Abstract
This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using non-idealized LR and HR pairs resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics, and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of non-idealized LR fields resulting from internal variability. Furthermore, we conduct a spectra-based feature-importance experiment allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.
Abstract
This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using non-idealized LR and HR pairs resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics, and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of non-idealized LR fields resulting from internal variability. Furthermore, we conduct a spectra-based feature-importance experiment allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.
Abstract
Under the background of global warming, the Arctic region has warmed faster than the Antarctic, which is referred to as asymmetric Arctic and Antarctic warming. The new generation of model simulations from the CMIP6 offers an opportunity to identify the major factors contributing to the asymmetric warming and its inter-model spread. In this study, the pre-industrial and abrupt-4 × CO2 experiments from eighteen CMIP6 models are examined to extract the asymmetric warming and its inter-model spread. A climate feedback-response analysis method is applied to reveal the contributions of external and internal feedback processes to the asymmetric warming and its inter-model spread, by decomposing total warming into the partial temperature changes caused by individual factors. It is found that a seasonal energy transfer mechanism (SETM) dominates in both polar warmings. The direct consequence of the sea ice declining in response to the anthropogenic forcing is an increase in the effective heat capacity of the ocean surface layer. Such increase in the effective heat capacity temporally withholds most of the extra solar energy absorbed during summer and then releases it during winter, contributing to stronger warming in winter. However, the background oceanic circulation in the Southern Ocean, namely the Antarctic Circumpolar Current, continually transports energy equatorward, resulting in a suppressed SETM and surface warming in the Antarctic. The key factor that accounts for inter-model spread in the asymmetric warming is the difference in their strength of SETM. The poleward atmospheric transport and water vapor feedback also contribute to the inter-model spread.
Abstract
Under the background of global warming, the Arctic region has warmed faster than the Antarctic, which is referred to as asymmetric Arctic and Antarctic warming. The new generation of model simulations from the CMIP6 offers an opportunity to identify the major factors contributing to the asymmetric warming and its inter-model spread. In this study, the pre-industrial and abrupt-4 × CO2 experiments from eighteen CMIP6 models are examined to extract the asymmetric warming and its inter-model spread. A climate feedback-response analysis method is applied to reveal the contributions of external and internal feedback processes to the asymmetric warming and its inter-model spread, by decomposing total warming into the partial temperature changes caused by individual factors. It is found that a seasonal energy transfer mechanism (SETM) dominates in both polar warmings. The direct consequence of the sea ice declining in response to the anthropogenic forcing is an increase in the effective heat capacity of the ocean surface layer. Such increase in the effective heat capacity temporally withholds most of the extra solar energy absorbed during summer and then releases it during winter, contributing to stronger warming in winter. However, the background oceanic circulation in the Southern Ocean, namely the Antarctic Circumpolar Current, continually transports energy equatorward, resulting in a suppressed SETM and surface warming in the Antarctic. The key factor that accounts for inter-model spread in the asymmetric warming is the difference in their strength of SETM. The poleward atmospheric transport and water vapor feedback also contribute to the inter-model spread.
Abstract
Since 2019, National Weather Service (NWS) offices have been able to issue 360-character Wireless Emergency Alert (“WEA360”) messages for tornadoes. NWS is now considering changing from a “deterministic” to a “probabilistic” warning paradigm. That change could possibly influence how WEA360 messages for tornado are issued in the future. Recent experimental studies have found that probabilistic hazard information (PHI) forecast graphics improve consumers’ risk perception for tornadoes, but findings from these studies concerning whether PHI forecast graphics improve people’s protective action decision-making are mixed. The present study therefore investigated how mock PHI-enhanced WEA360 messages might influence people’s risk perception and protective action decision-making. Analysis of qualitative data gathered from a combination of questionnaire and focus group interview methods conducted in collaboration with 31 community members in Denver, Colorado, indicated that inclusion of PHI forecast graphics within WEA360 messages elicited high levels of understanding and message believability but did not consistently lead to appropriate precautionary intent. Because warning response is a complex social phenomenon, PHI may not significantly improve protective action decision-making if PHI forecast graphics are eventually presented to consumers via the Wireless Emergency Alerts system. Factors that PHI stakeholders should consider before the adoption of PHI-enhanced WEA360 messages for consumers are discussed.
Significance Statement
This study examines how consumers respond to and talk about mock WEA360 messages for tornadoes that contain embedded PHI forecast graphics. As NWS considers moving to a probabilistic warning paradigm, stakeholders will need to determine how PHI forecast graphics might be communicated directly to consumers, if at all. Our findings suggest that combining WEA360 messages with PHI forecast graphics creates challenges and complexities related to consumers’ assessment of personal risk and protective action decision-making. Overall, the study suggests that any future PHI-enhanced WEA360 messages provided directly to consumers, if at all, must avoid discrepancies (even subtle) between the level of risk represented by the PHI forecast graphic and the protective action guidance included in the text of the messages.
Abstract
Since 2019, National Weather Service (NWS) offices have been able to issue 360-character Wireless Emergency Alert (“WEA360”) messages for tornadoes. NWS is now considering changing from a “deterministic” to a “probabilistic” warning paradigm. That change could possibly influence how WEA360 messages for tornado are issued in the future. Recent experimental studies have found that probabilistic hazard information (PHI) forecast graphics improve consumers’ risk perception for tornadoes, but findings from these studies concerning whether PHI forecast graphics improve people’s protective action decision-making are mixed. The present study therefore investigated how mock PHI-enhanced WEA360 messages might influence people’s risk perception and protective action decision-making. Analysis of qualitative data gathered from a combination of questionnaire and focus group interview methods conducted in collaboration with 31 community members in Denver, Colorado, indicated that inclusion of PHI forecast graphics within WEA360 messages elicited high levels of understanding and message believability but did not consistently lead to appropriate precautionary intent. Because warning response is a complex social phenomenon, PHI may not significantly improve protective action decision-making if PHI forecast graphics are eventually presented to consumers via the Wireless Emergency Alerts system. Factors that PHI stakeholders should consider before the adoption of PHI-enhanced WEA360 messages for consumers are discussed.
Significance Statement
This study examines how consumers respond to and talk about mock WEA360 messages for tornadoes that contain embedded PHI forecast graphics. As NWS considers moving to a probabilistic warning paradigm, stakeholders will need to determine how PHI forecast graphics might be communicated directly to consumers, if at all. Our findings suggest that combining WEA360 messages with PHI forecast graphics creates challenges and complexities related to consumers’ assessment of personal risk and protective action decision-making. Overall, the study suggests that any future PHI-enhanced WEA360 messages provided directly to consumers, if at all, must avoid discrepancies (even subtle) between the level of risk represented by the PHI forecast graphic and the protective action guidance included in the text of the messages.
Abstract
We present a new global oxygen atlas. This atlas uses all of the available full water column profiles of oxygen, salinity and temperature available as part of the World Ocean Atlas released in 2018. Instead of optimal interpolation we use the Data Interpolating Variational Analysis (DIVA) approach to map the available profiles onto 108 depth levels between the surface and 6800 m, covering more than 99% of ocean volume. This 1/2° × 1/2° degree atlas covers the period 1955 to 2018 in 1 year intervals. The DIVA method has significant benefits over traditional optimal interpolation. It allows the explicit inclusion of advection and boundary constraints thus offering improvements in the representations of oxygen, salinity and temperature in regions of strong flow and near coastal boundaries. We demonstrate these benefits of this mapping approach with some examples from this atlas. We can explore the regional and temporal variations of oxygen in the global oceans. Preliminary analyses confirm earlier analyses that the oxygen minimum zone in the eastern Pacific Ocean has expanded and intensified. Oxygen inventory changes between 1970 and 2010 are assessed and compared against prior studies. We find that the full ocean oxygen inventory decreased by 0.84%±0.42%. For this period temperature driven solubility changes explain about 21% of the oxygen decline over the full water column, in the upper 100 m solubility changes can explain all of the oxygen decrease, for the 100-600 m depth range it can explain only 29%, 19% between 600 m and 1000 m, and just 11% in the deep ocean.
Abstract
We present a new global oxygen atlas. This atlas uses all of the available full water column profiles of oxygen, salinity and temperature available as part of the World Ocean Atlas released in 2018. Instead of optimal interpolation we use the Data Interpolating Variational Analysis (DIVA) approach to map the available profiles onto 108 depth levels between the surface and 6800 m, covering more than 99% of ocean volume. This 1/2° × 1/2° degree atlas covers the period 1955 to 2018 in 1 year intervals. The DIVA method has significant benefits over traditional optimal interpolation. It allows the explicit inclusion of advection and boundary constraints thus offering improvements in the representations of oxygen, salinity and temperature in regions of strong flow and near coastal boundaries. We demonstrate these benefits of this mapping approach with some examples from this atlas. We can explore the regional and temporal variations of oxygen in the global oceans. Preliminary analyses confirm earlier analyses that the oxygen minimum zone in the eastern Pacific Ocean has expanded and intensified. Oxygen inventory changes between 1970 and 2010 are assessed and compared against prior studies. We find that the full ocean oxygen inventory decreased by 0.84%±0.42%. For this period temperature driven solubility changes explain about 21% of the oxygen decline over the full water column, in the upper 100 m solubility changes can explain all of the oxygen decrease, for the 100-600 m depth range it can explain only 29%, 19% between 600 m and 1000 m, and just 11% in the deep ocean.
Abstract
Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.
Significance Statement
Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.
Abstract
Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.
Significance Statement
Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.
Abstract
Soil moisture–precipitation (SM–PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s−1), high convective available potential energy (>1500 J kg−1), and low convective inhibition (<250 J kg−1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM–PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM–PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions.
Abstract
Soil moisture–precipitation (SM–PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s−1), high convective available potential energy (>1500 J kg−1), and low convective inhibition (<250 J kg−1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM–PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM–PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions.
Abstract
Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS), and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best performing individual component VTS. Ten-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1-6 while Both/ConVTS had better skill at later hours 7-15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits.
Abstract
Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS), and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best performing individual component VTS. Ten-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1-6 while Both/ConVTS had better skill at later hours 7-15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits.
Abstract
Although seasonal climate forecasts have major socioeconomic impacts, current forecast products, especially those for precipitation, are not yet reliable for forecasters and decision-makers. Here we developed a novel statistical–dynamical hybrid model for precipitation by applying weather regimes (WRs) and Gaussian mixture models (WR-GMM) to the National Oceanic and Atmospheric Administration’s Climate Forecast System, version 2 (CFSv2), precipitation forecasts across the continental United States. Instead of directly forecasting precipitation, WR-GMM uses observed precipitation from synoptic patterns similar to the future CFSv2 forecast. Traditionally K-means has been used to classify daily synoptic patterns into individual WRs, but the new GMM approach allows multiple WRs to be represented for the same day. The novel WR-GMM forecast model is trained on daily Climate Forecast System Reanalysis (CFSR) geopotential height and observed precipitation data during the 1981–2010 period and is verified for 2011–22. Overall, the WR-GMM method outperforms the CFSv2 ensemble forecast precipitation in terms of root-mean-square error and for Pearson correlation coefficient for lead months 1–4. Previous studies have used global climate models to forecast WRs in the Pacific Ocean and Mediterranean Sea regions, usually with an emphasis on winter months, but the WR-GMM model is the first of its kind that promises great untapped potential to improve precipitation forecasts produced by CFSv2 across the continental United States.
Abstract
Although seasonal climate forecasts have major socioeconomic impacts, current forecast products, especially those for precipitation, are not yet reliable for forecasters and decision-makers. Here we developed a novel statistical–dynamical hybrid model for precipitation by applying weather regimes (WRs) and Gaussian mixture models (WR-GMM) to the National Oceanic and Atmospheric Administration’s Climate Forecast System, version 2 (CFSv2), precipitation forecasts across the continental United States. Instead of directly forecasting precipitation, WR-GMM uses observed precipitation from synoptic patterns similar to the future CFSv2 forecast. Traditionally K-means has been used to classify daily synoptic patterns into individual WRs, but the new GMM approach allows multiple WRs to be represented for the same day. The novel WR-GMM forecast model is trained on daily Climate Forecast System Reanalysis (CFSR) geopotential height and observed precipitation data during the 1981–2010 period and is verified for 2011–22. Overall, the WR-GMM method outperforms the CFSv2 ensemble forecast precipitation in terms of root-mean-square error and for Pearson correlation coefficient for lead months 1–4. Previous studies have used global climate models to forecast WRs in the Pacific Ocean and Mediterranean Sea regions, usually with an emphasis on winter months, but the WR-GMM model is the first of its kind that promises great untapped potential to improve precipitation forecasts produced by CFSv2 across the continental United States.
Abstract
This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño–Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s.
Abstract
This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño–Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s.
