112 research outputs found

    The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

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    A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no com- parison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes. Here, the methods and ïŹrst results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been signiïŹcant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat ïŹ‚ux observations for an industrial area in Vancouver, British Columbia, Canada. The aim of the comparison is twofold: to identify those modeling ap- proaches that minimize the errors in the simulated ïŹ‚uxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual ïŹ‚uxes but no model performs best or worst for all ïŹ‚uxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat ïŹ‚ux

    Mesenchymal Stem Cells Prevent the Rejection of Fully Allogenic Islet Grafts by the Immunosuppressive Activity of Matrix Metalloproteinase-2 and -9

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    OBJECTIVE Mesenchymal stem cells (MSCs) are known to be capable of suppressing immune responses, but the molecular mechanisms involved and the therapeutic potential of MSCs remain to be clarified. RESEARCH DESIGN AND METHODS We investigated the molecular mechanisms underlying the immunosuppressive effects of MSCs in vitro and in vivo. RESULTS Our results demonstrate that matrix metalloproteinases (MMPs) secreted by MSCs, in particular MMP-2 and MMP-9, play an important role in the suppressive activity of MSCs by reducing surface expression of CD25 on responding T-cells. Blocking the activity of MMP-2 and MMP-9 in vitro completely abolished the suppression of T-cell proliferation by MSCs and restored T-cell expression of CD25 as well as responsiveness to interleukin-2. In vivo, administration of MSCs significantly reduced delayed-type hypersensitivity responses to allogeneic antigen and profoundly prolonged the survival of fully allogeneic islet grafts in transplant recipients. Significantly, these MSC-mediated protective effects were completely reversed by in vivo inhibition of MMP-2 and MMP-9. CONCLUSIONS We demonstrate that MSCs can prevent islet allograft rejection leading to stable, long-term normoglycemia. In addition, we provide a novel insight into the mechanism underlying the suppressive effects of MSCs on T-cell responses to alloantigen.</p

    Representing spatial variability of snow water equivalent in hydrologic and land-surface models: a review

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    This paper evaluates the use of field data on the spatial variability of snow water equivalent (SWE) to guide the design of distributed snow models. An extensive reanalysis of results from previous field studies in different snow environments around the world is presented, followed by an analysis of field data on spatial variability of snow collected in the headwaters of the Jollie River basin, a rugged mountain catchment in the Southern Alps of New Zealand. In addition, area-averaged simulations of SWE based on different types of spatial discretization are evaluated. Spatial variability of SWE is shaped by a range of different processes that occur across a hierarchy of spatial scales. Spatial variability at the watershed-scale is shaped by variability in near-surface meteorological fields (e.g., elevation gradients in temperature) and, provided suitable meteorological data is available, can be explicitly resolved by spatial interpolation/extrapolation. On the other hand, spatial variability of SWE at the hillslope-scale is governed by processes such as drifting, sloughing of snow off steep slopes, trapping of snow by shrubs, and the nonuniform unloading of snow by the forest canopy, which are more difficult to resolve explicitly. Subgrid probability distributions are often capable of representing the aggregate-impact of unresolved processes at the hillslope-scale, though they may not adequately capture the effects of elevation gradients. While the best modeling strategy is case-specific, the analysis in this paper provides guidance on both the suitability of several common snow modeling approaches and on the choice of parameter values in subgrid probability distributions.Martyn P. Clark, Jordy Hendrikx, Andrew G. Slater, Dmitri Kavetski, Brian Anderson, Nicolas J. Cullen, Tim Kerr, Einar Örn Hreinsson and Ross A. Wood

    Testing Above- and Below-Canopy Representations of Turbulent Fluxes in an Energy Balance Snowmelt Model

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    Turbulent fluxes of sensible and latent heat are important processes in the surface energy balance that drives snowmelt. Modeling these fluxes in a forested environment is complicated because of the canopy effects on the wind field. This paper presents and tests a turbulent flux model developed to represent these processes in an energy balance snowmelt model. The goal is to model these processes using the readily available inputs of canopy height and leaf area index in a way that minimizes the number of parameters, state variables, and assumptions about hard to quantify processes. Selected periods from 9 years of eddy-covariance (EC) measurements at Niwot Ridge, Colorado, were used to evaluate the effectiveness of this modeling approach. The model was able to reproduce the above-canopy sensible and latent heat fluxes reasonably with the correlation higher for sensible heat than latent heat. The modeled values of the below-canopy latent heat fluxes also matched the EC-measured values. The model captured the nighttime below-canopy sensible heat flux quite well, but there were discrepancies in daytime sensible heat flux possibly due to mountain slope circulation not quantifiable in this kind of model. Despite the uncertainties in the below-canopy sensible heat fluxes, the results are encouraging and suggest that reasonable predictions of turbulent flux energy exchanges and subsequent vapor losses from snow in forested environments can be obtained with a parsimonious single-layer representation of the canopy. The model contributes an improved physically based capability for predicting the snow accumulation and melt in a forested environment

    Surface Energy Budgets of Arctic Tundra During Growing Season

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    This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.Plain Language SummaryGrowing season latent and sensible heat fluxes are nearly equal over the Arctic permafrost tundra regions. Persistent ground heat flux into the subsurface layer leads to seasonal thaw of the top permafrost layer. The maximum energy production model accurately estimates the latent, sensible, and ground heat flux of the surface energy budget of the Arctic permafrost regions.Key PointThe MEP model is parsimonious and well suited to modeling surface energy budget in data‐sparse permafrost environmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/1/jgrd55584.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/2/jgrd55584_am.pd

    ESM-SnowMIP: Assessing snow models and quantifying snow-related climate feedbacks

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    This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP)

    Impact of intra- versus inter-annual snow depth variation on water relations and photosynthesis for two Great Basin Desert shrubs

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    Snowfall provides the majority of soil water in certain ecosystems of North America. We tested the hypothesis that snow depth variation affects soil water content, which in turn drives water potential (Κ) and photosynthesis, over 10 years for two widespread shrubs of the western USA. Stem Κ (Κ stem) and photosynthetic gas exchange [stomatal conductance to water vapor (g s), and CO2 assimilation (A)] were measured in mid-June each year from 2004 to 2013 for Artemisia tridentata var. vaseyana (Asteraceae) and Purshia tridentata (Rosaceae). Snow fences were used to create increased or decreased snow depth plots. Snow depth on +snow plots was about twice that of ambient plots in most years, and 20 % lower on -snow plots, consistent with several down-scaled climate model projections. Maximal soil water content at 40- and 100-cm depths was correlated with February snow depth. For both species, multivariate ANOVA (MANOVA) showed that Κ stem, g s, and A were significantly affected by intra-annual variation in snow depth. Within years, MANOVA showed that only A was significantly affected by spatial snow depth treatments for A. tridentata, and Κ stem was significantly affected by snow depth for P. tridentata. Results show that stem water relations and photosynthetic gas exchange for these two cold desert shrub species in mid-June were more affected by inter-annual variation in snow depth by comparison to within-year spatial variation in snow depth. The results highlight the potential importance of changes in inter-annual variation in snowfall for future shrub photosynthesis in the western Great Basin Desert
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