Articles | Volume 22, issue 12
https://doi.org/10.5194/acp-22-8151-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-8151-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jun 2022
Research article |
| 23 Jun 2022
| 23 Jun 2022
Impacts of active satellite sensors' low-level cloud detection limitations on cloud radiative forcing in the Arctic
Yinghui Liu
CORRESPONDING AUTHOR
Center for Satellite Applications and Research, NOAA/NESDIS, Madison,
WI, USA
Related authors
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 17, 2871–2889, https://doi.org/10.5194/tc-17-2871-2023, https://doi.org/10.5194/tc-17-2871-2023, 2023
Short summary
Short summary
Sea ice parcels may experience thickness changes primarily through two processes: due to freezing or melting or due to motion relative to other parcels. These processes are independent and will be affected differently in a changing climate. In order to better understand these processes and compare them against models, observational estimates of these process independent from one another are necessary. We present a large spatial- and temporal-scale observational estimate of these processes.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
Short summary
Short summary
A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Yinghui Liu, Jeffrey R. Key, Xuanji Wang, and Mark Tschudi
The Cryosphere, 14, 1325–1345, https://doi.org/10.5194/tc-14-1325-2020, https://doi.org/10.5194/tc-14-1325-2020, 2020
Short summary
Short summary
This study provides a consistent and accurate multi-decadal product of ice thickness and ice volume from 1984 to 2018 based on satellite-derived ice age. Sea ice volume trends from this dataset are stronger than the trends from other datasets. Changes in sea ice thickness contribute more to overall sea ice volume trends than changes in sea ice area do in all months.
Jui-Lin Frank Li, Mark Richardson, Wei-Liang Lee, Eric Fetzer, Graeme Stephens, Jonathan Jiang, Yulan Hong, Yi-Hui Wang, Jia-Yuh Yu, and Yinghui Liu
The Cryosphere, 13, 969–980, https://doi.org/10.5194/tc-13-969-2019, https://doi.org/10.5194/tc-13-969-2019, 2019
Short summary
Short summary
Observed summer Arctic sea ice retreat has been faster than simulated by the average CMIP5 models, most of which exclude falling ice particles from their radiative calculations.
We use controlled CESM1-CAM5 simulations to show for the first time that snowflakes' radiative effects can accelerate sea ice retreat. September retreat rates are doubled above current CO2 levels, highlighting falling ice radiative effects as a high priority for inclusion in future modelling of the Arctic.
Aaron Letterly, Jeffrey Key, and Yinghui Liu
The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, https://doi.org/10.5194/tc-12-3373-2018, 2018
Short summary
Short summary
Significant reductions in Arctic sea ice and snow cover on Arctic land have led to increases in absorbed solar energy by the surface. Does one play a more important role in Arctic climate change? Using 34 years of satellite data we found that solar energy absorption increased by 10 % over the ocean, which was 3 times greater than over land. Therefore, the decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant feedback mechanism over the last few decades.
Yinghui Liu, Matthew D. Shupe, Zhien Wang, and Gerald Mace
Atmos. Chem. Phys., 17, 5973–5989, https://doi.org/10.5194/acp-17-5973-2017, https://doi.org/10.5194/acp-17-5973-2017, 2017
Short summary
Short summary
Detailed and accurate vertical distributions of cloud properties are essential to accurately calculate the surface radiative flux and to depict the mean climate state, and such information is more desirable in the Arctic due to its recent rapid changes and the challenging observation conditions. This study presents a feasible way to provide such information by blending cloud observations from surface and space-based instruments with the understanding of their respective strength and limitations.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 17, 2871–2889, https://doi.org/10.5194/tc-17-2871-2023, https://doi.org/10.5194/tc-17-2871-2023, 2023
Short summary
Short summary
Sea ice parcels may experience thickness changes primarily through two processes: due to freezing or melting or due to motion relative to other parcels. These processes are independent and will be affected differently in a changing climate. In order to better understand these processes and compare them against models, observational estimates of these process independent from one another are necessary. We present a large spatial- and temporal-scale observational estimate of these processes.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
Short summary
Short summary
A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Yinghui Liu, Jeffrey R. Key, Xuanji Wang, and Mark Tschudi
The Cryosphere, 14, 1325–1345, https://doi.org/10.5194/tc-14-1325-2020, https://doi.org/10.5194/tc-14-1325-2020, 2020
Short summary
Short summary
This study provides a consistent and accurate multi-decadal product of ice thickness and ice volume from 1984 to 2018 based on satellite-derived ice age. Sea ice volume trends from this dataset are stronger than the trends from other datasets. Changes in sea ice thickness contribute more to overall sea ice volume trends than changes in sea ice area do in all months.
Jui-Lin Frank Li, Mark Richardson, Wei-Liang Lee, Eric Fetzer, Graeme Stephens, Jonathan Jiang, Yulan Hong, Yi-Hui Wang, Jia-Yuh Yu, and Yinghui Liu
The Cryosphere, 13, 969–980, https://doi.org/10.5194/tc-13-969-2019, https://doi.org/10.5194/tc-13-969-2019, 2019
Short summary
Short summary
Observed summer Arctic sea ice retreat has been faster than simulated by the average CMIP5 models, most of which exclude falling ice particles from their radiative calculations.
We use controlled CESM1-CAM5 simulations to show for the first time that snowflakes' radiative effects can accelerate sea ice retreat. September retreat rates are doubled above current CO2 levels, highlighting falling ice radiative effects as a high priority for inclusion in future modelling of the Arctic.
Aaron Letterly, Jeffrey Key, and Yinghui Liu
The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, https://doi.org/10.5194/tc-12-3373-2018, 2018
Short summary
Short summary
Significant reductions in Arctic sea ice and snow cover on Arctic land have led to increases in absorbed solar energy by the surface. Does one play a more important role in Arctic climate change? Using 34 years of satellite data we found that solar energy absorption increased by 10 % over the ocean, which was 3 times greater than over land. Therefore, the decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant feedback mechanism over the last few decades.
Yinghui Liu, Matthew D. Shupe, Zhien Wang, and Gerald Mace
Atmos. Chem. Phys., 17, 5973–5989, https://doi.org/10.5194/acp-17-5973-2017, https://doi.org/10.5194/acp-17-5973-2017, 2017
Short summary
Short summary
Detailed and accurate vertical distributions of cloud properties are essential to accurately calculate the surface radiative flux and to depict the mean climate state, and such information is more desirable in the Arctic due to its recent rapid changes and the challenging observation conditions. This study presents a feasible way to provide such information by blending cloud observations from surface and space-based instruments with the understanding of their respective strength and limitations.
Related subject area
Subject: Radiation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Understanding the trends in reflected solar radiation: a latitude- and month-based perspective
Evaluating the representation of Arctic cirrus solar radiative effects in the Integrated Forecasting System with airborne measurements
Uncertainty in simulated brightness temperature due to sensitivity to atmospheric gas spectroscopic parameters from the centimeter- to submillimeter-wave range
Direct observational evidence from space of the effect of CO2 increase on longwave spectral radiances: the unique role of high-spectral-resolution measurements
LIME: Lunar Irradiance Model of ESA, a new tool for absolute radiometric calibration using the Moon
Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect
Estimation of 1 km downwelling shortwave radiation over the Tibetan Plateau under all-sky conditions
Record-breaking statistics detect islands of cooling in a sea of warming
Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106
Longwave radiative effect of the cloud–aerosol transition zone based on CERES observations
Ice and mixed-phase cloud statistics on the Antarctic Plateau
Photovoltaic power potential in West Africa using long-term satellite data
A semi-empirical potential energy surface and line list for H216O extending into the near-ultraviolet
Global distribution and 14-year changes in erythemal irradiance, UV atmospheric transmission, and total column ozone for2005–2018 estimated from OMI and EPIC observations
Biomass-burning-induced surface darkening and its impact on regional meteorology in eastern China
Air pollution slows down surface warming over the Tibetan Plateau
Estimation of hourly land surface heat fluxes over the Tibetan Plateau by the combined use of geostationary and polar-orbiting satellites
Estimations of global shortwave direct aerosol radiative effects above opaque water clouds using a combination of A-Train satellite sensors
Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals
Simulated and observed horizontal inhomogeneities of optical thickness of Arctic stratus
Net radiative effects of dust in the tropical North Atlantic based on integrated satellite observations and in situ measurements
Comparison of global observations and trends of total precipitable water derived from microwave radiometers and COSMIC radio occultation from 2006 to 2013
Characterizing energy budget variability at a Sahelian site: a test of NWP model behaviour
Scale dependence of cirrus horizontal heterogeneity effects on TOA measurements – Part I: MODIS brightness temperatures in the thermal infrared
Aerosol scattering effects on water vapor retrievals over the Los Angeles Basin
Directional, horizontal inhomogeneities of cloud optical thickness fields retrieved from ground-based and airbornespectral imaging
Airborne observations of far-infrared upwelling radiance in the Arctic
Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table
Microwave signatures of ice hydrometeors from ground-based observations above Summit, Greenland
Shortwave direct radiative effects of above-cloud aerosols over global oceans derived from 8 years of CALIOP and MODIS observations
Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data
Instantaneous longwave radiative impact of ozone: an application on IASI/MetOp observations
A method to retrieve super-thin cloud optical depth over ocean background with polarized sunlight
Airborne observations and simulations of three-dimensional radiative interactions between Arctic boundary layer clouds and ice floes
Deriving polarization properties of desert-reflected solar spectra with PARASOL data
Using IASI to simulate the total spectrum of outgoing long-wave radiances
Investigation of the "elevated heat pump" hypothesis of the Asian monsoon using satellite observations
Improved retrieval of direct and diffuse downwelling surface shortwave flux in cloudless atmosphere using dynamic estimates of aerosol content and type: application to the LSA-SAF project
Surface-sensible and latent heat fluxes over the Tibetan Plateau from ground measurements, reanalysis, and satellite data
Influence of local surface albedo variability and ice crystal shape on passive remote sensing of thin cirrus
Combining MODIS, AVHRR and in situ data for evapotranspiration estimation over heterogeneous landscape of the Tibetan Plateau
Modeling polarized solar radiation from the ocean–atmosphere system for CLARREO inter-calibration applications
HIRS channel 12 brightness temperature dataset and its correlations with major climate indices
Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies
Multi-satellite aerosol observations in the vicinity of clouds
CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo
Quantitative comparison of the variability in observed and simulated shortwave reflectance
Regional radiative impact of volcanic aerosol from the 2009 eruption of Mt. Redoubt
Airborne hyperspectral observations of surface and cloud directional reflectivity using a commercial digital camera
Direct and semi-direct radiative forcing of smoke aerosols over clouds
Ruixue Li, Bida Jian, Jiming Li, Deyu Wen, Lijie Zhang, Yang Wang, and Yuan Wang
Atmos. Chem. Phys., 24, 9777–9803, https://doi.org/10.5194/acp-24-9777-2024, https://doi.org/10.5194/acp-24-9777-2024, 2024
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Hemispheric or interannual averages of reflected solar radiation (RSR) can mask signals from seasonally active or region-specific mechanisms. We examine RSR characteristics from latitude and month perspectives, revealing decreased trends observed by CERES in both hemispheres driven by clear-sky atmospheric and cloud components at 30–50° N and cloud components at 0–50° S. AVHRR achieves symmetry criteria within uncertainty and is suitable for the long-term analysis of hemispheric RSR symmetry.
Johannes Röttenbacher, André Ehrlich, Hanno Müller, Florian Ewald, Anna E. Luebke, Benjamin Kirbus, Robin J. Hogan, and Manfred Wendisch
Atmos. Chem. Phys., 24, 8085–8104, https://doi.org/10.5194/acp-24-8085-2024, https://doi.org/10.5194/acp-24-8085-2024, 2024
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Weather prediction models simplify the physical processes related to light scattering by clouds consisting of complex ice crystals. Whether these simplifications are the cause for uncertainties in their prediction can be evaluated by comparing them with measurement data. Here we do this for Arctic ice clouds over sea ice using airborne measurements from two case studies. The model performs well for thick ice clouds but not so well for thin ones. This work can be used to improve the model.
Donatello Gallucci, Domenico Cimini, Emma Turner, Stuart Fox, Philip W. Rosenkranz, Mikhail Y. Tretyakov, Vinia Mattioli, Salvatore Larosa, and Filomena Romano
Atmos. Chem. Phys., 24, 7283–7308, https://doi.org/10.5194/acp-24-7283-2024, https://doi.org/10.5194/acp-24-7283-2024, 2024
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Nowadays, atmospheric radiative transfer models are widely used to simulate satellite and ground-based observations. A meaningful comparison between observations and simulations requires an estimate of the uncertainty associated with both. This work quantifies the uncertainty in atmospheric radiative transfer models in the microwave range, providing the uncertainty associated with simulations of new-generation satellite microwave sensors.
João Teixeira, R. Chris Wilson, and Heidar Th. Thrastarson
Atmos. Chem. Phys., 24, 6375–6383, https://doi.org/10.5194/acp-24-6375-2024, https://doi.org/10.5194/acp-24-6375-2024, 2024
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This paper presents direct evidence from space (solely based on observations) that CO2 increase leads to the theoretically expected effects on longwave spectral radiances. This is achieved by using a methodology that allows us to isolate the CO2 effects from the temperature and water vapor effects. By searching for ensembles of temperature and water vapor profiles that are similar to each other but have different values of CO2, it is possible to estimate the direct effects of CO2 on the spectra.
Carlos Toledano, Sarah Taylor, África Barreto, Stefan Adriaensen, Alberto Berjón, Agnieszka Bialek, Ramiro González, Emma Woolliams, and Marc Bouvet
Atmos. Chem. Phys., 24, 3649–3671, https://doi.org/10.5194/acp-24-3649-2024, https://doi.org/10.5194/acp-24-3649-2024, 2024
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The calibration of Earth observation sensors is key to ensuring the continuity of long-term and global climate records. Satellite sensors, calibrated prior to launch, are susceptible to degradation in space. The Moon provides a stable calibration reference; however, its illumination depends on the Sun–Earth–Moon geometry and must be modelled. A new lunar irradiance model is presented, built upon observations over 5 years at a high-altitude observatory and a rigorous calibration and validation.
Adeleke S. Ademakinwa, Zahid H. Tushar, Jianyu Zheng, Chenxi Wang, Sanjay Purushotham, Jianwu Wang, Kerry G. Meyer, Tamas Várnai, and Zhibo Zhang
Atmos. Chem. Phys., 24, 3093–3114, https://doi.org/10.5194/acp-24-3093-2024, https://doi.org/10.5194/acp-24-3093-2024, 2024
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Clouds play a critical role in our climate system. At present and in the near future, satellite-based remote sensing is the only means to obtain regional and global observations of cloud properties. The current satellite remote sensing algorithms are mostly based on the so-called 1D radiative transfer. This deviation from the 3D world reality can lead to large errors. In this study we investigate how this error affects our estimation of cloud radiative effects.
Peizhen Li, Lei Zhong, Yaoming Ma, Yunfei Fu, Meilin Cheng, Xian Wang, Yuting Qi, and Zixin Wang
Atmos. Chem. Phys., 23, 9265–9285, https://doi.org/10.5194/acp-23-9265-2023, https://doi.org/10.5194/acp-23-9265-2023, 2023
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In this paper, all-sky downwelling shortwave radiation (DSR) over the entire Tibetan Plateau (TP) at a spatial resolution of 1 km was estimated using an improved parameterization scheme. The influence of topography and different radiative attenuations were comprehensively taken into account. The derived DSR showed good agreement with in situ measurements. The accuracy was better than six other DSR products. The derived DSR also provided more reasonable and detailed spatial patterns.
Elisa T. Sena, Ilan Koren, Orit Altaratz, and Alexander B. Kostinski
Atmos. Chem. Phys., 22, 16111–16122, https://doi.org/10.5194/acp-22-16111-2022, https://doi.org/10.5194/acp-22-16111-2022, 2022
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We used record-breaking statistics together with spatial information to create record-breaking SST maps. The maps reveal warming patterns in the overwhelming majority of the ocean and coherent islands of cooling, where low records occur more frequently than high ones. Some of these cooling spots are well known; however, a surprising elliptical area in the Southern Ocean is observed as well. Similar analyses can be performed on other key climatological variables to explore their trend patterns.
Carola Barrientos-Velasco, Hartwig Deneke, Anja Hünerbein, Hannes J. Griesche, Patric Seifert, and Andreas Macke
Atmos. Chem. Phys., 22, 9313–9348, https://doi.org/10.5194/acp-22-9313-2022, https://doi.org/10.5194/acp-22-9313-2022, 2022
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This article describes an intercomparison of radiative fluxes and cloud properties from satellite, shipborne observations, and 1D radiative transfer simulations. The analysis focuses on research for PS106 expedition aboard the German research vessel, Polarstern. The results are presented in detailed case studies, time series for the PS106 cruise and extended to the central Arctic region. The findings illustrate the main periods of agreement and discrepancies of both points of view.
Babak Jahani, Hendrik Andersen, Josep Calbó, Josep-Abel González, and Jan Cermak
Atmos. Chem. Phys., 22, 1483–1494, https://doi.org/10.5194/acp-22-1483-2022, https://doi.org/10.5194/acp-22-1483-2022, 2022
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The change in the state of sky from cloudy to cloudless (or vice versa) comprises an additional phase called
transition zonewith characteristics laying between those of aerosols and clouds. This study presents an approach for the quantification of the broadband longwave radiative effects of the cloud–aerosol transition zone at the top of the atmosphere during daytime over the ocean based on satellite observations and radiative transfer simulations.
William Cossich, Tiziano Maestri, Davide Magurno, Michele Martinazzo, Gianluca Di Natale, Luca Palchetti, Giovanni Bianchini, and Massimo Del Guasta
Atmos. Chem. Phys., 21, 13811–13833, https://doi.org/10.5194/acp-21-13811-2021, https://doi.org/10.5194/acp-21-13811-2021, 2021
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The presence of clouds over Concordia, in the Antarctic Plateau, is investigated. Results are obtained by applying a machine learning algorithm to measurements of the infrared radiation emitted by the atmosphere toward the surface. The clear-sky, ice cloud, and mixed-phase cloud occurrence at different timescales is studied. A comparison with satellite measurements highlights the ability of the algorithm to identify multiple cloud conditions and study their variability at different timescales.
Ina Neher, Susanne Crewell, Stefanie Meilinger, Uwe Pfeifroth, and Jörg Trentmann
Atmos. Chem. Phys., 20, 12871–12888, https://doi.org/10.5194/acp-20-12871-2020, https://doi.org/10.5194/acp-20-12871-2020, 2020
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Photovoltaic power is one current option to meet the rising energy demand with low environmental impact. Global horizontal irradiance (GHI) is the fuel for photovoltaic power installations and needs to be evaluated to plan and dimension power plants. In this study, 35 years of satellite-based GHI data are analyzed over West Africa to determine their impact on photovoltaic power generation. The major challenges for the development of a solar-based power system in West Africa are then outlined.
Eamon K. Conway, Iouli E. Gordon, Jonathan Tennyson, Oleg L. Polyansky, Sergei N. Yurchenko, and Kelly Chance
Atmos. Chem. Phys., 20, 10015–10027, https://doi.org/10.5194/acp-20-10015-2020, https://doi.org/10.5194/acp-20-10015-2020, 2020
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Water vapour has a complex spectrum and absorbs from the microwave to the near-UV where it dissociates. There is limited knowledge of the absorption features in the near-UV, and there is a large disagreement for the available models and experiments. We created a new ab initio model that is in good agreement with observation at 363 nm. At lower wavelengths, our calculations suggest that the latest experiments overestimate absorption. This has implications for trace gas retrievals in the near-UV.
Jay Herman, Alexander Cede, Liang Huang, Jerald Ziemke, Omar Torres, Nickolay Krotkov, Matthew Kowalewski, and Karin Blank
Atmos. Chem. Phys., 20, 8351–8380, https://doi.org/10.5194/acp-20-8351-2020, https://doi.org/10.5194/acp-20-8351-2020, 2020
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The amount of erythemal irradiance reaching the Earth's surface has been calculated from ozone, aerosol, and reflectivity data obtained from OMI and DSCOVR/EPIC satellite instruments showing areas with high levels of solar UV radiation. Changes in erythemal irradiance, cloud transmission, aerosol transmission, and ozone absorption have been estimated for 14 years 2005–2018 in units of percent per year for 191 locations, mostly large cities, and from EPIC for the entire illuminated Earth.
Rong Tang, Xin Huang, Derong Zhou, and Aijun Ding
Atmos. Chem. Phys., 20, 6177–6191, https://doi.org/10.5194/acp-20-6177-2020, https://doi.org/10.5194/acp-20-6177-2020, 2020
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Biomass-burning-induced large areas of dark char (i.e.
surface darkening) could influence the radiative energy balance. During the harvest season in eastern China, satellite retrieval shows that surface albedo was significantly decreased. Observational evidence of meteorological perturbations from the surface darkening is identified, which is further examined by model simulation. This work highlights the importance of burning-induced albedo change in weather forecast and regional climate.
Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang
Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, https://doi.org/10.5194/acp-20-881-2020, 2020
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The Tibetan Plateau (TP) plays a vital role in regional and global climate change due to its location and orography. After generating a long-term surface radiation (SR) dataset, we characterized the SR spatiotemporal variation along with temperature. Evidence from multiple data sources indicated that the TP dimming was primarily driven by increased aerosols from human activities, and the cooling effect of aerosol loading offsets TP surface warming, revealing the human impact on regional warming.
Lei Zhong, Yaoming Ma, Zeyong Hu, Yunfei Fu, Yuanyuan Hu, Xian Wang, Meilin Cheng, and Nan Ge
Atmos. Chem. Phys., 19, 5529–5541, https://doi.org/10.5194/acp-19-5529-2019, https://doi.org/10.5194/acp-19-5529-2019, 2019
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Fine-temporal-resolution turbulent heat fluxes at the plateau scale have significant importance for studying diurnal variation characteristics of atmospheric boundary and weather systems in the Tibetan Plateau (TP) and its surroundings. Time series of land surface heat fluxes with high temporal resolution over the entire TP were derived. The derived surface heat fluxes proved to be in good agreement with in situ measurements and were superior to GLDAS flux products.
Meloë S. Kacenelenbogen, Mark A. Vaughan, Jens Redemann, Stuart A. Young, Zhaoyan Liu, Yongxiang Hu, Ali H. Omar, Samuel LeBlanc, Yohei Shinozuka, John Livingston, Qin Zhang, and Kathleen A. Powell
Atmos. Chem. Phys., 19, 4933–4962, https://doi.org/10.5194/acp-19-4933-2019, https://doi.org/10.5194/acp-19-4933-2019, 2019
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Significant efforts are required to estimate the direct radiative effects of aerosols above clouds (DAREcloudy). We have used a combination of passive and active A-Train satellite sensors and derive mainly positive global and regional DAREcloudy values (e.g., global seasonal values between 0.13 and 0.26 W m-2). Despite differences in methods and sensors, the DAREcloudy values in this study are generally higher than previously reported. We discuss the primary reasons for these higher estimates.
Domenico Cimini, Philip W. Rosenkranz, Mikhail Y. Tretyakov, Maksim A. Koshelev, and Filomena Romano
Atmos. Chem. Phys., 18, 15231–15259, https://doi.org/10.5194/acp-18-15231-2018, https://doi.org/10.5194/acp-18-15231-2018, 2018
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The paper presents a general approach to quantify the uncertainty related to atmospheric absorption models. These models describe how the atmosphere interacts with radiation, and they have general implications for atmospheric sciences.
The presented approach contributes to a better understanding of the total uncertainty affecting atmospheric radiative properties, thus reducing the chances of systematic errors when observations are exploited for weather forecast or climate trend derivations.
Michael Schäfer, Katharina Loewe, André Ehrlich, Corinna Hoose, and Manfred Wendisch
Atmos. Chem. Phys., 18, 13115–13133, https://doi.org/10.5194/acp-18-13115-2018, https://doi.org/10.5194/acp-18-13115-2018, 2018
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Airborne observed horizontal fields of cloud optical thickness are compared with semi-idealized large eddy simulations of Arctic stratus. The comparison focuses on horizontal cloud inhomogeneities and directional features of the small-scale cloud structures. Using inhomogeneity parameters and autocorrelation analysis it is investigated, if the observed small-scale cloud inhomogeneities can be represented by the model. Forcings for cloud inhomogeneities are investigated in a sensitivity study.
Qianqian Song, Zhibo Zhang, Hongbin Yu, Seiji Kato, Ping Yang, Peter Colarco, Lorraine A. Remer, and Claire L. Ryder
Atmos. Chem. Phys., 18, 11303–11322, https://doi.org/10.5194/acp-18-11303-2018, https://doi.org/10.5194/acp-18-11303-2018, 2018
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Mineral dust is the most abundant atmospheric aerosol component in terms of dry mass. In this study, we integrate recent aircraft measurements of dust microphysical and optical properties with satellite retrievals of aerosol and radiative fluxes to quantify the dust direct radiative effects on the shortwave and longwave radiation at both the top of the atmosphere and the surface in the tropical North Atlantic during summer months.
Shu-Peng Ho, Liang Peng, Carl Mears, and Richard A. Anthes
Atmos. Chem. Phys., 18, 259–274, https://doi.org/10.5194/acp-18-259-2018, https://doi.org/10.5194/acp-18-259-2018, 2018
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In this study, we compare 7 years of atmospheric total precipitable water (TPW) derived from multiple microwave radiometers to collocated TPW estimates derived from COSMIC radio occultation under various atmospheric conditions over the oceans. Results show that these two TPW trends from independent observations are larger than previous estimates and are a strong indication of the positive water vapor–temperature feedback on a warming planet.
Anna Mackie, Paul I. Palmer, and Helen Brindley
Atmos. Chem. Phys., 17, 15095–15119, https://doi.org/10.5194/acp-17-15095-2017, https://doi.org/10.5194/acp-17-15095-2017, 2017
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We compare the balance of solar and thermal radiation at the surface and the top of the atmosphere from a forecasting model to observations at a site in Niamey, Niger, in the Sahel. To interpret the energy budgets we examine other factors, such as cloud properties, water vapour and aerosols, which we use to understand the differences between the observation and model. We find that some differences are linked to lack of ice in clouds, underestimated aerosol loading and surface temperatures.
Thomas Fauchez, Steven Platnick, Kerry Meyer, Céline Cornet, Frédéric Szczap, and Tamás Várnai
Atmos. Chem. Phys., 17, 8489–8508, https://doi.org/10.5194/acp-17-8489-2017, https://doi.org/10.5194/acp-17-8489-2017, 2017
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This study presents impact of cirrus cloud horizontal heterogeneity on simulated thermal infrared brightness temperatures at the top of the atmosphere for spatial resolutions ranging from 50 m to 10 km. The cirrus is generated by the 3DCLOUD code and the radiative transfer by the 3DMCPOL code. Brightness temperatures are mostly impacted by the horizontal transport effect and plane-parallel bias at high and coarse spatial resolutions, respectively, with a minimum around 100 m–250 m.
Zhao-Cheng Zeng, Qiong Zhang, Vijay Natraj, Jack S. Margolis, Run-Lie Shia, Sally Newman, Dejian Fu, Thomas J. Pongetti, Kam W. Wong, Stanley P. Sander, Paul O. Wennberg, and Yuk L. Yung
Atmos. Chem. Phys., 17, 2495–2508, https://doi.org/10.5194/acp-17-2495-2017, https://doi.org/10.5194/acp-17-2495-2017, 2017
Short summary
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We propose a novel approach to describing the scattering effects of atmospheric aerosols using H2O retrievals in the near infrared. We found that the aerosol scattering effect is the primary contributor to the variations in the wavelength dependence of the H2O SCD retrievals and the scattering effects can be derived using H2O retrievals from multiple bands. This proposed method could potentially contribute towards reducing biases in greenhouse gas retrievals from space.
Michael Schäfer, Eike Bierwirth, André Ehrlich, Evelyn Jäkel, Frank Werner, and Manfred Wendisch
Atmos. Chem. Phys., 17, 2359–2372, https://doi.org/10.5194/acp-17-2359-2017, https://doi.org/10.5194/acp-17-2359-2017, 2017
Short summary
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Cloud optical thickness fields, retrieved from solar spectral radiance measurements, are used to investigate the directional structure of horizontal cloud inhomogeneities with scalar one-dimensional inhomogeneity parameters, two-dimensional auto-correlation functions, and two-dimensional Fourier analysis. The investigations reveal that it is not sufficient to quantify horizontal cloud inhomogeneities by one-dimensional inhomogeneity parameters; two-dimensional parameters are necessary.
Quentin Libois, Liviu Ivanescu, Jean-Pierre Blanchet, Hannes Schulz, Heiko Bozem, W. Richard Leaitch, Julia Burkart, Jonathan P. D. Abbatt, Andreas B. Herber, Amir A. Aliabadi, and Éric Girard
Atmos. Chem. Phys., 16, 15689–15707, https://doi.org/10.5194/acp-16-15689-2016, https://doi.org/10.5194/acp-16-15689-2016, 2016
Short summary
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The first airborne measurements performed with the FIRR are presented. Vertical profiles of upwelling spectral radiance in the far-infrared are measured in the Arctic atmosphere for the first time. They show the impact of the temperature inversion on the radiative budget of the atmosphere, especially in the far-infrared. The presence of ice clouds also significantly alters the far-infrared budget, highlighting the critical interplay between water vapour and clouds in this very dry region.
Jani Huttunen, Harri Kokkola, Tero Mielonen, Mika Esa Juhani Mononen, Antti Lipponen, Juha Reunanen, Anders Vilhelm Lindfors, Santtu Mikkonen, Kari Erkki Juhani Lehtinen, Natalia Kouremeti, Alkiviadis Bais, Harri Niska, and Antti Arola
Atmos. Chem. Phys., 16, 8181–8191, https://doi.org/10.5194/acp-16-8181-2016, https://doi.org/10.5194/acp-16-8181-2016, 2016
Short summary
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For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is needed. One option to lengthen time series is to retrieve aerosol optical depth from solar radiation measurements. We have evaluated several methods for this task. Most of the methods produce aerosol optical depth estimates with a good accuracy. However, machine learning methods seem to be the most applicable not to produce any systematic biases, since they do not need constrain the aerosol properties.
Claire Pettersen, Ralf Bennartz, Mark S. Kulie, Aronne J. Merrelli, Matthew D. Shupe, and David D. Turner
Atmos. Chem. Phys., 16, 4743–4756, https://doi.org/10.5194/acp-16-4743-2016, https://doi.org/10.5194/acp-16-4743-2016, 2016
Short summary
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We examined four summers of data from a ground-based atmospheric science instrument suite at Summit Station, Greenland, to isolate the signature of the ice precipitation. By using a combination of instruments with different specialities, we identified a passive microwave signature of the ice precipitation. This ice signature compares well to models using synthetic data characteristic of the site.
Zhibo Zhang, Kerry Meyer, Hongbin Yu, Steven Platnick, Peter Colarco, Zhaoyan Liu, and Lazaros Oreopoulos
Atmos. Chem. Phys., 16, 2877–2900, https://doi.org/10.5194/acp-16-2877-2016, https://doi.org/10.5194/acp-16-2877-2016, 2016
Short summary
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The frequency of occurrence and shortwave direct radiative effects (DRE) of above-cloud aerosols (ACAs) over global oceans are investigated using 8 years of collocated CALIOP and MODIS observations. We estimated that ACAs have a global ocean annual mean diurnally averaged cloudy-sky DRE of 0.015 W m−2 (range of −0.03 to 0.06 W m−2) at TOA. The DREs at surface and within atmosphere are −0.15 W m−2 (range of −0.09 to −0.21 W m−2), and 0.17 W m−2 (range of 0.11 to 0.24 W m−2), respectively.
Wenjun Tang, Jun Qin, Kun Yang, Shaomin Liu, Ning Lu, and Xiaolei Niu
Atmos. Chem. Phys., 16, 2543–2557, https://doi.org/10.5194/acp-16-2543-2016, https://doi.org/10.5194/acp-16-2543-2016, 2016
Short summary
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In this paper, we develop a new method to quickly retrieve high-resolution surface solar radiation (SSR) over China by combining MODIS and MTSAT data. The RMSEs of the retrieved SSR at hourly, daily, and monthly scales are about 98.5, 34.2, and 22.1 W m−2. The accuracy is comparable to or even higher than other two satellite radiation products. Finally, we derive an 8-year high-resolution SSR data set (hourly, 5 km) from 2007 to 2014, which would contribute to studies of land surface processes.
S. Doniki, D. Hurtmans, L. Clarisse, C. Clerbaux, H. M. Worden, K. W. Bowman, and P.-F. Coheur
Atmos. Chem. Phys., 15, 12971–12987, https://doi.org/10.5194/acp-15-12971-2015, https://doi.org/10.5194/acp-15-12971-2015, 2015
W. Sun, R. R. Baize, G. Videen, Y. Hu, and Q. Fu
Atmos. Chem. Phys., 15, 11909–11918, https://doi.org/10.5194/acp-15-11909-2015, https://doi.org/10.5194/acp-15-11909-2015, 2015
Short summary
Short summary
A method is reported for retrieving super-thin cloud optical depth with polarized light. It is found that near-backscatter p-polarized light is sensitive to clouds, but not to ocean conditions. Near-backscatter p-polarized intensity linearly relates to super-thin cloud optical depth. Based on these findings, super-thin cloud optical depth can be retrieved with little effect from surface reflection.
M. Schäfer, E. Bierwirth, A. Ehrlich, E. Jäkel, and M. Wendisch
Atmos. Chem. Phys., 15, 8147–8163, https://doi.org/10.5194/acp-15-8147-2015, https://doi.org/10.5194/acp-15-8147-2015, 2015
W. Sun, R. R. Baize, C. Lukashin, and Y. Hu
Atmos. Chem. Phys., 15, 7725–7734, https://doi.org/10.5194/acp-15-7725-2015, https://doi.org/10.5194/acp-15-7725-2015, 2015
E. C. Turner, H.-T. Lee, and S. F. B. Tett
Atmos. Chem. Phys., 15, 6561–6575, https://doi.org/10.5194/acp-15-6561-2015, https://doi.org/10.5194/acp-15-6561-2015, 2015
M. M. Wonsick, R. T. Pinker, and Y. Ma
Atmos. Chem. Phys., 14, 8749–8761, https://doi.org/10.5194/acp-14-8749-2014, https://doi.org/10.5194/acp-14-8749-2014, 2014
X. Ceamanos, D. Carrer, and J.-L. Roujean
Atmos. Chem. Phys., 14, 8209–8232, https://doi.org/10.5194/acp-14-8209-2014, https://doi.org/10.5194/acp-14-8209-2014, 2014
Q. Shi and S. Liang
Atmos. Chem. Phys., 14, 5659–5677, https://doi.org/10.5194/acp-14-5659-2014, https://doi.org/10.5194/acp-14-5659-2014, 2014
C. Fricke, A. Ehrlich, E. Jäkel, B. Bohn, M. Wirth, and M. Wendisch
Atmos. Chem. Phys., 14, 1943–1958, https://doi.org/10.5194/acp-14-1943-2014, https://doi.org/10.5194/acp-14-1943-2014, 2014
Y. Ma, Z. Zhu, L. Zhong, B. Wang, C. Han, Z. Wang, Y. Wang, L. Lu, P. M. Amatya, W. Ma, and Z. Hu
Atmos. Chem. Phys., 14, 1507–1515, https://doi.org/10.5194/acp-14-1507-2014, https://doi.org/10.5194/acp-14-1507-2014, 2014
W. Sun and C. Lukashin
Atmos. Chem. Phys., 13, 10303–10324, https://doi.org/10.5194/acp-13-10303-2013, https://doi.org/10.5194/acp-13-10303-2013, 2013
L. Shi, C. J. Schreck III, and V. O. John
Atmos. Chem. Phys., 13, 6907–6920, https://doi.org/10.5194/acp-13-6907-2013, https://doi.org/10.5194/acp-13-6907-2013, 2013
M. J. Alvarado, V. H. Payne, E. J. Mlawer, G. Uymin, M. W. Shephard, K. E. Cady-Pereira, J. S. Delamere, and J.-L. Moncet
Atmos. Chem. Phys., 13, 6687–6711, https://doi.org/10.5194/acp-13-6687-2013, https://doi.org/10.5194/acp-13-6687-2013, 2013
T. Várnai, A. Marshak, and W. Yang
Atmos. Chem. Phys., 13, 3899–3908, https://doi.org/10.5194/acp-13-3899-2013, https://doi.org/10.5194/acp-13-3899-2013, 2013
A. Riihelä, T. Manninen, V. Laine, K. Andersson, and F. Kaspar
Atmos. Chem. Phys., 13, 3743–3762, https://doi.org/10.5194/acp-13-3743-2013, https://doi.org/10.5194/acp-13-3743-2013, 2013
Y. L. Roberts, P. Pilewskie, B. C. Kindel, D. R. Feldman, and W. D. Collins
Atmos. Chem. Phys., 13, 3133–3147, https://doi.org/10.5194/acp-13-3133-2013, https://doi.org/10.5194/acp-13-3133-2013, 2013
C. L. Young, I. N. Sokolik, and J. Dufek
Atmos. Chem. Phys., 12, 3699–3715, https://doi.org/10.5194/acp-12-3699-2012, https://doi.org/10.5194/acp-12-3699-2012, 2012
A. Ehrlich, E. Bierwirth, M. Wendisch, A. Herber, and J.-F. Gayet
Atmos. Chem. Phys., 12, 3493–3510, https://doi.org/10.5194/acp-12-3493-2012, https://doi.org/10.5194/acp-12-3493-2012, 2012
E. M. Wilcox
Atmos. Chem. Phys., 12, 139–149, https://doi.org/10.5194/acp-12-139-2012, https://doi.org/10.5194/acp-12-139-2012, 2012
Cited articles
Arias, P. A., Bellouin, N., Coppola, E., Jones, R. G., Krinner, G., Marotzke, J.,
Naik, V., Palmer, M. D., Plattner, G.-K., Rogelj, J.,
Rojas, M., Sillmann, J., Storelvmo, T., Thorne, P. W., Trewin, B., Achuta Rao, K.,
Adhikary, B., Allan, R. P., Armour, K., Bala, G.,
Barimalala, R., Berger, S., Canadell, J. G., Cassou, C., Cherchi, A., Collins, W.,
Collins, W. D., Connors, S. L., Corti, S., Cruz, F.,
Dentener, F. J., Dereczynski, C., Di Luca, A., Diongue Niang, A.,
Doblas-Reyes, F. J., Dosio, A., Douville, H., Engelbrecht, F.,
Eyring, V., Fischer, E., Forster, P., Fox-Kemper, B., Fuglestvedt, J. S.,
Fyfe, J. C., Gillett, N. P., Goldfarb, L., Gorodetskaya, I.,
Gutierrez, J. M., Hamdi, R., Hawkins, E., Hewitt, H. T., Hope, P., Islam, A. S.,
Jones, C., Kaufman, D. S., Kopp, R. E., Kosaka, Y.,
Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock, T. K.,
Meinshausen, M., Min, S.-K., Monteiro, P. M. S.,
Ngo-Duc, T., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasinghe, R.,
Ruane, A. C., Ruiz, L., Sallée, J.-B., Samset, B. H.,
Sathyendranath, S., Seneviratne, S. I., Sörensson, A. A., Szopa, S.,
Takayabu, I., Tréguier, A.-M., van den Hurk, B., Vautard, R.,
von Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical
Summary, in: Climate Change 2021: The
Physical Science Basis, Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A.,
Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y.,
Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B.:
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
33–144,
2021.
Blanchard, Y., Pelon, J., Eloranta, E. W., Moran, K. P., Delanoë, J., and
Sèze, G.: A Synergistic Analysis of Cloud Cover and Vertical
Distribution from A-Train and Ground-Based Sensors over the High Arctic
Station Eureka from 2006 to 2010, J. Appl. Meteorol. Clim., 53,
2553–2570, https://doi.org/10.1175/JAMC-D-14-0021.1, 2014.
Blanchard, Y., Pelon, J., Cox, C. J., Delanoë, J., Eloranta, E. W., and
Uttal, T.: Comparison of TOA and BOA LW Radiation Fluxes Inferred From
Ground-Based Sensors, A-Train Satellite Observations and ERA Reanalyzes at
the High Arctic Station Eureka Over the 2002–2020 Period,
J. Geophys. Res.-Atmos., 126, e2020JD033615, https://doi.org/10.1029/2020JD033615, 2021.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L.,
Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John,
V. O.: COSP: Satellite simulation software for model assessment,
B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and aerosols, Climate Change 2013: The
Physical Science Basis, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University
Press, 571–657, https://doi.org/10.1017/CBO9781107415324.016, 2013.
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and Boer, G.: Ubiquitous
low-level liquid-containing Arctic clouds: New observations and climate
model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39,
2012GL053385, https://doi.org/10.1029/2012GL053385, 2012.
Christensen, M. W., Stephens, G. L., and Lebsock, M. D.: Exposing biases in
retrieved low cloud properties from CloudSat: A guide for evaluating
observations and climate data, J. Geophys. Res.-Atmos., 118,
12120–12131, https://doi.org/10.1002/2013JD020224, 2013.
de Roode, S.: radiosonde profiles during the SHEBA experiment, ftp://eos.atmos.washington.edu/pub/roode/rawinsonde.nc, last access: May 2022a.
de Roode, S.: hourly averaged surface albedo during the SHEBA experiment, ftp://eos.atmos.washington.edu/pub/roode/surf_obs.nc, last access: May 2022b.
Devasthale, A., Tjernström, M., Karlsson, K.-G., Thomas, M. A., Jones,
C., Sedlar, J., and Omar, A. H.: The vertical distribution of thin features
over the Arctic analysed from CALIPSO observations, Tellus B, 63, 77–85, https://doi.org/10.1111/j.1600-0889.2010.00516.x, 2011.
Di Biagio, C., Pelon, J., Blanchard, Y., Loyer, L., Hudson, S. R., Walden,
V. P., Raut, J. -C., Kato, S., Mariage, V., and Granskog, M. A.: Toward a
Better Surface Radiation Budget Analysis Over Sea Ice in the High Arctic
Ocean: A Comparative Study Between Satellite, Reanalysis, and local-scale
Observations, J. Geophys. Res.-Atmos., 126, e2020JD032555, https://doi.org/10.1029/2020JD032555,
2021.
Dong, X., Ackerman, T. P., and Clothiaux, E. E.: Parameterizations of the
microphysical and shortwave radiative properties of boundary layer stratus
from ground-based measurements, J. Geophys. Res.-Atmos., 103,
31681–31693, https://doi.org/10.1029/1998JD200047, 1998.
Dong, X., Xi, B., Crosby, K., Long, C. N., Stone, R. S., and Shupe, M. D.: A
10 year climatology of Arctic cloud fraction and radiative forcing at
Barrow, Alaska, J. Geophys. Res., 115, D17212,
https://doi.org/10.1029/2009JD013489, 2010.
Ebert, E. E. and Curry, J. A.: A parameterization of ice cloud optical
properties for climate models, J. Geophys. Res., 97, 3831,
https://doi.org/10.1029/91JD02472, 1992.
Griesche, H. J., Ohneiser, K., Seifert, P., Radenz, M., Engelmann, R., and Ansmann, A.: Contrasting ice formation in Arctic clouds: surface-coupled vs. surface-decoupled clouds, Atmos. Chem. Phys., 21, 10357–10374, https://doi.org/10.5194/acp-21-10357-2021, 2021.
Haynes, J. M.: QuickBeam radar simulation software user's guide v 1.1 a, Fort
Collins, https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/Quickbeam-Userguide.pdf (last access: May 2022), 2007.
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G.
L.: A Multipurpose Radar Simulation Package: QuickBeam, B. Am. Meteorol. Soc., 88, 1723–1728, https://doi.org/10.1175/BAMS-88-11-1723, 2007.
Henderson, D. S., L'Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi, M.:
A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols,
J. Appl. Meteorol. Clim., 52, 853–871, https://doi.org/10.1175/JAMC-D-12-025.1,
2013.
Hu, X., Ge, J., Du, J., Li, Q., Huang, J., and Fu, Q.: A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar, Atmos. Meas. Tech., 14, 1743–1759, https://doi.org/10.5194/amt-14-1743-2021, 2021.
Hu, Y., Vaughan, M., McClain, C., Behrenfeld, M., Maring, H., Anderson, D., Sun-Mack, S., Flittner, D., Huang, J., Wielicki, B., Minnis, P., Weimer, C., Trepte, C., and Kuehn, R.: Global statistics of liquid water content and effective number concentration of water clouds over ocean derived from combined CALIPSO and MODIS measurements, Atmos. Chem. Phys., 7, 3353–3359, https://doi.org/10.5194/acp-7-3353-2007, 2007.
Huang, Y., Siems, S. T., Manton, M. J., Hande, L. B., and Haynes, J. M.: The
Structure of Low-Altitude Clouds over the Southern Ocean as Seen by
CloudSat, J. Climate, 25, 2535–2546, https://doi.org/10.1175/JCLI-D-11-00131.1, 2012.
Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L., and
Weimer, C.: CALIPSO Lidar Description and Performance Assessment,
J. Atmos. Ocean. Tech., 26, 1214–1228, https://doi.org/10.1175/2009JTECHA1223.1, 2009.
Intrieri, J. M., Fairall, C. W., Shupe, M. D., Persson, P. O. G., Andreas, E. L.,
Guest, P. S., and Moritz, R. E.: An annual cycle of Arctic surface cloud
forcing at SHEBA, J. Geophys. Res., 107, 8039,
https://doi.org/10.1029/2000JC000439, 2002a.
Intrieri, J. M., Shupe, M. D., Uttal, T., and McCarty, B. J.: An annual cycle of
Arctic cloud characteristics observed by radar and lidar at SHEBA, J.
Geophys. Res., 107, 8030, https://doi.org/10.1029/2000JC000423, 2002b.
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal
Arctic sea ice loss, J. Geophys. Res., 114, D18204,
https://doi.org/10.1029/2009JD011773, 2009.
Kay, J. E. and L'Ecuyer, T.: Observational constraints on Arctic Ocean
clouds and radiative fluxes during the early 21st century, J. Geophys. Res.-Atmos., 118, 7219–7236, https://doi.org/10.1002/jgrd.50489, 2013.
Key, J. R. and Schweiger, A. J.: Tools for atmospheric radiative transfer:
Streamer and FluxNet, Comput. Geosci., 24, 443–451,
https://doi.org/10.1016/S0098-3004(97)00130-1, 1998.
Key, J., Wang, X., Liu, Y., Dworak, R., and Letterly, A.: The AVHRR Polar
Pathfinder Climate Data Records, Remote Sens., 8, 167,
https://doi.org/10.3390/rs8030167, 2016.
L'Ecuyer, T. S., Wood, N. B., Haladay, T., Stephens, G. L., and Stackhouse,
P. W.: Impact of clouds on atmospheric heating based on the R04 CloudSat
fluxes and heating rates data set, J. Geophys. Res., 113, D00A15,
https://doi.org/10.1029/2008JD009951, 2008.
Letterly, A., Key, J., and Liu, Y.: Arctic climate: changes in sea ice extent outweigh changes in snow cover, The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, 2018.
Li, J., Huang, J., Stamnes, K., Wang, T., Lv, Q., and Jin, H.: A global survey of cloud overlap based on CALIPSO and CloudSat measurements, Atmos. Chem. Phys., 15, 519–536, https://doi.org/10.5194/acp-15-519-2015, 2015.
Liu, Y., Key, J. R., Frey, R. A., Ackerman, S. A., and Menzel, W. P.:
Nighttime polar cloud detection with MODIS, Remote Sens. Environ., 92,
181–194, https://doi.org/10.1016/j.rse.2004.06.004, 2004.
Liu, Y., Ackerman, S. A., Maddux, B. C., Key, J. R., and Frey, R. A.: Errors
in Cloud Detection over the Arctic Using a Satellite Imager and Implications
for Observing Feedback Mechanisms, J. Climate, 23, 1894–1907,
https://doi.org/10.1175/2009JCLI3386.1, 2010.
Liu, Y., Key, J. R., Ackerman, S. A., Mace, G. G., and Zhang, Q.: Arctic
cloud macrophysical characteristics from CloudSat and CALIPSO, Remote Sens.
Environ., 124, 159–173, https://doi.org/10.1016/j.rse.2012.05.006, 2012a.
Liu, Y., Key, J. R., Liu, Z., Wang, X., and Vavrus, S. J.: A cloudier Arctic
expected with diminishing sea ice, Geophys. Res. Lett., 39, L05705,
https://doi.org/10.1029/2012GL051251, 2012b.
Liu, Y., Shupe, M. D., Wang, Z., and Mace, G.: Cloud vertical distribution from combined surface and space radar–lidar observations at two Arctic atmospheric observatories, Atmos. Chem. Phys., 17, 5973–5989, https://doi.org/10.5194/acp-17-5973-2017, 2017.
Loyer, L., Raut, J.-C., Di Biagio, C., Maillard, J., Mariage, V., and Pelon, J.: Radiative fluxes in the High Arctic region derived from ground-based lidar measurements onboard drifting buoys, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-326, 2021.
Mace, G. G. and Zhang, Q.: The CloudSat radar-lidar geometrical profile
product (RL-GeoProf): Updates, improvements, and selected results, J. Geophys. Res.-Atmos., 119, 9441–9462, https://doi.org/10.1002/2013JD021374, 2014.
Mace, G. G., Zhang, Q., Vaughan, M., Marchand, R., Stephens, G., Trepte, C.,
and Winker, D.: A description of hydrometeor layer occurrence statistics
derived from the first year of merged Cloudsat and CALIPSO data, J. Geophys.
Res., 114, D00A26, https://doi.org/10.1029/2007JD009755, 2009.
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
Detection Using Cloudsat–An Earth-Orbiting 94-GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008.
Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A
comparison of simulated cloud radar output from the multiscale modeling
framework global climate model with CloudSat cloud radar observations, J.
Geophys. Res., 114, D00A20, https://doi.org/10.1029/2008JD009790, 2009.
Mioche, G., Jourdan, O., Ceccaldi, M., and Delanoë, J.: Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing, Atmos. Chem. Phys., 15, 2445–2461, https://doi.org/10.5194/acp-15-2445-2015, 2015.
Naud, C. M., Posselt, D. J., and van den Heever, S. C.: A CloudSat–CALIPSO
View of Cloud and Precipitation Properties across Cold Fronts over the
Global Oceans, J. Climate, 28, 6743–6762, https://doi.org/10.1175/JCLI-D-15-0052.1,
2015.
Persson, P. O. G.: Measurements near the Atmospheric Surface Flux Group
tower at SHEBA: Near-surface conditions and surface energy budget, J.
Geophys. Res., 107, 8045, https://doi.org/10.1029/2000JC000705, 2002.
Protat, A., Young, S. A., McFarlane, S. A., L'Ecuyer, T., Mace, G. G.,
Comstock, J. M., Long, C. N., Berry, E., and Delanoë, J.: Reconciling
Ground-Based and Space-Based Estimates of the Frequency of Occurrence and
Radiative Effect of Clouds around Darwin, Australia, J. Appl. Meteorol. Clim., 53, 456–478, https://doi.org/10.1175/JAMC-D-13-072.1, 2014.
Sassen, K. and Wang, Z.: Classifying clouds around the globe with the
CloudSat radar: 1-year of results, Geophys. Res. Lett., 35, L04805,
https://doi.org/10.1029/2007GL032591, 2008.
Sassen, K. and Wang, Z.: The Clouds of the Middle Troposphere: Composition,
Radiative Impact, and Global Distribution, Surv. Geophys., 33,
677–691, https://doi.org/10.1007/s10712-011-9163-x, 2012.
Serreze, M. C. and Stroeve, J.: Arctic sea ice trends, variability and
implications for seasonal ice forecasting, Philos. T. Roy. Soc. A, 373, 20140159, https://doi.org/10.1098/rsta.2014.0159, 2015.
Shupe, M. D.: A ground-based multisensor cloud phase classifier, Geophys.
Res. Lett., 34, L22809, https://doi.org/10.1029/2007GL031008, 2007.
Shupe, M. D.: Clouds at Arctic Atmospheric Observatories. Part II:
Thermodynamic Phase Characteristics, J. Appl. Meteorol. Clim., 50,
645–661, https://doi.org/10.1175/2010JAMC2468.1, 2011.
Shupe, M. D. and Intrieri, J. M.: Cloud Radiative Forcing of the Arctic
Surface: The Influence of Cloud Properties, Surface Albedo, and Solar Zenith
Angle, J. Climate, 17, 616–628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2, 2004.
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic Mixed-Phase Cloud
Properties Derived from Surface-Based Sensors at SHEBA, J. Atmos. Sci.,
63, 697–711, https://doi.org/10.1175/JAS3659.1, 2006.
Shupe, M. D., Walden, V. P., Eloranta, E., Uttal, T., Campbell, J. R.,
Starkweather, S. M., and Shiobara, M.: Clouds at Arctic Atmospheric
Observatories. Part I: Occurrence and Macrophysical Properties, J. Appl. Meteorol. Clim., 50, 626–644, https://doi.org/10.1175/2010JAMC2467.1, 2011.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang,
Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L.,
Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The CloudSat
Misson and the A-TRAIN, B. Am. Meteorol. Soc., 83, 1771–1790,
https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Tan, I. and Storelvmo, T.: Evidence of Strong Contributions From Mixed-Phase
Clouds to Arctic Climate Change, Geophys. Res. Lett., 46, 2894–2902,
https://doi.org/10.1029/2018GL081871, 2019.
Taylor, P. C., Kato, S., Xu, K., and Cai, M.: Covariance between Arctic sea
ice and clouds within atmospheric state regimes at the satellite footprint
level, J. Geophys. Res.-Atmos., 120, 12656–12678,
https://doi.org/10.1002/2015JD023520, 2015.
Tjernström, M. and Graversen, R. G.: The vertical structure of the lower
Arctic troposphere analysed from observations and the ERA-40 reanalysis,
Q. J. Roy. Meteor. Soc., 135, 431–443, https://doi.org/10.1002/qj.380, 2009.
Tjernström, M., Leck, C., Birch, C. E., Bottenheim, J. W., Brooks, B. J., Brooks, I. M., Bäcklin, L., Chang, R. Y.-W., de Leeuw, G., Di Liberto, L., de la Rosa, S., Granath, E., Graus, M., Hansel, A., Heintzenberg, J., Held, A., Hind, A., Johnston, P., Knulst, J., Martin, M., Matrai, P. A., Mauritsen, T., Müller, M., Norris, S. J., Orellana, M. V., Orsini, D. A., Paatero, J., Persson, P. O. G., Gao, Q., Rauschenberg, C., Ristovski, Z., Sedlar, J., Shupe, M. D., Sierau, B., Sirevaag, A., Sjogren, S., Stetzer, O., Swietlicki, E., Szczodrak, M., Vaattovaara, P., Wahlberg, N., Westberg, M., and Wheeler, C. R.: The Arctic Summer Cloud Ocean Study (ASCOS): overview and experimental design, Atmos. Chem. Phys., 14, 2823–2869, https://doi.org/10.5194/acp-14-2823-2014, 2014.
Turner, D. D.: Arctic Mixed-Phase Cloud Properties from AERI Lidar
Observations: Algorithm and Results from SHEBA, J. Appl. Meteorol., 44,
427–444, https://doi.org/10.1175/JAM2208.1, 2005.
Uttal, T. and Shupe, M.: 1 min cloud vertical properties from SHEBA experiment,
https://psl.noaa.gov/arctic/sheba/netcdf/shupeturner/microphysics/1min/, May 2022.
Uttal, T., Curry, J. A., Mcphee, M. G., Perovich, D. K., Moritz, R. E.,
Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R.,
Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A.,
Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe,
M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W.
S., Stanton, T. P., and Grenfeld, T. C.: Surface Heat Budget of the Arctic
Ocean, B. Am. Meteorol. Soc., 83, 255–275,
https://doi.org/10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2,
2002.
Vaughan, M. A., Powell, K. A., Winker, D. M., Hostetler, C. A., Kuehn, R.
E., Hunt, W. H., Getzewich, B. J., Young, S. A., Liu, Z., and McGill, M. J.:
Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar
Measurements, J. Atmos. Ocean. Tech., 26, 2034–2050,
https://doi.org/10.1175/2009JTECHA1228.1, 2009.
Vavrus, S., Waliser, D., Schweiger, A., and Francis, J.: Simulations of 20th
and 21st century Arctic cloud amount in the global climate models assessed
in the IPCC AR4, Clim. Dynam., 33, 1099–1115,
https://doi.org/10.1007/s00382-008-0475-6, 2009.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z.,
Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP
Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323,
https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Zhang, Y., Xie, S., Klein, S. A., Marchand, R., Kollias, P., Clothiaux, E.
E., Lin, W., Johnson, K., Swales, D., Bodas-Salcedo, A., Tang, S., Haynes,
J. M., Collis, S., Jensen, M., Bharadwaj, N., Hardin, J., and Isom, B.: The
ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and
Climate Models, B. Am. Meteorol. Soc., 99, 21–26,
https://doi.org/10.1175/BAMS-D-16-0258.1, 2018.
Zhao, M. and Wang, Z.: Comparison of Arctic clouds between European Center
for Medium-Range Weather Forecasts simulations and Atmospheric Radiation
Measurement Climate Research Facility long-term observations at the North
Slope of Alaska Barrow site, J. Geophys. Res., 115, D23202,
https://doi.org/10.1029/2010JD014285, 2010.
Zuidema, P., Baker, B., Han, Y., Intrieri, J., Key, J., Lawson, P.,
Matrosov, S., Shupe, M., Stone, R., and Uttal, T.: An Arctic Springtime
Mixed-Phase Cloudy Boundary Layer Observed during SHEBA, J. Atmos. Sci.,
62, 160–176, https://doi.org/10.1175/JAS-3368.1, 2005.
Short summary
Cloud detection from state-of-art satellite radar and lidar misses low-level clouds. Using in situ observations, this study confirms this cloud detection limitation over the Arctic Ocean. Impacts of this detection limitation from combined satellite radar and lidar on the monthly mean radiation flux estimations at the surface and at the top of the atmosphere in the Arctic are limited but larger from only satellite radar or satellite lidar in monthly mean and instantaneous values.
Cloud detection from state-of-art satellite radar and lidar misses low-level clouds. Using in...
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