the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Feb 2025
An observational estimate of Arctic UV-absorbing aerosol direct radiative forcing on instantaneous and climatic scales
Abstract. Using co-located satellite observations from the Aqua Moderate resolution Imaging Spectroradiometer, the Aqua Cloud and the Earth Radiant Energy System, the Special Sensor Microwave Imager / Sounder, and the Ozone Monitoring Instrument, we investigated changes in absorbing aerosol direct radiative forcing (ADRF) in the spring through fall Arctic from 2005 – 2020 through an observation based method, assisted by a neural network for estimating cloud and aerosol free sky Top-of-Atmosphere (TOA) radiative fluxes, and an innovative, Monte-Carlo-based method for estimating uncertainties in derived ADRF values. This study suggests that Arctic ADRF is a strong function of observing conditions, and changes in Arctic sea ice concentrations and cloud properties introduce a complex scenario for estimating ADRF. For example, the TOA ADRF reverses sign from negative (cooling) to positive (warming) for sea ice concentration above 60 % for a region with a relatively cloud free scene. ADRF trends over Arctic land surfaces are primarily negative. Strong negative ADRF trends of up to -4 Wm-2 were found over northern Russia and northern Canada in the summer months. Both positive and negative ADRF trends were found over the Arctic Ocean in the boreal summer, though these trends are much weaker than the over-land trends. Positive ADRF trends in the Arctic Ocean north of northeastern Russia and northern Canada are as high as +1.0 Wm-2 per study period. The trend results suggest that increasing amounts of absorbing aerosols in the Arctic have a cooling effect from TOA that could act to counter Arctic warming.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(3096 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3096 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-80', Anonymous Referee #1, 22 Feb 2025
- AC1: 'Reply on RC1', Blake Sorenson, 05 Jun 2025
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RC2: 'Comment on egusphere-2025-80', Anonymous Referee #2, 29 Apr 2025
This study presents an interesting data-driven approach, combining satellite observations, neural networks, and Monte Carlo uncertainty estimation, to derive Arctic absorbing aerosol direct radiative forcing (ADRF) trends over a 15-year period. The topic is highly relevant and timely, given the sensitivity of the Arctic climate. The manuscript is generally clear, but several areas require improvement to strengthen its scientific rigor and clarity.
- Data quality is critical for this analysis. Although the authors utilize several satellite products, many of these have primarily been validated over low- to mid-latitudes. Thorough validation over the Arctic region is necessary. More importantly, uncertainties associated with cloud, aerosol, and surface classification must be quantified. How reliable are the cloud-free and aerosol-free conditions as defined? Similar validation is needed for other aerosol and cloud products.
- The authors should clarify whether all retrieved data were used or if any quality control measures (e.g., quality flags) were applied.
- The dataset used for training the neural network appears limited, which could introduce biases, particularly given the uncertain data quality. This needs careful discussion.
- Figure 5 is not very informative for understanding the neural network architecture. A clearer schematic illustrating the network structure and flow is recommended.
- CERES data are used as the reference for shortwave flux (SWF) validation. However, the authors must first assess and validate the accuracy of CERES data specifically over the Arctic.
- The method used for trend estimation should be described in detail. Was data uncertainty incorporated into the trend analysis? In Section 4.3, the error analysis is not pixel-based—how representative is this approach?
- On line 564, the authors assume that daily Level 3 ADRF errors are normally distributed. This assumption should be justified with supporting analysis.
Citation: https://doi.org/10.5194/egusphere-2025-80-RC2 - AC2: 'Reply on RC2', Blake Sorenson, 05 Jun 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-80', Anonymous Referee #1, 22 Feb 2025
The authors use a data-driven approach (based on long-term satellite observations + neural networks + Monte Carlo methods) to study the impact of Arctic sea ice cover on absorbing aerosol direct radiative forcing (ADRF) and reveal its long-term trend. The following issues should be addressed before publication:
- The study’s methodology is not highly innovative, as NN + data has been used in previous studies. However, the scientific findings seem more novel, particularly regarding the impact of sea ice on ADRF and the long-term trend. The literature review should more clearly compare this work with existing studies to confirm the novelty of the sea ice-ADRF relationship, as many GCM studies have already estimated Arctic ADRF and explored aerosol-sea ice interactions.
- The 50+ samples used to train the NN might not be sufficient to represent all atmospheric conditions in the Arctic fully. If the NN primarily learns radiation fluxes from low-aerosol regions while the studied region has aerosols at higher altitudes, SWFcln may have systematic bias, leading to ADRF estimation errors. Stronger independent validation is needed to ensure the reliability of NN predictions.
- In the section on neural network design on pages 11-12 (lines 300-315), the authors mention: “All nodes in the hidden layer use the Leaky Rectified Linear Unit (LeakyReLU) activation (Maas et al., 2013), with this activation function having been identified to provide the best performance after testing with other activation functions.” Why does LeakyReLU provide the best performance？ It is suggested that comparative test results for different activation functions (such as ReLU, Sigmoid, etc.) be provided and that the specific advantages of LeakyReLU in handling TOA radiation flux estimation be explained.
- Monte Carlo methods quantify uncertainty but cannot verify potential systematic biases. How can the authors confirm that NN-predicted SWFcln has no systematic bias?
- Can Figure 2 include quantitative data on misclassification, such as the percentage of smoke misidentified as clouds?
- Why does ADRF shift from negative (cooling) to positive (warming) at a critical sea ice concentration of approximately 60%? Why is 60% the turning point? How does aerosol-surface multiple scattering influence ADRF?
Citation: https://doi.org/10.5194/egusphere-2025-80-RC1 - AC1: 'Reply on RC1', Blake Sorenson, 05 Jun 2025
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RC2: 'Comment on egusphere-2025-80', Anonymous Referee #2, 29 Apr 2025
This study presents an interesting data-driven approach, combining satellite observations, neural networks, and Monte Carlo uncertainty estimation, to derive Arctic absorbing aerosol direct radiative forcing (ADRF) trends over a 15-year period. The topic is highly relevant and timely, given the sensitivity of the Arctic climate. The manuscript is generally clear, but several areas require improvement to strengthen its scientific rigor and clarity.
- Data quality is critical for this analysis. Although the authors utilize several satellite products, many of these have primarily been validated over low- to mid-latitudes. Thorough validation over the Arctic region is necessary. More importantly, uncertainties associated with cloud, aerosol, and surface classification must be quantified. How reliable are the cloud-free and aerosol-free conditions as defined? Similar validation is needed for other aerosol and cloud products.
- The authors should clarify whether all retrieved data were used or if any quality control measures (e.g., quality flags) were applied.
- The dataset used for training the neural network appears limited, which could introduce biases, particularly given the uncertain data quality. This needs careful discussion.
- Figure 5 is not very informative for understanding the neural network architecture. A clearer schematic illustrating the network structure and flow is recommended.
- CERES data are used as the reference for shortwave flux (SWF) validation. However, the authors must first assess and validate the accuracy of CERES data specifically over the Arctic.
- The method used for trend estimation should be described in detail. Was data uncertainty incorporated into the trend analysis? In Section 4.3, the error analysis is not pixel-based—how representative is this approach?
- On line 564, the authors assume that daily Level 3 ADRF errors are normally distributed. This assumption should be justified with supporting analysis.
Citation: https://doi.org/10.5194/egusphere-2025-80-RC2 - AC2: 'Reply on RC2', Blake Sorenson, 05 Jun 2025
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Jianglong Zhang
Jeffrey S. Reid
Peng Xian
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3096 KB) - Metadata XML
The authors use a data-driven approach (based on long-term satellite observations + neural networks + Monte Carlo methods) to study the impact of Arctic sea ice cover on absorbing aerosol direct radiative forcing (ADRF) and reveal its long-term trend. The following issues should be addressed before publication: