Www.bsc.es DLR, 2016 Seasonal forecasting Omar Bellprat, Francois Massonet, Chloé Prodhomme, Virginie Guemas, Francisco Doblas-Reyes (BSC) Mathias Gröner.

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Presentation transcript:

DLR, 2016 Seasonal forecasting Omar Bellprat, Francois Massonet, Chloé Prodhomme, Virginie Guemas, Francisco Doblas-Reyes (BSC) Mathias Gröner and Ulrika Willén (SHMI) 15 March 2016

2 Questions from seasonal forecasting Initial conditions: Do the newly developed CCI products improve the skill of climate (seasonal) predictions? Forecast verification: Can verification seasonal predictions inform about observational quality?

3 CMUG Work Packages WP 4: Exploiting CCI products in AMIP experiments: Verification of high-resolution seasonal predictions WP 3: Cross-assessment of SST, SI, ocean colour and sea-level data for seasonal and decadal predictions and CCI4MIP simulations

4 CMUG Work Packages WP 4: Exploiting CCI products in AMIP experiments: Verification of high-resolution seasonal predictions WP 3: Cross-assessment of SST, SI, ocean colour and sea-level data for seasonal and decadal predictions and CCI4MIP simulations

5 High-resolution and ENSO ENSO prediction improves with increased horizontal resolution. Observational uncertainty similar – yet surprisingly systematic. Bellprat et al., 2015

6 Observational quality Correlation when using ERSST Correlation when using CCI SST August ENSO Skill Interannual correlation skill systematically higher in CCI SST 11 seasonal forecast systems

Best observation? ESA CCI SST gives the highest correlation scores on average. ERA-Interim the highest in the first month – initialization?

8 ESA CCI leads spatially to highest correlation, yet spatial variability is large. Best observation spatially Shipping routes? Satellite In-situ

9 Propagation of model error Observational errors need to be propagated to model scales (monthly, spatial average) – what approach to follow? Red-noise decayDifferent ObservationsTriple allocation L = 100 km / 1d Merchant et al., (2014) 5 Observational datasets Bellprat et al., (2014) Janssen et al. (2006) Niño 3.4 SST uncertainty = 0.01 K Niño 3.4 SST Uncertainty = 0.1 K ?

10 CMUG Work Packages WP 4: Exploiting CCI products in AMIP experiments: Verification of high-resolution seasonal predictions WP 3: Cross-assessment of SST, SI, ocean colour and sea-level data for seasonal and decadal predictions and CCI4MIP simulations

11 Coupled data assimilation at BSC Ensemble Kalman Filter assimilation in a coupled model system. Assimilation of SIC and SST in EC-Earth. Atmosphere updated implicitly after each time-step Standard deviation of sea ice concentration of a 25-member ensemble in September As the figure suggests, observational information will be particularly valuable around the ice edge, i.e. where the model is uncertain.

mgChl_a/m3 Coastal high productivity is underestimated due to lacking Riverine nutrient loads (will be included soon) Large-scale pattern is reproduced by the model: * Low chlorophyll in subtropical gyres, oligotropic open ocean regions * High chlorophyll in the North Atlantic and Arctic along coastal upwelling zones (e.g. west Africa) Observations: CCI occci-v2 Climatology, JJA Surface distribution of summer Chlorophyll-alpha SMHI, Matthias Gröger has made ocean stand alone simulations with EC-Earth ocean model, NEMO, including newly installed bio-geochemistry model, PISCES. How well does the ocean and bio-geochemistry components of EC-Earth compare to observations and to the CCI ocean colour, sea-ice and sea-level data? Models: EC-Earth NEMO-PISCES Spinup 100yrs, mean JJA

Conclusions Climate predictions systems allow to inform about observational quality – CCI SST leads overall to highest correlation values Propagation of observational uncertainty to model scales is a large cap for current practice in verification and data assimilation

14 Atlas of high-resolution predictions Prodhomme et al., (2016) CCI SST correlation ENSO and mid-latitude variability (NAO, blocking) improved 1° Ocean 80km Atm. 0.25° Ocean 40km Atm.