Atmospheric processes

Abstract:

<p>Mesoscale convective systems (MCSs) are important components of the Earth’s weather and climate systems. They produce a large fraction of tropical rainfall and their top-heavy heating profiles can feedback onto atmospheric dynamics. Understanding the large-scale environmental precursor conditions that cause their formation is normally done as case studies or on a regional basis. Here, we take a global view on this problem, linking tracked MCSs to the environmental conditions that lead to their growth and maintenance. We consider common variables associated with deep convection, such as CAPE, total column water vapour and moisture convergence. We take care to distinguish between conditions associated with deep convection, and conditions associated with MCSs specifically. Furthermore, we pose the question in a way that is useful for the development of an MCS parametrization scheme, by asking what environmental conditions lead to MCS occurrence, instead of locating an MCS and then finding the associated conditions.</p>

Abstract:

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the "sources of uncertainty" framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research.

Abstract:

The Madden-Julian oscillation (MJO) is the dominant source of sub-seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so sub-seasonal forecasts are generally probabilistic. We present a deep convolutional neural network (CNN) that produces skilful state-dependent probabilistic MJO forecasts. Importantly, the CNN's forecast uncertainty varies depending on the instantaneous predictability of the MJO. The CNN accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using Monte-Carlo dropout. Interpretation of the CNN mean forecasts highlights known MJO mechanisms, providing confidence in the model. Interpretation of forecast uncertainty indicates mechanisms governing MJO predictability. In particular, we find an initially stronger MJO signal is associated with more uncertainty, and that MJO predictability is affected by the state of the Walker Circulation.