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The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework.

 

Abstract Accurate representation of stratospheric trace gas transport is important for ozone modeling and climate projection. Intermodel spread can arise from differences in the representation of transport by the diabatic (overturning) circulation vs. comparatively faster adiabatic mixing by breaking waves, or through numerical errors, primarily diffusion. This study investigates the impact of these processes on transport using an idealised tracer, the age-of-air. Transport is assessed in two state-of-the-art dynamical cores based on fundamentally different numerical formulations: finite volume and spectral element. Integrating the models in free-running and nudged tropical wind configurations reveals the crucial impact of tropical dynamics on stratospheric transport. Using age-budget theory, vertical and horizontal gradients of age allow comparison of the roles of the diabatic circulation, adiabatic mixing, and the numerical diffusive flux. Their respective contribution is quantified by connecting the full 3-d model to the tropical leaky pipe framework of Neu and Plumb (1999). Transport by the two cores varies significantly in the free-running integrations, with the age in the middle stratosphere differing by about 2 years primarily due to differences in adiabatic mixing. When winds in the tropics are constrained, the difference in age drops to about 0.5 years; in this configuration, more than half the difference is due to the representation of the diabatic circulation. Numerical diffusion is very sensitive to the resolution of the core, but does not play a significant role in differences between the cores when they are run at comparable resolution. It is concluded that fundamental differences rooted in dynamical core formulation can account for a substantial fraction of transport bias between climate models. 

Wave‐induced adiabatic mixing in the winter midlatitudes is one of the key processes impacting stratospheric transport. Understanding its strength and structure is vital to understanding the distribution of trace gases and their modulation under a changing climate. Age‐of‐air is often used to understand stratospheric transport, and this study proposes refinements to the vertical age gradient theory of Linz et al. (2021),https://doi.org/10.1029/2021JD035199. The theory assumes exchange of air between a well‐mixed tropics and a well‐mixed extratropics, separated by a transport barrier, quantifying the adiabatic mixing flux across the interface using age‐based measures. These assumptions are re‐evaluated and a refined framework that includes the effects of meridional tracer gradients is established to quantify the mixing flux. This is achieved, in part, by computing a circulation streamfunction in age‐potential temperature coordinates to generate a complete distribution of parcel ages being mixed in the midlatitudes. The streamfunction quantifies the “true” age of parcels mixed between the tropics and the extratropics. Applying the revised theory to an idealized and a comprehensive climate model reveals that ignoring the meridional gradients in age leads to an underestimation of the wave‐driven mixing flux. Stronger, and qualitatively similar fluxes are obtained in both models, especially in the lower‐to‐middle stratosphere. While the meridional span of adiabatic mixing in the two models exhibits some differences, they show that the deep tropical pipe, that is, latitudes equatorward of 15° barely mix with older midlatitude air. The novel age‐potential temperature circulation can be used to quantify additional aspects of stratospheric transport.

 

Automated computational assessment of neuropsychological tests would enable widespread, cost‐effective screening for dementia.

A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects’ neuropsychological tests conducted by the Framingham Heart Study (n= 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants’ demographic characteristics.

Average area under the curve (AUC) on the held‐out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively.

The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

 

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.