Climate processes

Abstract:

<jats:p>Abstract. The impact of aerosol–radiation and aerosol–cloud interactions on the radiative forcing is subject to large uncertainties. This is caused by the limited understanding of aerosol optical properties and the role of aerosols as cloud condensation/ice nuclei (CCN/IN). On the other hand, aerosol optical properties and vertical distribution are highly related, and their uncertainties come from different processes. This work attempts to quantify the sensitivity of aerosol optical properties (i.e. aerosol optical depth; AOD) and their vertical distribution (using the extinction coefficient, backscatter coefficient, and concentrations' species profiles) to key processes. In order to achieve this objective, sensitivity tests have been carried out, using the WRF-Chem regional fully coupled model by modifying the dry deposition, sub-grid convective transport, relative humidity, and wet scavenging. The 2010 Russian heatwave–wildfires episode has been selected as case study. Results indicate that AOD is sensitive to these key processes in the following order of importance: (1) modification of relative humidity, causing AOD differences of up to 0.6; (2) modification of vertical convection transport with AOD differences around −0.4; and (3) the dry deposition with AOD absolute differences of up to −0.35 and 0.3. Moreover, these AOD changes exhibit a nonlinear response. Both an increase and a decrease in the RH result in higher AOD values. On the other hand, both the increase and offset of the sub-grid convective transport lead to a reduction in the AOD over the fire area. In addition, a similar nonlinear response is found when reducing the dry deposition velocity; in particular, for the accumulation mode where the concentration of several species increases (while a decrease might be expected). These nonlinear responses are highly dependent on the equilibrium of the thermodynamics system sulfate–nitrate–SOA (secondary organic aerosol). In this sense, small changes in the concentration of one species can strongly affect others, finally affecting aerosol optical properties. Changes in this equilibrium could come from modifications in relative humidity, dry deposition, or vertical convective transport. By itself, dry deposition also presents a high uncertainty influencing the AOD representation. </jats:p>

Abstract:

The radiative forcing of the aerosol–radiation interaction can be decomposed into clear-sky and cloudy-sky portions. Two sets of multi-model simulations within Aerosol Comparisons between Observations and Models (AeroCom), combined with observational methods, and the time evolution of aerosol emissions over the industrial era show that the contribution from cloudy-sky regions is likely weak. A mean of the simulations considered is 0.01±0.1 W m−2. Multivariate data analysis of results from AeroComPhase II shows that many factors influence the strength of the cloudy-sky contribution to the forcing of the aerosol–radiation interaction. Overall, single-scattering albedo of anthropogenic aerosols and the interaction of aerosols with the short-wave cloud radiative effects are found to be important factors. A more dedicated focus on the contribution from the cloud-free and cloud-covered sky fraction, respectively, to the aerosol–radiation interaction will benefit the quantification of the radiative forcing and its uncertainty range.

Abstract:

The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.