Bias correction (BC) of the cloud-affected infrared (IR) radiances is one of the most difficult challenges in the all-sky data assimilation. This study introduces an offline nonlinear bias correction model based on the machine learning (ML) technology of Random Forest to enhance the impacts of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) all-sky radiance data assimilation. The effects of the developed model were comprehensively evaluated through sensitivity experiments based on the NoBC, BC and modified BC schemes for two super typhoon cases. Among them, the modified BC scheme is designed to extract the features of cloud-affected systematic biases, which are more prevalent in the all-sky IR radiance assimilation. Results showed that the modified BC scheme outperforms other schemes in terms of removing the cloud-impacted systematic bias while retaining the useful meteorological signal. Whereas, those biases were improperly corrected by the original BC scheme when the inputs of a grid point were handled by the ML model site by site without the feature extraction, leading to a non-Gaussian error distribution. Assimilating those better-corrected IR radiances in the modified BC experiments would lead to a greater improvement in the analysis of the humidity and cloud ice. Based on the improved initial condition, the positive effects of the modified BC scheme are also evident in the forecasts of atmospheric variables and typhoon systems.
Reactive Transport Models (RTMs) are essential tools for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent watershed-scale RTMs have integrated ecohydrological and biogeochemical interactions between surface and subsurface. These emergent, watershed-scale RTMs are often spatially explicit and require extensive data, computational power, and computational expertise. There is however a pressing need to create parsimonious models that require minimal data and are accessible to scientists with limited computational background. To that end, we have developed BioRT-HBV 1.0, a watershed-scale, hydro-biogeochemical RTM that builds upon the widely used, bucket-type HBV model known for its simplicity and minimal data requirements. BioRT-HBV uses the conceptual structure and hydrology output of HBV to simulate processes including advective solute transport and biogeochemical reactions that depend on reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. The model uses time series of weather (air temperature, precipitation, and potential evapotranspiration) and initial biogeochemical conditions of subsurface water, soils, and rocks as input, and output times series of reaction rates and solute concentrations in subsurface waters and rivers. This paper presents the model structure and governing equations and demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, BioRT-HBV can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous-to-measure subsurface, and their influence on the observed stream or river chemistry and solute export. With its parsimonious structure and easy-to-use graphical user interface, BioRT-HBV can be a useful research tool for users without in-depth computational training. It can additionally serve as an educational tool that promotes pollination of ideas across disciplines and foster a diverse, equal, and inclusive user community.
Numerical simulation of land models without error control can be highly inaccurate. We present the incorporation of the Suite of Nonlinear and Differential-Algebraic Equation Solvers (SUNDIALS) package to solve the equations that simulate thermodynamics and hydrologic processes in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) land model. The algorithmic features of SUNDIALS, such as error estimation and adaptive order and step-size control, result in a SUMMA-SUNDIALS model that delivers substantially improved accuracy and relative computational efficiency compared to integration with the previous SUMMA model, which uses the low-order backward Euler method with no rigorous error control. The results are demonstrated through simulations over the North American continent with more than 500,000 spatial elements. Compared to the previous SUMMA model, we find that the simulations produced by the SUMMA-SUNDIALS model are orders of magnitude closer to converged solutions for the same computational cost. Being able to efficiently perform more reliable simulations makes the SUMMA-SUNDIALS model a powerful tool for improving our understanding of the terrestrial component of the Earth System.
Oceanic mesoscale flows are characterized by an inverse kinetic energy cascade and the subsequent formation of large coherent vortices, and these flow features are captured well by the quasi-geostrophic (QG) model. Oceanic submesoscale flow dynamics are however significantly different from those of mesoscales. The increase in unbalanced energy levels and the Rossby number at submesoscales results in cyclone-anticyclone asymmetry in vorticity structures, forward kinetic energy cascades, and enhanced small-scale dissipation. In this paper, we develop a reduced single-equation model that can generate submesoscale-like flows in two dimensions. We start from the two-dimensional barotropic QG equation and add an external random vorticity field, to mimic the effect of unbalanced flow components. Thereafter, we add a vorticity-squared term, to generate asymmetry in the vorticity structures. By varying the strength of these two terms, we observe that the model can generate submesoscale-like flows that compare qualitatively well with realistic flows generated by complex ocean models. The reduced model is seen to be capable of generating flows that are intermittent in nature, are characterized by a forward energy flux, and are composed of small-scale flow structures along with enhanced energy dissipation. We further demonstrate the practical utility of the model by applying it to a passive tracer dispersion and a plankton patchiness problem, these being applications that require submesoscale-like flows. Our investigation points out that the new model could serve as a convenient platform for various applications that require submesoscale-like flows, such as testing and developing different kinds of parameterizations.
This paper proposes algorithms for estimating parameters in Earth System Models (ESMs), specifically focusing on simulations that have not yet achieved statistical equilibrium and display climate drift. The basic idea is to treat ESM time series as outputs of an autoregressive process, with parameters that depend on those of the ESM. The maximum likelihood estimate of the parameters and the associated uncertainties are derived. This method requires solving a nonlinear system of equations and often results in unsatisfactory parameter estimates, especially in short simulations. This paper explores a strategy for overcoming this limitation by dividing the estimation process into two linear phases. This algorithm is applied to estimate parameters in the convection scheme of the Community Earth System Model version 2 (CESM2). The modified algorithm can produce accurate estimates from perturbation runs as short as 2 years, including those exhibiting climate drift. Despite accounting for climate drift, the accuracy of these estimates is comparable to that of algorithms that do not. While these initial results are not optimal, the autoregressive approach presented here remains a promising strategy for model tuning since it explicitly accounts for climate drift in a rigorous statistical framework. The current performance issues are believed to be technical in nature and potentially solvable through further investigation.
A deep learning (DL) model, based on a transformer architecture, is trained on a climate-model data set and compared with a standard linear inverse model (LIM) in the tropical Pacific. We show that the DL model produces more accurate forecasts compared to the LIM when tested on a reanalysis data set. We then assess the ability of an ensemble Kalman filter to reconstruct the monthly averaged upper ocean from a noisy set of 24 sea-surface temperature observations designed to mimic existing coral proxy measurements, and compare results for the DL model and LIM. Due to signal damping in the DL model, we implement a novel inflation technique by adding noise from hindcast experiments. Results show that assimilating observations with the DL model yields better reconstructions than the LIM for observation averaging times ranging from 1 month to 1 year. The improved reconstruction is due to the enhanced predictive capabilities of the DL model, which map the memory of past observations to future assimilation times.
This study utilizes the wealth of observational data collected during the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment to constrain and evaluate close to two-hundred daily Large-Eddy Simulations (LES) of Arctic boundary layers and clouds at high resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single-case LES studies, and allows for a robust assessment of model performance against independent data under a range of atmospheric conditions. A homogeneously forced domain is simulated in a Lagrangian frame of reference, initialized with radiosonde and value-added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time-constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for 3 hours, allowing turbulence and clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are well reproduced and maintained. The model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model's skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES for gaining further insight into Arctic boundary layer processes and their role in Arctic climate change.
Earth's tropics are characterized by quasi-steady precipitation with small oscillations about a mean value, which has led to the hypothesis that moist convection is in a state of quasi-equilibrium (QE). In contrast, very warm simulations of Earth's tropical convection are characterized by relaxation-oscillator-like (RO) precipitation, with short-lived convective storms and torrential rainfall forming and dissipating at regular intervals with little to no precipitation in between. We develop a model of moist convection by combining a zero-buoyancy model of bulk-plume convection with a QE heat engine model, and we use it to show that QE is violated at high surface temperatures. We hypothesize that the RO state emerges when the equilibrium condition of the convective heat engine is violated, that is, when the heating rate times a thermodynamic efficiency exceeds the rate at which work can be performed. We test our hypothesis against one- and three-dimensional numerical simulations and find that it accurately predicts the onset of RO convection. The proposed mechanism for RO emergence from QE breakdown is agnostic of the condensable, and can be applied to any planetary atmosphere undergoing moist convection. To date, RO states have only been demonstrated in three-dimensional convection-resolving simulations, which has made it seem that the physics of the RO state requires simulations that can explicitly resolve the three-dimensional interaction of cloudy plumes and their environment. We demonstrate that RO states also exist in single-column simulations of radiative-convective equilibrium with parameterized convection, albeit in a different surface temperature range and with much longer storm-free intervals.
This work integrates machine learning into an atmospheric parameterization to target uncertain mixing processes while maintaining interpretable, predictive, and well-established physical equations. We adopt an eddy-diffusivity mass-flux (EDMF) parameterization for the unified modeling of various convective and turbulent regimes. To avoid drift and instability that plague offline-trained machine learning parameterizations that are subsequently coupled with climate models, we frame learning as an inverse problem: Data-driven models are embedded within the EDMF parameterization and trained online in a one-dimensional vertical global climate model (GCM) column. Training is performed against output from large-eddy simulations (LES) forced with GCM-simulated large-scale conditions in the Pacific. Rather than optimizing subgrid-scale tendencies, our framework directly targets climate variables of interest, such as the vertical profiles of entropy and liquid water path. Specifically, we use ensemble Kalman inversion to simultaneously calibrate both the EDMF parameters and the parameters governing data-driven lateral mixing rates. The calibrated parameterization outperforms existing EDMF schemes, particularly in tropical and subtropical locations of the present climate, and maintains high fidelity in simulating shallow cumulus and stratocumulus regimes under increased sea surface temperatures from AMIP4K experiments. The results showcase the advantage of physically constraining data-driven models and directly targeting relevant variables through online learning to build robust and stable machine learning parameterizations.
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