1. Progress in the EUPORIAS project
Key messages
Not just services but science development – value of downscaling seasonal forecasts
indices
Impact models, crop models, hydrological models – if you want to maximise utitily of predictions have to find an optimal way of interfacing impact models with seasonal prediction – skill, coupling…
Lots of material on different topics
Practical output – software in R packages and ECOMS data portal, bias corrected etc.
Service development – didn’t pre-define case studies and prototypes, they emerged from iteraction with users, asked a panel to comment on these. Unusual approach
List of prototypes, pick a couple more interesting.
Criteria for selection was to have good understanding of link between drivers and impacts, and have a user – link to principles, and Marta’s work. Research can stay within an ivory tower but services cannot, need an engaged user. Acknowledge them and their knowledge about sector specific activities.
Could go through principles,
SIS currently not clear who the user is.
Important component of usability. Aims to inform decisions, find out what you can
Nicola Golding Met Office

2. Four-year collaborative project funded by the European commission under the seventh framework programme ( )
24 partners from across Europe and brings together a wide set of expertise from academia, the private sector and the national met services.
Main aim to demonstrate that the development of a suitable interface between users and providers f climate information can increase its societal usefulness, and ultimately strengthen the resilience of European society to climate variability and change.
Whilst

3. 24 partners from across Europe and brings together a wide set of expertise from academia, the private sector and the national met services.

4. Four-year collaborative project funded by the European commission under the seventh framework programme ( )
24 partners from across Europe and brings together a wide set of expertise from academia, the private sector and the national met services.
Improve our ability to maximise the societal benefit of climate prediction on timescales of months to decadal prediction.
Increase the resilience of the European society to climate variability and change by demonstrating how climate information can be directly useable by decision makers.
Working in close relation with over 70 European stakeholder organisations to develop fully working prototypes of climate services addressing the needs of specific users.
Main aim to demonstrate that the development of a suitable interface between users and providers f climate information can increase its societal usefulness, and ultimately strengthen the resilience of European society to climate variability and change.
Whilst

5. Working in close relation with over 70 European stakeholder organisations to develop fully working prototypes of climate services addressing the needs of specific users.

6. Timescales in scope? 1. Past climate 2. Near-term future climate
observations and monitoring,
climatologies
2. Near-term future climate
month-season-decade predictions
3. Long-term future climate
multi-decadal projections
Often an overlap with weather services

7. Project Structure Needs of stakeholders, users, decision makers
Stakeholder engagement
Research theme 1
Products for decision makers, stakeholders, and the international research community
Evaluate and communicate uncertainty
Research theme 3
Develop prototypes for decision making
Research theme 4
Improve predictions of relevant impacts
Research theme 2
Developing Science
Developing Services
Developing Tools
© Crown copyright Met Office

8. EUPORIAS’ structure Three main blocks: RT1: understand RT2: improve
Users needs and current use of S2D
Sector specific vulnerability
RT2: improve
Decision-relevant scales: downscale
Decision-relevant parameters: impact models and post-processing
CCT3: Uncertainty
Impact models’ uncertainties
Combining uncertainties
Communicating level of confidence
RT4: engage and demonstrate
Decision making process
Climate service prototypes
Delivery and engagement
Business opportunity

9. Assessing users’ needs
80 interviews and 450+ survey responses: mainly private companies & government organisations; larger companies working at the national and international level;
Complex landscape of users: different decision-making processes within and across organisations/sectors and hence, different needs;
Key findings:
Few users of seasonal forecasts in the energy, water, transport, health, agriculture, and insurance sectors; no use of decadal climate predictions;
Seasonal forecasts mainly used as qualitative information to help frame decision-making;
Perceived barriers linked to lack of reliability but also tradition of performing historical analysis and difficulty in integrating into existing operational models; lack of awareness and accessibility by some end-users.

10. Assessing users’ needs
Potential for using seasonal-to-decadal climate predictions (providing reliability is ensured):
Many interested in using seasonal forecasts e.g. to help improve planning of activities and decision-making processes;
Some organisations interested inter-annual/decadal predictions (mainly in the transport, energy and forestry sectors);
Decadal predictions could be used to improve efficiency of operations; understand and assess future risk conditions; develop more accurate asset management plans; assess how climate change projections are unfolding;
Variables of interest: temperature, precipitation, wind, humidity, & solar radiation; different (but finer) temporal/spatial resolutions;
Need for sector specific workshops on S2D and their use. (e.g. water, energy, wine production, tourism and health).
Areas of possible development: users-defined indices, integration with other sources of information, statistical-dynamical downscaling, integration with existing early warning systems.

11. Science Development E.g. Value of downscaling seasonal forecasts
Use of climate indices
Use of impact models with seasonal forecasts, e.g. crop models, hydrological models,
to add value …
How to use impact models with seasonal forecasts, crop models, hydrological models – if you want to maximise utility of predictions have to find an optimal way of interfacing impact models with seasonal prediction

12. The Value of Downscaling
Assess utility of both statistical and dynamical downscaling of global seasonal forecasts
The largest RCM downscaled seasonal hindcast ever generated in eastern Africa
Assess performance of statistical downscaling techniques
A number of different bias correction techniques have been selected for implementation in a software package.
Several bias-correction methods are implemented into the DownscaleR package
The DownscaleR package is used in FP7 SPECS and EUPORIAS for statistical downscaling and bias correction of seasonal forecasts
Downscaled seasonal forecasts for the hydropower sector in Sweden and France
RCM seasonal hindcasts are available in a consistent way (i.e., the same format).
Figure: probabilistic skill of downscaled full hindcasts (15 members) - SMHI-RCA4 (top) and DWD-CCLM (bottom). The figure shows probabilistic precipitation tercile forecasts for 2009 (top) and an evaluation of the skill for the full period (bottom)

13. Forecasts and skill analyses of climate information indices
prediction
observation
Examples:
Forecasts of heat wave related mortality (IC3, WHO)
Skill of forecast of fire weather index (Uni Cantabria):
Skill of forecast of underlying variable vs. skill of climate information index (MCH)
Correlation for lead month 3
RPSS temperature
RPSS heating degree days

14. EUPORIAS analysed the skill of the predictions of user relevant variables.
Skill in the indices is often similar to the skill of the underlying variables for all indices based on thresholds.
In the process a number of verification/visualisation packages have been developed (e.g. Probabilistic contingency table in the picture)
The plot show the distribution of the ensemble for different lead months (LM) as a function of the time .
In each plot a white dot represent the observations .
This work has been done by Uni. Cantabria who have led on the development of the R packages.
The work on the indices has been led by Meteo Swiss within WP22. The main conclusion is that there is no much point in looking at simple indices such as cooling degree days as the skill in their prediction is not better than the skill in the underlaying variable. On the other hand looking at indices which account for a complex set of interlaced variable has the potential of adding skill (e.g. Forest fire index).

15. Impacts Modelling
Aim: develop and apply impact models to seasonal hind-casts and forecasts, covering the sectors of:
Agriculture (crop) (WUR, U. Leeds, Met Office)
Forestry (U. Lund)
Water (WUR, SMHI, Meteo-France, CetAqua, Met Office, EDF)
Road conditions (Predictia)
Objectives
further develop complex impact models able to address users’ needs and inform case studies and the prototypes:
develop a prototype operational workflow to use these models in S2D forecast mode;
assess and improve their predictive skill by analysing hind casts of low- and high-end impact events (hi/lo discharge, crop yields, etc);
develop optimal geographical forecasting units, as a function of model physics and stakeholder needs.

16. River flow skill – EHYPE (SMHI), System 4 forcing (Beta metric)
Good
Lead month
Lead month 4
better skill in NE Europe, better for winter than summer, better for high flows vs. low flows
not only climate skill is important (snow, lakes, wetlands, reservoirs, human factors)
Poor

17. Uncertainty and confidence
How I feel it should be…
Account for the fact that beside the inherent uncertainty climate knowledge provides useful information
Climate is often one of the less uncertain elements users deal with. How many forecasted the financial meltdown of 2008?
Acknowledge the fact that users may find value in climate data in ways that are different from ours.
How the discussion goes…
Provider centric discussion
Tend to be focused on one specific aspect of the overall uncertainty; namely the one related to climate data
Often assume users have no understanding and/or experience.
It is often based on a linear model of science to user interaction

18. Communicating Uncertainty
Preference for different types of format influenced by both existing familiarity and statistical expertise.
Second order uncertainty (reliability, skill) not being clearly communicated to many users.
Organisations vary considerably in tolerance for false alarms and amount of ‘in house’ data processing conducted.
Acknowledgements
Visualisations from top to bottom
(1) Error bars representing spread in a dummy stream flow forecast, provided by DHI
(2) Map showing predicted likelihood of above average seasonal temperature, provided by Meteo Swiss
(3) Bar graph illustrating predicted likelihood of stream flow being above average, average, or below average (dummy forecast), provided by DHI
NOTE: These examples were provided by DHI and Meteo Swiss for use in euporias user needs surveys and should not be taken to reflect the methods of communication utilised by these organisations.
Example visualisations created for EUPORIAS user need surveys by Top DHI, Middle MeteoSwiss, Bottom DHI
Thanks to Andrea Taylor et al, University of Leeds

19. Service Development
No pre-defined case studies and prototypes, they emerged from interaction with users
All prototypes developed in close collaboration with users of varying expertise and knowledge
- Research can stay within an ivory tower but services cannot
- Acknowledge users and their
sector knowledge
- Important component of usability

20. Thanks to Rachel Lowe et al, IC3
Heat mortality
Users of climate information often require probabilistic information on which to base their decisions.
Communicating information contained within a probabilistic forecast presents a challenge.
OBJECTIVE: Demonstrate a novel visualisation technique to display ternary probabilistic forecasts on a map in order to inform decision making.
Information gained from using this technique, compared to more traditional
methods to display ternary probabilistic forecasts, is demonstrated.
Technique allows decision makers to identify areas where the model
predicts with certainty area-specific heat waves or cold snaps, to effectively
target resources to areas most at risk, for a given season.
It is hoped that this visualisation tool will facilitate the interpretation
of the probabilistic forecasts not only for public health decision makers
but also within a multi-sectoral climate service framework.
Thanks to Rachel Lowe et al, IC3

21. RESILIENCE Energy Management Tool
Objective: To provide monthly to seasonal probabilistic climate forecasts for safe and efficient energy management Stakeholders: Energy producers (e.g. EDF, grid operators (e.g. REE, renewable energy operators (e.g. EDP, energy investors (e.g. Iberdrola, The primary aim of the RESILIENCE prototype is to secure the provision of energy to society. It will facilitate important decisions related to the operations, planning and adaptation of energy systems, by providing robust knowledge of the future variability in energy supply and demand. More info: Melanie Davis,

22. Thanks to Erika Palin et al, Met Office
Transport Impacts
Establish extent of relationship between retrospective forecasts of the winter NAO index (as forecast by GloSea5, the Met Office seasonal forecast system) and a range of observed winter transport impacts (data provided by stakeholders) If relationships are skilful and significant, use these in conjunction with GloSea5 real-time forecast output to predict impacts for the coming winter
Thanks to Erika Palin et al, Met Office

23. correlation between observed/hindcast NAO and observed impact
probabilistic winter impact forecast
probabilistic winter NAO forecast

24. Tool Development E.g. ECOMS User Data Gateway
Portable programmes, R Interface
Climate Services Principles

25. ECOMS UDG
The ECOMS User Data Gateway (ECOMS UDG) provides a homogeneous access point to collections of impact- relevant variables.
The aim of ECOMS UDG is to gather different data sources with different terms of use (policies) in a single data server, so that users can access all the data and metadata they typically need (seasonal forecasts, reanalysis and observations) in a homogeneous and simple way,
without worrying about the inherent complexities of data access, download and post- processing of the variables stored in massive archive systems at different institutions.

26. R Interface Authentication → Data load → Data processing Regridding
library(ecomsUDG.Raccess)
loginECOMS_UDG('username', 'password')
Data load →
Data processing
Regridding
Bias (mock up)
Plot

27. Tools in SPECS/EUPORIAS
Tools in SPECS/EUPORIAS
easyVerification Application of verification metrics to large datasets MeteoSwiss
SpecsVerification New verification metrics and significance assessment Uni Exeter
ECOMS-UDG / downscaleR Access to seasonal forecast data, calibration and downscaling
Uni Cantabria
Series of R packages that can be used in conjunction to analyse seasonal forecasts
In progress: tutorial with worked examples for forecast verification using the above R packages

28. Be mindful of the limits
It takes (at least) two to “service”
Be mindful of the limits
Who are the users and possible users of the climate service?
What is the proposed approach?
What are the motivations of each participant to take part in the project?
Are all the relevant people involved in the discussion?
Does the project initiator have a good understanding of the end-users’ needs?
Do the providers have all the skills needed to deliver the service on time and in full?
What expertise will the users bring to the service development?
Listen to understand
Be open to be believed
It is essential that the scope is clearly defined at the beginning of the project AND to ensure there is a common understanding how the scope is evolving throughout the project.
What is in scope and what is not?
Be honest about what is and it is not achievable within the project.
Be open about new ideas that can alter your perception of what is and is not possible.
Spell out all the possible issues (scientific, technical, legal, political or commercial) which could limit the service
Take the journey together
Expect changes in the scope as this is part of human nature.
Maintaining a highly interactive and flexible work-programme you will be able to account for some of those changes.
Make clear what this means in terms of scope and what are the boundaries of flexibility.
Scope, deliver, evaluate: iterate
If possible divide the service in small components that can be delivered separately.
Scope each of these, deliver and evaluate them with the users and then, if necessary, re-scope.
Some project management practices are intrinsically designed for these sort of applications
Be flexible
The service (should) provide value to users but it is also important to identify value (not necessarily monetary) to the provider.
Make clear what each actor involved is expecting to get out of the service, meaning the journey can be more easily taken together
© Crown copyright Met Office

29. © Crown copyright Met Office
Key Learning
Development of novel science approaches to support climate services, e.g. downscaling, communicating uncertainty, use of impact models.
In-depth partnership with stakeholders and decision makers a key part of the success of climate services, and making use of the user’s knowledge.
Development of tools for use in climate service development beyond this project.
© Crown copyright Met Office

30. Thank you

31. Available datasets

32. Available datasets

33. Available variables

34. Land Management Tool
Objective: Enable land managers to make more weather-resilient decisions. Stakeholder: Clinton Devon Estates The aim is to develop a specific working tool for one application which can later be extended to other uses, while also serving as a blueprint for a weather-decision making tool for land managers and farmers in general. The specific decision is cover crop planting. More info: Pete Falloon