SPECS: Climate Prediction for Climate Services

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Presentation transcript:

SPECS: Climate Prediction for Climate Services 13 March 2017 SPECS: Climate Prediction for Climate Services Francisco J. Doblas-Reyes

SPECS objective and structure SPECS specs-fp7.eu produced quasi-operational and actionable local climate information with a new generation of reliable European climate forecast systems. BSC Climate data is not climate information Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation

Resolution brings leap forward in skill Correlation of the ensemble mean precipitation from DePreSys3 (N216-ORCA025). Multiyear Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation Inter-annual Sheen et al. (2017, J. Climate) © Crown copyright Met Office

Increased resolution improves skill Forecast quality from EC-Earth3.1 seasonal hindcasts (1993- 2009, Glorys2v1, ERAInt and ERALand initial conditions). Solid for ESA-CCI and dashed for ERSST. Blue for high resolution ocean and atmosphere, red for high resolution ocean, black for standard resolution. Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation Prodhomme et al. (2016, J. Climate)

Increased resolution modifies biases Bias from EC-Earth3.1 seasonal hindcasts (1993-2009, Glorys2v1, ERAInt and ERALand initial conditions). Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation Prodhomme et al. (2016, J. Climate)

Improving initialisation JJA near-surface temperature anomalies in 2010 from ERAInt (left) and odds ratio from experiments with a climatological (centre) and a realistic (right) land-surface initialisation. Results for EC-Earth2.3 started in May with initial conditions from ERAInt, ORAS4 and a sea-ice reconstruction over 1979-2010. C3S Climate Projections Workshop: Near-term predictions and projections, 21 April 2015 Prodhomme et al. (2015, Clim. Dyn.)

Sensitivity experiments at HR Correlation of JJA near-surface temperature from EC-Earth3.1 hindcasts started in May over 1993-2009 with climatological (left) and ERA-Land (centre) land-surface initial conditions, and their difference (right). Hard to detect differences in sensitivity experiments: use of the Steiger test for correlation differences (increased power). Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation Siegert et al. (2017, MWR)

Observational uncertainty Niño3.4 SST correlation of the ensemble mean for ECMWF System 4 started every May over 1993-2010. Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation Bellprat et al. (2017, Rem. Sens. Env.)

Computational efficiency Speed up of the NEMO3.6 (ORCA025L75) code when switching some parts of the code from double to single precision. O. Tintó, M. Castrillo (BSC)

PRIMAVERA: A HR EU concerted effort Aims to develop advanced and well-evaluated high-resolution global climate models, capable of simulating and predicting regional climate with unprecedented fidelity (contribution to HiResMIP). Substantial reduction in near-surface temperature RMSE in the northern high latitudes when using realistic sea ice initialization for the winter following Nov initialisation

Summary and some thoughts Progress: increased resolution improves both mean climate and skill for some areas and variables; further improvements require substantial experimentation for the model to be diagnosed at the same level as with the standard resolution configuration. Challenges: reference uncertainty, observations at equivalent resolutions, process understanding, leveraging knowledge from other communities (climate modelling and weather forecasting), etc. Technology: make the most of a context with rapidly evolving technology (heterogeneous nodes, software, mobile data capture, visualisation, storage/compression, computing and storage outsourcing) to reduce the model cost and increase the capability to experiment with expensive configurations. Services: who benefits from these expensive efforts?