A document by Tammas Francis Loughran. Click on the document to view its contents.

We enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow-aerosol-radiation interactions. Specifically, we incorporate the following model enhancements: (1) updating ice and aerosol optical properties with more realistic and accurate datasets, (2) adding multiple dust types, (3) adding multiple surface downward solar spectra to account for different atmospheric conditions, (4) incorporating a more accurate adding-doubling radiative transfer solver, (5) adding nonspherical snow grain representation, (6) adding black carbon-snow and dust-snow internal mixing representations, and (7) adding a hyperspectral (480-band versus the default 5-band) modeling capability. These model features/enhancements are included as new CLM physics/namelist options, which allows for quantification of model sensitivity to snow albedo processes and for multi-physics model ensemble analyses for uncertainty assessment. The model updates will be included in the next CLM version release. Sensitivity analyses reveal stronger impacts of using the new adding-doubling solver, nonspherical snow grains, and aerosol-snow internal mixing than the other new features/enhancements. These enhanced snow albedo representations improve the CLM simulated global snowpack evolution and land surface conditions, with reduced biases in simulated snow surface albedo, snow cover, snow water equivalent, snow depth, and surface temperature, particularly over northern mid-latitude mountainous regions and polar regions.

Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model vesion 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive dataset, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.

Lakes and reservoirs are an integral part of the terrestrial water cycle. In this work, we present the implementation of water balance models of lakes and reservoirs into mizuRoute, a vector-based routing model. The developments described here are termed mizuRoute-Lakes. The capabilities of mizuRoute-Lake in simulating the water balance of lakes and reservoirs are demonstrated. The main advantage of mizuRoute-Lake is flexibility in testing alternative lake water balance models within a given river and lake network topology. Users can choose between various types of parametric models that are already implemented in mizuRoute-Lake or data-driven models that provide time-series of the target volume and abstraction from a lake or reservoir from an external source such as historic observation or water management models. The parametric models for lake and reservoir water balance implemented in mizuRoute-Lake are Hanasaki, HYPE, and D{\"o}ll formulations. In general, the parametric models relate the outflow from lakes or reservoirs to the storage and various parameters including inflow, demand, volume of storage, etc. Additionally, this flexibility allows to easily evaluate and compare the effect of various water balance models for a lake or reservoir without needing to reconfigure the routing model. We show the flexibility of mizuRoute-Lake by presenting global, regional and local scale applications. The development of mizuRoute-Lake paves the way for better integration of water management models with existing and future observations such as the Surface Water and Ocean Topography (SWOT) mission, in the context of Earth system modeling.

The Arctic hydrological system is an interconnected system that is experiencing rapid change. It is comprised of permafrost, snow, glacier, frozen soils, and inland river systems. Permafrost degradation, trends towards earlier snow melt, a lengthening snow-free season, soil ice melt, and warming frozen soils all challenge hydrologic simulation under climate change in the Arctic. In this study, we provide an improved representation of the hydrologic cycle across a regional Arctic domain using a generalizable optimization methodology and workflow for the community. We applied the Community Terrestrial Systems Model (CTSM) across the US state of Alaska and the Yukon River Basin at 4-km spatial resolution. We highlight several potentially useful high-resolution CTSM configuration changes. Additionally, we performed a multi-objective optimization using snow and river flow metrics within an adaptive surrogate-based model optimization scheme. Four representative river basins across our study domain were selected for optimization based on observed streamflow and snow water equivalent observations at ten SNOTEL sites. Fourteen sensitive parameters were identified for optimization with half of them not directly related to hydrology or snow processes. Across fifteen out-of-sample river basins, thirteen had improved flow simulations after optimization and the median Kling-Gupta Efficiency of daily flow increased from 0.40 to 0.63. In addition, we adapted the Shapley Decomposition to disentangle each parameter’s contribution to streamflow performance changes, with the seven non-hydrological parameters providing a non-negligible contribution to performance gains. The snow simulation had limited improvement, likely because snow simulation is influenced more by meteorological forcing than model parameter choices.

Land-atmosphere interactions are central to the evolution of the atmospheric boundary layer and the subsequent formation of clouds and precipitation. Existing global climate models represent these connections with bulk approximations on coarse spatial scales, but observations suggest that small-scale variations in surface characteristics and co-located turbulent and momentum fluxes can significantly impact the atmosphere. Recent model development efforts have attempted to capture this phenomenon by coupling existing representations of subgrid-scale (SGS) heterogeneity between land and atmosphere models. Such approaches are in their infancy and it is not yet clear if they can produce a realistic atmospheric response to surface heterogeneity. Here, we implement a parameterization to capture the effects of SGS heterogeneity in the Community Earth System Model (CESM2), and compare single-column simulations against high-resolution Weather Research and Forecasting (WRF) large-eddy simulations (LESs), which we use as a proxy for observations. The CESM2 experiments increase the temperature and humidity variances in the lowest atmospheric levels, but the response is weaker than in WRF-LES. In part, this is attributed to an underestimate of surface heterogeneity in the land model due to a lack of SGS meteorology, a separation between deep and shallow convection schemes in the atmosphere, and a lack of explicitly represented mesoscale secondary circulations. These results highlight the complex processes involved in capturing the effects of SGS heterogeneity and suggest the need for parameterizations that communicate their influence not only at the surface but also vertically.

Multiple 50-member ensemble simulations with the Community Earth System Model version 2 are performed to estimate the coupled climate responses to the 2019-2020 Australian wildfires and COVID-19 pandemic policies. The climate response to the pandemic is found to be weak generally, with net top-of-atmosphere radiative anomalies of +0.23+-0.14 W m/2driving a gradual global warming of 0.05+-0.04 K by the end of 2022. While regional anomalies are detectible in aerosol burdens and clear-sky radiation, few significant anomalies exist in other fields due to internal variability. In contrast, the simulated response to Australian wildfires is a strong and rapid cooling, peaking at -0.95+-0.15 W m/2 in late 2019 with an anomalous global cooling of 0.06+-0.04 K by mid-2020. Transport of fire aerosols throughout the Southern Hemisphere increases albedo and drives a strong interhemispheric radiative contrast, with simulated responses that are consistent generally with those to a Southern Hemisphere volcanic eruption.

A key challenge in accurate simulations of desert dust emission is the parameterization of the threshold wind speed above which dust emission occurs. However, the existing parameterizations yield a unrealistically low dust emission threshold in some climate models such as the Community Earth System Model (CESM), leading to higher simulated dust source activation frequencies than observed and requiring global tuning constants to scale down dust emissions. Here we develop a more realistic parameterization for the dust emission threshold in CESM. In particular, we account for the dissipation of surface wind momentum by surface roughness elements such as vegetation, rocks, and pebbles, which reduce the wind momentum exerted on the bare soil surface. We achieve this by implementing a dynamic wind drag partition model by considering the roughness of the time-varying vegetation as quantified by the leaf area index (LAI), as well as the time-invariant rocks and pebbles using satellite-derived aeolian roughness length. Furthermore, we account for the effect of soil size on dust emission threshold by replacing the currently used globally constant soil median diameter with a spatially varying soil texture map. Results show that with the new parameterization dust emissions decrease by 20–80% over source regions such as Africa, Middle East, and Asia, thereby reducing the need for the global tuning constant. Simulated dust emissions match better in both spatiotemporal variability and emission frequency when compared against satellite observed dust activation frequency data. Our results suggest that including more physical dust emission parameterizations into climate models can lessen bias and improve simulation results, possibly eliminate the use of empirical source functions, and reduce the need for tuning constants. This development could improve assessments of dust impacts on the Earth system.

The diffuse radiation fertilization effect – the increase in plant productivity in the presence of higher diffuse radiation (K↓,d) – is an important yet understudied aspect of atmosphere-biosphere interactions and can modify the terrestrial carbon, energy, and water budgets. The K↓,d fertilization effect links the carbon cycle with clouds and aerosols, all of which are large sources of uncertainties for our current understanding of the Earth system and for future climate projections. Here we establish to what extent observational and modeling uncertainty in sunlight’s diffuse fraction (kd) affects simulated gross primary productivity (GPP) and terrestrial evapotranspiration (λE). We find only 48 eddy covariance sites with simultaneous sufficient measurements of K↓,d with none in the tropical climate zone, making it difficult to constrain this mechanism globally using observations. Using a land modeling framework based on the latest version of the Community Land Model, we find that global GPP ranges from 114 Pg C year-1 when using kd forcing from the MERRA-2 reanalysis to a ~7% higher value of 122 Pg C year-1 when using the CERES satellite product, with especially strong differences apparent over the tropical region (mean increase ~9%). The differences in λE, although smaller (-0.4%) due to competing changes in shaded and sunlit leaf transpiration, can be greater than regional impacts of individual forcing agents like aerosols. Our results demonstrate the importance of comprehensively and systematically validating the simulated kd by atmosphere modules as well as the response differences in diffuse fraction within land modules across Earth System Models.

Irrigation parameterizations in land surface models have been advanced over the past decade, but the newly available data from the Soil Moisture Active Passive (SMAP) satellite has seldom been used to improve irrigation modeling. Here, we investigate the potential of assimilating SMAP soil moisture (SM) data into the Community Land Model (CLM) to improve irrigation representation. Simulations are conducted at 3 arc-minute resolution over the highly irrigated region in the central US, fully enclosing the upstream areas of the river basins draining over the High Plains Aquifer (i.e., the Missouri and Arkansas), and Colorado River basins. We test the original CLM4.5 irrigation scheme and two new irrigation parameterizations using SMAP data assimilation by: (1) directly integrating raw SMAP data, and (2) integrating SMAP data using 1-D Kalman Filter (KF) smoother. An a priori scaling approach is also used to account for bias correction of the shortly-recorded SMAP data based on the ground observations, enabling us to use SMAP for out-of-sample tests (i.e., assessment of the new parameterizations during a non-SMAP period). The ground-based SM observations from three monitoring networks, namely Soil Climate Analysis Network (SCAN), US Climate Reference Network (USCRN), and SNOwpack TELemetry (SNOTEL) are employed for bias correcting SMAP data and validating SM simulations. Results show that SMAP data assimilation using 1-D KF significantly improves irrigation simulations. Bias correction of SMAP data further improves results from KF assimilation in some regions. However, the improvements are small compared to those achieved from 1-D KF application alone, indicating the robustness of using SMAP data and KF globally even for the regions where ground-based data are not available for bias correction. The data assimilation also improves the accuracy of the temporal dynamics and vertical profile of simulated SM. These results are expected to provide a basis for improved modeling of irrigation water use and land-atmosphere interactions.

Stratospheric aerosol injection (SAI) has been shown in climate models to reduce some impacts of global warming in the Arctic, including the loss of sea ice, permafrost thaw, and reduction of Greenland Ice Sheet (GrIS) mass; SAI at high latitudes could preferentially target these impacts. In this study, we use the Community Earth System Model to simulate two Arctic-focused SAI strategies, which inject at 60°N latitude each spring with injection rates adjusted to either maintain September Arctic sea ice at 2030 levels (“Arctic Low”) or restore it to 2010 levels (“Arctic High”). Both simulations maintain or restore September Arctic sea ice to within 10% of their respective targets, reduce permafrost thaw, and increase GrIS surface mass balance by reducing runoff. Arctic High reduces these impacts more effectively than a globally-focused SAI strategy that injects similar quantities of SO2 at lower latitudes. However, Arctic-focused SAI is not merely a “reset button” for the Arctic climate, but brings about a novel climate state, including changes to the seasonal cycles of Northern Hemisphere temperature and sea ice and less high-latitude carbon uptake relative to SSP2-4.5. Additionally, while Arctic-focused SAI predominantly cools the Arctic, its effects are not confined to the Arctic, including detectable cooling throughout most of the northern hemisphere for both simulations, increased mid-latitude sulfur deposition, and a southward shift of the location of the Intertropical Convergence Zone (ITCZ).