Radiative effects of ozone waves on the Northern Hemisphere polar vortex and its modulation by the QBO

The radiative effects induced by the zonally asymmetric part of the ozone field have been shown to significantly change the temperature of the NH winter polar cap, and correspondingly the strength of the polar vortex. In this paper, we aim to understand the physical processes behind these effects using the National Center for Atmospheric Research (NCAR)'s Whole Atmosphere Community Climate Model, run with 1960s ozone-depleting substances and greenhouse gases. We find a mid-winter polar vortex influence only when considering the quasi-biennial oscillation (QBO) phases separately, since ozone waves affect the vortex in an opposite manner. Specifically, the emergence of a midlatitude QBO signal is delayed by 1–2 months when radiative ozone-wave effects are removed. The influence of ozone waves on the winter polar vortex, via their modulation of shortwave heating, is not obvious, given that shortwave heating is largest during fall, when planetary stratospheric waves are weakest. Using a novel diagnostic of wave 1 temperature amplitude tendencies and a synoptic analysis of upward planetary wave pulses, we are able to show the chain of events that lead from a direct radiative effect on weak early fall upward-propagating planetary waves to a winter polar vortex modulation. We show that an important stage of this amplification is the modulation of individual wave life cycles, which accumulate during fall and early winter, before being amplified by wave–mean flow feedbacks. We find that the evolution of these early winter upward planetary wave pulses and their induced stratospheric zonal mean flow deceleration is qualitatively different between QBO phases, providing a new mechanistic view of the extratropical QBO signal. We further show how these differences result in opposite radiative ozone-wave effects between east and west QBOs

How does downward planetary wave coupling affect polar stratospheric ozone in the Arctic winter stratosphere?

It is well established that variable wintertime planetary wave forcing in the stratosphere controls the variability of Arctic stratospheric ozone through changes in the strength of the polar vortex and the residual circulation. While previous studies focused on the variations in upward wave flux entering the lower stratosphere, here the impact of downward planetary wave reflection on ozone is investigated for the first time. Utilizing the MERRA2 reanalysis and a fully coupled chemistry–climate simulation with the Community Earth System Model (CESM1(WACCM)) of the National Center for Atmospheric Research (NCAR), we find two downward wave reflection effects on ozone: (1) the direct effect in which the residual circulation is weakened during winter, reducing the typical increase of ozone due to upward planetary wave events and (2) the indirect effect in which the modification of polar temperature during winter affects the amount of ozone destruction in spring. Winter seasons dominated by downward wave reflection events (i.e., reflective winters) are characterized by lower Arctic ozone concentration, while seasons dominated by increased upward wave events (i.e., absorptive winters) are characterized by relatively higher ozone concentration. This behavior is consistent with the cumulative effects of downward and upward planetary wave events on polar stratospheric ozone via the residual circulation and the polar temperature in winter. The results establish a new perspective on dynamical processes controlling stratospheric ozone variability in the Arctic by highlighting the key role of wave reflection.</p

The Role of Cloud Radiative Effects in the Propagating Southern Annular Mode

The Southern Annular Mode (SAM) is the most dominant natural mode of variability in the mid-latitudes of the Southern Hemisphere (SH). However, both the sign and magnitude of the feedbacks from the diabatic processes, especially those associated with clouds, onto the SAM remain elusive. By applying the cloud locking technique to the Energy Exascale Earth System Model (E3SM) atmosphere model, this study isolates the positive feedback from the cloud radiative effect (CRE) to the SAM. Feedback analysis based on a wave activity-zonal momentum interaction framework corroborates this weak but positive feedback. While the magnitude of the CRE feedback appears to be secondary compared to the feedbacks from the dry and other diabatic processes, the indirect CRE effects through the interaction with other dynamical and thermodynamical processes appear to play as important a role as the direct CRE in the life cycle of the SAM. The cross-EOF analysis further reveals the obstructive effect of the interactive CRE on the propagation mode of the SH zonal wind directly through the CRE wave source and/or indirectly through modulating other diabatic processes. As a result, the propagation mode becomes more persistent and the SAM it represents becomes more predictable when the interactive CRE is disabled by cloud locking. Future efforts on inter-model comparisons of CRE-denial experiments are important to build consensus on the dynamical feedback of CRE

Coupled stratosphere-troposphere-Atlantic multidecadal oscillation and its importance for near-future climate projection

Northern Hemisphere (NH) climate has experienced various coherent wintertime multidecadal climate trends in stratosphere, troposphere, ocean, and cryosphere. However, the overall mechanistic framework linking these trends is not well established. Here we show, using long-term transient forced coupled climate simulation, that large parts of the coherent NH-multidecadal changes can be understood within a damped coupled stratosphere/troposphere/ocean-oscillation framework. Wave-induced downward propagating positive stratosphere/troposphere-coupled Northern Annular Mode (NAM) and associated stratospheric cooling initiate delayed thermohaline strengthening of Atlantic overturning circulation and extratropical Atlantic-gyres. These increase the poleward oceanic heat transport leading to Arctic sea-ice melting, Arctic warming amplification, and large-scale Atlantic warming, which in turn initiates wave-induced downward propagating negative NAM and stratospheric warming and therefore reverse the oscillation phase. This coupled variability improves the performance of statistical models, which project further weakening of North Atlantic Oscillation, North Atlantic cooling and hiatus in wintertime North Atlantic-Arctic sea-ice and global surface temperature just like the 1950s-1970s

Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM

Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are 1) data imbalance related to learning rare (often large-amplitude) samples; 2) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and 3) generalization to other climates, e.g., those with higher radiative forcing. Here, we examine the performance of methods for addressing these challenges using NN-based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics-based gravity wave (GW) parameterizations as the test case. WACCM has complex, state-of-the-art parameterizations for orography-, convection- and frontal-driven GWs. Convection- and orography-driven GWs have significant data imbalance due to the absence of convection or orography in many grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria on when a NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4XCO2). However, the generalization accuracy is significantly improved by applying transfer learning, e.g., re-training only one layer using ~1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data-driven parameterizations for various processes, including (but not limited) to GWs

Impact of Madden-Julian Oscillation (MJO) on global distribution of total water vapor and column ozone

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal variability in the tropical troposphere, characterized by an eastward moving 'pulse' of cloud and rainfall near the equator. In this study, total precipitable water (TPW) and total column ozone (TCO) datasets from ECMWF ERA-Interim reanalysis were used to analyse the impact of the MJO on the distribution of water vapor and column ozone in the tropics from 1979 to 2013. The results show that seasonal variations of TPW modulated by the MJO are maximized in the tropics of about 10°S-10°N during boreal winter, while the variation in TCO is maximized in the mid-latitudes of about 30°S - 40°N in the same season. The composite analysis shows that MJO modulates TPW and TCO anomalies eastward across the globe. The underlying mechanism of the MJO's impact on TPW is mainly associated with variation of tropical convection modulated by the MJO, while the underlying mechanism of the MJO's impact on TCO is mainly associated with an intra-seasonal variability of tropopause height modulated by the MJO activity. This knowledge helps to improve the prediction skill of the intra-seasonal variation of water vapor and column ozone in the tropics during boreal winter

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data-Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non-linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large-amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN-based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics-based gravity wave (GW) parameterizations as a test case. WACCM has complex, state-of-the-art parameterizations for orography-, convection-, and front-driven GWs. Convection- and orography-driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4&nbsp;×&nbsp;CO2). However, their performance is significantly improved by applying transfer learning, for example, re-training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data-driven parameterizations for various processes, including (but not limited to) GWs