Experiment List & Goals

Version 1.0  Drafted by John Scinocca, Tim Stockdale & Francois Lott

a) Present-Day Climate: Identify and distinguish the properties of and mechanisms underlying the different model simulations of the QBO in present-day conditions:

EXPERIMENT 1: AMIP – specified interannually varying SSTs, sea ice, and external forcings

EXPERIMENT 2: 1xCO2 - identical simulation to the AMIP above except employing repeated annual cycle SSTs, sea ice, and external forcings

These experiments will allow an evaluation of the realism of modelled QBOs under present-day climate conditions, employing diagnostics and metrics discussed in Section 5. The impact of interannual forcing on the model QBO can also be assessed, and Experiment 2 is a control for the climate projection experiments. 

b) Climate Projections: Subject each modelled QBO contribution to an external forcing that is similar to that typically applied for climate projections:

EXPERIMENT 3: 2xCO2 - identical to Experiment 2, but with a change in CO2 concentration and specified SSTs and sea ice appropriate for a 2xCO2 world

EXPERIMENT 4: 4xCO2 - identical to Experiment 2 but with a change in CO2 concentration and specified SSTs and sea ice appropriate for a 4xCO2 world

The response of the QBO, its forcing mechanisms, and its impact/influence will be evaluated by the same set of diagnostics used for diagnosing Experiments 1 and 2, but representing the response 2xCO2 - 1xCO2 and 4xCO2 - 1xCO2.  Obvious questions that will arise:

  • What is the spread/uncertainty of the forced model response?
  • Do different model contributions cluster in any particular way?
  • Can a connection/correlation be made between QBOs with similar metrics/diagnostics in present day climate and their response to CO2 forcing?

The hope is that this sort of sensitivity experiment might indicate what aspects of modelled QBOs determine the spread, or uncertainty, of the QBO response to CO2 forcing.  These aspects are the ones which should receive the most attention by QBOi in order to reduce uncertainty in future projections. Such experiments also will inform the community as to what the general uncertainty might be for state-of-the-art QBOs in CMIP6 projection experiments.

c) QBO Predictions and process study: Evaluate and compare the predictive skill of modelled QBOs in a seasonal prediction hindcast context, and study the model processes driving the evolution of the QBO.

EXPERIMENT 5: A set of initialized QBO hindcasts, with 9-12 month range.  Observed SSTs and forcings specified as in Experiment 1 (these are diagnostic experiments), with reanalysis data for the atmosphere inserted at a set of given start dates.

These are not strictly prediction experiments in the seasonal forecast sense (they use prescribed observed SST), but still represent a challenge as to how well the models can predict the evolution of the QBO from specified initial conditions. Obvious questions that will arise:

  • How much does model prediction skill vary between models, and to what extent are models able to predict the QBO evolution correctly at different vertical levels and different phases of the QBO?
  • How does the forecast skill relate to the behaviour of the QBO in Experiment 1? Does a realistic QBO in a long model run guarantee good predictions, or vice versa, or neither?
  • Do the models that cluster and/or do well in the prediction experiments cluster in the CO2 forcing experiments?

The hope is that this sort of prediction experiment might indicate what aspects of modelled QBOs determine the quality of QBO prediction, so that these aspects can receive attention in order to improve prediction.  Alternatively, the prediction framework may be helpful for directly assessing model changes, to help drive improvements in free-running models. Can prediction experiments help narrow the range of plausible models for climate change experiments?

Process Studies: Experiment 5 has a dual purpose: it not only provides information on the predictive capabilities of the models, it offers a unique opportunity to investigate and evaluate differences in wave dissipation and momentum deposition, so as to understand the processes driving the QBO in each model.  The initialization of the seasonal forecasts will necessarily present each QBO contribution with the same basic state. The evolution of that state immediately after the start of the forecast offers an opportunity to compare and contrast the properties of wave dissipation and momentum deposition between different models given an identical basic state. Specifying the same observed SST in all models (rather than allowing each model to predict its own SST evolution) helps focus attention on the model mechanisms that drive the QBO, and the extent to which they are correctly represented.

It is likely that a special, high-frequency, data request for an early period of each forecast should be defined which focuses on dissipation processes for this study.

No comments:

Post a Comment