LAUSR

Atmospheric moist available potential energy (MAPE) has been traditionally defined as the potential energy of a moist atmosphere relative to that of the adiabatically sorted reference state defining a global potential energy minimum. Finding such a reference state was recently shown to be a linear assignment problem, and therefore exactly solvable. However, this is computationally extremely expensive, so there has been much interest in developing heuristic methods for computing MAPE in practice. Comparisons of the accuracy of such approximate algorithms have so far been limited to a small number of test cases; this work provides an assessment of the algorithms' performance across a wide range of atmospheric soundings, in two different locations. We determine that the divide-and-conquer algorithm is the best suited to practical application, but suffers from the previously overlooked shortcoming that it can produce a reference state with higher potential energy than the actual state, resulting in a negative value of MAPE. Additionally, we show that it is possible to construct an algorithm exploiting a theoretical expression linking MAPE to Convective Available Potential Energy (CAPE) previously derived by Kerry Emanuel. This approach has a similar accuracy to existing approximate sorting algorithms, whilst providing greater insight into the physical source of MAPE. In light of these results, we discuss how to make progress towards constructing a satisfactory moist APE theory for the atmosphere. We also outline a method for vectorising the adiabatic lifting of moist air parcels, which increases the computational efficiency of algorithms for calculating MAPE, and could be used for other applications such as convection schemes.