Using the fast impact of anthropogenic aerosols on regional land temperature to constrain aerosol forcing

Anthropogenic aerosols have been postulated to have a cooling effect on climate, but its magnitude remains uncertain. Using atmospheric general circulation model simulations, we separate the land temperature response into a fast response to radiative forcings and a slow response to changing oceanic conditions and find that the former accounts for about one fifth of the observed warming of the Northern Hemisphere land during summer and autumn since the 1960s. While small, this fast response can be constrained by observations. Spatially varying aerosol effects can be detected on the regional scale, specifically warming over Europe and cooling over Asia. These results provide empirical evidence for the important role of aerosols in setting regional land temperature trends and point to an emergent constraint that suggests strong global aerosol forcing and high transient climate response

Using the fast impact of anthropogenic aerosols on regional land temperature to constrain aerosol forcing

Anthropogenic aerosols have been postulated to have a cooling effect on climate, but its magnitude remains uncertain. Using atmospheric general circulation model simulations, we separate the land temperature response into a fast response to radiative forcings and a slow response to changing oceanic conditions and find that the former accounts for about one fifth of the observed warming of the Northern Hemisphere land during summer and autumn since the 1960s. While small, this fast response can be constrained by observations. Spatially varying aerosol effects can be detected on the regional scale, specifically warming over Europe and cooling over Asia. These results provide empirical evidence for the important role of aerosols in setting regional land temperature trends and point to an emergent constraint that suggests strong global aerosol forcing and high transient climate response

Deep Image Harmonization

Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods