Climate impact indicators
Global essential climate variables/indicators used

S4 model outputs (precipitation and temperature Re-forecast). S4 Re-forecast initialized on each month 1993-2010 (6 lead times: 1-6 months)

Pre-processing: S4 daily data will be aggregated to monthly and quarterly data for downscaling

Regional/local indicators used and produced

Probabilities of rainfall and temperature (terciles)

Probability of exceeding certain threshold

Probability of having a season similar to an analogue year

Number of days with rain (without)

Global data to regional/local scale

Step 1: S5 data download: Downloading of S5 model outputs (precipitation and temperature Re-forecast). S5 Re-forecast initialized on each month 1993-2015 (7 lead times: 1-7 months). Data is being downloaded from NCS SMHI Publisher.

Step 2: Station data processing: Calculation of climate indices with stations data (reference climatology). Consecutive dry days (CDD), consecutive wet days (CWD), yearly total precipitation amount (RTOT), 95th precipitation percentile (R95P), heat waves or warm days (TXX), cold waves or cold nights (TNN). All climate indices for the period 1981-2010 were calculated with stations data. Indices from CHRIPS database are going to be calculated as well.

Step 3: Bias adjustment and quality control: Bias adjustment procedures applied to S5 data outputs. Quality control analysis was applied to stations data. Data was checked for outliers, duplicates, and inconsistency.

Step 4: Downscale to higher resolution: Downscaling of S5 monthly rainfall outputs to 0.05 degrees gridded data for all pilot basins. Downscaling will be performed using the IRI´s Climate Predictability Tool CPT, (S5 rainfall as predictor, CHIRPS data as response variable) and tailored for sectoral applications. Downscaled outputs will be presented in term of probability of exceedance of upper and lower percentiles, probability of exceedance of certain critical threshold, probability of having similar conditions as in El Niño or La Niña years, among others.

Step 5: Web mapping: Tailored downscaled outputs and vulnerability maps will be uploaded into a web system. The system will be developed using THREDDS data server technology in order to enhance the interaction of users with the demonstration products. This will allow users to visualize climate threats and the vulnerability of the basins, in order to facilitate decision making based on high quality and high resolution data.

Step 6: Feedback with NMHS and users: Meeting with NMHS´s technicians to discuss the concept of the study case. Development of a communication (provider-user-provider) strategy with local authorities. The strategy will consider both ways of communication from providers to users, and feedback from users to providers. Online workshops with local authorities to explain how to interpret and use the information provided, including limitations.

Centro Internacional para la Investigación del Fenómeno de El Nino 

Poster displayed at the Kick–Off meeting, 7/8 September 2017, Norrköping, Sweden

Local community in Santa Isabel (Jubones river basin – Ecuador) working together preparing land for the next season