Abstract. One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the
mapping of solution field data between different discretizations and numerical grids used in the coupled component
models.
The typical climate computational workflow involves evaluation and serialization of the remapping weights during
the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute
field projections.
Tools like Earth System Modeling Framework (ESMF) (Hill et al., 2004) and TempestRemap (Ullrich et al., 2013) offer
capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) (Larson et al., 2001)
that is utilized in many production climate models exposes functionality to make use of the operators to solve the
coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and
impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure
based on the Mesh Oriented datABase (MOAB) (Tautges et al., 2004; Mahadevan et al., 2015) library, which allows for a
complete description of the numerical grids and solution data used in each submodel.
Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed,
which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between
discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels
in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the
coupled workflow process.
We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science
tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element
atmosphere and finite volume discretizations on the polygonal ocean grids.
Data‐driven worldwide quantification of large‐scale hydroclimatic co‐variation patterns and comparison with reanalysis and Earth System modeling
