Climate processes

Abstract:

The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a deep learning algorithm to detect unreported industry sites in high-resolution satellite data. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement data sets of heavy industry sites.

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.

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.

Abstract:

Despite their small size, aerosols strongly influence Earth's climate. Aerosols scatter and absorb radiation referred to as aerosol-radiation interactions but also modify the properties of clouds, as cloud droplets form on aerosol particles, referred to as aerosol-cloud interactions. Kilometer-scale simulations allow us to examine long-standing questions related to these interactions. Such simulations resolve atmospheric motions on scales of a few kilometers and represent important atmospheric processes like convective updrafts that were parameterized previously. Regional simulations revealed significant effects of aerosols on convective clouds and provided insights into the underlying processes and drivers. To examine these interactions with the climate model ICON, we developed the simple aerosol model HAMlite based on and fully traceable to the complex aerosol model HAM. HAMlite represents aerosols as an ensemble of log-normal modes. To reduce the computational and physical complexity, aerosol microphysics are discarded and aerosol sizes and compositions are prescribed. The selection of modes is flexible and can include the Aitken, accumulation, and coarse modes. The calculation of aerosol properties and thermodynamics remains fully consistent with HAM. HAMlite is linked to the atmospheric processes of ICON. Aerosols are transported as tracers in the dynamical core and coupled to the radiation, turbulence, and cloud microphysics schemes.We present first results from global simulations with ICON-HAMlite. The atmosphere is governed by non-hydrostatic conservation equations, the land is represented with the dynamic vegetation model JSBACH, and the sea surface temperature and sea ice are prescribed with the AMIP database. The horizontal resolution is about 5 km and time period is about 40 days. First, we evaluate the global distributions of the different aerosol modes. And second, we investigate how aerosols influence the diurnal cycle and deep convection in the tropics. In contrast to regional simulations, global simulations include the large-scale circulation and in particular the budgetary constraints on precipitation due to the conservation of water and energy.

Abstract:

Aerosol-cloud interactions remain a large source of uncertainty in anthropogenic climate forcing. One of the reasons for this uncertainty is the confounding role of meteorology, influencing both aerosols and cloud properties. To untangle these variables, ship tracks, the clouds polluted by shipping emissions, have been widely studied. Recently, the use of shipping emissions locations and amounts, combined with reanalysis winds, has allowed us to study polluted clouds by following ship emissions to the locations they are advected to by the time of a satellite measurement of clouds. This is possible even when no visible tracks appear in satellite images. Here, we additionally use emission amounts data and investigate their effect on key cloud characteristics like droplet numbers and liquid water. This per-ship emissions data is valuable as it allows us to investigate cloud property changes stratified by region or meteorology. Between the ships with the lowest and highest emissions, droplet number anomalies increase by an order of magnitude from 0.25% to 2.5%, but the effect saturates at high emissions. We furthermore present evidence that increases of liquid water are insensitive to the amount of aerosol increases. Crossing data with a set of machine-learning detected ship tracks, we show that emissions amount has a similarly saturating effect on the formation of visible tracks as on droplet number, increasing roughly linearly for a large range of emissions before saturating (and even declining) at high emissions. The saturation of cloud responses at relatively high emissions could indicate that clouds react strongly to reductions in aerosol emissions.