New Measurements of Venus Winds with Ground-Based Doppler Velocimetry at CFHT

operations with observations from the ground using various techniques and spectral domains (Lellouch and Witasse, 2008). We present an analysis of Venus Doppler winds at cloud tops based on observations made at the Canada France Hawaii 3.6-m telescope (CFHT) with the ESPaDOnS visible spectrograph. These observations consisted of high-resolution spectra of Fraunhofer lines in the visible range (0.37-1.05 μm) to measure the winds at cloud tops using the Doppler shift of solar radiation scattered by cloud top particles in the observer's direction (Widemann et al., 2007, 2008). The observations were made during 19-20 February 2011 and were coordinated with Visual Monitoring Camera (VMC) observations by Venus Express. The complete optical spectrum was collected over 40 spectral orders at each point with 2-5 seconds exposures, at a resolution of about 80000. The observations included various points of the dayside hemisphere at a phase angle of 67°, between +10° and -60° latitude, in steps of 10° , and from +70° to -12° longitude relative to sub-Earth meridian in steps of 12°. The Doppler shift measured in scattered solar light on Venus dayside results from two instantaneous motions: (1) a motion between the Sun and Venus upper cloud particles; (2) a motion between the observer and Venus clouds. The measured Doppler shift, which results from these two terms combined, varies with the planetocentric longitude and latitude and is minimum at meridian ΦN = ΦSun - ΦEarth where the two components subtract to each other for a pure zonal regime. Due to the need for maintaining a stable velocity reference during the course of acquisition using high resolution spectroscopy, we measure relative Doppler shifts to ΦN. The main purpose of our work is to provide variable wind measurements with respect to the background atmosphere, complementary to simultaneous measurements made with the VMC camera onboard the Venus Express. We will present first results from this work, comparing with previous results by the CFHT/ESPaDOnS and VLT-UVES spectrographs (Machado et al., 2012), with Galileo fly-by measurements and with VEx nominal mission observations (Peralta et al., 2007, Luz et al., 2011). Acknowledgements: The authors acknowledge support from FCT through projects PTDC/CTE-AST/110702/2009 and PEst-OE/FIS/UI2751/2011. PM and TW also acknowledge support from the Observatoire de Paris. Lellouch, E., and Witasse, O., A coordinated campaign of Venus ground-based observations and Venus Express measurements, Planetary and Space Science 56 (2008) 1317-1319. Luz, D., et al., Venus's polar vortex reveals precessing circulation, Science 332 (2011) 577-580. Machado, P., Luz, D. Widemann, T., Lellouch, E., Witasse, O, Characterizing the atmospheric dynamics of Venus from ground-based Doppler velocimetry, Icarus, submitted. Peralta J., R. Hueso, A. Sánchez-Lavega, A reanalysis of Venus winds at two cloud levels from Galileo SSI images, Icarus 190 (2007) 469-477. Widemann, T., Lellouch, E., Donati, J.-F., 2008, Venus Doppler winds at Cloud Tops Observed with ESPaDOnS at CFHT, Planetary and Space Science, 56, 1320-1334

The asymptotic iteration method for the angular spheroidal eigenvalues with arbitrary complex size parameter c

The asymptotic iteration method is applied, to calculate the angular spheroidal eigenvalues $\lambda^{m}_{\ell}(c)$ with arbitrary complex size parameter $c$. It is shown that, the obtained numerical results of $\lambda^{m}_{\ell}(c)$ are all in excellent agreement with the available published data over the full range of parameter values $\ell$, $m$, and $c$. Some representative values of $\lambda^{m}_{\ell}(c)$ for large real $c$ are also given.Comment: 15 pages, 1 figur

Soil conservation II : know your farm.

This circular was prepared by O. T. Coleman, Extension Specialist in Soils, in Collaboration with E. T. Itschner, State Club Agent. Acknowledgment is given to A. W. Klemme, Extension Specialist in Soils, for the preparation of Chapter III; to W. R. Tascher, Extension Soil Conservationist, for the preparation of Chapter VI; to John Falloon, Extension Soil Conservationist, for the preparation of Chapter V; and to John Ferguson, Extension Soil Conservationist, for the preparation of Chapter IV. --Page 3."Cooperative Extension Work in Agriculture and Home Economics, University of Missouri, College of Agriculture and the United States Department of Agriculture cooperating.""March, 1939."Title from cover

Decomposing uncertainties in the future terrestrial carbon budget associated with emission scenarios, climate projections, and ecosystem simulations using the ISI-MIP results

We examined the changes to global net primary production (NPP), vegetation biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global vegetation models (GVMs) obtained from the Inter-Sectoral Impact Model Intercomparison Project. Simulation results were obtained using five global climate models (GCMs) forced with four representative concentration pathway (RCP) scenarios. To clarify which component (i.e., emission scenarios, climate projections, or global vegetation models) contributes the most to uncertainties in projected global terrestrial C cycling by 2100, analysis of variance (ANOVA) and wavelet clustering were applied to 70 projected simulation sets. At the end of the simulation period, changes from the year 2000 in all three variables varied considerably from net negative to positive values. ANOVA revealed that the main sources of uncertainty are different among variables and depend on the projection period. We determined that in the global VegC and SOC projections, GVMs are the main influence on uncertainties (60 % and 90 %, respectively) rather than climate-driving scenarios (RCPs and GCMs). Moreover, the divergence of changes in vegetation carbon residence times is dominated by GVM uncertainty, particularly in the latter half of the 21st century. In addition, we found that the contribution of each uncertainty source is spatiotemporally heterogeneous and it differs among the GVM variables. The dominant uncertainty source for changes in NPP and VegC varies along the climatic gradient. The contribution of GVM to the uncertainty decreases as the climate division becomes cooler (from ca. 80 % in the equatorial division to 40 % in the snow division). Our results suggest that to assess climate change impacts on global ecosystem C cycling among each RCP scenario, the long-term C dynamics within the ecosystems (i.e., vegetation turnover and soil decomposition) are more critical factors than photosynthetic processes. The different trends in the contribution of uncertainty sources in each variable among climate divisions indicate that improvement of GVMs based on climate division or biome type will be effective. On the other hand, in dry regions, GCMs are the dominant uncertainty source in climate impact assessments of vegetation and soil C dynamics

Reliability Ensemble Averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) <q>business as usual</q> emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095–2099) compared to 2001–2005, which is 2–3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y⁻¹. Using REA also leads to a 45–68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO₂ fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions

Psychometric Properties of an Assessment for Mental Health Recovery Programs

The concept of recovery can be operationalized from either the point of view of the consumer, or from the perspective of the agency providing services. The Milestones of Recovery Scale (MORS) was created to capture aspects of recovery from the agency perspective. Evidence establishing the psychometric properties of the MORS was obtained in three efforts: Inter-rater reliability using staff at The Village, a multi-service organization serving the homeless mentally ill in Long Beach, California; inter-rater reliability was also obtained from Vinfen Corporation, a large provider of housing services to mentally ill persons in Boston, Massachusetts. A test–retest reliability study was conducted using staff rating of clients at The Village, and evidence for validity was obtained using the Level of Care Utilization System (LOCUS) as a validity measure. The intra-class correlation coefficient for the inter-rater reliability study was r = .85 (CI .81, .89) for The Village and r = .86 (CI .80, .90) for Vinfen Corporation; test–retest reliability was r = .85 (CI .81, .87); and validity coefficients for the LOCUS were at or above r = .49 for all subscales except one. There is sufficient evidence for the reliability and validity of the MORS

Comparing projections of future changes in runoff and water resources from hydrological and ecosystem models in ISI-MIP

Projections of future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed. Projections of change from the baseline period (1981–2010) to the future (2070–2099) from a number of different ecosystems and hydrological models were studied. The differences between projections from the two types of model were looked at globally and regionally. Typically, across different regions the ecosystem models tended to project larger increases and smaller decreases in runoff than the hydrological models. However, the differences varied both regionally and seasonally. Sensitivity experiments were also used to investigate the contributions of varying CO2 and allowing vegetation distribution to evolve on projected changes in runoff. In two out of four models which had data available from CO2 sensitivity experiments, allowing CO2 to vary was found to increase runoff more than keeping CO2 constant, while in two models runoff decreased. This suggests more uncertainty in runoff responses to elevated CO2 than previously considered. As CO2 effects on evapotranspiration via stomatal conductance and leaf-area index are more commonly included in ecosystems models than in hydrological models, this may partially explain some of the difference between model types. Keeping the vegetation distribution static in JULES runs had much less effect on runoff projections than varying CO2, but this may be more pronounced if looked at over a longer timescale as vegetation changes may take longer to reach a new state

How can the First ISLSCP Field Experiment contribute to present-day efforts to evaluate water stress in JULESv5.0?

The First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), Kansas, US, 1987–1989, made important contributions to the understanding of energy and CO2 exchanges between the land surface and the atmosphere, which heavily influenced the development of numerical land-surface modelling. Now, 30 years on, we demonstrate how the wealth of data collected during FIFE and its subsequent in-depth analysis in the literature continue to be a valuable resource for the current generation of land-surface models. To illustrate, we use the FIFE dataset to evaluate the representation of water stress on tallgrass prairie vegetation in the Joint UK Land Environment Simulator (JULES) and highlight areas for future development. We show that, while JULES is able to simulate a decrease in net carbon assimilation and evapotranspiration during a dry spell, the shape of the diurnal cycle is not well captured. Evaluating the model parameters and results against this dataset provides a case study on the assumptions in calibrating “unstressed” vegetation parameters and thresholds for water stress. In particular, the responses to low water availability and high temperatures are calibrated separately. We also illustrate the effect of inherent uncertainties in key observables, such as leaf area index, soil moisture and soil properties. Given these valuable lessons, simulations for this site will be a key addition to a compilation of simulations covering a wide range of vegetation types and climate regimes, which will be used to improve the way that water stress is represented within JULES

Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2.

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 2007-2013) under Grant 238366. R.B., R.K., R.D., A.W., and P.D.F. were supported by the Joint Department of Energy and Climate Change/Department for Environment, Food and Rural Affairs Met Office Hadley Centre Climate Programme (GA01101). A.I. and K.N. were supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modeling groups responsible for the GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M models for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work has been conducted under the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (BMBF) with project funding Reference 01LS1201A.This is the author accepted manuscript. The final version is available from PNAS via http://dx.doi.org/10.1073/pnas.122247711