1.1 Global Earth energy budget and global energy fluxes

1.2 Regional energy fluxes and heat transports

1.3 Climate sensitivity and pattern effect

1.4. EEI in climate models and projections

1.4 Cause/interpretation of recent EEI changes

2.1 Status of space techniques (improvements, requirements): space radiometry, space geodesy, i.e., satellite altimetry and space gravimetry

2.2 EEI variables and their spatio-temporal monitoring: incoming shortwave radiation, albedo, outgoing longwave radiation, sea level, ocean mass, expansion efficiency of heat etc.

2.3 EEI uncertainty: can we list all the sources of uncertainty and can we provide a comprehensive estimate of the uncertainty based on a comprehensive error budget? If not, what are the key challenges to be addressed?

2.4 Regional estimate of the Ocean heat content from space geodesy: what is the effective time and space resolution? What is the regional uncertainty? Can we describe the time and space correlation in errors

2.5 Consistency of OHC estimates with TOA radiation budget estimates and atmospheric energy transport

2.6 Blending/fusion of large and diverse datasets. How can we take advantage of the in situ network and satellite-based product to reduce the uncertainty in mean, trend and time variations of the EEI?

 

3.1 Status of in situ technique (improvements, requirements) and status of the current spatio-temporal monitoring: Argo data, deep Argo data, XBT, CTD data. Current status and progress

3.2 OHC uncertainty from in situ measurements: can we list all the sources of uncertainty and can we provide a comprehensive estimate of the uncertainty based on a comprehensive error budget? If not, what are the key challenges to be addressed?

3.3 Status of the uncertainty estimates: uncertainty due to Quality control, uncertainty due to inhomogeneous spatio-temporal sampling and gap filling techniques, uncertainty due to climatology, uncertainty due to instrumental corrections 

3.4 Regional estimate of the Ocean heat content from in situ data: what is the effective time and space resolution? What is the local uncertainty? Can we describe the time and space correlation in errors

3.5 Consistency of OHC estimates with space-based estimates of OHC and with TOA radiation budget estimates combined with atmospheric energy transport

3.6 Blending/fusion of large and diverse datasets. How can we take advantage of the in situ network and satellite-based product to reduce the uncertainty in mean, trend and time variations of the EEI?

3.7 Discuss best-practices and recommendations for uncertainty quantification, integration of OHC to OHU, and trend analysis (could also go to other techniques)

4.1 Status of reanalyses estimates of EEI (improvements, requirements). Current status and progress

4.2 OHC uncertainty from reanalysis estimates: can we list all the sources of uncertainty and can we provide a comprehensive estimate of the uncertainty based on a comprehensive error budget? If not, what are the key challenges to be addressed?

4.3 Status of the uncertainty estimates: formal uncertainty due to errors in assimilated data, uncertainty related to the atmospheric forcing, uncertainty due to inhomogeneous sampling and drifts in the unsampled deep ocean. 

4.4 Regional estimate of the Ocean heat content from reanalyses: what is the spread in regional OHC across ocean reanalyses? Can we explain this spread?

4.5 Consistency of reanalyses OHC estimates with space-based estimates of OHC, in situ estimates of OHC and with TOA radiation budget estimates combined with atmospheric energy transport

4.6 Gaps and needs in terms of observations

4.7 Different assimilation techniques, advantages/disadvantage and best practices

5.1 The uncertainty in global mean net surface energy flux and the status of the balance of surface energy flux with most recent data products.

5.2 Regional energy flux uncertainties.

5.3 What is needed to improve estimates of surface heat fluxes? (maybe better for round table)

6.1 Future space mission concepts

6.2 Future in situ concepts

6.3 Future climate models and their needs in terms of observational datasets?