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- Mauritsen, Thorsten, et al.
(author)
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Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2
- 2019
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In: Journal of Advances in Modeling Earth Systems. - 1942-2466. ; 11:4, s. 998-1038
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Journal article (peer-reviewed)abstract
- A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low-level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two-layer model.
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| 2. |
- Kageyama, Masa, et al.
(author)
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The PMIP4 contribution to CMIP6-Part 4 : Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments
- 2017
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In: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 10:11, s. 4035-4055
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Journal article (peer-reviewed)abstract
- The Last Glacial Maximum (LGM, 21 000 years ago) is one of the suite of paleoclimate simulations included in the current phase of the Coupled Model Intercomparison Project (CMIP6). It is an interval when insolation was similar to the present, but global ice volume was at a maximum, eustatic sea level was at or close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. The LGM has been a focus for the Paleoclimate Modelling Intercomparison Project (PMIP) since its inception, and thus many of the problems that might be associated with simulating such a radically different climate are well documented. The LGM state provides an ideal case study for evaluating climate model performance because the changes in forcing and temperature between the LGM and pre-industrial are of the same order of magnitude as those projected for the end of the 21st century. Thus, the CMIP6 LGM experiment could provide additional information that can be used to constrain estimates of climate sensitivity. The design of the Tier 1 LGM experiment (lgm) includes an assessment of uncertainties in boundary conditions, in particular through the use of different reconstructions of the ice sheets and of the change in dust forcing. Additional (Tier 2) sensitivity experiments have been designed to quantify feedbacks associated with land-surface changes and aerosol loadings, and to isolate the role of individual forcings. Model analysis and evaluation will capitalize on the relative abundance of paleoenvironmental observations and quantitative climate reconstructions already available for the LGM.
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| 3. |
- Leissner, Johanna, et al.
(author)
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Climate for Culture : assessing the impact of climate change on the future indoor climate in historic buildings using simulations
- 2015
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In: Heritage Science. - : Springer Science and Business Media LLC. - 2050-7445. ; 3
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Journal article (peer-reviewed)abstract
- Background The present study reports results from the large-scale integrated EU project "Climate for Culture". The full name, or title, of the project is Climate for Culture: damage risk assessment, economic impact and mitigation strategies for sustainable preservation of cultural heritage in times of climate change. This paper focusses on implementing high resolution regional climate models together with new building simulation tools in order to predict future outdoor and indoor climate conditions. The potential impact of gradual climate change on historic buildings and on the vast collections they contain has been assessed. Two moderate IPCC emission scenarios A1B and RCP 4.5 were used to predict indoor climates in historic buildings from the recent past until the year 2100. Risks to the building and to the interiors with valuable artifacts were assessed using damage functions. A set of generic building types based on data from existing buildings were used to transfer outdoor climate conditions to indoor conditions using high resolution climate projections for Europe and the Mediterranean. Results The high resolution climate change simulations have been performed with the regional climate model REMO over the whole of Europe including the Mediterranean region. Whole building simulation tools and a simplified building model were developed for historic buildings; they were forced with high resolution climate simulations. This has allowed maps of future climate-induced risks for historic buildings and their interiors to be produced. With this procedure future energy demands for building control can also be calculated. Conclusion With the newly developed method described here not only can outdoor risks for cultural heritage assets resulting from climate change be assessed, but also risks for indoor collections. This can be done for individual buildings as well as on a larger scale in the form of European risk maps. By using different standardized and exemplary artificial buildings in modelling climate change impact, a comparison between different regions in Europe has become possible for the first time. The methodology will serve heritage owners and managers as a decision tool, helping them to plan more effectively mitigation and adaption measures at various levels.
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| 4. |
- Renoult, Martin, et al.
(author)
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A Bayesian framework for emergent constraints : case studies of climate sensitivity with PMIP
- 2020
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In: Climate of the Past. - : Copernicus GmbH. - 1814-9324 .- 1814-9332. ; 16:5, s. 1715-1735
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Journal article (peer-reviewed)abstract
- In this paper we introduce a Bayesian framework, which is explicit about prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on ordinary least squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7K (0.6-5.2, 5th-95th percentiles) using the PMIP2, PMIP3, and PMIP4 datasets for the LGM and 2.3K (0.5-4.4) with the PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7K (0.7-5.2) using the LGM and 2.3K (0.4-4.5) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a tighter constraint of 2.5K (0.8-4.0) using the restricted ensemble. We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95% probability of climate sensitivity mostly below 5K and only exceeding 6K in a single and most uncertain case assuming a large structural uncertainty. The approach is compared with other approaches based on OLS, a Kalman filter method, and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, an artefact due to a flatter regression line in the case of lack of correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation for their potential use in future probabilistic estimations of climate sensitivity.
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