Weakly coupled assimilation for decadal prediction with MPI-ESM

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Weakly coupled assimilation for decadal prediction with MPI-ESM Sebastian Brune, Johanna Baehr Institute of Oceanography CEN, University of Hamburg Holger Pohlmann, Wolfgang Müller Max Planck Institute for Meteorology Hamburg Thanks to Lars Nerger for PDAF, AWI Bremerhaven http://www.fona-miklip.de/ Pohlmann et al. 2013: Improved forecast skill in the tropics in the new MiKlip decadal climate predictions, GRL. Brune et al., 2015: Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter, Ocean Modelling. Marotzke et al., 2016: MiKlip - a National Research Project on Decadal Climate Prediction, BAMS. 1

Outline MiKlip - decadal climate prediction: 2-10 years Weakly coupled assimilation in MPI-ESM: nudging, Evaluation of decadal hindcasts: global, SPG Time dependency of evaluation: 20 year sliding window 1961-2013 How does assimilation quality fit with hindcast quality? How does the hindcast evaluation depends on the time period? 2

MiKlip decadal climate prediction Mittelfristige Klimaprognose = mid-term climate forecast http://www.fona-miklip.de, Marotzke et al., 2016, BAMS Establish decadal prediction system in pre-operational mode 8 years of funding by German Federal Ministry for Education and Research Based on coupled global model MPI-ESM: atmosphere: ECHAM T63L47 / T127L91 ocean: MPIOM GR15L40 coupling: OASIS daily / hourly Giorgetta et al. 2013 External forcing with data (past) and RCP-scenarios (future) Initialization of atmosphere and ocean with weakly coupled DA Observational record very sparse before 1950: assimilation starts in the 1950s Evaluation of hindcasts 1961-today 3

Assimilation in MPI-ESM-LR Atmosphere, ECHAM6 T63L47 Full nudging of Vor, Div, T, SLP to monthly ERA40/ERAInterim Vor, Div, T no nudging in lower 5 SLP SST T, S daily coupling Ocean, MPIOM GR15L40 Pohlmann et al. 2013, GRL Baseline1 Anomaly nudging T, S to monthly ORAS4 Brune et al. 2015, Oc. Mod. Ensemble Kalman filter (PDAF) Full value, 8 members, global EN4 T and S profiles + HadISST monthly assimilation window 4

Global assimilation results (1961-2013) T2m (correlation with HadCRUT4, Morice et al. 2012) - - UOHC (correlation with NOAA OCL, Levitus et al. 2012) 5

Global hindcast results, lead year 2-5 (1961-2013) T2m (HadCRUT4) - - 6

Global hindcast results, lead year 2-5 (1961-1981) T2m (HadCRUT4) - - 7

Global hindcast results, lead year 2-5 (1990-2009) T2m (HadCRUT4) - - 8

Global hindcast results, lead year 2-5 (1961-2013) T2m (HadCRUT4) - - North Atlantic 9

SPG assimilation results: correlation 1961-2013 - 1961-1981 - 1990-2009 - 10

SPG hindcast results lead year 5: ACC 1961-2013 - 1961-1981 - 1990-2009 - 11

SPG hindcast results lead year 5: ACC 1961-2013 - 1961-1981 - 1990-2009 - subpolar gyre mean 12

SPG ACC vs. lead year T2m (HadCRUT4) 1961-2013 1961-1981 anom.nudg. anom.nudg. 1990-2009 anom.nudg. anom.nudg. anom.nudg. anom.nudg. 13

SPG results assimilation and hindcasts: ACC T2m (HadCRUT4) lead year lead year 10 8 6 4 2 0 0.0 10 8 6 4 2 0 1961 1980 0.0 1970 1989 1980 1999 1990 2009 1961 1980 1970 1989 1980 1999 1990 2009 1961 1980 1970 1989 1980 1999 1990 2009 14

Summary Decadal climate prediction in MiKlip with MPI-ESM-LR: Different types of weakly coupled assimilation, May work for lead years 1 to 5, 1961-2013. Time dependency of hindcast evaluation: Strong regional time dependency, Address forecast uncertainty. How does assimilation quality fit with hindcast quality? Good assimilation = good hindcast: not always true, Model deficiencies vs assimilation. What is the right time period for hindcast evaluation? Longer period delivers more robust statistics, Sliding window analysis adds the details. 15

Summary Decadal climate prediction in MiKlip with MPI-ESM-LR: Different types of weakly coupled assimilation, May work for lead years 1 to 5, 1961-2013. Time dependency of hindcast evaluation: Strong regional time dependency, Address forecast uncertainty. How does assimilation quality fit with hindcast quality? Good assimilation = good hindcast: not always true, Model deficiencies vs assimilation. What is the right time period for hindcast evaluation? Longer period delivers more robust statistics, Sliding window analysis adds the details. Next: Improve weakly coupled DA, but keep the hindcast perspective. 16

The End 17

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