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Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser


Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

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Ann M. Fridlind, Bastiaan van Diedenhoven, Andrew S. Ackerman, Alexander Avramov, Agnieszka Mrowiec, Hugh Morrison, Paquita Zuidema, and Matthew D. Shupe


Observations of long-lived mixed-phase Arctic boundary layer clouds on 7 May 1998 during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE)/Surface Heat Budget of the Arctic Ocean (SHEBA) campaign provide a unique opportunity to test understanding of cloud ice formation. Under the microphysically simple conditions observed (apparently negligible ice aggregation, sublimation, and multiplication), the only expected source of new ice crystals is activation of heterogeneous ice nuclei (IN) and the only sink is sedimentation. Large-eddy simulations with size-resolved microphysics are initialized with IN number concentration N IN measured above cloud top, but details of IN activation behavior are unknown. If activated rapidly (in deposition, condensation, or immersion modes), as commonly assumed, IN are depleted from the well-mixed boundary layer within minutes. Quasi-equilibrium ice number concentration Ni is then limited to a small fraction of overlying N IN that is determined by the cloud-top entrainment rate we divided by the number-weighted ice fall speed at the surface υf. Because wc < 1 cm s−1 and υf > 10 cm s−1, Ni/N IN ≪ 1. Such conditions may be common for this cloud type, which has implications for modeling IN diagnostically, interpreting measurements, and quantifying sensitivity to increasing N IN (when we/υf < 1, entrainment rate limitations serve to buffer cloud system response). To reproduce observed ice crystal size distributions and cloud radar reflectivities with rapidly consumed IN in this case, the measured above-cloud N IN must be multiplied by approximately 30. However, results are sensitive to assumed ice crystal properties not constrained by measurements. In addition, simulations do not reproduce the pronounced mesoscale heterogeneity in radar reflectivity that is observed.

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Anthony J. Prenni, Jerry Y. Harrington, Michael Tjernström, Paul J. DeMott, Alexander Avramov, Charles N. Long, Sonia M. Kreidenweis, Peter Q. Olsson, and Johannes Verlinde

Mixed-phase stratus clouds are ubiquitous in the Arctic and play an important role in climate in this region. However, climate and regional models have generally proven unsuccessful at simulating Arctic cloudiness, particularly during the colder months. Specifically, models tend to underpredict the amount of liquid water in mixed-phase clouds. The Mixed-Phase Arctic Cloud Experiments (M-PACE), conducted from late September through October 2004 in the vicinity of the Department of Energy's Atmospheric Radiation Measurement (ARM) North Slope of Alaska field site, focused on characterizing low-level Arctic stratus clouds. Ice nuclei (IN) measurements were made using a continuous-flow ice thermal diffusion chamber aboard the University of North Dakota's Citation II aircraft. These measurements indicated IN concentrations that were significantly lower than those used in many models. Using the Regional Atmospheric Modeling System (RAMS), we show that these low IN concentrations, as well as inadequate parameterizations of the depletion of IN through nucleation scavenging, may be partially responsible for the poor model predictions. Moreover, we show that this can lead to errors in the modeled surface radiative energy budget of 10–100 Wm2. Finally, using the measured IN concentrations as input to RAMS and comparing to a mixed-phase cloud observed during M-PACE, we show excellent agreement between modeled and observed liquid water content and net infrared surface flux.

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