NACLIM GA, 14 OCT 2014 Predictability of climate in the North Atlantic and Arctic Sectors: GREENICE and EPOCASA projects Noel Keenlyside Geophysical Institute, University of Bergen Ingo Bethke, Francois Counillon, Tor Eldevik, Anne Britt Sandø, Øystein Skagseth, Yongqi Gao, Helene Langehaug, Shuting Yang, Vladimir Semenov, Yvan Orsolini and others

Initialization provides prediction skill in the North Atlantic Doblas-Reyes et al Multi-model prediction, surface temperature, year 2-5 Root mean square skill score RMSE Init/RMSE No Init

Enhancing seasonal-to-decadal Prediction of climate for the North Atlantic Sector and Arctic Research Council of Norway, 6 partners, Key objectives To investigate key mechanisms for seasonal-to-decadal predictability North Atlantic and Nordic Seas Teleconnections from SST, sea ice, and snow cover anomalies Assess decadal predictability and role of external forcing from 1900 onwards To develop the Norwegian Climate Prediction model (NorCPM) Norwegian Earth System model with ensemble Kalman Filter assimilation

Impacts of Sea-Ice and Snow-Cover Changes on Climate, Green Growth and Society NordForsk, 9 partners from 5 Nordic countries and Russia, Key objectives To better understand the impact of sea-ice and snow-cover changes on the atmosphere, and near-term changes in climate To provide stakeholders and northern communities with better information on climate change To increase understanding of adaptation of Arctic communities to global change

First results and on going work with the Norwegian Climate Prediction Model (NorCPM)

Norwegian Climate Prediction Model Atmosphere (CAM) Ocean (MICOM) Sea ice Norwegian Earth System Model External factors (e.g., greenhouse gases) External factors (e.g., greenhouse gases) External factors (e.g., greenhouse gases) External factors (e.g., greenhouse gases) Data assimilation (e.g., EnKF) Uncertainties Flow-dependent update

Current experiments 1.Idealised predictability experiments – low-resolution ~ 4º atm and 3.6º ocean – assimilation of monthly SST data from a control simulation – SST data used to update the full water column – 30 member ensemble 2.Assimilation of observed SST anomalies – Medium resolution ~2° atm and ocean – Initialisation using only SST from observations – Time varying radiative forcing – Analysis for the period (cover the SPG shift) – 30 member ensemble

Idealised experiments: Nordic sea heat content (0- 300m) predictable based on only SST assimilation AMOC at 42N: Control, EnKF-SST, Perfect Counillon et al Similar results found for AMOC at 42N

Observed SST data can constrain Nordic Sea Atlantic Layer Temperature anomaly Salinity anomaly Ingo Bethke

Observed SST data captures the weakening of the North Atlantic subpolar gyre in the mid-90’s North Atlantic Subpolar Gyre Strength: Observed, EnKF-SST analysis, Free run Francois Counillon

Points for possible cooperation on prediction work Long term ocean reanalysis using SST assimilation from 1900 to present, and corresponding prediction experiments to assess roles of internal and external variability Assess the value of different observations (hydrography, SSH, atmospheric nudging, sea ice, and snow cover) for initialisation of climate predictions Assess impact of various configurations on predictions: High vs low-top model; Full versus anomaly coupling

1. Capturing climate predictability from ocean, sea ice, and snow cover impacts 2. Factors explaining recent changes over the northern hemisphere

Model evidence: Cold winters – EC-EARTH experiments Robust response to Arctic sea ice reductions? Follow the experiment setup as Petoukhov and Semenov (2010) EC-EARTH – atm – T159 L31 – Forced with prescribed SSTs from a cold winter year and climatological/reduced sea ice in Barents and Kara Seas Surface T change 500 hPa Height change Prob(T<-1.5σ) Prob(T>1.5σ) Shuting Yang

RCP Historical Model evidence: Cold winters in CMIP5 Composite T2M anomalies for European cold Januaries Yang & Christiansen (2012) 13 CMIP5 model ensembles Cold winters and changes defined with respect to

Snow initialisation impact on the negative NAO phase in winter 2009/10 Yvan Orsolini, NILU ensemble-mean Series 1 – Series 2 15-day lead DEC 1, 2009 start date Series1 (realistic snow) has more negative NAO index than Series2 (scrambled snow) -> Consistent signal of NAO negative phase: seen in T2m, SLP, jet stream -> Snow contributes to maintaining negative NAO -> one of the factors influencing negative NAO phase 2009/10 : very cold winter in Europe and US, and over Far East : cold air outbreaks 2009/10: Most negative winter (DJF) NAO in 145-Year Record T2mSLP200hPa wind speed ”SNOWGLACE” experiments at ECMWF: cooperation NILU/ECMWF (not operational system 4)  High horizontal resolution (T255;l62) coupled ocean-atmosphere model (IFS HOPE V4)  ensemble prediction system atmospheric model: 36R1, 62L, (low) top at 5hPa Orsolini, Y.J., Senan, R., Balsamo, G., Doblas-Reyes, F., Vitart, D., Weisheimer, A., Carrasco, A., Benestad, R. (2013), Impact of snow initialization on sub-seasonal forecasts, Clim. Dyn., DOI: /s

Stratosphere required to capture observed winter (JFM) response to North Atlantic SST High-top model NCEP/NCAR Omrani et al anomaly

Coordinated AGCM model experiments Motivation is to better understand atmospheric impact of SST, sea ice, and snow cover Four models: CAM5/WACM, IFS, ECHAM5, IAP – Low and high top, low and high-horizontal resolution Tier 1 – AGCM with full sea ice and SST variations – AGCM with full sea ice and climatological SST variations Tier 2 – Case studies based on analysis of tier 1 experiments – Experiments to assess impact of snow cover variations

Points for possible cooperation on the atmospheric impact of SST and sea ice Coordinated experiments and analysis to understand sensitivity of the atmospheric response to model configuration and bias Analysis of high- and low-top coupled models to assess if better representation of O-A interaction might impact simulated North Atlantic variability Thank you! Collaboration very welcome!!