zone scale does not differentiate between clear and cloudy conditions with the same bulk stability. A posteriori simulations are then performed over a range of resolutions to evaluate the performance of a recently developed scale-adaptive planetary boundary layer (PBL) scheme. Simulation results suggest indifferences to the scale-adaptive capability. Detailed analyses of flux partition reveal that, in the absence of a shallow cumulus scheme, overly energetic resolved fluxes develop in the CL at gray zone and coarse resolutions, and are responsible for overpredicted resolved convection in the ML. These results suggest that modifications are needed for scale-adaptive PBL schemes under shallow cumulus-topped conditions.
Significance Statement
Shallow cumulus (ShCu) clouds play an important role in the dynamical and radiative processes of the atmospheric boundary layer. As the grid resolution of modern numerical weather prediction models approach kilometer and subkilometer scales, also known as the gray zone, accurate modeling of ShCu clouds becomes challenging due to difficulties in their parameterization. This study identifies the spatial scale that sets the gray zone of ShCu clouds, providing the key to building better parameterizations. Performance of existing parameterizations developed for clear-sky conditions is evaluated for cloudy conditions, exposing deficiencies and motivating further development.
Abstract
This study investigates simulated fair-weather shallow cumulus-topped boundary layer (SCTBL) on kilometer- and subkilometer-scale horizontal resolutions, also known as the numerical gray zone of boundary layer turbulence. Based on a priori analysis of a simulated classic SCTBL with large-eddy simulation, its gray zone scale is determined. The dominant length scale of the cloud layer (CL) is found to be the effective cloud diameter, while that of the underlying mixed layer (ML) is the size of organized convection. The two scales are linked by a simple geometric argument based on vertically coherent updrafts, and are quantified through spectral analysis. Comparison to a simulated dry convective boundary layer (CBL) further reveals that the ML gray zone scale does not differentiate between clear and cloudy conditions with the same bulk stability. A posteriori simulations are then performed over a range of resolutions to evaluate the performance of a recently developed scale-adaptive planetary boundary layer (PBL) scheme. Simulation results suggest indifferences to the scale-adaptive capability. Detailed analyses of flux partition reveal that, in the absence of a shallow cumulus scheme, overly energetic resolved fluxes develop in the CL at gray zone and coarse resolutions, and are responsible for overpredicted resolved convection in the ML. These results suggest that modifications are needed for scale-adaptive PBL schemes under shallow cumulus-topped conditions.
Significance Statement
Shallow cumulus (ShCu) clouds play an important role in the dynamical and radiative processes of the atmospheric boundary layer. As the grid resolution of modern numerical weather prediction models approach kilometer and subkilometer scales, also known as the gray zone, accurate modeling of ShCu clouds becomes challenging due to difficulties in their parameterization. This study identifies the spatial scale that sets the gray zone of ShCu clouds, providing the key to building better parameterizations. Performance of existing parameterizations developed for clear-sky conditions is evaluated for cloudy conditions, exposing deficiencies and motivating further development.
Abstract
The onset of summer monsoon associated with global warming is of great concern to scientific community. While observational data diagnosis has shown the impact of intraseasonal oscillation (ISO) on the monsoon onset, how the ISO may affect the onset of monsoon under global warming remains unknown. Here, by analyzing the onset of the summer monsoon over the South Asian marginal seas projected by CMIP6 (Coupled Model Intercomparison Project Phase 6) models under SSP5-8.5 scenario, we show evidence that the majority of models (>70%) project an earlier onset over the Arabian Sea (ArS), while a delayed onset over the Bay of Bangle (BoB) and the South China Sea (SCS). The temporal shifts of the monsoon onset are attributed to the changes in the pre-monsoon northward migration of equatorial ISO (NMISO), which is a trigger of monsoon onset and will be advanced (postponed) over the ArS (BoB and SCS). The subtropical upper-level westerly anomaly, inducing delayed occurrence of easterly shear, acts to delay the NMISO over the entire Indian Ocean. However, the intensified low-level southerly wind over the ArS, as well as its induced asymmetric pattern of boundary-layer moisture work together to advance the pre-monsoon NMISO in the area, outweighing the delayed impact from vertical shear. These large-scale circulation changes are driven by tropical warming in the upper troposphere, land warming over the Arabian Peninsula and ocean warming over the eastern Pacific. This analysis enriches monsoon onset projections by highlighting the role of ISO in influencing the future changes in monsoon onset.
Abstract
The onset of summer monsoon associated with global warming is of great concern to scientific community. While observational data diagnosis has shown the impact of intraseasonal oscillation (ISO) on the monsoon onset, how the ISO may affect the onset of monsoon under global warming remains unknown. Here, by analyzing the onset of the summer monsoon over the South Asian marginal seas projected by CMIP6 (Coupled Model Intercomparison Project Phase 6) models under SSP5-8.5 scenario, we show evidence that the majority of models (>70%) project an earlier onset over the Arabian Sea (ArS), while a delayed onset over the Bay of Bangle (BoB) and the South China Sea (SCS). The temporal shifts of the monsoon onset are attributed to the changes in the pre-monsoon northward migration of equatorial ISO (NMISO), which is a trigger of monsoon onset and will be advanced (postponed) over the ArS (BoB and SCS). The subtropical upper-level westerly anomaly, inducing delayed occurrence of easterly shear, acts to delay the NMISO over the entire Indian Ocean. However, the intensified low-level southerly wind over the ArS, as well as its induced asymmetric pattern of boundary-layer moisture work together to advance the pre-monsoon NMISO in the area, outweighing the delayed impact from vertical shear. These large-scale circulation changes are driven by tropical warming in the upper troposphere, land warming over the Arabian Peninsula and ocean warming over the eastern Pacific. This analysis enriches monsoon onset projections by highlighting the role of ISO in influencing the future changes in monsoon onset.
Abstract
This study investigates the seasonal features and generation mechanisms of submesoscale processes (SMPs) in the southern Bay of Bengal (BoB) during 2011/12, based on the output of a high-resolution model, LLC4320 (latitude–longitude–polar cap). The results show that the southern BoB exhibits the most energetic SMPs, with significant seasonal variations. The SMPs are more active during the summer and winter monsoon periods. During the monsoon periods, the sharpening horizontal buoyancy gradients associated with strong straining effects favor the frontogenesis and mixed layer instability (MLI), which are responsible for the SMPs generation. The symmetric instability (SI) scale is about 3–10 km in the southern BoB, which can be partially resolved by LLC4320. The SI is more active during summer and winter, with a proportion of 40%–80% during the study period when the necessary conditions for SI are satisfied. Energetics analysis suggests that the energy source of SMPs is mainly from the local large-scale and mesoscale processes. Baroclinic instability at submesoscales plays a significant role, further confirming the importance of frontogenesis and MLI. Barotropic instability also has considerable contribution to the submesoscale kinetic energy, especially during summer.
Significance Statement
Submesoscale processes (SMPs) are ubiquitous in the Bay of Bengal (BoB). Affected by the seasonally reversing monsoon, abundant rainfall and runoff, and equatorial remote forcing, the upper circulation in the BoB is complex, featuring active mesoscale eddies and rich submesoscale phenomena, making the BoB a “natural test ground” for submesoscale studies. It is found in this work that characteristics of SMPs in the BoB are quite different from other regions. In the southern bay, SMPs are most active during the summer and winter monsoons due to the frontogenesis, enhanced mixed layer instability (MLI), and symmetric instability. These findings could deepen our understanding on multiscale dynamic processes and energy cascade in the BoB and have implications for the study of marine ecology and biogeochemical processes.
Abstract
This study investigates the seasonal features and generation mechanisms of submesoscale processes (SMPs) in the southern Bay of Bengal (BoB) during 2011/12, based on the output of a high-resolution model, LLC4320 (latitude–longitude–polar cap). The results show that the southern BoB exhibits the most energetic SMPs, with significant seasonal variations. The SMPs are more active during the summer and winter monsoon periods. During the monsoon periods, the sharpening horizontal buoyancy gradients associated with strong straining effects favor the frontogenesis and mixed layer instability (MLI), which are responsible for the SMPs generation. The symmetric instability (SI) scale is about 3–10 km in the southern BoB, which can be partially resolved by LLC4320. The SI is more active during summer and winter, with a proportion of 40%–80% during the study period when the necessary conditions for SI are satisfied. Energetics analysis suggests that the energy source of SMPs is mainly from the local large-scale and mesoscale processes. Baroclinic instability at submesoscales plays a significant role, further confirming the importance of frontogenesis and MLI. Barotropic instability also has considerable contribution to the submesoscale kinetic energy, especially during summer.
Significance Statement
Submesoscale processes (SMPs) are ubiquitous in the Bay of Bengal (BoB). Affected by the seasonally reversing monsoon, abundant rainfall and runoff, and equatorial remote forcing, the upper circulation in the BoB is complex, featuring active mesoscale eddies and rich submesoscale phenomena, making the BoB a “natural test ground” for submesoscale studies. It is found in this work that characteristics of SMPs in the BoB are quite different from other regions. In the southern bay, SMPs are most active during the summer and winter monsoons due to the frontogenesis, enhanced mixed layer instability (MLI), and symmetric instability. These findings could deepen our understanding on multiscale dynamic processes and energy cascade in the BoB and have implications for the study of marine ecology and biogeochemical processes.
Abstract
In this study, El Niño–Southern Oscillation (ENSO) retrospective forecasts were performed for the 120 yr from 1881 to 2000 using three realistic models that assimilate the historic dataset of sea surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, several important issues related to ENSO predictability have been explored, including its interdecadal variability and the dominant factors that control the interdecadal variability.
The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late nineteenth century and in the middle–late twentieth century, and low skill during the period from 1900 to 1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally, the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed.
Abstract
In this study, El Niño–Southern Oscillation (ENSO) retrospective forecasts were performed for the 120 yr from 1881 to 2000 using three realistic models that assimilate the historic dataset of sea surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, several important issues related to ENSO predictability have been explored, including its interdecadal variability and the dominant factors that control the interdecadal variability.
The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late nineteenth century and in the middle–late twentieth century, and low skill during the period from 1900 to 1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally, the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed.
Abstract
In this study, the Providing Regional Climates for Impacts Studies (PRECIS) and the Regional Climate Model (RegCM) system as well as the Variable Infiltration Capacity (VIC) macroscale hydrologic model were integrated into a general framework to investigate impacts of future climates on the hydrologic regime of the Athabasca River basin. Regional climate models (RCMs) including PRECIS and RegCM were used to develop ensemble high-resolution climate projections for 1979–2099. RCMs were driven by the boundary conditions from the Hadley Centre Global Environment Model, version 2 with Earth system configurations (HadGEM2-ES); the Second Generation Canadian Earth System Model (CanESM2); and the Geophysical Fluid Dynamics Laboratory Earth System Model with MOM (GFDL-ESM2M) under the representative concentration pathways (RCPs). The ensemble climate simulations were validated through comparison with observations for 1984–2003. The RCMs project increases in temperature, precipitation, and wind speed under RCPs across most of the Athabasca River basin. Meanwhile, VIC was calibrated using the University of Arizona Shuffled Complex Evolution method (SCE-UA). The performance of the VIC model in replicating the characteristics of the observed streamflow was validated for 1994–2003. Changes in runoff and streamflow under RCPs were then simulated by the validated VIC model. The validation results demonstrate that the ensemble-RCM-driven VIC model can effectively reproduce historical climatological and hydrological patterns in the Athabasca River basin. The ensemble-RCM-driven VIC model shows that monthly streamflow is projected to increase in the 2050s and 2080s under RCPs, with notably higher flows expected in the spring for the 2080s. This will have substantial impacts on water balance on the Athabasca River basin, thus affecting the surrounding industry and ecosystems. The developed framework can be applied to other regions for exploration of hydrologic impacts under climate change.
Abstract
In this study, the Providing Regional Climates for Impacts Studies (PRECIS) and the Regional Climate Model (RegCM) system as well as the Variable Infiltration Capacity (VIC) macroscale hydrologic model were integrated into a general framework to investigate impacts of future climates on the hydrologic regime of the Athabasca River basin. Regional climate models (RCMs) including PRECIS and RegCM were used to develop ensemble high-resolution climate projections for 1979–2099. RCMs were driven by the boundary conditions from the Hadley Centre Global Environment Model, version 2 with Earth system configurations (HadGEM2-ES); the Second Generation Canadian Earth System Model (CanESM2); and the Geophysical Fluid Dynamics Laboratory Earth System Model with MOM (GFDL-ESM2M) under the representative concentration pathways (RCPs). The ensemble climate simulations were validated through comparison with observations for 1984–2003. The RCMs project increases in temperature, precipitation, and wind speed under RCPs across most of the Athabasca River basin. Meanwhile, VIC was calibrated using the University of Arizona Shuffled Complex Evolution method (SCE-UA). The performance of the VIC model in replicating the characteristics of the observed streamflow was validated for 1994–2003. Changes in runoff and streamflow under RCPs were then simulated by the validated VIC model. The validation results demonstrate that the ensemble-RCM-driven VIC model can effectively reproduce historical climatological and hydrological patterns in the Athabasca River basin. The ensemble-RCM-driven VIC model shows that monthly streamflow is projected to increase in the 2050s and 2080s under RCPs, with notably higher flows expected in the spring for the 2080s. This will have substantial impacts on water balance on the Athabasca River basin, thus affecting the surrounding industry and ecosystems. The developed framework can be applied to other regions for exploration of hydrologic impacts under climate change.
