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Together with the University of Cologne and AWI the IUP currently is participating in the RV Polarstern cruise PS144 ArcWatch2. From onboard the ship we are measuring atmosphere and sea ice properties with microwave radiometers and radars (upper right) to improve numerical weather prediction and satellite observations. During ice stations (upper left) the physical properties of the sea ice and snow are measured in detail: for example, the size of snow grains, ice thickness, salinity, and temperature. This information is needed to interpret the microwave measurements on the ship and also from satellites. During the ice station, the upward-looking cloud radar observed cirrus clouds (lower left), which you can also see in the lower right and upper left photos. To also observe the sea ice thickness on a larger scale, helicopter flights are conducted with an instrument called EM-bird, hanging below the helicopter (lower right photo). Find more information under https://follow-polarstern.awi.de and https://blog.uni-koeln.de/awares/ (photo credits Janna Rückert and Linnea Bühler).

The AWI-IUP Phytooptics-Group participated in the RV Polarstern expedition PS143-2 in Fram Strait (East Greenland Sea) from 12 July to 5 August 2024 where the biodiversity and biogeochemistry of ocean surface water was studied in comparison to ocean physical properties.

From optical measurements the Phytooptics-Group determined the spectral absorption of under water light by particles in general and phytoplankton specifically. First results are shown from measurements on water samples measured with the quantitative filtration technique using an integrative cavity absorption measurement system (details in Liu et al. 2018: https://doi.org/10.1364/OE.26.00A678)

The absorption properties are used to determine from the spectral shape and strength of the absorption the composition of the phytoplankton, but here the overall biomass (chlorophyll concentration) from the peak height around 667 nm is presented but also. While the eastern part shows higher concentrations which is associated to the Atlantic water, the western part shows very low chlorophyll concentration. Overall the biomass is no very high, indicating rather post algal bloom conditions.

Figure from Astrid Bracher (bracher@uni-bremen.de).

Conventional steel production is a major contributor to global industrial CO2 emissions due to its reliance on carbon-rich materials. To meet climate targets, the systematic decarbonisation of the steel industry is essential, with one key approach being the transition to hydrogen-based production, which produces water vapour instead of CO2. Another by-product of the conventional steelmaking process is carbon monoxide (CO), which can be better monitored with satellite sensors from space than the greenhouse gas CO2 itself. Consequently, co-emitted CO may serve as a valuable indicator of the carbon footprint of steel plants.

The figure shows an example of CO plumes from the two most productive steel production sites in Germany detected by a single overpass of the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. By systematically analysing the satellite data over the long term, the CO emissions of all German integrated steelworks can be determined (Schneising et al., 2024). If the estimated CO emissions of these steel production sites are related to the associated CO2 emissions reported by the steel manufacturers for the same period, there is a very high correlation between CO and CO2 emissions across all analysed sites. This linear relationship justifies using CO as a proxy for CO2 emissions from comparable steel production sites, i.e. the greenhouse gas emissions of modern conventional steel plants can be specifically derived from space using the corresponding estimated CO emissions and the CO/CO2 emission ratio calibrated to German steelworks.

Reference

Schneising, O., Buchwitz, M., Reuter, M., Weimer, M., Bovensmann, H., Burrows, J. P., and Bösch, H.: Towards a sector-specific CO∕CO2 emission ratio: satellite-based observations of CO release from steel production in Germany, Atmos. Chem. Phys., 24, 7609–7621, https://doi.org/10.5194/acp-24-7609-2024, 2024.

Aerosols are small suspended liquid or solid particles in the terrestrial atmosphere. A recent study conducted by the IUP group "Cloud Aerosol Surface PArameter Retrieval" in the framework of the transregional project (AC)⊃3; investigated the spatial and temporal distribution of aerosols in the Arctic.
Arctic aerosols are important drivers of the Arctic climate due to their ability to act as cloud condensation nuclei or ice nucleating particles. As a consequence of their poorly known distribution and physico-chemical characterization, Arctic clouds, which in turn are strong Arctic climate agents, are also poorly understood.

The image shows the Aerosol Optical Depth (AOD), a parameter describing the total atmospheric aerosol load. The AOD is derived from satellite observations and modeled by GEOS-Chem, a chemical transport model. The satellite observations are based on a novel algorithm which has been developed and quality-assessed by the group. On the other hand, the model results, reflect state-of-the-art understanding of aerosol processes in the Arctic. Relating satellite based observations and model will therefore tell us how much of our (model) understanding is confirmed by the observations or not.

The seasonal AOD over the central Arctic cryosphere is averaged over the period 2003-2011. The stacked plot shows the GEOS-Chem model and the black dots the mean AOD derived from satellite data. Obviously the model results are different from the satellite data in summer. This indicates weaknesses of the model with respect to the distribution and characterization of aerosols during Arctic summer. This in turn can lead to significant problems in the characterization of Arctic clouds. Further investigations are in progress.

Reference: Swain, B., Vountas, M., Singh, A., Anchan, N. L., Deroubaix, A., Lelli, L., Ziegler, Y., Gunthe, S. S., Bösch, H., and Burrows, J. P.: Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer, Atmos. Chem. Phys., 24, 5671–5693, https://doi.org/10.5194/acp-24-5671-2024, 2024.

Kontakt/contact: Marco Vountas: vountas@iup.physik.uni-bremen.de

https://www.iup.uni-bremen.de/aerosol

The latitudinal variation of the mean difference (md) and standard deviation (sdev) between TROPOMI tropospheric ozone column retrieved using the four algorithms, ESA (European Space Agency, magenta) and our CPC (red), CLC (blue), and CLCT (yellow) retrievals, and collocated ozone sonde columns during the time period from 2018 to 2022. All TROPOMI algorithms use the convective-cloud-differential (CCD) method that subtracts from the clear-sky total ozone column the column amount above high convective clouds (above-cloud-column-ozone, ACCO) to obtain tropospheric ozone column amounts (TrOC). ESA and CPC use ACCO only from the Pacific region to obtain TrOC over the entire tropical region, while the advanced algorithms CLC and CLCT use ACCO from nearby clouds (local cloud algorithm). The latter two algorithms lead to a better agreement of TROPOMI tropospheric ozone with collocated ozone sondes and make this algorithm also more suitable for application in the subtropics and extratropics (e.g. middle latitudes). Tropospheric ozone is a potent greenhouse gas and a pollutant, harmful to our ecosystem. Global measurements of tropospheric ozone are only available from satellite instruments like TROPOMI.

Reference: Maratt Satheesan, S., Eichmann, K.-U., Burrows, J. P., Weber, M., Stauffer, R., Thompson, A. M., and Kollonige, D.: Improved CCD tropospheric ozone from S5P TROPOMI satellite data using local cloud fields, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2825, 2024.

Our figure of the month shows the gross primary production (GPP) from Coupled Model Intercomparison Project Phase 6 (CMIP6; red; Eyring et al., 2016) and CMIP5 (blue) models with (dashed) and without (solid) interactive nitrogen cycle (Ncycle) compared to reference data sets based on measurements of the FLUXNET eddy covariance tower network: a data set upscaled with the model tree ensembles (MTE) approach and FLUXCOM data, which additionally incorporate Moderate Resolution Imaging Spectroradiometer (MODIS) as well as meteorological data using various machine learning methods. Furthermore, both simulations with prescribed CO2emissions (e, darker colors) and prescribed concentrations (c, lighter colors) are used. GPP represents the CO2 uptake on land due to photosynthesis. This was one of the biggest weaknesses of the CMIP5 ensemble, with most models overestimating photosynthesis. In the figure, this can be seen by the large overestimation of non-Ncycle CMIP5 compared to the reference data sets. The CMIP6 non-Ncycle MMMs show a much better approximation across all latitudes, with a slight reduction of the bias in the NH but a significant overestimation in the tropics. The CMIP6 Ncycle models show a very good agreement with the reference data across all latitudes, now with slight underestimations at high latitudes.

This figure is part of Gier et al (2024), which investigates carbon cycle variables in CMIP6 and CMIP5 simulations and is currently under discussion for Biogeosciences. Gier et al. (2024) concludes, that there is a significant improvement in the simulation of photosynthesis in models with nitrogen cycle, as well as only small differences between emission and concentration-based simulations. Thus, it recommends to use emission driven simulations in the upcoming CMIP7 simulations as default setup as well as to view the nitrogen cycle as a necessary part of all future carbon cycle models.

The study was supported by the Climate-Carbon Interactions in the Current Century (4C) project, which is funded by the EU under the Horizon 2020 program. The figure was made using the Earth System Model Evaluation Tool v2 (ESMValTool; Eyring et al., 2020). ESMValTool includes many diagnostics to use with CMIP models and observations, as well as common preprocessor functions, such as the computation of multi-model means, area averages, and the derivation of custom variables, which were used for this work.

Related projects:

Climate-Carbon Interactions in the Current Century (4C)
Coupled Model Intercomparison Project (CMIP)

REFERENCES

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci Model Dev, 9, 1937-1958, 10.5194/gmd-9-1937-2016, 2016.

Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L. P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383-3438, 10.5194/gmd-13-3383-2020, 2020.

Gier, B. K., Schlund, M., Friedlingstein, P., Jones, C. D., Jones, C., Zaehle, S., and Eyring, V.: Representation of the Terrestrial Carbon Cycle in CMIP6, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-277, 2024.

Since climate model studies project a decline of the Atlantic Meridional Overturning Circulation (AMOC) in the 21st century, monitoring AMOC changes remains essential. While AMOC variability is expected to be coherent across latitudes on longer than decadal timescales, connectivity on inter-annual and seasonal timescales is less clear. Model studies and observational estimates disagree on the regions and timescales of meridional connectivity and AMOC observations at multiple latitudes are needed to study its connectivity. We calculate basin-wide AMOC volume transports (1993-2018) from measurements of the North Atlantic Changes (NOAC) array at 47°N, combining data from moored instruments with hydrography and satellite altimetry. The mean NOAC AMOC is 17.2\,Sv exhibiting no long-term trend. Both the unfiltered and low-pass filtered NOAC AMOC show a significant correlation with the RAPID-MOCHA-WBTS AMOC at 26°N when the NOAC AMOC leads by about one year.

More information:

Wett, S., M. Rhein, D. Kieke, C. Mertens, und M. Moritz (2023), Meridional connectivity of a 25-year observational AMOC record at 47°N. Geophysical Research Letters, 50, e2023GL103284, doi:10.1029/2023GL103284.

Vertical distributions of the stratospheric aerosol extinction coefficient were retrieved at the University of Bremen from measurements of scattered solar light in the limb viewing geometry made by the OMPS-LP instrument of NASA/NOAA. The obtained data enable us to investigate the evolution of the aerosol plume after the eruption of the Hunga Tonga–Hunga Haʻapai volcano in January 2022.

The left panel shows the zonal mean aerosol extinction coefficient distribution in tropics. The plume maximum reached the altitude of about 26 km and remained at these altitudes until the middle of May 2022. Thereafter, a rapid sinking of the plume maximum is observed. The right panel shows the vertical optical depth of the stratospheric aerosol (18 – 30 km). The plume mostly propagated to the southern hemisphere. Some transport events into the northern hemisphere are also seen, e.g. at the end of 2022. At the end of the analyzed record (December 2023), elevated values of the stratospheric aerosol optical depth are still observed in the tropics and southern mid-latitudes.

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