Impact of prescribed SSTs on climatologies and long-term trends in CCM simulations

Garny, Hella; Dameris, Martin; Stenke, Andrea

doi:10.5194/acp-9-6017-2009

Cited by 31 publications

(10 citation statements)

References 53 publications

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“…This would suggest a sensitivity in some models to the SSTs, as well as the need for accurate prediction of the SSTs to simulate the recovery of ozone. See also Garny et al [2009] and Austin and Wilson [2010].…”

Section: Evolution Of the Ozone Holementioning

confidence: 99%

Chemistry‐climate model simulations of spring Antarctic ozone

Austin¹,

Struthers²,

Scinocca³

et al. 2010

J. Geophys. Res.

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[1] Coupled chemistry-climate model simulations covering the recent past and continuing throughout the 21st century have been completed with a range of different models. Common forcings are used for the halogen amounts and greenhouse gas concentrations, as expected under the Montreal Protocol (with amendments) and Intergovernmental Panel on Climate Change A1b Scenario. The simulations of the Antarctic ozone hole are compared using commonly used diagnostics: the minimum ozone, the maximum area of ozone below 220 DU, and the ozone mass deficit below 220 DU. Despite the fact that the processes responsible for ozone depletion are reasonably well understood, a wide range of results is obtained. Comparisons with observations indicate that one of the reasons for the model underprediction in ozone hole area is the tendency for models to underpredict, by up to 35%, the area of low temperatures responsible for polar stratospheric cloud formation. Models also typically have species gradients that are too weak at the edge of the polar vortex, suggesting that there is too much mixing of air across the vortex edge. Other models show a high bias in total column ozone which restricts the size of the ozone hole (defined by a 220 DU threshold). The results of those models which agree best with observations are examined in more detail. For several models the ozone hole does not disappear this century but a small ozone hole of up to three million square kilometers continues to occur in most springs even after 2070.

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Section: Evolution Of the Ozone Holementioning

confidence: 99%

Chemistry‐climate model simulations of spring Antarctic ozone

Austin¹,

Struthers²,

Scinocca³

et al. 2010

J. Geophys. Res.

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“…The simulation is identical to the SCN-B2d simulation described in Garny et al (2009), and a brief description of the model and the simulation is given in Appendix A. The demonstration of the method is largely independent of the details of the model.…”

Section: Introductionmentioning

confidence: 99%

“…the solar cycle, the quasi-biennial oscillation and major volcanic eruptions in the past are considered. Details on the simulation can be found in Garny et al (2009).…”

Section: Appendix a The E39c-a Model And Simulation Descriptionmentioning

confidence: 99%

Attribution of ozone changes to dynamical and chemical processes in CCMs and CTMs

et al. 2011

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Abstract. Chemistry-climate models (CCMs) are commonly used to simulate the past and future development of Earth's ozone layer. The fully coupled chemistry schemes calculate the chemical production and destruction of ozone interactively and ozone is transported by the simulated atmospheric flow. Due to the complexity of the processes acting on ozone it is not straightforward to disentangle the influence of individual processes on the temporal development of ozone concentrations. A method is introduced here that quantifies the influence of chemistry and transport on ozone concentration changes and that is easily implemented in CCMs and chemistry-transport models (CTMs). In this method, ozone tendencies (i.e. the time rate of change of ozone) are partitioned into a contribution from ozone production and destruction (chemistry) and a contribution from transport of ozone (dynamics). The influence of transport on ozone in a specific region is further divided into export of ozone out of that region and import of ozone from elsewhere into that region. For this purpose, a diagnostic is used that disaggregates the ozone mixing ratio field into 9 separate fields according to in which of 9 predefined regions of the atmosphere the ozone originated. With this diagnostic the ozone mass fluxes between these regions are obtained. Furthermore, this method is used here to attribute long-term changes in ozone to chemistry and transport. The relative change in ozone from one period to another that is due to changes in production or destruction rates, or due to changes in import or export of ozone, are quantified. As such, the diagnostics introduced here can be used to attribute changes in ozone on monthly, interannual and long-term time-scales to the responsible mechanisms. Results from a CCM simulation are shown here as examples, with the main focus of the paper being on introducing the method.

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“…2). Taking stratospheric ozone as an example (Q1), Braesicke and Pyle (2004) and Garny et al (2009) showed that the representation of sea surface temperatures have an impact on tropospheric and stratospheric ozone in the order of 10 %. The uncertainties in the input data give a limit to which a model can be evaluated.…”

Section: The Model Input Datamentioning

confidence: 99%

The ACCENT-protocol: a framework for benchmarking and model evaluation

et al. 2012

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Abstract.We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.

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Impact of prescribed SSTs on climatologies and long-term trends in CCM simulations

Cited by 31 publications

References 53 publications

Chemistry‐climate model simulations of spring Antarctic ozone

Chemistry‐climate model simulations of spring Antarctic ozone

Attribution of ozone changes to dynamical and chemical processes in CCMs and CTMs

The ACCENT-protocol: a framework for benchmarking and model evaluation

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