Atmospheric processes

Abstract:

The ERA5 environments of mesoscale convective systems (MCSs), tracked from satellite observations, are assessed over a 20-yr period. The use of a large set of MCS tracks allows us to robustly test the sensitivity of the results to factors such as region, latitude, and diurnal cycle. We aim to provide novel information on environments of observed MCSs for assessments of global atmospheric models and to improve their ability to simulate MCSs. Statistical analysis of all tracked MCSs is performed in two complementary ways. First, we investigate the environments when an MCS has occurred at different spatial scales before and after MCS formation. Several environmental variables are found to show marked changes before MCS initiation, particularly over land. The vertically integrated moisture flux convergence shows a robust signal across different regions and when considering MCS initiation diurnal cycle. We also found spatial scale dependence of the environments between 200 and 500 km, providing new evidence of a natural length scale for use with MCS parameterization. In the second analysis, the likelihood of MCS occurrence for given environmental conditions is evaluated, by considering all environments and determining the probability of being in an MCS core or shield region. These are compared to analogous non-MCS environments, allowing discrimination between conditions suitable for MCS and non-MCS occurrence. Three environmental variables are found to be useful predictors of MCS occurrence: total column water vapor, midlevel relative humidity, and total column moisture flux convergence. Such relations could be used as trigger conditions for the parameterization of MCSs, thereby strengthening the dependence of the MCS scheme on the environment.

Abstract:

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.