Overview
This potential climate intervention technique modifies the albedo of the low clouds over water by introducing cloud condensing nuclei-effective aerosols to produce a larger amount of smaller in size cloud droplets that reflect more sunlight back into space. Latham et al. (2008) estimated that a 6% increase of the albedo of marine stratocumulus clouds can offset the warming caused by the CO2 doubling.
A natural analogue for this solar radiation modification deployment comes from ship exhaust aerosol particles directly entering marine stratocumulus clouds (Christensen and Stephens, 2011) altering cloud microphysical and macrophysical properties. Cloud albedo responses to ship tracks depends on several parameters such as the mesoscale cloud structure, the free tropospheric humidity, and cloud top height (Chen et al., 2012).
Marine Cloud Brightening - the benefits and options
The ACtIon4Cooling project will evaluate the effectiveness in producing measurable cooling of the Earth system - at global and regional scale - resulting from MCB deployment.
The project will analyse marine clouds that are co-located with ship-track signatures over the Mediterranean Sea (and elsewhere): Changes in cloud cover and reflectivity of those marine clouds with ship emission signature will be evaluated.
Marine cloud properties will come initially from the spaceborne Ultraviolet Visible Near-infrared (UVN) spectrometers, such as TROPOMI on Sentinel-5 Precursors (Veefkind et al., 2012). The TROPOMI operational algorithms for the retrieval of cloud parameters (Loyola et al., 2018) make use of Earth-shine reflectance measurements in the spectral windows of UV, VIS and NIR. Complementary information for the marine clouds captured by TROPOMI instrument will be exploited from VIIRS on Suomi-NPP. The MCB aerosol information is captured by the TROPOMI sensor in the Oxygen absorption bands (i.e., Aerosol Layer Height), and in the UV spectral window as well. In particular, the ultraviolet (UV) Absorbing Aerosol Index (AAI) is widely used as an indicator for the presence of absorbing aerosols in the atmosphere (Kooreman et al., 2020; Torres et al., 1998a). The ship-track signature of aerosols in the TROPOMI cloud retrievals will be investigated via the scientific NASA TropOMAER (TROPOMI aerosol algorithm), which simultaneously retrieves aerosol optical depth (AOD), single-scattering albedo (SSA), and the qualitative UV aerosol index (UVAI) (Torres et al., 2020).
Key knowledge gaps addressed by ACtIon4Cooling
ACtIon4Cooling addressed key knowledge gaps related to MCB through observational analysis of natural and anthropogenic analogues such as ship tracks. The project focused on:
- Identifying regions where marine low clouds exhibit high susceptibility to aerosol perturbations.
- Quantifying associated radiative and precipitation responses at regional and global scales.
- Monitoring changes in cloud microphysics and top-of-atmosphere radiative properties using Earth Observation data.
- Providing empirical constraints to improve aerosol–cloud interaction parameterizations in climate models.
- Developing methodologies to distinguish MCB-like signals from natural variability and broader anthropogenic aerosol effects.
Summary & Results
For MCB the vessel density maps from the European Marine Observation and Data Network (EMODnet) were used for defining where the ships are located. The primary information on cloud properties was acquired from Sentinel-5 Precursor/TROPOMI. Complementary information for the clouds captured by TROPOMI instrument was taken from VIIRS on Suomi-NPP. The TROPOMI NO₂ Tropospheric Vertical Column Densities (VCDs) were analyzed to quantify shipping-related nitrogen dioxide enhancements along major maritime corridors in the Mediterranean Sea and North Eastern Atlantic.
The shipping emissions can be systematically detected in the NO2 Tropospheric column. The sign of the perturbation is always positive; the magnitude of the NO2 perturbation is large (~30% for the Mediterranean region). On the contrary, the perturbations of the cloud parameters may change sign from day to day. The natural variability of the clouds masks the signal of the modification due to the ship-emitted particles at their cloud base.
Therefore, the automatic ship-track detection in all conditions could be challenging with the use of Machine Learning (ML) techniques. The primary goal is to develop a ship-track detection model accurate enough to enable the estimation of local pixel-by-pixel cloud perturbations, computed as the difference between ship-affected pixels and background reference pixels within the same scene and meteorological regime. The latter is only possible with densely populated ship-relevant datasets which could be used for the training of a ML classifier. Until that trustworthy ship-track detection model is built, the most robust way to quantify cloud perturbations due to ships is the regional mean perturbation formula (i.e., the difference of the mean of ship-affected pixels per day and grid box minus the mean of background pixels per day and grid box).
The regional daily perturbation dataset (ship-mean versus background-mean approach) is more directly aligned with policy-relevant detectability questions. By aggregating signals at regional and daily scales, it reflects how monitoring systems for SRM would likely be operationalized in practice. This approach enables statistical robustness and provides a bridge between process-level understanding and operational climate intervention monitoring strategies.
The satellite-observed ship-affected marine cloud perturbations were reproduced in the ICON simulation to evaluate the global impacts of MCB. Pairs of global simulations were performed for attribution of effects, with and without the observations-based cloud perturbation. In the perturbed simulation, the liquid water path was increased by 1%, as suggested by observations over the region of interest. For the observations-derived perturbation of a mere 1%, no clear perturbation to the top-of-atmosphere radiation budget or surface air temperature is detected within the region of interest suggesting that the imposed perturbation is masked by signals arising from cloud adjustments. In turn, for a strong perturbation of a factor of 10, a regional effective radiative forcing of 15 Wm-2 was obtained, with little perturbation to the top-of-atmosphere radiation budget outside the region of interest.
In consequence, there is no discernible perturbation of temperatures in the observations-tied perturbation. For the strong perturbation, in turn, surface air temperature increased locally by up to 0.5K, suggesting the relevance of Earth system feedbacks for the analysis of MCB climate effects. Precipitation responses extend beyond the region of imposed perturbation, reflecting the strong coupling between latent heating, large-scale circulation, and atmospheric energy balance. A similar spatial pattern of precipitation response is obtained for the strong and the weak perturbation simulations. This suggests that the precipitation changes are instead dominated by internal variability and rapid adjustment processes.
References
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