DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience

ArXiv 2306.06269 (2023)

Authors:

Wenlu Sun, Yao Sun, Chenying Liu, Conrad M Albrecht

Publisher Correction: Sea surface warming patterns drive hydrological sensitivity uncertainties

Nature Climate Change Nature Research 13:9 (2023) 997-997

Authors:

Shipeng Zhang, Philip Stier, Guy Dagan, Chen Zhou, Minghuai Wang

Sea surface warming patterns drive hydrological sensitivity uncertainties

Nature Climate Change Springer Nature (2023) 545-553

Authors:

Shipeng Zhang, Philip Stier, Guy Dagan, Chen Zhou, Minghuai Wang

Abstract:

The increase in global-mean precipitation with global-mean temperature (hydrological sensitivity; η) is constrained by the atmospheric energy budget, but its magnitude remains uncertain. Here we apply warming patch experiments to a climate model to demonstrate that the spatial pattern of sea surface warming can explain a wide range of η. Warming in tropical strongly ascending regions produces η values even larger than suggested by the Clausius–Clapeyron relationship (7% K−1), as the warming and moisture increases can propagate vertically and be transported globally through atmospheric dynamics. Differences in warming patterns are as important as different treatments of atmospheric physics in determining the spread of η in climate models. By accounting for the pattern effect, the global-mean precipitation over the past decades can be well reconstructed in terms of both magnitude and variability, indicating the vital role of the pattern effect in estimating future intensification of the hydrological cycle.

Biomass Estimation and Uncertainty Quantification from Tree Height

ArXiv 2305.09555 (2023)

Authors:

Qian Song, Conrad M Albrecht, Zhitong Xiong, Xiao Xiang Zhu

Rapid saturation of cloud water adjustments to shipping emissions

EGU Sphere European Geosciences Union (2023) egusphere-2023-813

Authors:

Peter Manshausen, Duncan Watson-Parris, Matthew Christensen, Jukka-Pekka Jalkanen, Philip Stier

Abstract:

Human aerosol emissions change cloud properties by providing additional cloud condensation nuclei. This increases cloud droplet numbers, which in turn affects other cloud properties like liquid water content, and ultimately cloud albedo. These adjustments are poorly constrained, making aerosol effects the most uncertain part of anthropogenic climate forcing. Here we show that cloud droplet number and water content react differently to changing emission amounts in shipping exhausts. We use information about ship positions and modelled emission amounts together with reanalysis winds and satellite retrievals of cloud properties. The analysis reveals that cloud droplet numbers respond linearly to emission amount over a large range (1–10 kg h−1), before the response saturates. Liquid water increases in raining clouds, and increases are constant over the emission ranges observed. There is evidence that this is due to compensating effects under rainy and non-rainy conditions, consistent with suppression of rain by enhanced aerosol. This has implications for our understanding of cloud processes and may improve the way clouds are represented in climate models, in particular by changing parameterizations of liquid water responses to aerosol.