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- Author or Editor: Jia Hu x
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Abstract
The current work investigated the interdecadal changes in the leading empirical orthogonal function (EOF) pattern of the interannual variation in spring [March–May (MAM)] snow-cover extent (SCE) over the Tibetan Plateau (TP) (SSC_TP). The leading EOF pattern of the SSC_TP is transformed from an east to west dipole pattern during the period 1970–89 (P1) to a monopole structure during the period 1991–2020 (P2). Observational analysis shows that during P1, the negative Antarctic Oscillation (AAO) (−AAO) is associated with low-level cross-equator southeasterly anomalies across the Bay of Bengal and transports more water vapor to the eastern TP. Moreover, at a high level, anomalous northerly winds accompanied by an anomalous sinking motion dominate the western TP, favoring an east-wet–west-dry dipole pattern of SSC_TP. Further analysis shows that the −AAO induces anomalous divergence over the Antarctic, which contributes to the formation of a Rossby wave source (RWS). This RWS is related to a northeastward-propagating atmospheric wave train that crosses the equator and contributes to the SSC_TP variation during P1. In contrast, in P2, the Arctic Oscillation (AO) is associated with a barotropic atmospheric wave train originating from southern Greenland, moving across the North Atlantic Ocean and North Africa and reaching the TP. This wave train results in significant positive vorticity and ascending airflow above the TP and favors a monopole pattern of the SSC_TP. Further analysis shows that the AO can induce divergence anomalies over southeastern Greenland and RWS anomalies there. This RWS induces an atmospheric wave train that propagates eastward and reaches the TP during P2. The above mechanisms have been supported by the results of numerical experiments performed using the linear baroclinic model.
Abstract
The current work investigated the interdecadal changes in the leading empirical orthogonal function (EOF) pattern of the interannual variation in spring [March–May (MAM)] snow-cover extent (SCE) over the Tibetan Plateau (TP) (SSC_TP). The leading EOF pattern of the SSC_TP is transformed from an east to west dipole pattern during the period 1970–89 (P1) to a monopole structure during the period 1991–2020 (P2). Observational analysis shows that during P1, the negative Antarctic Oscillation (AAO) (−AAO) is associated with low-level cross-equator southeasterly anomalies across the Bay of Bengal and transports more water vapor to the eastern TP. Moreover, at a high level, anomalous northerly winds accompanied by an anomalous sinking motion dominate the western TP, favoring an east-wet–west-dry dipole pattern of SSC_TP. Further analysis shows that the −AAO induces anomalous divergence over the Antarctic, which contributes to the formation of a Rossby wave source (RWS). This RWS is related to a northeastward-propagating atmospheric wave train that crosses the equator and contributes to the SSC_TP variation during P1. In contrast, in P2, the Arctic Oscillation (AO) is associated with a barotropic atmospheric wave train originating from southern Greenland, moving across the North Atlantic Ocean and North Africa and reaching the TP. This wave train results in significant positive vorticity and ascending airflow above the TP and favors a monopole pattern of the SSC_TP. Further analysis shows that the AO can induce divergence anomalies over southeastern Greenland and RWS anomalies there. This RWS induces an atmospheric wave train that propagates eastward and reaches the TP during P2. The above mechanisms have been supported by the results of numerical experiments performed using the linear baroclinic model.
Abstract
Central Africa (CA) is identified as a location of a large positive trend of the occurrence of heat waves (HWs) during 1979–2016, appearing to result mostly from a regime shift around the year 2000. Therefore, we study the evolution of synoptic features associated with the occurrence of HW events in CA. It is found that the HW-related circulation is typically characterized by an anomalous convergence in the upper troposphere but there are important differences for HW events occurring in the south region of CA (CA_S) versus the north region (CA_N). For the occurrence of the HW events in CA_S, the anomalous subsidence associated with upper troposphere anomalous convergence is the dominant factor for their occurrence and magnitude: the strong subsidence leads to warming through greater solar insolation. The HW events in CA_S are also accompanied by an anomalous surface anticyclone in the north with anomalous northerly flow transporting heat into the CA_S region. In contrast, although the HW events in CA_N are also associated with upper troposphere anomalous convergence, the intensity of the convergence is weak with small solar insolation. Instead, the anomalous warm advection is the main factor for determining the magnitude of the HW events in CA_N, induced by the prevailing northerly winds acting on the anomalous temperature gradient. Thus, the synoptic features associated with HW events in the CA_N and CA_S are quite different despite their nearby locations. The discovered dominant factors for the HW events in CA can be used to improve the forecast skill.
Abstract
Central Africa (CA) is identified as a location of a large positive trend of the occurrence of heat waves (HWs) during 1979–2016, appearing to result mostly from a regime shift around the year 2000. Therefore, we study the evolution of synoptic features associated with the occurrence of HW events in CA. It is found that the HW-related circulation is typically characterized by an anomalous convergence in the upper troposphere but there are important differences for HW events occurring in the south region of CA (CA_S) versus the north region (CA_N). For the occurrence of the HW events in CA_S, the anomalous subsidence associated with upper troposphere anomalous convergence is the dominant factor for their occurrence and magnitude: the strong subsidence leads to warming through greater solar insolation. The HW events in CA_S are also accompanied by an anomalous surface anticyclone in the north with anomalous northerly flow transporting heat into the CA_S region. In contrast, although the HW events in CA_N are also associated with upper troposphere anomalous convergence, the intensity of the convergence is weak with small solar insolation. Instead, the anomalous warm advection is the main factor for determining the magnitude of the HW events in CA_N, induced by the prevailing northerly winds acting on the anomalous temperature gradient. Thus, the synoptic features associated with HW events in the CA_N and CA_S are quite different despite their nearby locations. The discovered dominant factors for the HW events in CA can be used to improve the forecast skill.
Abstract
In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of ~8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Niño–Southern Oscillation patterns.
Abstract
In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of ~8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Niño–Southern Oscillation patterns.
Abstract
We used a mesoscale atmospheric model to simulate Typhoon Hagupit (2008) in the South China Sea (SCS). First, we chose optimized parameterization schemes based on a series of sensitivity tests. The results suggested that a combination of the Kain–Fritsch cumulus scheme and the Goddard microphysics scheme was the best choice for reproducing both the track and intensity of Typhoon Hagupit. Next, the simulated rainfall was compared with microwave remote sensing products. This comparison validated the model results for both the magnitude of rainfall and the location of heavy rain relative to the typhoon’s center. Furthermore, the potential vorticity and vertical wind speed displayed the asymmetric horizontal and tilted vertical structures of Typhoon Hagupit. Finally, we compared the simulation of air–sea turbulent fluxes with estimations from an in situ buoy. The time series of momentum fluxes were roughly consistent, while the model still overestimated heat fluxes, especially right before the typhoon’s arrival at the buoy.
Abstract
We used a mesoscale atmospheric model to simulate Typhoon Hagupit (2008) in the South China Sea (SCS). First, we chose optimized parameterization schemes based on a series of sensitivity tests. The results suggested that a combination of the Kain–Fritsch cumulus scheme and the Goddard microphysics scheme was the best choice for reproducing both the track and intensity of Typhoon Hagupit. Next, the simulated rainfall was compared with microwave remote sensing products. This comparison validated the model results for both the magnitude of rainfall and the location of heavy rain relative to the typhoon’s center. Furthermore, the potential vorticity and vertical wind speed displayed the asymmetric horizontal and tilted vertical structures of Typhoon Hagupit. Finally, we compared the simulation of air–sea turbulent fluxes with estimations from an in situ buoy. The time series of momentum fluxes were roughly consistent, while the model still overestimated heat fluxes, especially right before the typhoon’s arrival at the buoy.
Abstract
Satellite and in situ observations in the equatorial Atlantic Ocean during 2002–03 show dominant spectral peaks at 40–60 days and secondary peaks at 10–40 days in sea level and thermocline within the intraseasonal period band (10–80 days). A detailed investigation of the dynamics of the intraseasonal variations is carried out using an ocean general circulation model, namely, the Hybrid Coordinate Ocean Model (HYCOM). Two parallel experiments are performed in the tropical Atlantic Ocean basin for the period 2000–03: one is forced by daily scatterometer winds from the Quick Scatterometer (QuikSCAT) satellite together with other forcing fields, and the other is forced by the low-passed 80-day version of the above fields. To help in understanding the role played by the wind-driven equatorial waves, a linear continuously stratified ocean model is also used.
Within 3°S–3°N of the equatorial region, the strong 40–60-day sea surface height anomaly (SSHA) and thermocline variability result mainly from the first and second baroclinic modes equatorial Kelvin waves that are forced by intraseasonal zonal winds, with the second baroclinic mode playing a more important role. Sharp 40–50-day peaks of zonal and meridional winds appear in both the QuikSCAT and Pilot Research Moored Array in the Tropical Atlantic (PIRATA) data for the period 2002–03, and they are especially strong in 2002. Zonal wind anomaly in the central-western equatorial basin for the period 2000–06 is significantly correlated with SSHA across the equatorial basin, with simultaneous/lag correlation ranging from −0.62 to 0.74 above 95% significance. Away from the equator (3°–5°N), however, sea level and thermocline variations in the 40–60-day band are caused largely by tropical instability waves (TIWs).
On 10–40-day time scales and west of 10°W, the spectral power of sea level and thermocline appears to be dominated by TIWs within 5°S–5°N of the equatorial region. The wind-driven circulation, however, also provides a significant contribution. Interestingly, east of 10°W, SSHA and thermocline variations at 10–40-day periods result almost entirely from wind-driven equatorial waves. During the boreal spring of 2002 when TIWs are weak, Kelvin waves dominate the SSHA across the equatorial basin (2°S–2°N). The observed quasi-biweekly Yanai waves are excited mainly by the quasi-biweekly meridional winds, and they contribute significantly to the SSHA and thermocline variations in 1°–5°N and 1°–5°S regions.
Abstract
Satellite and in situ observations in the equatorial Atlantic Ocean during 2002–03 show dominant spectral peaks at 40–60 days and secondary peaks at 10–40 days in sea level and thermocline within the intraseasonal period band (10–80 days). A detailed investigation of the dynamics of the intraseasonal variations is carried out using an ocean general circulation model, namely, the Hybrid Coordinate Ocean Model (HYCOM). Two parallel experiments are performed in the tropical Atlantic Ocean basin for the period 2000–03: one is forced by daily scatterometer winds from the Quick Scatterometer (QuikSCAT) satellite together with other forcing fields, and the other is forced by the low-passed 80-day version of the above fields. To help in understanding the role played by the wind-driven equatorial waves, a linear continuously stratified ocean model is also used.
Within 3°S–3°N of the equatorial region, the strong 40–60-day sea surface height anomaly (SSHA) and thermocline variability result mainly from the first and second baroclinic modes equatorial Kelvin waves that are forced by intraseasonal zonal winds, with the second baroclinic mode playing a more important role. Sharp 40–50-day peaks of zonal and meridional winds appear in both the QuikSCAT and Pilot Research Moored Array in the Tropical Atlantic (PIRATA) data for the period 2002–03, and they are especially strong in 2002. Zonal wind anomaly in the central-western equatorial basin for the period 2000–06 is significantly correlated with SSHA across the equatorial basin, with simultaneous/lag correlation ranging from −0.62 to 0.74 above 95% significance. Away from the equator (3°–5°N), however, sea level and thermocline variations in the 40–60-day band are caused largely by tropical instability waves (TIWs).
On 10–40-day time scales and west of 10°W, the spectral power of sea level and thermocline appears to be dominated by TIWs within 5°S–5°N of the equatorial region. The wind-driven circulation, however, also provides a significant contribution. Interestingly, east of 10°W, SSHA and thermocline variations at 10–40-day periods result almost entirely from wind-driven equatorial waves. During the boreal spring of 2002 when TIWs are weak, Kelvin waves dominate the SSHA across the equatorial basin (2°S–2°N). The observed quasi-biweekly Yanai waves are excited mainly by the quasi-biweekly meridional winds, and they contribute significantly to the SSHA and thermocline variations in 1°–5°N and 1°–5°S regions.
Abstract
Monitoring changes in river runoff at the Third Pole (TP) is important because rivers in this region support millions of inhabitants in Asia and are very sensitive to climate change. Under the influence of climate change and intensified cryospheric melt, river runoff has changed markedly at the TP, with significant effects on the spatial and temporal water resource distribution that threaten water supply and food security for people living downstream. Despite some in situ observations and discharge estimates from state-of-the-art remote sensing technology, the total river runoff (TRR) for the TP has never been reliably quantified, and its response to climate change remains unclear. As part of the Chinese Academy of Sciences’ “Pan-Third Pole Environment Study for a Green Silk Road,” the TP-River project aims to construct a comprehensive runoff observation network at mountain outlets (where rivers leave the mountains and enter the plains) for 13 major rivers in the TP region, thereby enabling TRR to be accurately quantified. The project also integrates discharge estimates from remote sensing and cryosphere–hydrology modeling to investigate long-term changes in TRR and the relationship between the TRR variations and westerly/monsoon. Based on recent efforts, the project provides the first estimate (656 ± 23 billion m3) of annual TRR for the 13 TP rivers in 2018. The annual river runoff at the mountain outlets varies widely between the different TP rivers, ranging from 2 to 176 billion m3, with higher values mainly corresponding to rivers in the Indian monsoon domain, rather than in the westerly domain.
Abstract
Monitoring changes in river runoff at the Third Pole (TP) is important because rivers in this region support millions of inhabitants in Asia and are very sensitive to climate change. Under the influence of climate change and intensified cryospheric melt, river runoff has changed markedly at the TP, with significant effects on the spatial and temporal water resource distribution that threaten water supply and food security for people living downstream. Despite some in situ observations and discharge estimates from state-of-the-art remote sensing technology, the total river runoff (TRR) for the TP has never been reliably quantified, and its response to climate change remains unclear. As part of the Chinese Academy of Sciences’ “Pan-Third Pole Environment Study for a Green Silk Road,” the TP-River project aims to construct a comprehensive runoff observation network at mountain outlets (where rivers leave the mountains and enter the plains) for 13 major rivers in the TP region, thereby enabling TRR to be accurately quantified. The project also integrates discharge estimates from remote sensing and cryosphere–hydrology modeling to investigate long-term changes in TRR and the relationship between the TRR variations and westerly/monsoon. Based on recent efforts, the project provides the first estimate (656 ± 23 billion m3) of annual TRR for the 13 TP rivers in 2018. The annual river runoff at the mountain outlets varies widely between the different TP rivers, ranging from 2 to 176 billion m3, with higher values mainly corresponding to rivers in the Indian monsoon domain, rather than in the westerly domain.
Abstract
In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system.
Significance Statement
Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.
Abstract
In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system.
Significance Statement
Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.
A four-day educational cruise navigated around the leeward side of Oahu and Kauai to observe the thermodynamic and dynamic features of the trade-wind wakes of these small islands by using weather balloons and other onboard atmospheric and oceanographic sensors. This cruise was proposed, designed, and implemented completely by graduate students from the School of Ocean and Earth Science and Technology at the University of Hawaii. The data collected during the cruise show, for the first time, strong sea/land breezes during day/night and their thermal effects on the island wake. This cruise provided the students with a significant, valuable, and meaningful opportunity to experience the complete process of proposing and undertaking field observations, as well as analyzing data and writing a scientific article.
A four-day educational cruise navigated around the leeward side of Oahu and Kauai to observe the thermodynamic and dynamic features of the trade-wind wakes of these small islands by using weather balloons and other onboard atmospheric and oceanographic sensors. This cruise was proposed, designed, and implemented completely by graduate students from the School of Ocean and Earth Science and Technology at the University of Hawaii. The data collected during the cruise show, for the first time, strong sea/land breezes during day/night and their thermal effects on the island wake. This cruise provided the students with a significant, valuable, and meaningful opportunity to experience the complete process of proposing and undertaking field observations, as well as analyzing data and writing a scientific article.
Abstract
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.
Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.
Abstract
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.
Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.
