Simulations to explore impact of calibration of model parameters on climate sensitivity

Raw data used in study, in preparation, by Tett et al. PP data, is a format used by the Met Office for its weather and climate model data output. Data can be read by the Iris Python module from conda-forge. See https://scitools.org.uk/iris/docs/latest/ for documentaton on the package. ## Access ## This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh users. External users are very welcome to request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page once the paper has been accepted. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: http://www.ed.ac.uk/is/research-support/datavault .This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to multiple parameters and the strength of the climate feedbacks investigated with a suite of standard coupled simulations. This dataset supports a manuscript which, at the time of deposit, is still in preparation.Tett, S; Mineter, M "Simulations to explore impact of calibration of model parameters on climate sensitivity" [dataset] (2019) Edinburgh DataVault. https://doi.org/10.7488/84b585fc-57d2-4e5a-b3a3-694f70534a0

Automated parameter tuning applied to sea ice in a global climate model

Raw data used in study by Roach et al. PP data, is a format used by the Met Office for its weather and climate model data output. Data can be read by the iris python module from conda-forge. See https://scitools.org.uk/iris/docs/latest/ for documentaton on the package. ## Access ## This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh users. External users are very welcome to request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: http://www.ed.ac.uk/is/research-support/datavault .This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters result in a simulated sea-ice time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence from observed Antarctic trends and mean regional sea ice distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology. This simple approach provides evidence for the contribution of parameter uncertainty to spread in sea ice extent trends and could be customized to investigate uncertainties in other climate variables.Tett, Simon "Automated parameter tuning applied to sea ice in a global climate model" (2019) [dataset] Edinburgh DataVault https://doi.org/10.7488/758c86a0-765c-495d-b776-f728c500857

10 member ensemble of HadCM3 simulations

Numerical climate output from the HadCM3 climate model simulation runs produced for the ERC funded TITAN project covering the time period 1780-2009 . These data were produced by Andrew Schurer, using the HadCM3 climate model on the EDDIE supercomputer at the University of Edinburgh and the forcings follow the CMIP5 convention. The data is at monthly resolution and is mainly in NetDCF format. Due to a data processing error only data for 9 ensemble members exist for the AMOC and the sea ice coverage fields. The model data was first described and used in the publication: Brönnimann, S., Franke, J., Nussbaumer, S.U. et al. Last phase of the Little Ice Age forced by volcanic eruptions. Nat. Geosci. 12, 650–656 (2019). https://doi.org/10.1038/s41561-019-0402-y. And was then used in the particle filter experiments: Schurer, A. P., Hegerl, G. C., Goosse, H., Bollasina, M. A., England, M. H., Mineter, M. J., Smith, D. M., and Tett, S. F. B. (2023): Quantifying the contribution of forcing and three prominent modes of variability on historical climate, Clim. Past Discuss. And. Schurer, A., Hegerl, G. C., Goosse, H., Bollasina, M. A., England, M. H., Smith, D., & Tett, S. F. (2023). Role of multi-decadal variability of the winter North Atlantic oscillation on northern hemisphere climate. Environmental Research Letters

Continuation part of CMIP5/PMIP3 HadCM3 past1000 simulation

Numerical climate output from the HadCM3 climate model simulation runs for the NERC funded Euroclim500 project: Causes of change in European mean and extreme climate over the past 500 years. These data were produced by Andrew Schurer, Mike Mineter and Simon Tett using the HadCM3 climate model on the HECTOR supercomputer at the University of Edinburgh. They have been published as part of a larger collection at CEDA - but are here in CMOR format. On CEDA, this simulation is labelled experiment ALL, ensemble 1. The first part of this simulation 850-1849 is the PMIP3/CMIP5 past1000 run. The part archived here (1850-2000) is the historical continuation part. The format is identical to that on the PMIP/CMIP database. The simulations form the historical continuation from the CMIP5/PMIP3 HadCM3 past1000 simulation and start from the past1000 experiment in 01/1850. The data is all at monthly resolution and is contained in one compressed folder which is subdivided into atmosphere, ocean, land, sea ice and land ice variables, following the CMIP5 convention.Schurer, A; Mineter, M; Tett, S. (2021). Continuation part of CMIP5/PMIP3 HadCM3 past1000 simulation, 1850-2000 [dataset]. University of Edinburgh. School of GeoSciences. https://doi.org/10.7488/ds/2995

Does model calibration reduce uncertainty in climate projections?

Uncertainty in climate projections is large as shown by the likely uncertainty ranges in Equilibrium Climate Sensitivity (ECS) of 2.5-4K and in the Transient Climate Response (TCR) of 1.4-2.2K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles which were objectively calibrated to minimise differences from observed large scale atmospheric climatology, uncertainties in ECS and TCR are about two to six times smaller than in the CMIP5 or CMIP6 multi-model ensemble. We also find that projected uncertainties in surface temperature, precipitation and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea-ice feedbacks. The 20+ year old HadAM3 standard model configuration simulates observed hemispheric scale observations and pre-industrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimised configurations simulates these better than almost all the CMIP5 and CMIP6 models. Hemispheric scale observations and pre-industrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 though the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimised HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parametrisation schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor

Particle filter HadCM3 simulation

Numerical climate output from the HadCM3 climate model simulation runs produced for the ERC funded TITAN project covering the time period 1780-2009 using a particle-filter data-assimilation approach. These data were produced by Andrew Schurer, using the HadCM3 climate model on the EDDIE supercomputer at the University of Edinburgh and the forcings follow the CMIP5 convention. These simulations investigate the role of three modes of climate variability, the North Atlantic Oscillation, El-Niño Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near free running model simulation with the time-evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated, and evaluate to what extent these are confounded with the forced response. A description of this dataset can be found in: Schurer, A.P., Hegerl, G.C., Goosse, Bollasina, M.A., England, M.H., Mineter, M.J., Smith, D.M., and Tett, S.F.B. (2022). Quantifying the contribution of forcing and three prominent modes of variability on historical climate. Climate of the Past Discussions, 1-25 - doi.org/10.5194/cp-2022-55