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1. A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

   https://doi.org/10.5194/ascmo-6-223-2020  

   Dagon, Katherine ; Sanderson, Benjamin M. ; Fisher, Rosie A. ; Lawrence, David M. ( January 2020 , Advances in Statistical Climatology, Meteorology and Oceanography)

   null (Ed.)

   Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections. 

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2. Deep Convective Adjustment of Temperature and Moisture

   https://doi.org/10.1175/JAS-D-19-0227.1  

   Ahmed, Fiaz ; Adames, Ángel F. ; Neelin, J. David ( June 2020 , Journal of the Atmospheric Sciences)

   Simple process models and complex climate models are remarkably sensitive to the time scale of convective adjustment τ, but this parameter remains poorly constrained and understood. This study uses the linear-range slope of a semiempirical relationship between precipitation and a lower-free-tropospheric buoyancy measure B_L. The B_Lmeasure is a function of layer-averaged moist enthalpy in the boundary layer (150-hPa-thick layer above surface), and temperature and moisture in the lower free troposphere (boundary layer top to 500 hPa). Sensitivity parameters with units of time quantify the B_Lresponse to its component perturbations. In moist enthalpy units, B_Lis more sensitive to temperature than equivalent moisture perturbations. However, column-integrated moist static energy conservation ensures that temperature and moisture are equally altered during the adjustment process. Multiple adjusted states with different temperature–moisture combinations exist; the B_Lsensitivity parameters govern the relationship between adjusted states, and also combine to yield a time scale of convective adjustment ~2 h. This value is comparable to τ values used in cumulus parameterization closures. Disparities in previously reported values of τ are attributed to the neglect of the temperature contribution to precipitation, and to averaging operations that include data from both precipitating and nonprecipitating regimes. A stochastic model of tropical convection demonstrates how either averaging operations or neglected environmental influences on precipitation can yield τ estimates longer than the true τ value built into the model. The analysis here culminates in construction of a precipitation closure with both moisture and temperature adjustment ( q– T closure), suitable for use in both linearized and nonlinear, intermediate-complexity models.

    

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3. Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind Using the Alfvén Wave Solar Atmosphere Model

   https://doi.org/10.1029/2022SW003262  

   Jivani, Aniket ; Sachdeva, Nishtha ; Huang, Zhenguang ; Chen, Yang ; van der Holst, Bart ; Manchester, Ward ; Iong, Daniel ; Chen, Hongfan ; Zou, Shasha ; Huan, Xun ; et al ( January 2023 , Space Weather)

   Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework is a state‐of‐the‐art framework that offers full Sun‐to‐Earth simulations by computing the background solar wind, CME propagation, and magnetospheric impact. However, reliable long‐term predictions of CME events require uncertainty quantification (UQ) and data assimilation. We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol' indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol' indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced‐dimension parameter space. We present an efficient procedure for computing the Sobol' indices using polynomial chaos expansion surrogates and space‐filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM.

    

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4. Quantifying Dynamical Proxy Potential Through Shared Adjustment Physics in the North Atlantic

   https://doi.org/10.1029/2020JC016112  

   Loose, N. ; Heimbach, P. ; Pillar, H. R. ; Nisancioglu, K. H. ( August 2020 , Journal of Geophysical Research: Oceans)

   Oceanic quantities of interest (QoIs), for example, ocean heat content or transports, are often inaccessible to direct observation, due to the high cost of instrument deployment and logistical challenges. Therefore, oceanographers seek proxies for undersampled or unobserved QoIs. Conventionally, proxy potential is assessed via statistical correlations, which measure covariability without establishing causality. This paper introduces an alternative method: quantifying dynamical proxy potential. Using an adjoint model, this method unambiguously identifies the physical origins of covariability. A North Atlantic case study illustrates our method within the ECCO (Estimating the Circulation and Climate of the Ocean) state estimation framework. We find that wind forcing along the eastern and northern boundaries of the Atlantic drives a basin‐wide response in North Atlantic circulation and temperature. Due to these large‐scale teleconnections, a single subsurface temperature observation in the Irminger Sea informs heat transport across the remote Iceland‐Scotland ridge (ISR), with a dynamical proxy potential of 19%. Dynamical proxy potential allows two equivalent interpretations: Irminger Sea subsurface temperature (i) shares 19% of its adjustment physics with ISR heat transport and (ii) reduces the uncertainty in ISR heat transport by 19% (independent of the measured temperature value), if the Irminger Sea observation is added without noise to the ECCO state estimate. With its two interpretations, dynamical proxy potential is simultaneously rooted in (i) ocean dynamics and (ii) uncertainty quantification and optimal observing system design, the latter being an emerging branch in computational science. The new method may therefore foster dynamics‐based, quantitative ocean observing system design in the coming years.

    

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5. The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls

   https://doi.org/10.1002/cnm.3351  

   Seo, Jongmin ; Schiavazzi, Daniele E. ; Kahn, Andrew M. ; Marsden, Alison L. ( June 2020 , International Journal for Numerical Methods in Biomedical Engineering)

   Cardiovascular simulations are increasingly used for noninvasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an Arbitrary‐Lagrangian‐Eulerian framework for fluid‐structure interaction, with a prescribed inlet pressure and open‐loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra‐coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi Monte Carlo sampling, stochastic collocation, and multi‐wavelet stochastic expansion. Variabilities in the quantities of interest, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. From the simulation results, we conclude that the multi‐wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.

    

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