ORCID Profile
0000-0002-0008-5943
Current Organisation
ETH Zurich
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IOP Publishing
Date: 25-08-2023
Abstract: Very large volcanic eruptions have substantial impacts on the climate, causing global cooling and major changes to the hydrological cycle. While most studies have focused on changes to mean climate, here we use a large ensemble to assess the impact on extreme climate for three years following tropical and extratropical eruptions of different sulfur emission strength. We focus on the impact of an extremely large eruption, injecting 40 Tg sulfur into the stratosphere, which could be expected to occur approximately twice a millennium. Our findings show that the eruption would have a profound effect on large areas of the globe, resulting in extremely rare drought events that under normal circumstances would occur once every century becoming very likely. Several regions such as West Africa, South and East Asia and the Maritime continent are particularly affected with the expected climate shifting well outside the usual range, by up to five standard deviations. These results have important consequences as they indicate that a severe drought in multiple breadbasket regions should be expected following a large eruption. The risk of heavy rainfall tends to decrease over the same regions but by a reduced amount, heatwaves become extremely rare, however the chance of extreme Winter cold surges do not increase by a corresponding amount, since widespread parts of the Northern Hemisphere display a winter warming. Our results show that the location of the eruption is crucial for the change in extremes, with overall changes larger for a Northern Hemisphere eruption than a tropical and Southern Hemisphere eruption, although there is a regional dependency. Simulations of different eruptions with similar forcing distributions but with different sizes are consistent with a linear relationship, however for smaller eruptions the internal variability tends to become dominant and the effect on extreme climate less detectable.
Publisher: Springer Science and Business Media LLC
Date: 29-05-2021
DOI: 10.1007/S00382-021-05821-W
Abstract: We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-9811
Abstract: & & In a collaborative effort, data management specialists at the German Climate Computing Centre (Deutsches Klimarechenzentrum, DKRZ) and researchers at the Max Planck Institute for Meteorology (MPI-M) are joining forces to achieve long-term and effective global availability of a high-volume flagship climate model dataset: the MPI-M Grand Ensemble (MPI-GE, Maher et al. 2019& sup& & /sup& ), which is the largest ensemble of a single state-of-the-art comprehensive climate model (MPI-ESM1.1-LR) currently available. The MPI-GE has formed the basis for a number of scientific publications over the past 4 years& sup& & /sup& . However, the wealth of data available from the MPI-GE simulations was essentially invisible to potential data users outside of DKRZ and MPI-M.& & & & In this contribution, we showcase the adopted strategy, experiences made and the current status of FAIR long-term preservation of the MPI-GE dataset in the World Data Center for Climate (WDCC), hosted at DKRZ. The importance of synergistic cooperation between domain-expert data providers and knowledgeable repository staff will be highlighted.& & & & Recognising the demand for MPI-GE data access outside of its native environment, the development of a strategy to make MPI-GE data globally available began in mid 2018. A two-stage dissemination reservation process was decided upon.& & & & In a first step, MPI-GE data would be published and made globally available via the Earth System Grid Federation (ESGF) infrastructure. Second, the ESGF-published data would be transferred to DKRZ& #8217 s long-term and FAIR archiving service WDCC. Datasets preserved in the WDCC can be made accessible via ESGF - global access via the established system would thus still be ensured.& & & & To date, the first stage of the above process is completed and data are available via the ESGF& sup& & /sup& . Data published in the ESGF has to comply with strict data standards in order to ensure efficient data retrieval and interoperability of the dataset. Standardization of the MPI-GE data required selection of an applicable data standard (CMIP5 in this case) and an appropriate variable subset, adaptation and application of fit-for-purpose DKRZ-supplied post-processing software and of course the post-processing of the data itself. All steps required dedicated communication and collaboration between DKRZ and MPI-M staff and required significant time resources. Currently, some 87 TB, comprised of more than 55 000 records, of standardized MPI-GE data are available for search and download from the ESGF. About three to four thousand records with an accumulated volume of several hundred GB are downloaded by ESGF users each month.& & & & The long-term archival of the standardized MPI-GE data using DKRZ& #8217 s WDCC-service is planned to begin within the first half of 2020. All preparatory work done so far, especially the data standardization, significantly reduces the effort and resources required for achieving FAIR MPI-GE data preservation in the WDCC.& & & & & sup& & /sup& Maher, N. et al. ( 2019). J. Adv. Model Earth Sy., 11, 2050& #8211 2069. 0.1029/2019MS001639& & & & & sup& & /sup& www.mpimet.mpg.de/en/grand-ensemble ublications/& & & & & sup& & /sup& esgf-data.dkrz.de rojects/mpi-ge/& & & & & br& & br& & &
Publisher: American Geophysical Union (AGU)
Date: 07-2019
DOI: 10.1029/2019MS001639
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10531
Abstract: Projecting how temperature variability is likely to change in the future is important for understanding future extreme events. This comes from the fact that such extremes can change due to both changes in the mean climate and its variability. The recent IPCC report found large regions of low model agreement in the change of temperature variability in both December, January, February (DJF) and June, July, August (JJA) when considering 7 Single Model Initial-Condition Large Ensembles (SMILEs). In this study we use the framework described by Suarez-Gutierrez et al, (2021) to constrain future projections of temperature variability by selecting the SMILEs that best represent observed variability. We use 11 SMILEs with CMIP5 and CMIP6 forcing and consider 9 ocean regions and 24 land regions. We then assess, for both DJF and JJA, whether temperature variability projections are constrained by selecting for models capture observed variability in in idual regions and seasons. We consider projected changes at various warming levels to account for differences in warming between models and the use of different future scenarios across CMIP5 and 6. We identify MPI-GE and CESM2 as the SMILEs that capture observed variability sufficiently. across most regions (29 & 30 out of 33 in DJF and 28 and 26 in JJA respectively). Whether temperature variability projections are constrained depends on both season and region. For ex le, in DJF over South East Asia the constraint does not change the already large spread of projections. Conversely, over the Amazon the constraint tells us temperature variability will increase in DJF whereas the entire model archive does not agree on the sign of the change. This method can be used to better constrain our uncertainty in temperature variability projections by selecting SMILEs that best represent observed variability.
No related grants have been discovered for Laura Suarez-Gutierrez.