Stratosphere Sub-Project

Last modified by s2s_wiki on 2021/04/25 22:59

Points of Contact

Co-chairs of sub-project: Amy Butler and Chaim Garfinkel

S2S committee member: Daniela Domeisen 


The stratosphere is one of the major contributors to sub-seasonal to seasonal predictability of surface climate in winter and spring. Our activity is continuing to lead international efforts to quantify and understand this predictability so that, ultimately, we might improve our models and forecasting systems so they can better exploit it.

Summary of Previous Activities

We have contributed an overview chapter led by Amy Butler to the S2S prediction book “The gap between weather and climate forecasting” on subseasonal predictability and the stratosphere:

We have produced two major review papers led by Daniela Domeisen to gather the international community on the topic of predictability of the stratosphere and stratosphere-troposphere coupling on sub-seasonal timescales using the S2S project database. Both studies are published in the JGR special issue on “Bridging Weather and Climate: S2S Prediction”. This was a major milestone for our project.

  • Stratospheric predictability:
  • Stratosphere - troposphere coupling:

Another milestone was a joint meeting held with SPARC DynVar in Madrid that brought together a wide range of scientists working on dynamics and predictability, including but not limited to stratosphere topics.

Ongoing Activities

Our initial analysis of stratospheric predictability and processes in the S2S prediction systems revealed issues with biases in these models that vary as a function of lead-time and initialization time. A community project is ongoing to evaluate stratosphere-troposphere coupling biases in S2S prediction systems, and how these biases may influence predictive skill.     We currently have researchers from 11 countries contributing to this analysis (Switzerland, Israel, Spain, United States, Finland, South Korea, United Kingdom, Japan, Australia, Norway, Argentina).  Core questions we seek to answer include:

a.      What are the lead time dependent mean biases in the stratosphere and in stratosphere-troposphere coupling processes, and how do they compare among models? What about biases in variability?

b.     Which biases have the greatest impact on predictive skill? How large are the impacts?

c.      What are the sources of the biases? Can they be linked to biases in the troposphere, and/or can biases in the troposphere be linked to those in the stratosphere?

d.     Are biases linked to stratospheric processes/variability? E.g., are biases larger before or after SSWs, vortex intensifications, phases of the QBO, etc?

 These efforts are being led by Zachary Lawrence. The current plan is to submit 1-2 community papers in late summer of 2021. If you would like to join these efforts, please contact the SNAP co-chairs (Amy Butler, NOAA and Chaim Garfinkel, Hebrew University) or Zachary Lawrence.

Upcoming Activities: experiments for operational centres to perform that isolate the role of the stratosphere on predictive skill

Recent studies including our review papers have highlighted that key to understanding stratosphere-troposphere coupling in the extra-tropics and S2S predictability more broadly is a greater understanding how the representation of tropospheric eddy-jet feedbacks contribute to this predictability. Similarly, in the tropics, a focus on the links between the QBO and MJO predictability is very important. With this in mind, our next major project is to plan, coordinate, and analyze an experiment in which the stratosphere is damped towards observed stratospheric events, replicating earlier idealised studies in simpler, low- resolution models which helped to develop more detailed theories for how planetary and synoptic scale eddies contribute to coupling between the stratosphere and troposphere. Peter Hitchcock will be the scientific lead.

The test cases will be NH SSWs during two recent winters with very different tropospheric impact (2018 and 2019) and the 2019 SH warming, which has likely contributed to the extreme wildfires over Australia in 2019/20. The goals of this project are:

a.     To assess the contribution of the stratospheric evolution to forecast skill in a controlled fashion.

b.     To assess the representation of coupling processes across different operational models.

c.      Attribute particular meteorological events to stratospheric conditions

d.     To assess the representation of stratospheric wave driving.

The basic experimental protocol consists of a set of forecast ensembles: (1) a standard, free running forecast ensemble, (2) a 'perfect stratosphere' forecast in which the stratosphere is relaxed towards the observed evolution, and (3) a 'control' forecast in which the stratosphere is relaxed towards climatology. Further details of the experimental protocol will be described in an article soon to be submitted to a peer-reviewed journal. To date, twelve modeling groups at eleven centers are planning to contribute integrations following this protocol. This will allow for an unprecedented, multi-model comparison of the dynamics underlying the surface responses to sudden stratospheric warmings. Moreover, by including 'counterfactual' forecasts in which the stratospheric circulation remains in a climatological state, the experimental protocol will allow for formal attribution statements to be made regarding the surface extremes that followed the stratospheric anomalies.

A preliminary version of the Experiment Protocol for damping experiments can be viewed here:

We have already held very fruitful discussions with QBOi about a joint initiative that would explore stratosphere-troposphere coupling in both the extra-tropics and tropics and for both initialised and free-running models using a combined and agreed upon damping framework. A rough timeline is:

Q2 2021 – submission of paper outlining experimental protocol (with input from modelling centres)
Q2 2021 – begin modelling experiments
Q4 2021 – completion of modelling experiments and transfer of detailed dynamical diagnostics to a shared, open data repository
Q1-Q3 2022 – analysis of damped runs and production of ~4 papers on outcomes

Q4 2022 Data embargo lifted

Created by Administrator on 2019/11/28 16:50
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