Abstract
The tropical cloud forest ecosystem in western equatorial Africa (WEA) is known to be sensitive to the presence of an extensive and persistent low-level stratiform cloud deck during the long dry season from June to September (JJAS). Here, we present a new climatology of the diurnal cycle of the low-level cloud cover from surface synoptic stations over WEA during JJAS 1971–2019. For the period JJAS 2008–19, we also utilized estimates of cloudiness from four satellite products, namely, the Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (SAFNWC) cloud classification, the Day and Night Microphysical Schemes (DMS/NMS), and cross sections from CALIPSO and CloudSat (2B-GEOPROF-lidar). A comparison with surface stations reveals that the NMS at night together with SAFNWC at daytime yield the smallest biases. The climatological analysis reveals that low-level clouds persist during the day over the coastal plains and windward side of the low mountain ranges. Conversely, on their leeward sides, i.e., over the plateaus, a decrease of the low-level cloud frequency is observed in the afternoon, together with a change from stratocumulus to cumulus. At night, the low-level cloud deck reforms over this region with the largest cloud occurrence frequencies in the morning. Vertical profiles from 2B-GEOPROF-lidar reveal cloud tops below 3000 m even at daytime. The station data and the suitable satellite products form the basis to better understand the physical processes controlling the clouds and to evaluate cloudiness from reanalyses and models.
Abstract
The tropical cloud forest ecosystem in western equatorial Africa (WEA) is known to be sensitive to the presence of an extensive and persistent low-level stratiform cloud deck during the long dry season from June to September (JJAS). Here, we present a new climatology of the diurnal cycle of the low-level cloud cover from surface synoptic stations over WEA during JJAS 1971–2019. For the period JJAS 2008–19, we also utilized estimates of cloudiness from four satellite products, namely, the Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (SAFNWC) cloud classification, the Day and Night Microphysical Schemes (DMS/NMS), and cross sections from CALIPSO and CloudSat (2B-GEOPROF-lidar). A comparison with surface stations reveals that the NMS at night together with SAFNWC at daytime yield the smallest biases. The climatological analysis reveals that low-level clouds persist during the day over the coastal plains and windward side of the low mountain ranges. Conversely, on their leeward sides, i.e., over the plateaus, a decrease of the low-level cloud frequency is observed in the afternoon, together with a change from stratocumulus to cumulus. At night, the low-level cloud deck reforms over this region with the largest cloud occurrence frequencies in the morning. Vertical profiles from 2B-GEOPROF-lidar reveal cloud tops below 3000 m even at daytime. The station data and the suitable satellite products form the basis to better understand the physical processes controlling the clouds and to evaluate cloudiness from reanalyses and models.
Abstract
Flooding, including inland and coastal flooding, is one of the most devastating and costly natural hazards in Canada, Mexico, and the United States. Recent research conducted by an international team has focused on understanding the true and comprehensive economic costs of floods, with an eye towards addressing their impacts, allocating adequate resources for monitoring and preparedness, and building resilient communities. Flood-costing methods vary greatly among federal and sub-national jurisdictions across the three North American countries. Because the rigor and consistency of existing datasets across the three countries vary significantly, it is also difficult to determine the economic impacts of cross-border events. This paper aims to critically analyze the research methods used to conduct this trinational project and develop recommendations for enhancing impacts of such work in future. We discuss three major research barriers: gaps in knowledge and research capacity; differences in data collation and analysis methods across the three countries; and, linguistic barriers in working across disciplines and economic sectors. We also explore how the COVID-19 pandemic significantly added to these three barriers. We propose creation of new institutional mechanisms that can play a major role in developing comprehensive, consistent, and cohesive data gathering approaches in Canada, Mexico, and the United States.
Abstract
Flooding, including inland and coastal flooding, is one of the most devastating and costly natural hazards in Canada, Mexico, and the United States. Recent research conducted by an international team has focused on understanding the true and comprehensive economic costs of floods, with an eye towards addressing their impacts, allocating adequate resources for monitoring and preparedness, and building resilient communities. Flood-costing methods vary greatly among federal and sub-national jurisdictions across the three North American countries. Because the rigor and consistency of existing datasets across the three countries vary significantly, it is also difficult to determine the economic impacts of cross-border events. This paper aims to critically analyze the research methods used to conduct this trinational project and develop recommendations for enhancing impacts of such work in future. We discuss three major research barriers: gaps in knowledge and research capacity; differences in data collation and analysis methods across the three countries; and, linguistic barriers in working across disciplines and economic sectors. We also explore how the COVID-19 pandemic significantly added to these three barriers. We propose creation of new institutional mechanisms that can play a major role in developing comprehensive, consistent, and cohesive data gathering approaches in Canada, Mexico, and the United States.
Abstract
A ‘protracted’ El Niño episode occurred from March-April 2018 until April-May 2020. It was manifest by the interlinked Indo-Pacific influences of two components of El Niño phases. Positive Indian Ocean Dipoles (IODs) in 2018 and 2019, suppressed the formation of northwest cloud bands and southern Australia rainfall, and a persistent teleconnection, with enhanced convection generated by positive Niño 4 region sea surface temperature (SST) anomalies and strong subsidence over eastern Australia, exacerbated this Australian drought. As with ‘classical’ El Niño Southern Oscillation (ENSO) events, which usually last 12-18 months, ‘protracted’ ENSO episodes, which last for more than 2 years, show a similar pattern of impacts on society and the environment across the Indo-Pacific domain, and often extend globally. The second half of this study puts the impact of the 2018-2020 ‘protracted’ El Niño episode on both the Australian terrestrial agricultural and marine ecophysiological environments in a broader context. These impacts are often not only modulated by the direct effects of ENSO events and episodes, but by interrelated local to region ocean-atmosphere interactions and synoptic weather patterns. Even though the indices of ‘protracted’ ENSO episodes are often weaker in magnitude than those of major ‘classical’ ENSO events, it is the longer duration of the former which poses its own set of problems. Thus, there is an urgent need to investigate the potential to forecast ‘protracted’ ENSO episodes, particularly when the mid-2020 to current 2022 period has been experiencing a major ‘protracted’ La Niña episode with near-global impacts.
Abstract
A ‘protracted’ El Niño episode occurred from March-April 2018 until April-May 2020. It was manifest by the interlinked Indo-Pacific influences of two components of El Niño phases. Positive Indian Ocean Dipoles (IODs) in 2018 and 2019, suppressed the formation of northwest cloud bands and southern Australia rainfall, and a persistent teleconnection, with enhanced convection generated by positive Niño 4 region sea surface temperature (SST) anomalies and strong subsidence over eastern Australia, exacerbated this Australian drought. As with ‘classical’ El Niño Southern Oscillation (ENSO) events, which usually last 12-18 months, ‘protracted’ ENSO episodes, which last for more than 2 years, show a similar pattern of impacts on society and the environment across the Indo-Pacific domain, and often extend globally. The second half of this study puts the impact of the 2018-2020 ‘protracted’ El Niño episode on both the Australian terrestrial agricultural and marine ecophysiological environments in a broader context. These impacts are often not only modulated by the direct effects of ENSO events and episodes, but by interrelated local to region ocean-atmosphere interactions and synoptic weather patterns. Even though the indices of ‘protracted’ ENSO episodes are often weaker in magnitude than those of major ‘classical’ ENSO events, it is the longer duration of the former which poses its own set of problems. Thus, there is an urgent need to investigate the potential to forecast ‘protracted’ ENSO episodes, particularly when the mid-2020 to current 2022 period has been experiencing a major ‘protracted’ La Niña episode with near-global impacts.
Abstract
A unique, high resolution, hydroclimate re-analysis, 40-plus-year (October 1979 – September 2021) 4-km (named as CONUS404), has been created using the Weather Research and Forecast model by dynamically downscaling of the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous U.S. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)-U. S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous U.S. The data are archived and accessible on the USGS Black Pearl Tape system and on the NCAR super-computer Campaign storage system.
Abstract
A unique, high resolution, hydroclimate re-analysis, 40-plus-year (October 1979 – September 2021) 4-km (named as CONUS404), has been created using the Weather Research and Forecast model by dynamically downscaling of the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous U.S. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)-U. S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous U.S. The data are archived and accessible on the USGS Black Pearl Tape system and on the NCAR super-computer Campaign storage system.
Abstract
Directional wave spectra are of importance for numerous practical applications such as seafaring and ocean engineering. The wave spectral densities at a certain point in the open ocean are significantly correlated to the local wind field and historical remote wind field. This feature can be used to predict the wave spectrum at that point using the wind field. In this study, a Convolutional Neural Network (CNN) model was established to estimate wave spectra at a target point using the wind field from the ERA5 dataset. A geospatial range where the wind could impact the target point was selected and then the historical wind field data within the range was analyzed to extract the nonlinear quantitative relationships between wind fields and wave spectra. For the spectral densities at a given direction, the wind data along the direction where waves come from were used as the input of the CNN. The model was trained to minimize the Mean Square Error (MSE) between the CNN-predicted and ERA5 re-analysis spectral density. The data structure of the wind input is reorganized into a polar grid centered on the target point to make the model applicable to different open-ocean locations worldwide. The results show that the model can well predict the wave spectrum shapes and integral wave parameters. The model allows for the prediction of single-point wave spectra in the open ocean with low computational cost and can be helpful for the study of spectral wave climate.
Abstract
Directional wave spectra are of importance for numerous practical applications such as seafaring and ocean engineering. The wave spectral densities at a certain point in the open ocean are significantly correlated to the local wind field and historical remote wind field. This feature can be used to predict the wave spectrum at that point using the wind field. In this study, a Convolutional Neural Network (CNN) model was established to estimate wave spectra at a target point using the wind field from the ERA5 dataset. A geospatial range where the wind could impact the target point was selected and then the historical wind field data within the range was analyzed to extract the nonlinear quantitative relationships between wind fields and wave spectra. For the spectral densities at a given direction, the wind data along the direction where waves come from were used as the input of the CNN. The model was trained to minimize the Mean Square Error (MSE) between the CNN-predicted and ERA5 re-analysis spectral density. The data structure of the wind input is reorganized into a polar grid centered on the target point to make the model applicable to different open-ocean locations worldwide. The results show that the model can well predict the wave spectrum shapes and integral wave parameters. The model allows for the prediction of single-point wave spectra in the open ocean with low computational cost and can be helpful for the study of spectral wave climate.
Abstract
In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼ 20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that non-linear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012) — one with a semi-permanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer — shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 while, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback — together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first–time” ice-free Arctic.
Abstract
In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼ 20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that non-linear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012) — one with a semi-permanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer — shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 while, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback — together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first–time” ice-free Arctic.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA and, surprisingly, we find that it depends directly on the number of independent observations, but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA and, surprisingly, we find that it depends directly on the number of independent observations, but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Abstract
The Madden–Julian oscillation (MJO) exhibits pronounced seasonality, with one of the key unanswered questions being the following: what controls the maximum in MJO precipitation variance in the Southern Hemisphere during boreal winter? In this study, we examine a set of global climate model simulations in which the eccentricity and precession of Earth’s orbit are altered to change the boreal winter mean state in an attempt to reveal the processes that are responsible for the MJO’s amplitude in the boreal winter. In response to the forced insolation changes, the north–south asymmetry in sea surface temperature is amplified in boreal fall, which intensifies the Hadley circulation in boreal winter. The stronger Hadley circulation yields higher mean precipitation and stronger mean lower-tropospheric westerlies in the southern part of the Indo-Pacific warm pool. The MJO precipitation variability increases significantly where the mean precipitation and lower-tropospheric westerlies strengthen. In the column-integrated moisture budget of the simulated MJO, only surface latent heat flux feedback shows a trend that is consistent with the MJO’s amplitude, suggesting an important role for the surface latent heat flux feedback in the MJO’s amplitude during the boreal winter. An analysis of the moisture–precipitation relationship in the simulations shows that the increase in the mean precipitation lowers the convective moisture adjustment time scale, leading to the increase in precipitation variance. Our results suggest that the mean-state precipitation plays a critical role in the maintenance mechanism of the MJO.
Abstract
The Madden–Julian oscillation (MJO) exhibits pronounced seasonality, with one of the key unanswered questions being the following: what controls the maximum in MJO precipitation variance in the Southern Hemisphere during boreal winter? In this study, we examine a set of global climate model simulations in which the eccentricity and precession of Earth’s orbit are altered to change the boreal winter mean state in an attempt to reveal the processes that are responsible for the MJO’s amplitude in the boreal winter. In response to the forced insolation changes, the north–south asymmetry in sea surface temperature is amplified in boreal fall, which intensifies the Hadley circulation in boreal winter. The stronger Hadley circulation yields higher mean precipitation and stronger mean lower-tropospheric westerlies in the southern part of the Indo-Pacific warm pool. The MJO precipitation variability increases significantly where the mean precipitation and lower-tropospheric westerlies strengthen. In the column-integrated moisture budget of the simulated MJO, only surface latent heat flux feedback shows a trend that is consistent with the MJO’s amplitude, suggesting an important role for the surface latent heat flux feedback in the MJO’s amplitude during the boreal winter. An analysis of the moisture–precipitation relationship in the simulations shows that the increase in the mean precipitation lowers the convective moisture adjustment time scale, leading to the increase in precipitation variance. Our results suggest that the mean-state precipitation plays a critical role in the maintenance mechanism of the MJO.
Abstract
This study examines the changes in the outer size distribution of landfalling tropical cyclones (TCs) over Chinese mainland from 1977 to 2020. The period was divided into two epochs: 1977-1998 and 1999-2020. The results show that the size distribution of landfalling TCs over South China has no apparent change, while that of landfalling TCs over East China (LTCEC) is narrower in the second epoch, and the difference in the median sizes between East China and South China become more significant. Furthermore, it is found that LTCEC formed over the western part of the western North Pacific (W-WNP) shifted to a larger size range (300-500 km) at landfall, while those formed over the eastern part of the western North Pacific (E-WNP) rarely grew to extremely large size (>500 km).
Further investigation revealed that over the W-WNP, the genesis position of LTCEC migrated equatorward during the second epoch, leading to a longer TC lifetime before landfall. Also, the increase of background relative vorticity and moisture associated with the southward migration is conducive to larger initial vortices. For TCs originating from the E-WNP, the change in the active area of TC passages reduced the frequency of TCs affecting the Chinese coast. Moreover, the growth of TC size during the intensification stage was significantly suppressed, lowering the occurrence probability of extremely large TCs. Changes in the large-scale thermodynamic environments between the two epochs were explored. Increased static stability and decreased convective available potential energy are possible factors limiting TC size increase.
Abstract
This study examines the changes in the outer size distribution of landfalling tropical cyclones (TCs) over Chinese mainland from 1977 to 2020. The period was divided into two epochs: 1977-1998 and 1999-2020. The results show that the size distribution of landfalling TCs over South China has no apparent change, while that of landfalling TCs over East China (LTCEC) is narrower in the second epoch, and the difference in the median sizes between East China and South China become more significant. Furthermore, it is found that LTCEC formed over the western part of the western North Pacific (W-WNP) shifted to a larger size range (300-500 km) at landfall, while those formed over the eastern part of the western North Pacific (E-WNP) rarely grew to extremely large size (>500 km).
Further investigation revealed that over the W-WNP, the genesis position of LTCEC migrated equatorward during the second epoch, leading to a longer TC lifetime before landfall. Also, the increase of background relative vorticity and moisture associated with the southward migration is conducive to larger initial vortices. For TCs originating from the E-WNP, the change in the active area of TC passages reduced the frequency of TCs affecting the Chinese coast. Moreover, the growth of TC size during the intensification stage was significantly suppressed, lowering the occurrence probability of extremely large TCs. Changes in the large-scale thermodynamic environments between the two epochs were explored. Increased static stability and decreased convective available potential energy are possible factors limiting TC size increase.
Abstract
In 2018, Hurricanes Florence and Michael affected the southeastern portion of the United States, with widespread impacts in Florida, North Carolina, South Carolina, Georgia, and Virginia. The two storms were markedly different in terms of their meteorological history: Hurricane Florence made landfall as a category-1 storm approximately 2 weeks after formation, whereas Hurricane Michael made landfall as an “unprecedented” category-5 storm just 3 days after formation. The stark meteorological differences provided the opportunity to explore whether and to what extent public attention is influenced by storm severity. This study utilized both direct (i.e., tweet volume and search volume) and indirect (i.e., number of newspaper articles) measures to explore public attention at different scales. Data showed that Hurricane Florence received more attention than Hurricane Michael, both regionally and nationally, across all three measures. The findings also underscore the importance of time for the process of attention building, especially at the national level. Taken together, the results suggest that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways. The paper concludes with some opportunities for research that may provide additional insights into the linkages between attention, perception, and decision-making.
Significance Statement
The purpose of this study was to better understand the factors that influence public attention to extreme weather. This is important because attention is often noted for its mediating effect on decision-making. We found that public attention was greater during Hurricane Florence, despite the fact that Hurricane Michael was an “unprecedented” category-5 storm. Taken together, this suggests that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways.
Abstract
In 2018, Hurricanes Florence and Michael affected the southeastern portion of the United States, with widespread impacts in Florida, North Carolina, South Carolina, Georgia, and Virginia. The two storms were markedly different in terms of their meteorological history: Hurricane Florence made landfall as a category-1 storm approximately 2 weeks after formation, whereas Hurricane Michael made landfall as an “unprecedented” category-5 storm just 3 days after formation. The stark meteorological differences provided the opportunity to explore whether and to what extent public attention is influenced by storm severity. This study utilized both direct (i.e., tweet volume and search volume) and indirect (i.e., number of newspaper articles) measures to explore public attention at different scales. Data showed that Hurricane Florence received more attention than Hurricane Michael, both regionally and nationally, across all three measures. The findings also underscore the importance of time for the process of attention building, especially at the national level. Taken together, the results suggest that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways. The paper concludes with some opportunities for research that may provide additional insights into the linkages between attention, perception, and decision-making.
Significance Statement
The purpose of this study was to better understand the factors that influence public attention to extreme weather. This is important because attention is often noted for its mediating effect on decision-making. We found that public attention was greater during Hurricane Florence, despite the fact that Hurricane Michael was an “unprecedented” category-5 storm. Taken together, this suggests that storm severity, forecast lead time, previous meteorological history, and population density intersect with one another to influence public attention in complex ways.
