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:

Distinguishing the influences of externally forced responses and internal variability on the observed climate is critical for attributing historical climate change and for evaluating the forced responses simulated by climate models. Statistical methods such as optimal fingerprinting, low-frequency component analysis (LFCA), and dynamical adjustment have proven useful for this application. The skill of such statistical methods can be evaluated using climate model large ensembles, where the forced response is estimated by averaging over many realizations. Our study uses large ensemble simulations from five different climate models to evaluate the performance of three statistical methods for this application: (1) low-frequency component analysis, (2) signal-to-noise maximizing pattern optimal fingerprinting (SNMP-OF), which uses the patterns from an ensemble-based signal-to-noise maximizing pattern (SNMP) analysis for optimal fingerprinting, and (3) a novel method based on SNMP analysis called fingerprint maximizing patterns (FMP), which finds patterns within observed variability that have the maximum fingerprint of the model-based forced response. We investigate how the root mean square error (RMSE) of these three methods varies across the choices of hyperparameters and show that all methods have a similar maximum skill. However, the contribution to the RMSE from the mean bias in the forced response estimate varies across the methods, with SNMP-OF and FMP showing a larger mean bias than LFCA. This demonstrates that methods that largely rely on the model forced response to obtain the observed forced response may give biased estimates and underestimate the uncertainty in these estimates due to the bias-variance tradeoff. Additionally, we apply these methods to observed Sahel precipitation, which is extensively debated in terms of its forced component, and closely related North Atlantic sea surface temperatures (SSTs). We show that while the methods give a robust estimate of the forced response in North Atlantic SSTs from 1950 to 2022, their estimates of the forced response in Sahel precipitation over the same period differ in sign. The fact that these estimates of the Sahel precipitation response differ substantially, despite all methods performing similarly well for large ensembles, suggests substantial epistemic uncertainty in estimates of the forced precipitation response in this region.

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.