Lei Wang, Zhiming Kuang
The eddy straining mechanism of Shutts (1983; S83) has long been considered a main process for explaining the maintenance of atmospheric blocking. As hypothesized in S83, incoming synoptic eddies experience a meridional straining effect when approaching a split jetstream, and as a result, enhanced PV fluxes reinforce the block. A two-layer QG model is adopted here as a minimal model to conduct mechanism-denial experiments. While transient eddies' forcing is clearly critical to the formation and maintenance of a block, using a large ensemble, the authors demonstrate that the straining of generic eddies does not maintain blocks, thus challenge the idea of eddy straining serving as a positive feedback for the blocks. These results indicate that specific configurations of the eddy field are required for the maintenance stage. The authors also remark on the main supporting evidence in S83: the second-order induced flow is sensitive to the location of the wavemaker.
Aviv Solodoch, William Boos, Zhiming Kuang, Eli Tziperman
The intraseasonal Madden-Julian oscillation (MJO) involves a slow eastward-propagating signal in the tropical atmosphere which significantly influences climate yet is not well understood despite significant theoretical and observational progress. We study the atmosphere's response to nonlinear "Wind Induced Surface Heat Exchange" (WISHE) forcing in the tropics using a simple shallow water atmospheric model. The model produces an interestingly rich interannual behavior including a slow, eastward propagating equatorial westerly multiscale signal, not consistent with any free linear waves, and with MJO-like characteristics. It is shown that the slow signal is due to a Kelvin wave forced by WISHE due to the meridional wind induced by a Yanai wave group. The forced Kelvin wave has a velocity similar to the group velocity of the Yanai waves, allowing the two to interact nonlinearly via the WISHE term while slowly propagating eastward. These results may have implications for observed tropical WISHE-related atmospheric intraseasonal phenomena.
Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) relates the mean-response of a nonlinear system to weak external forcings and vice versa. Even for simple models of the general circulation, such as the dry dynamical core, the LRF cannot be calculated from first principles due to the lack of a complete theory for eddy-mean flow feedbacks. According to the Fluctuation-Dissipation Theorem (FDT), the LRF can be calculated using only the covariance and lag-covariance matrices of the unforced system. However, efforts in calculating the LRFs for GCMs using FDT have produced mixed results, and the reason(s) behind the poor performance of the FDT remains unclear. In Part 1 of this study, the LRF of an idealized GCM, the dry dynamical core with Held-Suarez physics, is accurately calculated using Green's functions. In this paper (Part 2), the LRF of the same model is computed using FDT, which is found to perform poorly for some of the test cases. The accurate LRF of Part 1 is used with a linear stochastic equation to show that dimension-reduction by projecting the data onto leading EOFs, which is commonly used for FDT, can alone be a significant source of error. Simplified equations and examples of 2 x 2 matrices are then used to demonstrate that this error arises because of the non-normality of the operator. These results suggest that errors caused by dimension-reduction are a major, if not the main, contributor to the poor performance of the LRF calculated using FDT, and that further investigations of dimension-reduction strategies with a focus on non-normality are needed.
Qiyu Song, Zhiming Kuang
Data-driven convective parameterization aims to accurately represent convective adjustments to large-scale forcings in a computationally economic manner. While previous studies have demonstrated success using various model architectures, challenges persist in developing physically interpretable models and assessing generalizability and confidence level. In this study, we develop a recurrent neural network to predict time series of temperature, moisture, and precipitation of a cumulus ensemble in response to large-scale forcings. The recurrent cell combines a linear component, pre-identified as a time-invariant state-space model within the linear limit of the problem, and a multilayer neural network for the nonlinear component. Trained on ensembles of limited-domain cloud-resolving model simulation data, the model exhibits stable and realistic performance in long-term emulations, both with prescribed large-scale forcings and when coupled with two-dimensional gravity waves. We further calculate linear responses to perturbations for the coupled emulation, revealing physically interpretable, state-dependent properties of the convectively coupled gravity wave system.
Giuseppe Torri, Zhiming Kuang, Yang Tian
Cold pools are fundamental ingredients of deep convection. They contribute to organizing the sub-cloud layer and are considered key elements in triggering convective cells. It was long known that this could happen mechanically, through lifting by the cold pools' fronts. More recently, it has been suggested that convection could also be triggered thermodynamically, by accumulation of moisture around the edges of cold pools. A method based on Lagrangian tracking is here proposed to disentangle the signatures of both forcings and quantify their importance in a given environment. Results from a simulation of radiative-convective equilibrium over the ocean show that parcels reach their level of free convection through a combination of both forcings, each being dominant at different stages of the ascent. Mechanical forcing is an important player in lifting parcels from the surface, whereas thermodynamic forcing reduces the inhibition encountered by parcels before they reach their level of free convection.
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I. Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R. Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid ML-physics simulations require domain-specific data and workflows that have been inaccessible to many ML experts. As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various ML baselines, alongside a hybrid baseline simulator, to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid ML-physics and high-fidelity climate simulations.
Pedram Hassanzadeh, Zhiming Kuang
The linear response function (LRF) of an idealized GCM, the dry dynamical core with Held-Suarez physics, is used to accurately compute how eddy momentum and heat fluxes change in response to the zonal wind and temperature anomalies of the annular mode at the low-frequency limit. Using these results and knowing the parameterizations of surface friction and thermal radiation in Held-Suarez physics, the contribution of each physical process (meridional and vertical eddy fluxes, surface friction, thermal radiation, and meridional advection) to the annular mode dynamics is quantified. Examining the quasi-geostrophic potential vorticity balance, it is shown that the eddy feedback is positive and increases the persistence of the annular mode by a factor of more than two. Furthermore, how eddy fluxes change in response to only the barotropic component of the annular mode, i.e., vertically averaged zonal wind (and no temperature) anomaly, is also calculated similarly. The response of eddy fluxes to the barotropic-only component of the annular mode is found to be drastically different from the response to the full (barotropic+baroclinic) annular mode anomaly. In the former, the barotropic governor effect significantly suppresses the eddy generation leading to a negative eddy feedback that decreases the persistence of the annular mode by nearly a factor of three. These results suggest that the baroclinic component of the annular mode anomaly, i.e., the increased low-level baroclinicity, is essential for the persistence of the annular mode, consistent with the baroclinic mechanism but not the barotropic mechanism proposed in the previous studies.
Ding Ma, Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) that relates the temporal tendency of zonal mean temperature and zonal wind to their anomalies and external forcing is used to accurately quantify the strength of the eddy-jet feedback associated with the annular mode in an idealized GCM. Following a simple feedback model, the results confirm the presence of a positive eddy-jet feedback in the annular mode dynamics, with a feedback strength of 0.137 day$^{-1}$ in the idealized GCM. Statistical methods proposed by earlier studies to quantify the feedback strength are evaluated against results from the LRF. It is argued that the mean-state-independent eddy forcing reduces the accuracy of these statistical methods because of the quasi-oscillatory nature of the eddy forcing. A new method is proposed to approximate the feedback strength as the regression coefficient of low-pass filtered eddy forcing onto low-pass filtered annular mode index, which converges to the value produced by the LRF when timescales longer than 200 days are used for the low-pass filtering. Applying the new low-pass filtering method to the reanalysis data, the feedback strength in the Southern annular mode is found to be 0.121 day$^{-1}$, which is presented as an improvement over previous estimates. This work also highlights the importance of using sub-daily data in the analysis by showing the significant contribution of medium-scale waves of periods less than 2 days to the annular mode dynamics, which was under-appreciated in most of previous research. The present study provides a framework to quantify the eddy-jet feedback strength in models and reanalysis data.
Zeyuan Hu, Akshay Subramaniam, Zhiming Kuang, Jerry Lin, Sungduk Yu, Walter M. Hannah, Noah D. Brenowitz, Josh Romero, Michael S. Pritchard
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale Modeling Framework (MMF), which embeds a kilometer-resolution cloud-resolving model within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning (ML) offers a unique opportunity to make MMF more accessible by emulating the embedded cloud-resolving model and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2K, water vapor bias within 1 g/kg, and a precipitation RMSE of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.
Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) determines the mean-response of a nonlinear climate system to weak imposed forcings, and an eddy flux matrix (EFM) determines the eddy momentum and heat flux responses to mean-flow changes. Neither LRF nor EFM can be calculated from first principles due the lack of a complete theory for turbulent eddies. Here the LRF and EFM for an idealized dry atmosphere are computed by applying numerous localized weak forcings, one at a time, to a GCM with Held-Suarez physics and calculating the mean-responses. The LRF and EFM for zonally-averaged responses are then constructed using these forcings and responses through matrix inversion. Tests demonstrate that LRF and EFM are fairly accurate. Spectral analysis of the LRF shows that the most excitable dynamical mode, the neutral vector, strongly resembles the model's Annular Mode. The framework described here can be employed to compute the LRF/EFM for zonally-asymmetric responses and more complex GCMs. The potential applications of the LRF/EFM constructed here are i) forcing a specified mean-flow for hypothesis-testing, ii) isolating/quantifying the eddy-feedbacks in complex eddy-mean flow interaction problems, and iii) evaluating/improving more generally-applicable methods currently used to construct LRFs or diagnose eddy-feedbacks in comprehensive GCMs or observations. As an example for iii, in Part 2, the LRF is also computed using the fluctuation-dissipation theorem (FDT), and the previously-calculated LRF is exploited to investigate why FDT performs poorly in some cases. It is shown that dimension-reduction using leading EOFs, which is commonly used to construct LRFs from the FDT, can significantly degrade the accuracy due to the non-normality of the operator.