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- Author or Editor: Fan Wang x
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Abstract
The subtropical oceans between 35° and 20°S in the Southern Hemisphere (SH) have exhibited prevailingly rapid sea level rise (SLR) rates since the mid-twentieth century, amplifying damages of coastal hazards and exerting increasing threats to South America, Africa, and Australia. Yet, mechanisms of the observed SLR have not been firmly established, and its representation in climate models has not been examined. By analyzing observational sea level estimates, ocean reanalysis products, and ocean model hindcasts, we show that the steric SLR of the SH subtropical oceans between 35° and 20°S is faster than the global mean rate by 18.2% ± 9.9% during 1958–2014. However, present climate models—the fundamental bases for future climate projections—generally fail to reproduce this feature. Further analysis suggests that the rapid SLR in the SH subtropical oceans is primarily attributable to the persistent upward trend of the southern annular mode (SAM). Physically, this trend in SAM leads to the strengthening of the SH subtropical highs, with the strongest signatures observed in the southern Indian Ocean. These changes in atmospheric circulation promote regional SLR in the SH subtropics by driving upper-ocean convergence. Climate models show systematic biases in the simulated structure and trend magnitude of SAM and significantly underestimate the enhancement of subtropical highs. These biases lead to the inability of models to correctly simulate the observed subtropical SLR. This work highlights the paramount necessity of reducing model biases to provide reliable regional sea level projections.
Abstract
The subtropical oceans between 35° and 20°S in the Southern Hemisphere (SH) have exhibited prevailingly rapid sea level rise (SLR) rates since the mid-twentieth century, amplifying damages of coastal hazards and exerting increasing threats to South America, Africa, and Australia. Yet, mechanisms of the observed SLR have not been firmly established, and its representation in climate models has not been examined. By analyzing observational sea level estimates, ocean reanalysis products, and ocean model hindcasts, we show that the steric SLR of the SH subtropical oceans between 35° and 20°S is faster than the global mean rate by 18.2% ± 9.9% during 1958–2014. However, present climate models—the fundamental bases for future climate projections—generally fail to reproduce this feature. Further analysis suggests that the rapid SLR in the SH subtropical oceans is primarily attributable to the persistent upward trend of the southern annular mode (SAM). Physically, this trend in SAM leads to the strengthening of the SH subtropical highs, with the strongest signatures observed in the southern Indian Ocean. These changes in atmospheric circulation promote regional SLR in the SH subtropics by driving upper-ocean convergence. Climate models show systematic biases in the simulated structure and trend magnitude of SAM and significantly underestimate the enhancement of subtropical highs. These biases lead to the inability of models to correctly simulate the observed subtropical SLR. This work highlights the paramount necessity of reducing model biases to provide reliable regional sea level projections.
Abstract
Variability of oceanic salinity, an indicator of the global hydrological cycle, plays an important role in the basin-scale ocean circulation. In this study, interannual to decadal variability of salinity in the upper layer of the Indian Ocean is investigated using Argo observations since 2004 and data assimilating model outputs (1992–2015). The southeastern Indian Ocean shows the strongest interannual to decadal variability of upper-ocean salinity in the Indian Ocean. Westward propagation of salinity anomalies along isopycnal surfaces is detected in the southern Indian Ocean and attributed to zonal salinity advection anomalies associated with the Indonesian Throughflow and the South Equatorial Current. Composite and salinity budget analyses show that horizontal advection is a major contributor to the interannual to decadal salinity variability of the southern Indian Ocean, and the local air–sea freshwater flux plays a secondary role. The Pacific decadal oscillation (PDO) and El Niño–Southern Oscillation (ENSO) modulate the salinity variability in the southeastern Indian Ocean, with low salinity anomalies occurring during the negative phases of the PDO and ENSO and high salinity anomalies during their positive phases. The Indonesian Throughflow plays an essential role in transmitting the PDO- and ENSO-related salinity signals into the Indian Ocean. A statistical model is proposed based on the PDO index, which successfully predicts the southeastern Indian Ocean salinity variability with a lead time of 10 months.
Abstract
Variability of oceanic salinity, an indicator of the global hydrological cycle, plays an important role in the basin-scale ocean circulation. In this study, interannual to decadal variability of salinity in the upper layer of the Indian Ocean is investigated using Argo observations since 2004 and data assimilating model outputs (1992–2015). The southeastern Indian Ocean shows the strongest interannual to decadal variability of upper-ocean salinity in the Indian Ocean. Westward propagation of salinity anomalies along isopycnal surfaces is detected in the southern Indian Ocean and attributed to zonal salinity advection anomalies associated with the Indonesian Throughflow and the South Equatorial Current. Composite and salinity budget analyses show that horizontal advection is a major contributor to the interannual to decadal salinity variability of the southern Indian Ocean, and the local air–sea freshwater flux plays a secondary role. The Pacific decadal oscillation (PDO) and El Niño–Southern Oscillation (ENSO) modulate the salinity variability in the southeastern Indian Ocean, with low salinity anomalies occurring during the negative phases of the PDO and ENSO and high salinity anomalies during their positive phases. The Indonesian Throughflow plays an essential role in transmitting the PDO- and ENSO-related salinity signals into the Indian Ocean. A statistical model is proposed based on the PDO index, which successfully predicts the southeastern Indian Ocean salinity variability with a lead time of 10 months.
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
Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.
Abstract
Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.
Abstract
The principal modes of the diurnal cycle of rainfall (DCR) over South China during the presummer rainy season are examined using 23-yr satellite observations and reanalysis data. Three distinctly different DCR modes are identified via empirical orthogonal function analysis, that is, the early-afternoon precipitation (EAP) mode, the late-afternoon precipitation (LAP) mode, and the morning precipitation (MP) mode. Under the EAP mode, the rainfall starts to increase from midnight and reaches its peak in the early afternoon. The nocturnal to morning rainfall generally concentrates on the northeastern Pearl River delta (PRD) and along the coastline. The coastal rainfall is initiated from the convergence zone induced by the strong onshore wind and is further enhanced via the establishment of a land breeze in the early morning. The northeastern PRD center is mainly attributed to the windward mechanical lifting associated with the strong low-level wind. The afternoon rainfall is pronounced over inland areas and exhibits significantly regional diversity. The eastern inland rainfall develops from the early-morning rainfall over the northeastern PRD, whereas the eastward-propagating rain belts associated with frontal activities are responsible for the formation of western inland rainfall. The LAP mode features a late-afternoon peak, which is triggered and developed locally with favorable thermal–dynamic conditions over western inland South China. The MP mode exhibits a single early-morning peak. Nocturnal to morning rainfall is prominent on the northeastern PRD and near-offshore region. The near-offshore rainfall is basically induced by the convergence between the onshore wind and land breeze in the early morning, which further propagates far offshore in the morning due to effects of gravity waves.
Abstract
The principal modes of the diurnal cycle of rainfall (DCR) over South China during the presummer rainy season are examined using 23-yr satellite observations and reanalysis data. Three distinctly different DCR modes are identified via empirical orthogonal function analysis, that is, the early-afternoon precipitation (EAP) mode, the late-afternoon precipitation (LAP) mode, and the morning precipitation (MP) mode. Under the EAP mode, the rainfall starts to increase from midnight and reaches its peak in the early afternoon. The nocturnal to morning rainfall generally concentrates on the northeastern Pearl River delta (PRD) and along the coastline. The coastal rainfall is initiated from the convergence zone induced by the strong onshore wind and is further enhanced via the establishment of a land breeze in the early morning. The northeastern PRD center is mainly attributed to the windward mechanical lifting associated with the strong low-level wind. The afternoon rainfall is pronounced over inland areas and exhibits significantly regional diversity. The eastern inland rainfall develops from the early-morning rainfall over the northeastern PRD, whereas the eastward-propagating rain belts associated with frontal activities are responsible for the formation of western inland rainfall. The LAP mode features a late-afternoon peak, which is triggered and developed locally with favorable thermal–dynamic conditions over western inland South China. The MP mode exhibits a single early-morning peak. Nocturnal to morning rainfall is prominent on the northeastern PRD and near-offshore region. The near-offshore rainfall is basically induced by the convergence between the onshore wind and land breeze in the early morning, which further propagates far offshore in the morning due to effects of gravity waves.
Abstract
The identification of the world’s hottest and coldest cities fascinates both the public and the scientific community. However, the ranking of city temperatures, especially from the perspective of human discomfort, has been difficult. Here we estimated the monthly mean maximum and minimum 1-km resolution urban temperatures of 13,135 cities worldwide (2003–19) from the thermal discomfort perspective by combining in situ measurements, satellite-based land surface temperatures, fine-resolution intracity data, and reanalysis data. Manama, Bahrain, was identified as the hottest city (48.18° ± 1.31°C) and Yakutsk, Russia (−42.96° ± 0.72°C), as the coldest city. The global city temperatures followed a power-law pattern, characterized by cities with <0.3 million inhabitants covering 80% of the top 20% global cities with extreme temperatures. Our study reveals an inequitable pattern of global city temperature extremes and highlights the urgency of developing appropriate strategies to reduce climate change risks in small- and medium-sized cities with low development levels.
Abstract
The identification of the world’s hottest and coldest cities fascinates both the public and the scientific community. However, the ranking of city temperatures, especially from the perspective of human discomfort, has been difficult. Here we estimated the monthly mean maximum and minimum 1-km resolution urban temperatures of 13,135 cities worldwide (2003–19) from the thermal discomfort perspective by combining in situ measurements, satellite-based land surface temperatures, fine-resolution intracity data, and reanalysis data. Manama, Bahrain, was identified as the hottest city (48.18° ± 1.31°C) and Yakutsk, Russia (−42.96° ± 0.72°C), as the coldest city. The global city temperatures followed a power-law pattern, characterized by cities with <0.3 million inhabitants covering 80% of the top 20% global cities with extreme temperatures. Our study reveals an inequitable pattern of global city temperature extremes and highlights the urgency of developing appropriate strategies to reduce climate change risks in small- and medium-sized cities with low development levels.
Abstract
As the second-largest shifting sand desert worldwide, the Taklimakan Desert (TD) represents the typical aeolian landforms in arid regions as an important source of global dust aerosols. It directly affects the ecological environment and human health across East Asia. Thus, establishing a comprehensive environment and climate observation network for field research in the TD region is essential to improve our understanding of the desert meteorology and environment, assess its impact, mitigate potential environmental issues, and promote sustainable development. With a nearly 20-yr effort under the extremely harsh conditions of the TD, the Desert Environment and Climate Observation Network (DECON) has been established completely covering the TD region. The core of DECON is the Tazhong station in the hinterland of the TD. Moreover, the network also includes 4 satellite stations located along the edge of the TD for synergistic observations, and 18 automatic weather stations interspersed between them. Thus, DECON marks a new chapter of environmental and meteorological observation capabilities over the TD, including dust storms, dust emission and transport mechanisms, desert land–atmosphere interactions, desert boundary layer structure, ground calibration for remote sensing monitoring, and desert carbon sinks. In addition, DECON promotes cooperation and communication within the research community in the field of desert environments and climate, which promotes a better understanding of the status and role of desert ecosystems. Finally, DECON is expected to provide the basic support necessary for coordinated environmental and meteorological monitoring and mitigation, joint construction of ecologically friendly communities, and sustainable development of central Asia.
Abstract
As the second-largest shifting sand desert worldwide, the Taklimakan Desert (TD) represents the typical aeolian landforms in arid regions as an important source of global dust aerosols. It directly affects the ecological environment and human health across East Asia. Thus, establishing a comprehensive environment and climate observation network for field research in the TD region is essential to improve our understanding of the desert meteorology and environment, assess its impact, mitigate potential environmental issues, and promote sustainable development. With a nearly 20-yr effort under the extremely harsh conditions of the TD, the Desert Environment and Climate Observation Network (DECON) has been established completely covering the TD region. The core of DECON is the Tazhong station in the hinterland of the TD. Moreover, the network also includes 4 satellite stations located along the edge of the TD for synergistic observations, and 18 automatic weather stations interspersed between them. Thus, DECON marks a new chapter of environmental and meteorological observation capabilities over the TD, including dust storms, dust emission and transport mechanisms, desert land–atmosphere interactions, desert boundary layer structure, ground calibration for remote sensing monitoring, and desert carbon sinks. In addition, DECON promotes cooperation and communication within the research community in the field of desert environments and climate, which promotes a better understanding of the status and role of desert ecosystems. Finally, DECON is expected to provide the basic support necessary for coordinated environmental and meteorological monitoring and mitigation, joint construction of ecologically friendly communities, and sustainable development of central Asia.
Abstract
Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.
Abstract
Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.
Abstract
The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP’s precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies’ preparation for future environmental challenges. Some key issues for future TP studies are also discussed.
Abstract
The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP’s precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies’ preparation for future environmental challenges. Some key issues for future TP studies are also discussed.
