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- Author or Editor: Ning Jiang x
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Abstract
Mathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.
Abstract
Mathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.
Abstract
Although the use of anomaly fields in the forecast process has been shown to be useful and has caught forecasters’ attention, current short-range (1–3 days) weather analyses and forecasts are still predominantly total-field based. This paper systematically examines the pros and cons of anomaly- versus total-field-based approaches in weather analysis using a case from 1 July 1991 (showcase) and 41 cases from 1998 (statistics) of heavy rain events that occurred in China. The comparison is done for both basic atmospheric variables (height, temperature, wind, and humidity) and diagnostic parameters (divergence, vorticity, and potential vorticity). Generally, anomaly fields show a more enhanced and concentrated signal (pattern) directly related to surface anomalous weather events, while total fields can obscure the visualization of anomalous features due to the climatic background. The advantage is noticeable in basic atmospheric variables, but is marginal in nonconservative diagnostic parameters and is lost in conservative diagnostic parameters. Sometimes a mix of total and anomaly fields works the best; for example, in the moist vorticity when anomalous vorticity combines with total moisture, it can depict the heavy rain area the best when comparing to either the purely total or purely anomalous moist vorticity. Based on this study, it is recommended that anomaly-based weather analysis could be a valuable supplement to the commonly used total-field-based approach. Anomalies can help a forecaster to more quickly identify where an abnormal weather event might occur as well as more easily pinpoint possible meteorological causes than a total field. However, one should not use the anomaly structure approach alone to explain the underlying dynamics without a total field.
Abstract
Although the use of anomaly fields in the forecast process has been shown to be useful and has caught forecasters’ attention, current short-range (1–3 days) weather analyses and forecasts are still predominantly total-field based. This paper systematically examines the pros and cons of anomaly- versus total-field-based approaches in weather analysis using a case from 1 July 1991 (showcase) and 41 cases from 1998 (statistics) of heavy rain events that occurred in China. The comparison is done for both basic atmospheric variables (height, temperature, wind, and humidity) and diagnostic parameters (divergence, vorticity, and potential vorticity). Generally, anomaly fields show a more enhanced and concentrated signal (pattern) directly related to surface anomalous weather events, while total fields can obscure the visualization of anomalous features due to the climatic background. The advantage is noticeable in basic atmospheric variables, but is marginal in nonconservative diagnostic parameters and is lost in conservative diagnostic parameters. Sometimes a mix of total and anomaly fields works the best; for example, in the moist vorticity when anomalous vorticity combines with total moisture, it can depict the heavy rain area the best when comparing to either the purely total or purely anomalous moist vorticity. Based on this study, it is recommended that anomaly-based weather analysis could be a valuable supplement to the commonly used total-field-based approach. Anomalies can help a forecaster to more quickly identify where an abnormal weather event might occur as well as more easily pinpoint possible meteorological causes than a total field. However, one should not use the anomaly structure approach alone to explain the underlying dynamics without a total field.
Abstract
A seasonal evolution of rainbands over East China is evident and shows remarkable year-to-year variations. The present study identifies two dominant interannual modes of the seasonal evolution of rainbands over East China from 1981 to 2018: 1) the sudden change pattern, in which the anomalous rainfall changes abruptly from boreal spring to summer, especially over South China; and 2) the northward migration pattern, which shows a gradual poleward migration of the anomalous rainband over East China with the East Asian summer monsoon (EASM). Both of them are regulated by the sea surface temperature anomalies (SSTAs) in the Northern Hemisphere from spring to summer. In the sudden change pattern, the SSTAs in the Pacific modulate spring rainfall over South China via the ENSO–EASM teleconnection. By contrast, the North Atlantic SSTAs change the midlatitude wave train and modify summer rainfall over South and North China, in conjunction with the anomalous tropical circulation due to the Indian Ocean SSTAs. In the northward migration pattern, the North Pacific SSTAs alter spring rainfall over South China by varying the low-level western North Pacific subtropical high and the zonal land–sea thermal contrast over East Asia. Afterward, the ENSO-like SSTAs induce a Pacific–Japan teleconnection and shift the anomalous rainband northward to the Yangtze–Huai River and North China in summer. The seasonal switch of the SSTAs regulating these two modes is physically linked from boreal spring to summer. This mechanism provides potential seasonal predictability of the seasonal evolution of the anomalous rainband over East China.
Abstract
A seasonal evolution of rainbands over East China is evident and shows remarkable year-to-year variations. The present study identifies two dominant interannual modes of the seasonal evolution of rainbands over East China from 1981 to 2018: 1) the sudden change pattern, in which the anomalous rainfall changes abruptly from boreal spring to summer, especially over South China; and 2) the northward migration pattern, which shows a gradual poleward migration of the anomalous rainband over East China with the East Asian summer monsoon (EASM). Both of them are regulated by the sea surface temperature anomalies (SSTAs) in the Northern Hemisphere from spring to summer. In the sudden change pattern, the SSTAs in the Pacific modulate spring rainfall over South China via the ENSO–EASM teleconnection. By contrast, the North Atlantic SSTAs change the midlatitude wave train and modify summer rainfall over South and North China, in conjunction with the anomalous tropical circulation due to the Indian Ocean SSTAs. In the northward migration pattern, the North Pacific SSTAs alter spring rainfall over South China by varying the low-level western North Pacific subtropical high and the zonal land–sea thermal contrast over East Asia. Afterward, the ENSO-like SSTAs induce a Pacific–Japan teleconnection and shift the anomalous rainband northward to the Yangtze–Huai River and North China in summer. The seasonal switch of the SSTAs regulating these two modes is physically linked from boreal spring to summer. This mechanism provides potential seasonal predictability of the seasonal evolution of the anomalous rainband over East China.
Abstract
Properly including moisture effects into a dynamical parameter can significantly increase the parameter’s ability to diagnose heavy rain locations. The relative humidity–based weighting approach used to extend the moist potential vorticity (MPV) to the generalized moist potential vorticity (GMPV) is analyzed and demonstrates such an improvement. Following the same approach, two new diagnostic parameters, moist vorticity (MV) and moist divergence (MD), have been proposed in this study by incorporating moisture effects into the traditional vorticity and divergence. A regional heavy rain event that occurred along the Yangtze River on 1 July 1991 is used as a case study, and 41 daily regional heavy rain events during the notorious flooding year of 1998 in eastern China are used for a systematic evaluation. Results show that after the moisture effects were properly incorporated, the improved ability of all three parameters to capture a heavy rain area is significant (statistically at the 99% confidence level): the GMPV is improved over the MPV by 194%, the MD over the divergence by 60%, and the MV over the vorticity by 34% in terms of the threat score (TS). The average TS is 0.270 for the MD, 0.262 for the MV, and 0.188 for the GMPV. Application of the MV and MD to assess heavy rain potential is not intended to replace a complete, multiscale forecasting methodology; however, the results from this study suggest that the MV and MD could be used to postprocess a model forecast to potentially improve heavy rain location predictions.
Abstract
Properly including moisture effects into a dynamical parameter can significantly increase the parameter’s ability to diagnose heavy rain locations. The relative humidity–based weighting approach used to extend the moist potential vorticity (MPV) to the generalized moist potential vorticity (GMPV) is analyzed and demonstrates such an improvement. Following the same approach, two new diagnostic parameters, moist vorticity (MV) and moist divergence (MD), have been proposed in this study by incorporating moisture effects into the traditional vorticity and divergence. A regional heavy rain event that occurred along the Yangtze River on 1 July 1991 is used as a case study, and 41 daily regional heavy rain events during the notorious flooding year of 1998 in eastern China are used for a systematic evaluation. Results show that after the moisture effects were properly incorporated, the improved ability of all three parameters to capture a heavy rain area is significant (statistically at the 99% confidence level): the GMPV is improved over the MPV by 194%, the MD over the divergence by 60%, and the MV over the vorticity by 34% in terms of the threat score (TS). The average TS is 0.270 for the MD, 0.262 for the MV, and 0.188 for the GMPV. Application of the MV and MD to assess heavy rain potential is not intended to replace a complete, multiscale forecasting methodology; however, the results from this study suggest that the MV and MD could be used to postprocess a model forecast to potentially improve heavy rain location predictions.
Abstract
It is known that the southwest vortex (SWV) is an important weather system that may induce severe weather. The southward deviation of an SWV track forecasted by the Global Assimilation and Prediction System of the China Meteorological Administration (CMA-GFS) is systematically diagnosed in this study. The southward shift of the SWV is directly attributed to the deviation of the steering flow caused by the weak forecast of the upper-level trough. According to the diagnosis of potential tendency, the underestimation of the initial vorticity advection forecasted by CMA-GFS dominates the weak development of the upper-level trough. The underestimation of the vorticity advection is eventually sourced to the weak geostrophic wind caused by the weak initial meridional and zonal gradients of the midlevel height in front of the trough. The assimilation process on the initial field of the CMA-GFS acts a negative effect on forecasting this SWV track. It weakens the π field at midmodel level, resulting in the weak midlevel height gradient in front of the trough. A verified numerical experiment initialized by a more reasonable field is carried out and the southward shift of the SWV is obviously modified. This study suggests that a reasonable analysis field is crucial for the accurate forecast of the SWV track.
Significance Statement
The important impact of initial field deviation in key regions on the forecast in the late period is highlighted. A systematic diagnosis process for identifying and addressing forecast issues on SWV track is proposed. This research provides a comprehensive approach for diagnosing the forecast deviation associated with SWV track.
Abstract
It is known that the southwest vortex (SWV) is an important weather system that may induce severe weather. The southward deviation of an SWV track forecasted by the Global Assimilation and Prediction System of the China Meteorological Administration (CMA-GFS) is systematically diagnosed in this study. The southward shift of the SWV is directly attributed to the deviation of the steering flow caused by the weak forecast of the upper-level trough. According to the diagnosis of potential tendency, the underestimation of the initial vorticity advection forecasted by CMA-GFS dominates the weak development of the upper-level trough. The underestimation of the vorticity advection is eventually sourced to the weak geostrophic wind caused by the weak initial meridional and zonal gradients of the midlevel height in front of the trough. The assimilation process on the initial field of the CMA-GFS acts a negative effect on forecasting this SWV track. It weakens the π field at midmodel level, resulting in the weak midlevel height gradient in front of the trough. A verified numerical experiment initialized by a more reasonable field is carried out and the southward shift of the SWV is obviously modified. This study suggests that a reasonable analysis field is crucial for the accurate forecast of the SWV track.
Significance Statement
The important impact of initial field deviation in key regions on the forecast in the late period is highlighted. A systematic diagnosis process for identifying and addressing forecast issues on SWV track is proposed. This research provides a comprehensive approach for diagnosing the forecast deviation associated with SWV track.
Abstract
This paper studies the atmospheric moisture residence times over China for the period 1980–2009 using the dynamic recycling model (DRM). We define both the residence times for atmospheric moisture of precipitation (backward tracking) and evaporation (forward tracking) and show that each has significant spatial and seasonal variations. The area-averaged precipitation-moisture residence time is approximately 8.3 days, while the evaporation residence time is approximately 6.3 days. In addition, we investigate the concept of “tracking time” or time selected for moisture tracking in numerical source–sink studies. The area-averaged backward and forward tracking times at the 90% threshold (i.e., when 90% of initial moisture is attributed for tracking) are approximately 22 and 15 days, respectively. Finally, we theoretically deduced the explicit expressions for residence and tracking times for idealized cases and found the analytical proportional relationship between these times. In this way, the analytical link between residence time and e-folding time was reestablished. This proportional relationship was further verified against the DRM-derived values. In the DRM results, the proportional relation generally fluctuates along the trajectory, which leads to the differences between the theoretical and the DRM-derived values. These results can enhance our understanding of water cycling, and they are likely to help choose tracking times in relevant studies.
Abstract
This paper studies the atmospheric moisture residence times over China for the period 1980–2009 using the dynamic recycling model (DRM). We define both the residence times for atmospheric moisture of precipitation (backward tracking) and evaporation (forward tracking) and show that each has significant spatial and seasonal variations. The area-averaged precipitation-moisture residence time is approximately 8.3 days, while the evaporation residence time is approximately 6.3 days. In addition, we investigate the concept of “tracking time” or time selected for moisture tracking in numerical source–sink studies. The area-averaged backward and forward tracking times at the 90% threshold (i.e., when 90% of initial moisture is attributed for tracking) are approximately 22 and 15 days, respectively. Finally, we theoretically deduced the explicit expressions for residence and tracking times for idealized cases and found the analytical proportional relationship between these times. In this way, the analytical link between residence time and e-folding time was reestablished. This proportional relationship was further verified against the DRM-derived values. In the DRM results, the proportional relation generally fluctuates along the trajectory, which leads to the differences between the theoretical and the DRM-derived values. These results can enhance our understanding of water cycling, and they are likely to help choose tracking times in relevant studies.
Abstract
The spatial and temporal variations in terrestrial carbon storage play a pivotal role in regulating future climate change. However, Earth system models (ESMs), which have coupled the terrestrial biosphere and atmosphere, show great uncertainty in simulating the global land carbon storage. Here, based on multiple global datasets and a traceability analysis, we diagnosed the uncertainty source of terrestrial carbon storage in 22 ESMs that participated in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The modeled global terrestrial carbon storage has converged among ESMs from CMIP5 (1936.9 ± 739.3 PgC) to CMIP6 (1774.4 ± 439.0 PgC) but is persistently lower than the observation-based estimates (2285 ± 669 PgC). By further decomposing terrestrial carbon storage into net primary production (NPP) and ecosystem carbon residence time (τE ), we found that the decreased intermodel spread in land carbon storage primarily resulted from more accurate simulations on NPP among ESMs from CMIP5 to CMIP6. The persistent underestimation of land carbon storage was caused by the biased τE . In CMIP5 and CMIP6, the modeled τE was far shorter than the observation-based estimates. The potential reasons for the biased τE could be the lack of or incomplete representation of nutrient limitation, vertical soil biogeochemistry, and the permafrost carbon cycle. Moreover, the modeled τE became the key driver for the intermodel spread in global land carbon storage in CMIP6. Overall, our study indicates that CMIP6 models have greatly improved the terrestrial carbon cycle, with a decreased model spread in global terrestrial carbon storage and less uncertain productivity. However, more efforts are needed to understand and reduce the persistent data–model disagreement on carbon storage and residence time in the terrestrial biosphere.
Abstract
The spatial and temporal variations in terrestrial carbon storage play a pivotal role in regulating future climate change. However, Earth system models (ESMs), which have coupled the terrestrial biosphere and atmosphere, show great uncertainty in simulating the global land carbon storage. Here, based on multiple global datasets and a traceability analysis, we diagnosed the uncertainty source of terrestrial carbon storage in 22 ESMs that participated in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The modeled global terrestrial carbon storage has converged among ESMs from CMIP5 (1936.9 ± 739.3 PgC) to CMIP6 (1774.4 ± 439.0 PgC) but is persistently lower than the observation-based estimates (2285 ± 669 PgC). By further decomposing terrestrial carbon storage into net primary production (NPP) and ecosystem carbon residence time (τE ), we found that the decreased intermodel spread in land carbon storage primarily resulted from more accurate simulations on NPP among ESMs from CMIP5 to CMIP6. The persistent underestimation of land carbon storage was caused by the biased τE . In CMIP5 and CMIP6, the modeled τE was far shorter than the observation-based estimates. The potential reasons for the biased τE could be the lack of or incomplete representation of nutrient limitation, vertical soil biogeochemistry, and the permafrost carbon cycle. Moreover, the modeled τE became the key driver for the intermodel spread in global land carbon storage in CMIP6. Overall, our study indicates that CMIP6 models have greatly improved the terrestrial carbon cycle, with a decreased model spread in global terrestrial carbon storage and less uncertain productivity. However, more efforts are needed to understand and reduce the persistent data–model disagreement on carbon storage and residence time in the terrestrial biosphere.
Abstract
The persistence barrier (PB), one of the El Niño–Southern Oscillation (ENSO) properties, has exhibited a significant decadal variability, showing enhanced and weakened behavior before and after the late 1970s, respectively. In the present study, both the theoretical solution and the observations indicate that the variability of PB intensity is linearly proportional to the seasonal amplitude of ENSO growth rate, which accounts for the ENSO PB decadal variability. With further use of the Bjerknes–Jin (BJ) index analysis, we find that the decadal reduction in PB intensity since the late 1970s is mainly attributed to the mean advection and the thermocline feedback. In addition, the stronger spring thermal damping delayed the timing of PB in the 1980s and 1990s. Our study establishes a linear relationship between PB intensity and ENSO growth rate, which carries implications for understanding the ENSO predictability and the systematic changes in ENSO properties under climate change.
Abstract
The persistence barrier (PB), one of the El Niño–Southern Oscillation (ENSO) properties, has exhibited a significant decadal variability, showing enhanced and weakened behavior before and after the late 1970s, respectively. In the present study, both the theoretical solution and the observations indicate that the variability of PB intensity is linearly proportional to the seasonal amplitude of ENSO growth rate, which accounts for the ENSO PB decadal variability. With further use of the Bjerknes–Jin (BJ) index analysis, we find that the decadal reduction in PB intensity since the late 1970s is mainly attributed to the mean advection and the thermocline feedback. In addition, the stronger spring thermal damping delayed the timing of PB in the 1980s and 1990s. Our study establishes a linear relationship between PB intensity and ENSO growth rate, which carries implications for understanding the ENSO predictability and the systematic changes in ENSO properties under climate change.
