Accuracy and uncertainty of global ocean reanalyses in reproducing the OHC
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- Norah Day
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1 Accuracy and uncertainty of global ocean reanalyses in reproducing the OHC Andrea Storto, Simona Masina, Chunxue Yang CMCC, Bologna, Italy CONCEPT-HEAT Exeter, UK 29/09-1/
2 C-GLORS Reanalysis: Skill assessment V4.1, eddy-permitting (¼), [HYD+SLA+SST+SIC] Non-assimilative integrations show no skill (w.r.t. to climatology) before 1997 or below 100 m of depth. Skill calculated through bootstrapping validating observations to prevent the observation amount and sampling to affect the skill scores Skill vs time in the top 100 m Storto et al., 2015, QJRMS Skill vs vertical layers
3 C-GLORS Reanalysis: Evolution of OHC components Comparison of the global OHC components (atmospheric forcing and assimilation source/sink) Cross-covariances and compensation effects between different assimilation components prevent from robust conclusions
4 ORA-IP Global SSL ( ) vs Satellites From the ORA-IP InterComparison of Steric sea level (Storto et al. 2015, Clim.Dyn.) [20 products, 16 REAs] All products except 4 exhibit significant correlations wrt to validation dataset Seasonal signal correlations are larger than inter-annual ones REAENS outperforms OAENS and ALLENS Particularly at interannual scale
5 ORA-IP Regional SSL ( ) vs Satellites Correlation with ALT-GRV Full Signal REAENS REAENS-OAENS REAENS-ALLENS Correlation with ALT-GRV Interannual Signal
6 SSL : Significance of regional trends (halo/thermo) THERMOSTERIC HALOSTERIC Percentage of the Ocean having significant steric trends (wrt to the ensemble spread)
7 SSL : Significance of regional trends (depth) Below 700 m of depth, trends are generally non significant (w.r.t. to the spread) 2000-bottom Same results qualitatively apply to the halosteric trends Similar results found in the MyOcean reanalyses (all with NEMO/ORCA025) at depth and for salinity fields (Masina et al. 2015, Clim. Dyn.)
8 Multi-forcing Ensemble Reanalysis System Goal: to assess the impact of atmospheric forcing on ocean reanalyses using state-of-the-art atmospheric products Pros: - Straightforward ensemble generation - Implied assessment of atmospheric reanalysis accuracy from the ocean point of view Cons: - The ensemble is under-dispersive, ie only the uncertainty in the atmospheric forcing is spanned
9 Multi-forcing Ensemble Reanalysis System ENSEMBLE MEANS Very different climate signals between assimilative and non-assimilative runs Heat flux from atmospheric reanalyses (heat flux inter-comparison, Valdivieso et al. 2015, Clim. Dyn.) have large diversity. 0-bottom signal affected also by ICs
10 Multi-forcing Ensemble Reanalysis System ENSEMBLE SPREAD Very different spread behaviors: in-situ network (Argo in particular) are an attractor for the ensemble system
11 Multi-forcing Ensemble Reanalysis System Heat Content Trend from the multi-forcing ensemble [W/m2] Layer / [8%] / [18%] / [7%] 0.93+/ [13%] / [4%] / [10%] 0-bottom / [8%] / [ 7%] Based on our reanalysis system, the atmospheric reanalysis forcing impacts the OHC trends only marginally Their impact increases for the last period, namely for the more rapidly warming and shorter period, atmospheric reanalyses lead to larger relative spread
12 CMCC Historical reanalyses (CHOR): ½ degree REA during XX century Historical reanalyses (assimilating ICOADS SST, EN4 hydrographic profiles) as an ensemble of realizations with different atmospheric forcing (ERA-20, 20CRv2) and different SST assimilation strategies OHC 0-Bottom Promising approach vs OAs or model-only integrations or assimilation of surface observation only Historical reanalyses are an initial value problem Yang et al., 2015, in preparation
13 CMCC Historical reanalyses (CHOR): ½ degree REA during XX century Historical reanalyses (assimilating ICOADS SST, EN4 hydrographic profiles) as an ensemble of realizations with different atmospheric forcing (ERA-20, 20CRv2) and different SST assimilation strategies OHC 0-Bottom Below 700 m (see Balmaseda et al GRL) OAs CTRLs SODA Promising approach vs OAs or model-only integrations or assimilation of surface observation only Historical reanalyses are an initial value problem Yang et al., 2015, in preparation
14 CMCC Historical reanalyses: observational homogeneity Comparison of short temporal variability [3-year running standard deviation] OHC 0-Bottom Historical reanalyses: observational inhomogeneity vs natural variability
15 Conclusions Ocean reanalyses are becoming a robust tool to investigate ocean variability, at all time scales (decadal to centennial) During observation abundant period (2000s) at least, ocean reanalyses may outperform OAs During the last decades (satellite era), the deep ocean (below 700m) results much too unconstrained in reanalyses to provide robust and significant trends (in an ensemble sense) The uncertainty of atmospheric reanalyses in ocean reanalyses seems marginal during observation abundant periods Historical reanalyses are a promising way to investigate longterm OHC variability. However, ICs and temporal observation inhomogeneity need to be better addressed
16 Thank you Contact: Info about CMCC reanalyses at
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