Climate Change Predicted to Shift Wolverine Distributions, Connectivity, and Dispersal Corridors

Boreal species sensitive to the timing and duration of snow cover are particularly vulnerable to global climate change. Recent work has shown a link between wolverine (Gulo gulo) habitat and persistent spring snow cover through 15 May, the approximate end of the wolverine’s reproductive denning period. We modeled the distribution of snow cover within the Columbia, Upper Missouri, and Upper Colorado River Basins using a downscaled ensemble climate model. The ensemble model was based on the arithmetic mean of 10 global climate models (GCMs) that best fit historical climate trends and patterns within these three basins. Snow cover was estimated from resulting downscaled temperature and precipitation patterns using a hydrologic model. We bracketed our ensemble model predictions by analyzing warm (miroc 3.2) and cool (pcm1) downscaled GCMs. Because Moderate-Resolution Imaging Spectroradiometer (MODIS)-based snow cover relationships were analyzed at much finer grain than downscaled GCM output, we conducted a second analysis based on MODIS-based snow cover that persisted through 29 May, simulating the onset of spring two weeks earlier in the year. Based on the downscaled ensemble model, 67% of predicted spring snow cover will persist within the study area through 2030–2059, and 37% through 2070–2099. Estimated snow cover for the ensemble model during the period 2070– 2099 was similar to persistent MODIS snow cover through 29 May. Losses in snow cover were greatest at the southern periphery of the study area (Oregon, Utah, and New Mexico, USA) and least in British Columbia, Canada. Contiguous areas of spring snow cover become smaller and more isolated over time, but large (.1000 km2) contiguous areas of wolverine habitat are predicted to persist within the study area throughout the 21st century for all projections. Areas that retain snow cover throughout the 21st century are British Columbia, north-central Washington, northwestern Montana, and the Greater Yellowstone Area. By the late 21st century, dispersal modeling indicates that habitat isolation at or above levels associated with genetic isolation of wolverine populations becomes widespread. Overall, we expect wolverine habitat to persist throughout the species range at least for the first half of the 21st century, but populations will likely become smaller and more isolated

Comparing Large-Scale Hydrological Model Predictions with Observed Streamflow in the Pacific Northwest: Effects of Climate and Groundwater

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In particular, the adequacy of VIC simulations in groundwater- versus runoff-dominated watersheds using a range of flow metrics relevant for water supply and aquatic habitat was examined. These flow metrics were 1) total annual streamflow; 2) total fall, winter, spring, and summer season streamflows; and 3) 5th, 25th, 50th, 75th, and 95th flow percentiles. The effect of climate on model performance was also evaluated by comparing the observed and simulated streamflow sensitivities to temperature and precipitation. Model performance was evaluated using four quantitative statistics: nonparametric rank correlation ρ, normalized Nash–Sutcliffe efficiency NNSE, root-mean-square error RMSE, and percent bias PBIAS. The VIC model captured the sensitivity of streamflow for temperature better than for precipitation and was in poor agreement with the corresponding temperature and precipitation sensitivities derived from observed streamflow. The model was able to capture the hydrologic behavior of the study watersheds with reasonable accuracy. Both total streamflow and flow percentiles, however, are subject to strong systematic model bias. For example, summer streamflows were underpredicted (PBIAS = -13%) in groundwater-dominated watersheds and overpredicted (PBIAS = 48%) in runoff-dominated watersheds. Similarly, the 5th flow percentile was underpredicted (PBIAS = -51%) in groundwater-dominated watersheds and overpredicted (PBIAS = 19%) in runoff-dominated watersheds. These results provide a foundation for improving model parameterization and calibration in ungauged basins.Keywords: Model errors, Hydrologic models, Model evaluation/performanc

Synoptic sensitivities of subtropical clouds: Separating aerosol effects from meteorology

The fundamental goals of this study are to (1) quantify the link between aerosols, low-level clouds, and meteorology, and (2) evaluate model representation of aerosol-cloud interactions. Recent in-situ and remote sensing studies indicate that meteorological effects which influence cloud liquid water path dominate the aerosol signal in stratocumulus clouds. To address this issue, Chapter II undertakes a synoptic-scale investigation of aerosol-cloud interactions. Using parcel back-trajectories, we develop a method to isolate meteorological from aerosol impacts on clouds, and evaluate results over the Northeast Atlantic stratocumulus regime. Using MODIS observations and ECMWF analyses, we show that controlling for variations in lower tropospheric stability reduces the dependence of cloud fraction on AOD by at least 24%. We conclude that meteorological forcing must be accounted for in assessing aerosol impacts on cloud forcing, and that doing so requires a Lagrangian analysis of parcel histories. Chapter III extends the analysis in Chapter II by performing an in-depth analysis of the meteorological sensitivities of Northeast Atlantic stratocumulus clouds. Additional satellite observations are obtained from CERES, SSM/I and QuikScat. Compositing is used to quantify the sensitivity of cloud fraction to variations in meteorological state along 72 hour Lagrangian back trajectories. Clouds are found to respond to variations in stability, free tropospheric humidity, and sea surface temperature (SST) advection over long time scales while being influenced by changes in surface divergence over much shorter time scales. Cloud sensitivity to both divergence and stability is shown to be robust and not significantly affected through covariance with other forcings. An additional finding is that the sign of the relationship between cloud fraction and several quantities, including divergence, temperature advection, and surface fluxes, is reversed for long time lags. Along with the differences in stability, SST, and boundary layer humidity that are maintained throughout the trajectories, it is suggested that these point to decoupling of the cloud and sub-cloud layers as a possible cause for cloud dissipation. In contrast, the large cloud fraction composite appears to be more shallow and well-mixed at earlier times in the trajectory, thus maintaining a strong coupling with the surface. In Chapter IV, the observational sensitivities observed in Chapters II and III are compared with model representations of aerosol-cloud interactions. Since model parameterizations of aerosol-cloud interactions can be switched on or off, climate simulations can be used to separately quantify the impacts of aerosols and meteorology on cloud cover. Both the ECMWF and GFDL models are analyzed using the trajectory method developed in previous chapters. Both are consistent with Chapter II in showing that a significant fraction of the correlation between AOD and CF results from meteorological covariations. However, the two differ significantly in the magnitude of the correction and in their representation of low-level clouds. The ECMWF model, which shows a 43% correction to the AOD-CF sensitivity, also shows a weak correlation (R2=14.4%) when MODIS cloud cover is compared with ECMWF cloud cover predictions. Alternatively, if the dynamical sensitivities of MODIS clouds are compared to those of the ECMWF clouds, the two compare reasonably well. In contrast, the GFDL model shows a 100% correction, indicating that within the confidence limits of our analysis, low-level cloud cover is not affected by changes in aerosol. However, the GFDL model's treatment of clouds does not compare well with the observational cloud sensitivities identified in Chapter III. Since the model results are only relevant to the observational analysis if the simulations accurately represent cloud cover, from these results it is not possible to quantitatively conclude on a corrected sensitivity of cloud cover to changes in aerosol optical depth. However, the results do qualitatively confirm the results of Chapter II. In addition, the Lagrangian technique developed in previous chapters proves useful in providing detailed diagnostic information on model performance, in particular as a means of testing cloud parameterizations

Synoptic sensitivities of subtropical clouds separating aerosol effects from meteorology

The fundamental goals of this study are to 1) quantify the link between aerosols, low-level clouds, and meteorology, and 2) evaluate model representation of aerosol-cloud interactions. Recent in-situ and remote sensing studies indicate that meteorological effects which influence cloud liquid water path dominate the aerosol signal in stratocumulus clouds. To address this issue, Chapter II undertakes a synoptic-scale investigation of aerosol-cloud interactions. Using parcel back-trajectories, we develop a method to isolate meteorological from aerosol impacts on clouds, and evaluate results over the Northeast Atlantic stratocumulus regime. Using MODIS observations and ECMWF analyses, we show that controlling for variations in lower tropospheric stability reduces the dependence of cloud fraction on AOD by at least 24%. We conclude that meteorological forcing must be accounted for in assessing aerosol impacts on cloud forcing, and that doing so requires a Lagrangian analysis of parcel histories. Chapter III extends the analysis in Chapter II by performing an in-depth analysis of the meteorological sensitivities of Northeast Atlantic stratocumulus clouds. Additional satellite observations are obtained from CERES, SSM/I and QuikScat. Compositing is used to quantify the sensitivity of cloud fraction to variations in meteorological state along 72 hour Lagrangian back trajectories. Clouds are found to respond to variations in stability, free tropospheric humidity, and sea surface temperature (SST) advection over long time scales while being influenced by changes in surface divergence over much shorter time scales. Cloud sensitivity to both divergence and stability is shown to be robust and not significantly affected through covariance with other forcings. An additional finding is that the sign of the relationship between cloud fraction and several quantities, including divergence, temperature advection, and surface fluxes, is reversed for long time lags. Along with the differences in stability, SST, and boundary layer humidity that are maintained throughout the trajectories, it is suggested that these point to decoupling of the cloud and sub-cloud layers as a possible cause for cloud dissipation. In contrast, the large cloud fraction composite appears to be more shallow and well-mixed at earlier times in the trajectory, thus maintaining a strong coupling with the surface. In Chapter IV the observational sensitivities observed in Chapters II and III are compared with model representations of aerosol-cloud interactions. Since model parameterizations of aerosol-cloud interactions can be switched on or off, climate simulations can be used to separately quantify the impacts of aerosols and meteorology on cloud cover. Both the ECMWF and GFDL models are analyzed using the trajectory method developed in previous chapters. Both are consistent with Chapter \\ref}chapter2} in showing that a significant fraction of the correlation between AOD and CF results from meteorological covariations. However, the two differ significantly in the magnitude of the correction and in their representation of low-level clouds. The ECMWF model, which shows a 43% correction to the AOD-CF sensitivity, also shows a weak correlation (R²=14.4%) when MODIS cloud cover is compared with ECMWF cloud cover predictions. Alternatively, if the dynamical sensitivities of MODIS clouds are compared to those of the ECMWF clouds, the two compare reasonably well. In contrast, the GFDL model shows a 100\\% correction, indicating that within the confidence limits of our analysis, low-level cloud cover is not affected by changes in aerosol. However, the GFDL model's treatment of clouds does not compare well with the observational cloud sensitivities identified in Chapter III. Since the model results are only relevant to the observational analysis if the simulations accurately represent cloud cover, from these results it is not possible to quantitatively conclude on a corrected sensitivity of cloud cover to changes in aerosol optical depth. However, the results do qualitatively confirm the results of Chapter II. In addition, the Lagrangian technique developed in previous chapters proves useful in providing detailed diagnostic information on model performance, in particular as a means of testing cloud parameterization

Regional Climate and Hydrologic Change in the Northern US Rockies and Pacific Northwest: Internally Consistent Projections of Future Climate for Resource Management

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Proficiency of medical students at obtaining pressure measurement readings using Automated ankle and toe measuring devices for diagnosis of lower extremity peripheral artery disease

International audienc

Magnetic polarons and the metal-semiconductor transitions in (

We present inelastic light scattering measurements of EuO and Eu$_{1-x}$La$_{x}$B$_6$ ($x$=0, 0.005, 0.01, 0.03, and 0.05) as functions of doping, B isotope, magnetic field, and temperature. Our results reveal a variety of distinct regimes as a function of decreasing T: (a) a paramagnetic semimetal regime, which is characterized by a collision-dominated electronic scattering response whose scattering rate $\Gamma$ decreases with decreasing temperature; (b) a spin-disorder scattering regime, which is characterized by a collision-dominated electronic scattering response whose scattering rate $\Gamma$ scales with the magnetic susceptibility; (c) a magnetic polaron (MP) regime, in which the development of an $H$=0 spin-flip Raman response betrays the formation of magnetic polarons in a narrow temperature range above the Curie temperature T$_{\rm C}$; and (d) a ferromagnetic metal regime, characterized by a flat electronic continuum response typical of other strongly correlated metals. By exploring the behavior of the Raman responses in these various regimes in response to changing external parameters, we are able to investigate the evolution of charge and spin degrees of freedom through various transitions in these materials.Comment: 19 pages, 13 figures on 5 pages (Gif format