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acp.copernicus.org

Abstract. Mesoscale bands develop within winter cyclones as concentrated regions of locally enhanced radar reflectivity, often corresponding to intensified precipitation rates lasting several hours. Surface precipitation characteristics are governed by the microphysical properties of the ice-phase particles aloft, yet their unique microphysical evolutionary pathways and ambient environmental dependencies in banded regions remain poorly understood, in part due to a paucity of observations within clouds. Addressing this need, the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms measured properties of winter cyclones from airborne in situ and remote sensing platforms. Observations collected within a banded region of a decaying-stage northeast United States cyclone revealed a microphysical pathway characterized by precipitation fallout from a weak generating cell layer through an ∼ 2 km deep subsaturated downdraft region. Sublimation was a dominant evolutionary process, resulting in a > 70 % reduction in the initial characteristic ice water content (IWC). This vertical evolution was reproduced by a one-dimensional (1D) particle-based model simulation constrained by observations, conveying accuracy in the process representation. Four sensitivity simulations assessed evolutionary dependencies based on observationally informed perturbations of the ambient relative humidity, RH, and vertical air motion, w. Perturbations of ∼ 2 % RH significantly varied the resultant characteristic IWC loss, by as much as 29 %, whereas comparable perturbations of w had negligible effects. Intrinsic particle evolution during sublimation demonstrated a notable imprint on vertical profiles of radar reflectivity, but the Doppler velocity was more strongly governed by the ambient w profile. These findings contextualize radar-based discrimination of sublimation from other ice-phase processes, including riming and aggregation.

gmd.copernicus.org

Abstract. Spatial predictions of total organic carbon (TOC) concentrations and stocks are crucial for understanding marine sediments’ role as a significant carbon sink in the global carbon cycle. In this study, we present a geospatial prediction of global TOC concentrations and stocks on a 5 × 5 arcmin grid, using a novel neural network approach. We also provide and apply a new compilation of over 21 000 global TOC measurements and a new set of predictors, including features such as seafloor lithologies, benthic oxygen fluxes, and chlorophyll-a satellite data. Moreover, we compare different machine learning models based on their performance metrics and predictions and assess their strengths and limitations. For the dataset used, we find that the performance metrics of the models are comparable and that the neural network approach outperforms, on unseen data, methods such as k-nearest neighbours and random forests, which tend to overfit the training data. We provide estimates of mean TOC concentrations and stocks, both on continental shelves and in deep-sea settings across various marine regions and oceans. Our model suggests that the upper 10 cm of oceanic sediments harbour approximately 156 Pg of TOC stocks and have a mean TOC concentration of 0.61 %. Furthermore, we introduce a standardized methodology for quantifying predictive uncertainty using Monte Carlo dropout. The method was applied to our neural network model and underlying features to generate a map of information gain that measures the expected increase in model knowledge, achieved through additional sampling at specific locations, which is pivotal for sampling strategy planning.

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nature.com

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