1. Prediction for Climate Services: Highlights
SPECS
Climate
Prediction for Climate Services: Highlights
F.J. Doblas-Reyes
ICREA and Barcelona Supercomputing Center Barcelona, Spain

2. SPECS motivation http://www.specs-fp7.eu
What: to produce quasi-operational and actionable local climate information
Why: need information with improved forecast quality, a focus on extreme climate events and enhanced communication and services for RCOFs, NHMSs and a wide range of public and private stakeholders
How: with a new generation of reliable European climate forecast systems, including initialised ESMs, efficient regionalisation tools and combination methods, and an enhanced dissemination and communication protocol
Where: over land, focus on Europe, Africa, South America
When: seasonal-to-decadal time scales over the longest possible observational period
Where we got started from
ECOMS Conference, 7 October 2016

3. Climate data is not climate information
SPECS Overview
Strong links to EUPORIAS, but also NACLIM, IS-ENES2, PREFACE, …
BSC
Climate data is not climate information
The project structure and motto
ECOMS Conference, 7 October 2016

4. Coordinated experiments
Tenths of climate prediction experiments, seven models, with different configurations and parameterisations
Focus on both skill improvements and processes (case studies)
130 TB of output (and growing), most of it available from ESGF (a primer in climate prediction), curated in the long term
Data management and experiment documentation are fundamental. They shouldn’t be underestimated
The main message here is that the coordination of all those experiments and the availability of all these data requires a lot of effort from everyone, and needs to be taken into account from the early stages of the project. The legacy of the project depends largely on this.
However, you could also mention that ESGF still poses a lot of problems and that the access to the data via other means is still necessary
ECOMS Conference, 7 October 2016

5. Open access tools
Portals are useful, but open access tools allow to go beyond what is initially considered by portal developers
The packages created in SPECS can be better adapted to address specific problems in an interaction with the users
There is no magic recipe, users should be accompanied to make an efficient use of these tools
A big effort has been made to create tools that benefited the project partners, but also those in other projects (EUPORIAS) and the users in general
ECOMS Conference, 7 October 2016

6. Forecast initialisation
Initialisation matters, but the initialisation problem is far from being resolved; the initial condition uncertainty remains large (ocean, land surface and sea ice); better use of data assimilation techniques is needed
The forecast drift is a huge problem that has been characterised; some links between drift and skill have been identified
Simplified models can be very useful to explore solutions like coupled initialisation
Models predict in a non-stationary state (because of the drift) and the systematic error is too large at times to benefit from good initial conditions
A lot of work has gone into improving the initialisation of the systems (with the sensitivity to the initialisation of the sea ice, land surface, etc); improvements can be found at times with a better initialisation, including both better observations and better ensemble generation methods, but the systematic error (which is not stationary when the model is drifting) might prevent the model from making the most of having the best observations possible or the best way to perturb the system
ECOMS Conference, 7 October 2016

7. Process understanding
Case studies based on observed extreme events (European summer and 2010, North Atlantic warming and cooling) have been analysed
Interesting sources of predictability for some events have been identified
The process-based analysis of hits, misses and false alarms help improving the forecast systems if it is done in a coordinated multi-model context
There was a lot of analysis of case studies, most of them chosen by the partners as a function of the extremes discussed at the GA3. The role of soil moisture, resolution, sea ice, etc was discussed in the improved representation of the events in the models. In some way, this is an analysis of the hits and misses conditioned on the observed events. However, there was very little about the analysis of the false alarms, which also make a lot of harm to the forecasts
ECOMS Conference, 7 October 2016

8. Better forecast systems
Improvements in the resolution, vegetation treatment, stochastic parameterisations, initialisation, sampling (ensemble and hindcast size), …
Improvements in forecast quality are found, but statistical significance is an issue
Improving the forecast systems takes long time; need to focus on those aspects that have a stronger impact for a wide range of users (e-g- NAO)
Lots of results could be included here. I inserted a plot from Chloé about the impact of the horizontal resolution for Niño3.4 forecasts, the impact of the ensemble size from MiKlip and the improvement of the Z500 systematic error from Lauriane using the stochastic dynamics. The main point is that a lot of work has gone into improving the systems and transferring this information to the operational centres. However, the improvements are incremental, the impact of the schemes depend on the model configuration (the case of the stochastic physics is a good example) and requires testing in more than one model to obtain robust results
ECOMS Conference, 7 October 2016

9. Much more than global models
The operationalisation of decadal forecasts, the creation of robust empirical benchmarks, the exhaustive illustration of the (limited) benefits of downscaling and model combination, the generation of entry-level documentation, have been rewarding aspects of the project activity
Multidisciplinarity is fundamental in climate services, and this should include climate modellers and forecasters, which requires profiles that are not readily available
The idea here is to make clear that the modelling part should be complemented by many other indispensable stages: downscaling, calibration, combination, documentation, operationalisation, dissemination of the knowledge within and outside the climate research community.
The rightmost plot is an example of downscaling for the Philippines from José’s group
ECOMS Conference, 7 October 2016

10. Legacy
SPECS has tried to make a difference in engaging with multiple communities like WCRP (WGSIP drift project), Polar Prediction Project (role of sea ice and snow in climate prediction), GFCS (tools and examples for the RCOFs), Copernicus (verification and standards), …
The sustainability of the outcome of research projects in an international context can be ensured by the operationalisation of their conclusions 10
ECOMS Conference, 5 October 2016
ECOMS Conference, 7 October 2016

11. Climate data is not climate information
SPECS Overview
Strong links to EUPORIAS, but also NACLIM, IS-ENES2, PREFACE, …
Climate data is not climate information
… but there is no climate information without reliable climate data
BSC
This might sound obvious, but somehow the climate services community shouldn’t consider that the climate modelling problem is solved
ECOMS Conference, 7 October 2016

12. SPECS
Climate
Prediction for Climate Services