CL3.1.3

How to cite: Menary, M., Allan, R., Robson, J., Booth, B., Cassou, C., Gregory, J., Hodson, D., Jones, C., Mignot, J., Ringer, M., Sutton, R., Wilcox, L., Zhang, R., and Gastineau, G.: Aerosol-forced AMOC changes in CMIP6 historical simulations., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8693, https://doi.org/10.5194/egusphere-egu21-8693, 2021.

Previous work has shown that anthropogenic aerosol emissions drive a strengthening in the Atlantic Meridional Overturning Circulation (AMOC) in CMIP6 historical simulations over ~1850-1985. However, the mechanisms driving the increase are not fully understood. Previously, forced AMOC changes have been linked to changes in surface heat fluxes, changes in salinity, and interhemispheric energy imbalances. Here we will show that across CMIP6 historical simulations there is a strong correlation between ocean heat loss from the subpolar North Atlantic and the forced change in the AMOC. Furthermore, the model spread in the surface heat flux change explains the spread of the AMOC response and is correlated with the strength of the models’ aerosol forcing.  However, the AMOC change is not strongly related to changes in downwelling surface shortwave radiation over the North Atlantic, showing that anthropogenic aerosols do not drive AMOC change through changes in the local surface radiation budget. Rather, by separating the models into those with ‘strong’ and ‘weak’ aerosol forcing, we show that aerosols appear to predominantly imprint their impact on the AMOC through changes in surface air temperature over the Northern Hemisphere and the consequent impact on latent and sensible heat flux. This thermodynamic driver (i.e. more heat loss from the North Atlantic) is enhanced both by the increase in the AMOC itself, which acts as a positive feedback, and by a response in atmospheric circulation. 

How to cite: Robson, J., Menary, M., Gregory, J., Jones, C., Sinha, B., Stevens, D., Sutton, R., and Wilcox, L.: How does aerosol forcing drive a strengthening of the AMOC in CMIP6 historical simulations?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8913, https://doi.org/10.5194/egusphere-egu21-8913, 2021.

As the global climate is warming, with important regional differences, there is a growing need to (i) better understand whether and how internal variability controls the regional warming trends, and (ii) to identify the regions in which both the trends and the superimposed interannual variability are predictable. In this study we investigate trends, variability and predictive skill of the upper ocean heat content in the North Atlantic basin. This region is a source of decadal variability, in which internal ocean processes can locally modulate the global warming trends and add additional prediction skill. The analysis is focused on the period 1970-2014, and combines the study of an ensemble of ocean reanalyses, with two sets of CMIP6 experiments performed with the Earth system model EC-Earth3: (i) a 10-member historical ensemble; and (ii) an initialized 10-member retrospective decadal prediction system. External forcings are found to be important for the development of the regional trends, but on their own are unable to reproduce the exact geographical pattern. Our results also show that not all regions in the North Atlantic are equally predictable, which is explained by different contributions of the forcings and internal climate variability. While high levels of predictive skill in regions like the Eastern Subpolar North Atlantic, or the Irminger and Iceland Seas are clearly enabled by initialization, with a negligible influence of the external forcings, skill in others areas like the Subtropical North Atlantic, or the Gulf Stream extension mostly comes from the externally forced trends. The Labrador Sea is a particular case where predictive skill has both an external and internal origin. Large observational and modeling uncertainties affect the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain its very poor predictive skill. We would like to acknowledge the financial support from FCT through projects FCT-UIDB/50019/2020 and PD/BD/142785/2018.

How to cite: Carmo-Costa, T., Bilbao, R., Ortega, P., Teles-Machado, A., and Dutra, E.: Trends, variability and predictive skill of the ocean heat content in North Atlantic: An analysis with the EC-Earth3 model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3077, https://doi.org/10.5194/egusphere-egu21-3077, 2021.

How to cite: Bellomo, K., Angeloni, M., Corti, S., and von Hardenberg, J.: Future climate change scenarios shaped by inter-model differences in Atlantic Meridional Overturning Circulation response, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8452, https://doi.org/10.5194/egusphere-egu21-8452, 2021.

How to cite: Bretones, A., Hestnes Nisancioglu, K., and Fjalstad Jensen, M.: Deep-water formation and Arctic circulation under sea-ice retreat: a comparison between CMIP6 models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12155, https://doi.org/10.5194/egusphere-egu21-12155, 2021.

As one of the major carbon sinks in the global ocean, the North Atlantic is a key player in mediating and ameliorating the ongoing global warming. Projections of the North Atlantic carbon sink in a high-CO₂ future vary greatly among models, with some showing that a slowdown in carbon uptake has already begun and others predicting that this slowdown will not occur until nearly 2100.

Discrepancies among models largely originate because of differences in the efficiency of the high-latitude transport of carbon from the surface to the deep ocean. This transport occurs through biological production, deep convection and subsequent transport via the deep western boundary current. For an ensemble of 11 CMIP5-models, we studied the efficiency of this transport and identified two indicators of contemporary model behavior that are highly correlated with a model´s projected future carbon-uptake. The first indicator is the high latitude summer pCO₂^sea-anomaly of a model, which is tightly linked to winter mixing and nutrient supply, but also to deep convection. The second indicator is the fraction of the anthropogenic carbon-inventory stored below 1000-m depth, indicating how efficient carbon is transported into the deep ocean. By comparing to the observational database, these indicators allow us to better constrain the model ensemble, and demonstrate that the models with more efficient surface to deep transport are best aligned with current observations. These models also show the largest future North Atlantic carbon uptake, which we then conclude is the more plausible future evolution. We further study if the high correlations between our contemporary indicators and a model´s future North Atlantic carbon uptake is also upheld for the next model generation, CMIP6. We hypothesize that this is the case and that our indicators can not only help us to constrain the CMIP6 model ensemble but also inform us about progress made between CMIP5 and CMIP6 in terms of North Atlantic carbon uptake, winter mixing, nutrient supply, deep convection and transport of carbon into the deep ocean.

How to cite: Goris, N., Tjiputra, J., Ohlsen, A., Schwinger, J., Lauvset, S., and Jeansson, E.: Efficient carbon drawdown allows for a high future carbon uptake in the North Atlantic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3894, https://doi.org/10.5194/egusphere-egu21-3894, 2021.

The Atlantic Meridional Overturning Circulation (AMOC) is a key mechanism of poleward heat transport and an important part of the global climate system. How it responded to past changes inforcing, such as experienced during Quaternary interglacials, is an intriguing and open question. Previous modelling studies suggest an enhanced AMOC in the mid-Holocene compared to the pre-industrial period. In previous simulations from the Palaeoclimate Modelling Intercomparison Project (PMIP), this arose from feedbacks between sea ice and AMOC changes, which also depended on resolution. Here I present aninitial analysis of the recently available PMIP4 simulations. This shows the overall strength of the AMOC does not markedly change between the mid-Holocene and piControl experiments (at least looking at the maximum of the mean meridional mass overturning streamfunction below 500m at 30^oN and 50^oN). This is not inconsistent with the proxy reconstructions using sortable silt and Pa/Th for the mid-Holocene. Here we analyse changes in the spatial structure of the meridional overturning circulation, along with their fingerprints on the surface temperature (computed through regression). We then estimate the percentage of the simulated surface temperature changes between the mid-Holocene and pre-industrial period that can be explained by AMOC. Furthermore, the analysis for the changes in the AMOC spatial structure has been extended to see if the same patterns of change hold for the last interglacial. The simulations will be compared to existing proxy reconstructions, as well as new palaeoceanographic reconstructions.

How to cite: Jiang, Z., Brierley, C., Thornalley, D., and Sax, S.: The Atlantic Meridional Overturning Circulation (AMOC) simulated during interglacials and its impact on climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3432, https://doi.org/10.5194/egusphere-egu21-3432, 2021.

A pronounced multi-centennial variability of the Atlantic meridional overturning circulation (AMOC) is found to be regulated by the salinity exchanges between the Atlantic and Arctic ocean in the IPSL-CM6A-LR atmosphere-ocean coupled model. The AMOC variations are preceded by salinity-driven density anomalies in the main deep convection sites in the Labrador and Greenland seas. Associated with a strong AMOC, the Arctic sea ice export through the Fram Strait reduces due to the decreased sea ice volume and anomalous northward currents. Anomalous freshwater hence accumulates at the surface in the Central Arctic. Meanwhile, the enhanced Atlantic inflow enters the Arctic through the Barents Sea and leads to a positive salinity in the Eastern Arctic subsurface. The surface freshwater anomalies last for 4 to 5 decades before they eventually reach the Lincoln Sea north of Greenland. The associated oceanic currents around Greenland reorganize, favoring the anomalous Arctic freshwater export to the North Atlantic and intensifying the stratification in deep convection sites. The AMOC then weakens, and the Central Arctic presents a positive surface salinity anomaly in turn. The oscillation switches to the opposite phase. These AMOC and sea ice fluctuations modulate climate worldwide, with a strong AMOC leading to a warming of 0.4°C in the northern extratropics, reaching up to 1°C in the Arctic lower troposphere during winter. In all seasons, a northward displacement of the intertropical convergence zone is also simulated.

How to cite: Jiang, W., Gastineau, G., and Codron, F.: Atlantic multi-centennial variability in IPSL-CM6A-LR climate model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7336, https://doi.org/10.5194/egusphere-egu21-7336, 2021.

The Atlantic Multidecadal Variability (AMV) is a key factor in modulating climate change and its impacts around the world. Therefore, understanding of its physical mechanism will be crucial to achieving predictability on decadal timescales. However, details of the mechanism are not fully understood. This is evident in the wide range of simulated AMV timescales and spatial patterns exhibited by climate models in both pre-industrial and historical simulations.

In this study, we assess the impact of model resolution on the internal AMV mechanism by taking advantage of the close physical similarities between the medium- and low-resolution versions of the HadGEM3 models. Here, we present results from analysing the N96ORCA1 (~135km atmosphere, 1° ocean) and N216ORCA025 (~60km, 0.25°) pre-industrial simulations.

At both resolutions, we found that the internal AMV has a timescale of 70-100 years, comparable to the observed record. The processes driving decadal SST variability varies by latitude. Ocean heat transport changes associated with the AMOC drive subpolar variability, while surface fluxes associated with cloud and wind changes are more important in the subtropics. The AMOC strengthening is induced by density forcing from two sources. First, a Labrador Sea surface cooling driven by low-frequency positive NAO leads the AMOC by 5 years. Second, a source of anomalously saline Arctic water flowing into the subpolar North Atlantic also leads the AMOC by 5 years. Interestingly, the two resolutions disagree on the relative importance of these AMOC drivers. In the lower resolution model, the Arctic contribution is more important. However, the NAO dominates in the medium resolution model, and decadal NAO variability is more strongly associated with the AMV. Differences between the models are likely due to mean state differences including the strength and position of ocean currents such as the Gulf Stream, and their impacts on upper ocean properties.

How to cite: Lai, M., Robson, J., Wilcox, L., and Dunstone, N.: Internal Atlantic Multidecadal Variability mechanism at two model resolutions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8747, https://doi.org/10.5194/egusphere-egu21-8747, 2021.

Tired of downloading tons of model results? Is your internet connection flakey? Are you about to overload your computer’s memory with the constant increase of data volume and you need more computing resources? You can request free of charge computing time at one of the supercomputers of the Infrastructure of the European Network of Earth System modelling (IS-ENES)¹, the European part of Earth System Grid Federation (ESGF)², which also hosts and maintains more than 6 Petabytes of CMIP6 and CORDEX data.

Thanks to this new EU Comission funded service, you can run your own scripts in your favorite programming language and straightforward pre- and post-process model data. There is no need for heavy data transfer, just load with one line of code the data slice you need because your script will directly access the data pool. Therefore, days-lasting calculations will be done in seconds. You can test the service, we very easily provide pre-access activities.

In this session we will run Jupyter notebooks directly on the German Climate Computing Center (DKRZ)³, one of the ENES high performance computers and a ESGF data center, showing how to load, filter, concatenate, take means, and plot several CMIP6 models to compare their results, use some CMIP6 models to calculate some climate indexes for any location and period, and evaluate model skills with observational data. We will use Climate Data Operators (cdo)⁴ and Python packages for Big Data manipulation, as Intake⁵, to easily extract the data from the huge catalog, and Xarray⁶, to easily read NetDCF files and scale to parallel computing. We are continuously creating more use cases for multi-model evaluation, mechanisms of variability, and impact analysis, visit the demos, find more information, and apply here: https://portal.enes.org/data/data-metadata-service/analysis-platforms.

[1] https://is.enes.org/
[2] https://esgf.llnl.gov/
[3] https://www.dkrz.de/
[4] https://code.mpimet.mpg.de/projects/cdo/
[5] https://intake.readthedocs.io/en/latest/
[6] http://xarray.pydata.org/en/stable/

How to cite: Moreno de Castro, M., Kulüke, M., Wachsmann, F., Kwee-Hinzmann, R., Kindermann, S., Nassisi, P., Levavasseur, G., Fiore, S., Pascoe, C., Juckes, M., Morellon, S., and Joussaume, S.: Skip high-volume data transfer and access free computing resources for your CMIP6 multi-model analyses, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2513, https://doi.org/10.5194/egusphere-egu21-2513, 2021.