AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium IEEE (2024) 2023-2027

Authors:

Chenying Liu, Hunsoo Song, Anamika Shreevastava, Conrad M Albrecht

Climatic & Anthropogenic Hazards to the Nasca World Heritage: Application of Remote Sensing, AI, and Flood Modelling

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium IEEE (2024) 2212-2215

Authors:

Masato Sakai, Marcus Freitag, Akihisa Sakurai, Conrad M Albrecht, Hendrik F Hamann

Multi-Label Guided Supervised Contrastive Learning for Earth Observation Pretraining

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium IEEE (2024) 7568-7571

Authors:

Yi Wang, Conrad M Albrecht, Xiao Xiang Zhu

Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium IEEE (2024) 7040-7044

Authors:

Chenying Liu, Conrad M Albrecht, Yi Wang, Xiao Xiang Zhu

General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path

Atmospheric Chemistry and Physics European Geosciences Union 24:12 (2024) 7331-7345

Authors:

Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S Ackerman, Ann M Fridlind, Florian Tornow, Susanne E Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W Christensen, Adam C Varble, L Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G Partridge, Philip Stier, Toshihiko Takemura

Abstract:

General circulation models' (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs' inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between the present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in the simulated liquid water path due to the parameterized precipitation suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree.