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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. Toward Understanding Parametric Controls on Runoff Sensitivity to Climate in the Community Land Model: A Case Study Over the Colorado River Headwaters

   https://doi.org/10.1029/2024WR037718  

   Elkouk, Ahmed; Pokhrel, Yadu; Livneh, Ben; Payton, Elizabeth; Luo, Lifeng; Cheng, Yifan; Dagon, Katherine; Swenson, Sean; Wood, Andrew W; Lawrence, David M; et al (December 2024, Water Resources Research)

   Abstract Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. 

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3. A Method for Interpreting the Role of Parameterized Turbulence on Global Metrics in the Community Earth System Model

   https://doi.org/10.1029/2024MS004482  

   Nardi, Kyle M; Zarzycki, Colin M; Larson, Vincent E (October 2024, Journal of Advances in Modeling Earth Systems)

   The parameterization of subgrid‐scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one‐at‐a‐time (MOAT) parameter sensitivity analysis using short‐term (3‐day), initialized hindcasts of CAM6‐CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally‐averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid‐latitude storm tracks). We next evaluate several experimental 20‐year simulations of CAM6‐CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short‐term and long‐term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long‐term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study. 

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4. Urban Land Expansion Amplifies Surface Warming More in Dry Climate than in Wet Climate: A Global Sensitivity Study

   https://doi.org/10.1029/2024JD041696  

   Zhang, Keer; Fang, Bowen; Oleson, Keith; Zhao, Lei; He, Chunyang; Huang, Qingxu; Liu, Zhifeng; Cao, Chang; Lee, Xuhui (February 2025, Journal of Geophysical Research: Atmospheres)

   Abstract Urbanization changes Earth's climate by contributing to the buildup of atmospheric greenhouse gases and altering surface biophysical properties. In climate models, the greenhouse aspect is prescribed with urbanization and emission trajectories embedded in socioeconomic pathways (SSPs). However, the biophysical aspect is omitted because no models currently simulate spatially explicit urban land transition. Urban land is typically warmer than adjacent natural land due to a large urban‐versus‐natural land contrast in biophysical properties. The lack of biophysical representation of urbanization in climate models raises the possibility that model projection of future warming may be biased low, especially in areas with intense urban land expansion. Here, we conduct a global sensitivity study using a dynamic urban scheme in the Community Earth System Model to quantify the biophysical effect of urban land expansion under the SSP5‐RCP8.5 scenario. Constant urban radiative, thermal, and morphological properties are used. We find that the biophysical effect depends on land aridity. In climate zones where surface evaporation is water‐limited, the biophysical effect causes a significant increase in air temperature (0.28 ± 0.19 K; mean ± one standard deviation of nine ensemble pairs;p < 0.01) in areas where urban expansion exceeds 5% by 2070. The majority of this warming signal is attributed to an indirect effect associated with atmospheric and land feedback, with the direct effect of land replacement playing a minor role. These atmospheric feedback processes, including solar brightening, soil drying, and stomatal closure, act to enhance the warming initiated by surface property changes of urban land replacement. 

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5. An Improved Ensemble of Land Surface Air Temperatures Since 1880 Using Revised Pair-Wise Homogenization Algorithms Accounting for Autocorrelation

   https://doi.org/10.1175/JCLI-D-23-0338.1  

   Chan, Duo; Gebbie, Geoffrey; Huybers, Peter (April 2024, Journal of Climate)

   Abstract Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA’s monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth’s and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA’s GHCNm(V4), PHA₀, we propose two revisions to account for autocorrelation in climate variables. One version, PHA₁, makes minimal modification to PHA₀by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA₂, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA₂. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA₁and PHA₂both similarly outperform PHA₀in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA₁- and 50 PHA₂-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century⁻¹(95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900. Significance StatementAccurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century⁻¹greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively. 

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