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- Author or Editor: Sang-Woo Kim x
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Abstract
It is known that an increase of water vapor over the Arctic is one of most plausible causes driving Arctic amplification. However, debate continues with regard to the explanation of the underlying mechanisms driving the increase of moisture over the Arctic region in the observations. Here, we used the Community Atmosphere Model with prescribed sea surface temperature along with reanalysis datasets to examine the role of fast atmospheric responses to the increase of anthropogenic sulfate aerosol concentrations in China. We found that it plays an additive role in moisture transport from the midlatitudes, resulting in warming of the Arctic region, especially around the Barents–Kara Seas. Specifically, sulfate aerosol forcing in China reduces the meridional temperature gradient and leads to the increase of moisture transport into the Arctic by altering atmospheric circulation. The resulting increase of moisture then leads to surface warming through the enhancement of the downwelling longwave radiation. This implies that Arctic warming around the Barents–Kara Seas has been accelerated, at least in part, by a fast atmospheric response to anthropogenic sulfate aerosol emissions in China in the recent past.
Abstract
It is known that an increase of water vapor over the Arctic is one of most plausible causes driving Arctic amplification. However, debate continues with regard to the explanation of the underlying mechanisms driving the increase of moisture over the Arctic region in the observations. Here, we used the Community Atmosphere Model with prescribed sea surface temperature along with reanalysis datasets to examine the role of fast atmospheric responses to the increase of anthropogenic sulfate aerosol concentrations in China. We found that it plays an additive role in moisture transport from the midlatitudes, resulting in warming of the Arctic region, especially around the Barents–Kara Seas. Specifically, sulfate aerosol forcing in China reduces the meridional temperature gradient and leads to the increase of moisture transport into the Arctic by altering atmospheric circulation. The resulting increase of moisture then leads to surface warming through the enhancement of the downwelling longwave radiation. This implies that Arctic warming around the Barents–Kara Seas has been accelerated, at least in part, by a fast atmospheric response to anthropogenic sulfate aerosol emissions in China in the recent past.
Abstract
The Arctic summer sea ice area has been rapidly decreasing in recent decades. In addition to this trend, substantial interannual variability is present, as is highlighted by the recovery in sea ice area in 2013 following the record minimum in 2012. This interannual variability of the Arctic summer sea ice area has been attributed to the springtime weather disturbances. Here, by utilizing reanalysis- and satellite-based sea ice data, this study shows that summers with unusually small sea ice area are preceded by winters with anomalously strong downward longwave radiation over the Eurasian sector of the Arctic Ocean. This anomalous wintertime radiative forcing at the surface is up to 10–15 W m−2, which is about twice as strong than that during the spring. During the same winters, the poleward moisture and warm-air intrusions into the Eurasian sector of the Arctic Ocean are anomalously strong and the resulting moisture convergence field closely resembles positive anomalies in column-integrated water vapor and tropospheric temperature.
Climate model simulations support the above-mentioned findings and further show that the anomalously strong wintertime radiative forcing can decrease sea ice thickness over wide areas of the Arctic Ocean, especially over the Eurasian sector. During the winters preceding the anomalously small summer sea ice area, the upper ocean of the model is anomalously warm over the Barents Sea, indicating that the upper-ocean heat content contributes to winter sea ice thinning. Finally, mass divergence by ice drift in the preceding winter and spring contributes to the thinning of sea ice over the East Siberian and Chukchi Seas, where radiative forcing and upper-ocean heat content anomalies are relatively weak.
Abstract
The Arctic summer sea ice area has been rapidly decreasing in recent decades. In addition to this trend, substantial interannual variability is present, as is highlighted by the recovery in sea ice area in 2013 following the record minimum in 2012. This interannual variability of the Arctic summer sea ice area has been attributed to the springtime weather disturbances. Here, by utilizing reanalysis- and satellite-based sea ice data, this study shows that summers with unusually small sea ice area are preceded by winters with anomalously strong downward longwave radiation over the Eurasian sector of the Arctic Ocean. This anomalous wintertime radiative forcing at the surface is up to 10–15 W m−2, which is about twice as strong than that during the spring. During the same winters, the poleward moisture and warm-air intrusions into the Eurasian sector of the Arctic Ocean are anomalously strong and the resulting moisture convergence field closely resembles positive anomalies in column-integrated water vapor and tropospheric temperature.
Climate model simulations support the above-mentioned findings and further show that the anomalously strong wintertime radiative forcing can decrease sea ice thickness over wide areas of the Arctic Ocean, especially over the Eurasian sector. During the winters preceding the anomalously small summer sea ice area, the upper ocean of the model is anomalously warm over the Barents Sea, indicating that the upper-ocean heat content contributes to winter sea ice thinning. Finally, mass divergence by ice drift in the preceding winter and spring contributes to the thinning of sea ice over the East Siberian and Chukchi Seas, where radiative forcing and upper-ocean heat content anomalies are relatively weak.
The Estimation of the Total Freeboard of Arctic Sea Ice in Winter Using Passive Microwave Satellite MeasurementsAbstract
A method for estimating the total freeboard hf of the sea ice in the Arctic basin was developed in this study. To utilize the dielectric properties of microwave measurements on the sea ice freeboard, we adopted the spectral difference between the microwave frequencies (e.g., the gradient ratio). Satellite lidar altimetry data were utilized as a reference, and two pairs of gradient ratios [GR(36.5, 18.7) and GR(10.7, 6.9)] and an optional brightness temperature for first-year ice [that is, TB H (6.9)] were converted from passive microwave sensors into hf using the multiple linear regression equation. Using this method, we estimated hf without direct altimetry measurements. The developed method was evaluated using Operation IceBridge data and the relationship between the regressed hf and Operation IceBridge hf had a correlation coefficient R of 0.761 and a nearly unbiased (approximately −0.4 cm) pattern. Because passive microwave measurements are taken in the Arctic daily, the approach presented in this study has the potential to enable daily hf estimations for the Arctic.
Significance Statement
Arctic sea ice is one of the most critical indicators when monitoring climate change. Precise and continuous observations of sea ice thickness are essential to understand Arctic sea ice. This study attempts to estimate sea ice thickness using passive microwave satellite data. Passive microwave satellite observations are advantageous because of their wide spatial coverage and long-term records. Therefore, the suggested method in this study can be used for filling in gaps in coverage between sea ice thickness estimates from L-band radiometry and radar/lidar altimetry. The total freeboard is proportional to the thickness of sea ice, which is converted into thickness using the hydrostatic equation. The estimated total freeboard during two winter periods (2018/19 and 2019/20) demonstrates a plausible geographical distribution over the Arctic and indicates good agreement with airborne measurements.
Abstract
A method for estimating the total freeboard hf of the sea ice in the Arctic basin was developed in this study. To utilize the dielectric properties of microwave measurements on the sea ice freeboard, we adopted the spectral difference between the microwave frequencies (e.g., the gradient ratio). Satellite lidar altimetry data were utilized as a reference, and two pairs of gradient ratios [GR(36.5, 18.7) and GR(10.7, 6.9)] and an optional brightness temperature for first-year ice [that is, TB H (6.9)] were converted from passive microwave sensors into hf using the multiple linear regression equation. Using this method, we estimated hf without direct altimetry measurements. The developed method was evaluated using Operation IceBridge data and the relationship between the regressed hf and Operation IceBridge hf had a correlation coefficient R of 0.761 and a nearly unbiased (approximately −0.4 cm) pattern. Because passive microwave measurements are taken in the Arctic daily, the approach presented in this study has the potential to enable daily hf estimations for the Arctic.
Significance Statement
Arctic sea ice is one of the most critical indicators when monitoring climate change. Precise and continuous observations of sea ice thickness are essential to understand Arctic sea ice. This study attempts to estimate sea ice thickness using passive microwave satellite data. Passive microwave satellite observations are advantageous because of their wide spatial coverage and long-term records. Therefore, the suggested method in this study can be used for filling in gaps in coverage between sea ice thickness estimates from L-band radiometry and radar/lidar altimetry. The total freeboard is proportional to the thickness of sea ice, which is converted into thickness using the hydrostatic equation. The estimated total freeboard during two winter periods (2018/19 and 2019/20) demonstrates a plausible geographical distribution over the Arctic and indicates good agreement with airborne measurements.
Abstract
The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).
Abstract
The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).
Abstract
To estimate global aerosol radiative forcing, measurements of aerosol optical properties are made by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL)’s Global Monitoring Division (GMD) and their collaborators at 30 monitoring locations around the world. Many of the sites are located in regions influenced by specific aerosol types (Asian and Saharan desert dust, Asian pollution, biomass burning, etc.). This network of monitoring stations is a shared endeavor of NOAA and many collaborating organizations, including the World Meteorological Organization (WMO)’s Global Atmosphere Watch (GAW) program, the U.S. Department of Energy (DOE), several U.S. and foreign universities, and foreign science organizations. The result is a long-term cooperative program making atmospheric measurements that are directly comparable with those from all the other network stations and with shared data access. The protocols and software developed to support the program facilitate participation in GAW’s atmospheric observation strategy, and the sites in the NOAA/ESRL network make up a substantial subset of the GAW aerosol observations. This paper describes the history of the NOAA/ESRL Federated Aerosol Network, details about measurements and operations, and some recent findings from the network measurements.
Abstract
To estimate global aerosol radiative forcing, measurements of aerosol optical properties are made by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL)’s Global Monitoring Division (GMD) and their collaborators at 30 monitoring locations around the world. Many of the sites are located in regions influenced by specific aerosol types (Asian and Saharan desert dust, Asian pollution, biomass burning, etc.). This network of monitoring stations is a shared endeavor of NOAA and many collaborating organizations, including the World Meteorological Organization (WMO)’s Global Atmosphere Watch (GAW) program, the U.S. Department of Energy (DOE), several U.S. and foreign universities, and foreign science organizations. The result is a long-term cooperative program making atmospheric measurements that are directly comparable with those from all the other network stations and with shared data access. The protocols and software developed to support the program facilitate participation in GAW’s atmospheric observation strategy, and the sites in the NOAA/ESRL network make up a substantial subset of the GAW aerosol observations. This paper describes the history of the NOAA/ESRL Federated Aerosol Network, details about measurements and operations, and some recent findings from the network measurements.
