Climate models’ limitations in representing the bottom-heavy vertical circulation over the
eastern equatorial Pacific result in erroneously simulated ENSO–induced teleconnections. Over Hawaii and USAPI, direct impacts of this weakness include errors in representing multi-
seasonal persistence of droughts/floods. Our overarching hypothesis is: The eastern equatorial Pacific cause and effect relationship for variations in convection can be determined from a
comprehensive process-based diagnosis (down to the individual parameterization level) of
systematic changes in vertical structure in response to changes in ocean-atmosphere surface
characteristics. This includes meridional SST gradient-induced surface convergence.
Our objective, towards identifying the initial source(s) of model errors in climatological
mean states, is to develop process-oriented diagnostics (PODs) that: (i) Assess “co-occurring
parameterized processes in models; (ii) Include heterogeneous observational sources; (iii) Can
be applied to daily and shorter timescales (model time steps at 30 minutes) to identify errors
due to fast processes; and (iv) Quantify model development progress in CAM7/AM5. The
proposed PODs target processes related to: (a) Atmospheric boundary layer and near surface
interactions; (b) Vertical distributions in the troposphere and (c) Upper-ocean mixing.
Crucially PODs developed here will be used interactively during CAM7/AM5 development,
where each successive prototype simulation can be objectively assessed. This analysis
workflow will improve the model development processes considerably, in that performance
changes can be linked directly to parameterization improvements.
Our proposed research targets the MAPP competition that focuses on “key issues in the
representation of Earth system processes in CMIP6-era and developmental models to improve
model fidelity”, with a particular focus on “clearly-identified gaps in the existing MDTF
software package”. Continuing assessment in moist convection processes, our proposed PODs
branch-off from the ongoing efforts with primary focus on processes related to climatological
basic-states, atmospheric and oceanic boundary layer, and co-located column processes.
Recognizing that in data-sparse regions native model biases can dominate in reanalysis, we
employ currently under-utilized in-situ, field, and radiosonde observations (taken and
maintained by NOAA), to develop PODs based on ground observations. Process-based
diagnosis of CFSv2 (NOAA operational model) is lacking, and the PODs developed here will
be applied to forecasts. Our proposed work has close synergy with NOAA strategic plan for
improved understanding and model applications relevant to high-priority climate risk areas.
Specific to CPO are extreme droughts and heat, and coastal flooding over Hawaii and USAPI.
In their studies, the PIs have extensively employed most of the PODs. Implementing them into the MDTF framework will therefore be straightforward. Deliverables include a set of process-based metrics and post-processed data from models and observations that aid in assessing the improvements in recent model versions.