tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Geoscientific Model Development Copernicus GmbH 17:13 (2024) 5309-5330
Abstract:
<jats:p>Abstract. There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. </jats:p>General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path
Atmospheric Chemistry and Physics Copernicus GmbH 24:12 (2024) 7331-7345
Abstract:
<jats:p>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. </jats:p>Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometre-Scale Models
(2024)
Isolating aerosol-climate interactions in global kilometre-scale simulations
EGU Sphere European Geosciences Union (2024)
Abstract:
Anthropogenic aerosols are a primary source of uncertainty in future climate projections. Changes to aerosol concentrations modify cloud radiative properties, radiative fluxes and precipitation from the micro to the global scale. Due to computational constraints, we have been unable to explicitly simulate cloud dynamics, leaving key processes, such as convective updrafts parameterized. This has significantly limited our understanding of aerosol impacts on convective clouds and climate. However, new state-of-the-art climate models running on exascale supercomputers are capable of representing these scales. In this study, we use the kilometre-scale earth system model ICON to explore, for the first time, the global response of clouds and precipitation to anthropogenic aerosol via aerosol-cloud-interactions (ACI) and aerosol-radiation-interactions (ARI). In our month-long simulations, we find that the aerosol impact on clouds and precipitation exhibits strong regional dependence, highlighting the complex interplay with atmospheric dynamics. The impact of ARI and ACI on clouds in isolation shows some consistent behaviour, but the magnitude and additive nature of the effects are regionally dependent. This behaviour suggests that the findings of isolated case studies from regional simulations may not be representative, and that ARI and ACI processes should both be accounted for in modelling studies. The simulations also highlight some limitations to be considered in future studies. Differences in internal variability between the simulations makes large-scale comparison difficult after the initial 10 – 15 days. Longer averaging periods or ensemble simulations will be beneficial for perturbation experiments in future kilometre-scale model simulations.Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans
(2024)