Spatial-Temporal Variation of Snow Black Carbon Concentration in Snow Cover in Northeast China from 2001 to 2016 Based on Remote Sensing

The amount of black carbon (BC) on snow surface can significantly reduce snow surface albedo in the visible-light range and change the surface radiative forcing effect. Therefore, it is key to study regional and global climate changes to understand the BC concentration on snow. In this study, we simulated the BC concentration on the surface snow of northeast China using an asymptotic radiative transfer model. From 2001 to 2016, the BC concentration showed no significant increase, with an average increase of 82.104 ng/g compared with that in the early 21st century. The concentration of BC in December was the largest (1344.588 ng/g) and decreased in January and February (1248.619 ng/g and 983.635 ng/g, respectively). The high black carbon content centers were concentrated in the eastern and central regions with dense populations and concentrated industries, with a concentration above 1200 ng/g, while the BC concentration in the southwest region with less human activities was the lowest (below 850 ng/g), which indicates that human activities played an important role in snow BC pollution. Notably, Heilongjiang province has the highest concentration, which may be related to its atmospheric stability in winter. These findings suggest that the BC pollution in northeast China has been aggravated from 2001 to 2016. It is estimated that the snow surface albedo will decrease by 16.448% due to the BC pollution of snow in northeast China. The problem of radiative forcing caused by black carbon to snow reflectivity cannot be ignored.

Hyperspectral Satellite Remote Sensing of Aerosol Parameters: Sensitivity Analysis and Application to TROPOMI/S5P

Precise knowledge about aerosols in the lower atmosphere (optical properties and vertical distribution) is particularly important for studying the Earth’s climatic and weather conditions. Measurements from satellite sensors in sun-synchronous and geostationary orbits can be used to map distributions of aerosol parameters in global or regional scales. The new-generation sensor Tropospheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P) measures a wide variety of atmospheric trace gases and aerosols that are associated with climate change and air quality using a number of spectral bands between the ultraviolet and the shortwave infrared. In this study, we perform a sensitivity analysis of the forward model parameters and instrument information that are associated with the retrieval accuracy of aerosol layer height (ALH) and optical depth (AOD) using the oxygen (O2) A-band. Retrieval of aerosol parameters from hyperspectral satellite measurements requires accurate surface representation and parameterization of aerosol microphysical properties and precise radiative transfer calculations. Most potential error sources arising from satellite retrievals of aerosol parameters, including uncertainties in aerosol models, surface properties, solar/satellite viewing geometry, and wavelength shift, are analyzed. The impact of surface albedo accuracy on retrieval results can be dramatic when surface albedo values are close to the critical surface albedo. An application to the real measurements of two scenes indicates that the retrieval works reasonably in terms of retrieved quantities and fit residuals.

Attribution of the spatial heterogeneity of Arctic surface albedo feedback to the dynamics of vegetation, snow and soil properties and their interactions

The Arctic warming rate is triple the global average, which is partially caused by surface albedo feedback (SAF). Understanding the varying pattern of SAF and the mechanisms is therefore critical for predicting future Arctic climate under anthropogenic warming. To date, however, how the spatial pattern of seasonal SAF is influenced by various land surface factors remains unclear. Here, we aim to quantify the strengths of seasonal SAF across the Arctic and to attribute its spatial heterogeneity to the dynamics of vegetation, snow and soil as well as their interactions. The results show a large positive SAF above -5%·K-1 across Baffin Island in January and eastern Yakutia in June, while a large negative SAF beyond 5%·K-1 is observed in Canada, Chukotka and low latitudes of Greenland in January and Nunavut, Baffin Island and Krasnoyarsk Krai in July. Overall, a great spatial heterogeneity of Arctic land warming induced by positive SAF is found with a coefficient of variation (CV) larger than 61.5%, and the largest spatial difference is detected in wintertime with a CV > 643.9%. Based on the optimal parameter-based geographic detector model, the impacts of snow cover fraction (SCF), land cover type (LC), normalized difference vegetation index (NDVI), soil water content (SW), soil substrate chemistry (SC) and soil type (ST) on the spatial pattern of positive SAF are quantified. The rank of determinant power is SCF > LC > NDVI > SW > SC > ST, which indicates that the spatial patterns of snow cover, land cover and vegetation coverage dominate the spatial heterogeneity of positive SAF in the Arctic. The interactions between SCF, LC and SW exert further influences on the spatial pattern of positive SAF in March, June and July. This work could provide a deeper understanding of how various land factors contribute to the spatial heterogeneity of Arctic land warming at the annual cycle.

Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale

Methane (CH4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Remote sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of the instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance, and detector exposure times by using a realistic instrument noise model. In addition, we formally analyzed the impact of spectrally complex surface albedo features on retrievals using the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modeling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH4 plume over several natural and artificial surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 mole m−2 or 1.0 % of total atmospheric CH4 column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modeling framework provides the basis for assessing tradeoffs for future remote sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision, as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH4 concentration fields to determine methane sources accurately and efficiently at scale.

Lab-Controlled Experimental Evaluation of Heat-Reflective Coatings by Increasing Surface Albedo for Cool Pavements in Urban Areas

Many studies were conducted to find possible strategies for reducing the urban heat island (UHI) effect during the hot summer months. One of the largest contributors to UHI is the role that paved surfaces play in the warming of urban areas. Solar-reflective cool pavements stay cooler in the sun than traditional pavements. Pavement reflectance can be enhanced by using a reflective surface coating. The use of heat-reflective coatings to combat the effects of pavements on UHI was previously studied but no consistent conclusions were drawn. To find a conclusive solution, this work focuses on the abilities of heat-reflective pavement coatings to reduce UHI in varying weather conditions. Within this context, both concrete and asphalt samples were subject to a series of performance tests when applied to a heat-reflective coating, under the influence of normal, windy, and humid conditions. During these tests, the samples were heated with a halogen lamp and the surface temperature profile was measured using an infrared thermal camera. The air temperature was recorded with a thermometer, and the body temperature at multiple depths of the samples was measured using thermocouples. The results from these tests show that the effectiveness of the heat-reflective coating varies under different weather conditions. For instance, the coated samples were about 1 °C cooler for concrete and nearly 5 °C cooler for asphalt, on average. However, this temperature difference was reduced significantly under windy conditions. As such, the findings from this work conclude that the heat-reflective coatings can effectively cool down the pavement by increasing the surface albedo, and thus might be a viable solution to mitigate UHI impacts in the city/urban areas.

Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders – Part II: impact on NO₂ retrieval and mitigation strategies

Operational retrievals of tropospheric trace gases from space-borne spectrometers are based on one-dimensional radiative transfer models. To minimize cloud effects, trace gas retrievals generally implement Lambertian cloud models based on radiometric cloud fraction estimates and photon path length corrections. The latter relies on measurements of the oxygen collision pair (O2-O2) absorption at 477 nm or on the oxygen A-band around 760 nm. In reality however, the impact of clouds is much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighboring pixels and cloud shadow effects, such that unresolved three-dimensional effects due to clouds may introduce significant biases in trace gas retrievals. In order to quantify this impact, we study NO2 as a trace gas example, and apply standard retrieval methods including approximate cloud corrections to synthetic data generated by the state-of-the-art three-dimensional Monte Carlo radiative transfer model MYSTIC. A sensitivity study is performed for simulations including a box-cloud, and the dependency on various parameters is investigated. The most significant bias is found for cloud shadow effects under polluted conditions. Biases depend strongly on cloud shadow fraction, NO2 profile, cloud optical thickness, solar zenith angle, and surface albedo. Several approaches to correct NO2 retrievals under cloud shadow conditions are explored. We find that air mass factors calculated using fitted surface albedo or corrected using the O2-O2 slant column density can partly mitigate cloud shadow effects. However, these approaches are limited to cloud-free pixels affected by surrounding clouds. A parameterization approach is presented based on relationships derived from the sensitivity study. This allows identifying measurements for which the standard NO2 retrieval produces a significant bias, and therefore provides a way to improve the current data flagging approach.

The surface energy balance of Austre Lovénbreen, Svalbard, during the ablation period in 2014

The ability to simulate the surface energy balance is key to studying land–atmosphere interactions; however, it remains a weakness in Arctic polar sciences. Based on the analysis of meteorological data from 1 June to 30 September 2014 from an automatic weather station on the glacier Austre Lovénbreen, near Ny–Ålesund, Svalbard, we established a surface energy balance model to simulate surface melt. The results reveal that the net shortwave radiation accounts for 87% (39 W m–2) of the energy sources, and is controlled by cloud cover and surface albedo. The sensible heat equals 6 W m–2 and is a continuous energy source at the glacier surface. Net longwave radiation and latent heat account for 31% and 5% of heat sinks, respectively. The simulated summer mass balance equals –793 mm w.e., agreeing well with the observation by an ultrasonic ranger.