Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?

NASA Astrophysics Data System (ADS)

Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.

2016-12-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.

Global-scale high-resolution ( 1 km) modelling of mean, maximum and minimum annual streamflow

NASA Astrophysics Data System (ADS)

Barbarossa, Valerio; Huijbregts, Mark; Hendriks, Jan; Beusen, Arthur; Clavreul, Julie; King, Henry; Schipper, Aafke

2017-04-01

Quantifying mean, maximum and minimum annual flow (AF) of rivers at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. AF metrics can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict AF metrics based on climate and catchment characteristics. Yet, so far, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. We developed global-scale regression models that quantify mean, maximum and minimum AF as function of catchment area and catchment-averaged slope, elevation, and mean, maximum and minimum annual precipitation and air temperature. We then used these models to obtain global 30 arc-seconds (˜ 1 km) maps of mean, maximum and minimum AF for each year from 1960 through 2015, based on a newly developed hydrologically conditioned digital elevation model. We calibrated our regression models based on observations of discharge and catchment characteristics from about 4,000 catchments worldwide, ranging from 100 to 106 km2 in size, and validated them against independent measurements as well as the output of a number of process-based global hydrological models (GHMs). The variance explained by our regression models ranged up to 90% and the performance of the models compared well with the performance of existing GHMs. Yet, our AF maps provide a level of spatial detail that cannot yet be achieved by current GHMs.

Coupled local facilitation and global hydrologic inhibition drive landscape geometry in a patterned peatland

NASA Astrophysics Data System (ADS)

Acharya, S.; Kaplan, D. A.; Casey, S.; Cohen, M. J.; Jawitz, J. W.

2015-05-01

Self-organized landscape patterning can arise in response to multiple processes. Discriminating among alternative patterning mechanisms, particularly where experimental manipulations are untenable, requires process-based models. Previous modeling studies have attributed patterning in the Everglades (Florida, USA) to sediment redistribution and anisotropic soil hydraulic properties. In this work, we tested an alternate theory, the self-organizing-canal (SOC) hypothesis, by developing a cellular automata model that simulates pattern evolution via local positive feedbacks (i.e., facilitation) coupled with a global negative feedback based on hydrology. The model is forced by global hydroperiod that drives stochastic transitions between two patch types: ridge (higher elevation) and slough (lower elevation). We evaluated model performance using multiple criteria based on six statistical and geostatistical properties observed in reference portions of the Everglades landscape: patch density, patch anisotropy, semivariogram ranges, power-law scaling of ridge areas, perimeter area fractal dimension, and characteristic pattern wavelength. Model results showed strong statistical agreement with reference landscapes, but only when anisotropically acting local facilitation was coupled with hydrologic global feedback, for which several plausible mechanisms exist. Critically, the model correctly generated fractal landscapes that had no characteristic pattern wavelength, supporting the invocation of global rather than scale-specific negative feedbacks.

Coupled local facilitation and global hydrologic inhibition drive landscape geometry in a patterned peatland

NASA Astrophysics Data System (ADS)

Acharya, S.; Kaplan, D. A.; Casey, S.; Cohen, M. J.; Jawitz, J. W.

2015-01-01

Self-organized landscape patterning can arise in response to multiple processes. Discriminating among alternative patterning mechanisms, particularly where experimental manipulations are untenable, requires process-based models. Previous modeling studies have attributed patterning in the Everglades (Florida, USA) to sediment redistribution and anisotropic soil hydraulic properties. In this work, we tested an alternate theory, the self-organizing canal (SOC) hypothesis, by developing a cellular automata model that simulates pattern evolution via local positive feedbacks (i.e., facilitation) coupled with a global negative feedback based on hydrology. The model is forced by global hydroperiod that drives stochastic transitions between two patch types: ridge (higher elevation) and slough (lower elevation). We evaluated model performance using multiple criteria based on six statistical and geostatistical properties observed in reference portions of the Everglades landscape: patch density, patch anisotropy, semivariogram ranges, power-law scaling of ridge areas, perimeter area fractal dimension, and characteristic pattern wavelength. Model results showed strong statistical agreement with reference landscapes, but only when anisotropically acting local facilitation was coupled with hydrologic global feedback, for which several plausible mechanisms exist. Critically, the model correctly generated fractal landscapes that had no characteristic pattern wavelength, supporting the invocation of global rather than scale-specific negative feedbacks.

Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E.; Talbert, Marian; Talbert, Colin

2018-01-01

Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.

Global validation of a process-based model on vegetation gross primary production using eddy covariance observations.

PubMed

Liu, Dan; Cai, Wenwen; Xia, Jiangzhou; Dong, Wenjie; Zhou, Guangsheng; Chen, Yang; Zhang, Haicheng; Yuan, Wenping

2014-01-01

Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world. Our results indicated that the IBIS model explained 60% of the observed variation in daily GPP at all validation sites. Comparison with a satellite-based vegetation model (Eddy Covariance-Light Use Efficiency, EC-LUE) revealed that the IBIS simulations yielded comparable GPP results as the EC-LUE model. Global mean GPP estimated by the IBIS model was 107.50±1.37 Pg C year(-1) (mean value ± standard deviation) across the vegetated area for the period 2000-2006, consistent with the results of the EC-LUE model (109.39±1.48 Pg C year(-1)). To evaluate the uncertainty introduced by the parameter Vcmax, which represents the maximum photosynthetic capacity, we inversed Vcmax using Markov Chain-Monte Carlo (MCMC) procedures. Using the inversed Vcmax values, the simulated global GPP increased by 16.5 Pg C year(-1), indicating that IBIS model is sensitive to Vcmax, and large uncertainty exists in model parameterization.

Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus)

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E; Sheffels, Trevor R.; Carter, Jacoby; Systma, Mark D.; Talbert, Colin

2017-01-01

Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.

Exploratory Model Analysis of the Space Based Infrared System (SBIRS) Low Global Scheduler Problem

DTIC Science & Technology

1999-12-01

solution. The non- linear least squares model is defined as Y = f{e,t) where: 0 =M-element parameter vector Y =N-element vector of all data t...NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS EXPLORATORY MODEL ANALYSIS OF THE SPACE BASED INFRARED SYSTEM (SBIRS) LOW GLOBAL SCHEDULER...December 1999 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE EXPLORATORY MODEL ANALYSIS OF THE SPACE BASED INFRARED SYSTEM

Ionospheric Simulation System for Satellite Observations and Global Assimilative Modeling Experiments (ISOGAME)

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Mannucci, Anthony J.; Verkhoglyadova, Olga P.; Stephens, Philip; Wilson, Brian D.; Akopian, Vardan; Komjathy, Attila; Lijima, Byron A.

2013-01-01

ISOGAME is designed and developed to assess quantitatively the impact of new observation systems on the capability of imaging and modeling the ionosphere. With ISOGAME, one can perform observation system simulation experiments (OSSEs). A typical OSSE using ISOGAME would involve: (1) simulating various ionospheric conditions on global scales; (2) simulating ionospheric measurements made from a constellation of low-Earth-orbiters (LEOs), particularly Global Navigation Satellite System (GNSS) radio occultation data, and from ground-based global GNSS networks; (3) conducting ionospheric data assimilation experiments with the Global Assimilative Ionospheric Model (GAIM); and (4) analyzing modeling results with visualization tools. ISOGAME can provide quantitative assessment of the accuracy of assimilative modeling with the interested observation system. Other observation systems besides those based on GNSS are also possible to analyze. The system is composed of a suite of software that combines the GAIM, including a 4D first-principles ionospheric model and data assimilation modules, an Internal Reference Ionosphere (IRI) model that has been developed by international ionospheric research communities, observation simulator, visualization software, and orbit design, simulation, and optimization software. The core GAIM model used in ISOGAME is based on the GAIM++ code (written in C++) that includes a new high-fidelity geomagnetic field representation (multi-dipole). New visualization tools and analysis algorithms for the OSSEs are now part of ISOGAME.

Next Generation Community Based Unified Global Modeling System Development and Operational Implementation Strategies at NCEP

NASA Astrophysics Data System (ADS)

Tallapragada, V.

2017-12-01

NOAA's Next Generation Global Prediction System (NGGPS) has provided the unique opportunity to develop and implement a non-hydrostatic global model based on Geophysical Fluid Dynamics Laboratory (GFDL) Finite Volume Cubed Sphere (FV3) Dynamic Core at National Centers for Environmental Prediction (NCEP), making a leap-step advancement in seamless prediction capabilities across all spatial and temporal scales. Model development efforts are centralized with unified model development in the NOAA Environmental Modeling System (NEMS) infrastructure based on Earth System Modeling Framework (ESMF). A more sophisticated coupling among various earth system components is being enabled within NEMS following National Unified Operational Prediction Capability (NUOPC) standards. The eventual goal of unifying global and regional models will enable operational global models operating at convective resolving scales. Apart from the advanced non-hydrostatic dynamic core and coupling to various earth system components, advanced physics and data assimilation techniques are essential for improved forecast skill. NGGPS is spearheading ambitious physics and data assimilation strategies, concentrating on creation of a Common Community Physics Package (CCPP) and Joint Effort for Data Assimilation Integration (JEDI). Both initiatives are expected to be community developed, with emphasis on research transitioning to operations (R2O). The unified modeling system is being built to support the needs of both operations and research. Different layers of community partners are also established with specific roles/responsibilities for researchers, core development partners, trusted super-users, and operations. Stakeholders are engaged at all stages to help drive the direction of development, resources allocations and prioritization. This talk presents the current and future plans of unified model development at NCEP for weather, sub-seasonal, and seasonal climate prediction applications with special emphasis on implementation of NCEP FV3 Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) into operations by 2019.

An observationally constrained estimate of global dust aerosol optical depth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ridley, David A.; Heald, Colette L.; Kok, Jasper F.

Here, the role of mineral dust in climate and ecosystems has been largely quantified using global climate and chemistry model simulations of dust emission, transport, and deposition. However, differences between these model simulations are substantial, with estimates of global dust aerosol optical depth (AOD) that vary by over a factor of 5. Here we develop an observationally based estimate of the global dust AOD, using multiple satellite platforms, in situ AOD observations and four state-of-the-science global models over 2004–2008. We estimate that the global dust AOD at 550 nm is 0.030 ± 0.005 (1σ), higher than the AeroCom model medianmore » (0.023) and substantially narrowing the uncertainty. The methodology used provides regional, seasonal dust AOD and the associated statistical uncertainty for key dust regions around the globe with which model dust schemes can be evaluated. Exploring the regional and seasonal differences in dust AOD between our observationally based estimate and the four models in this study, we find that emissions in Africa are often overrepresented at the expense of Asian and Middle Eastern emissions and that dust removal appears to be too rapid in most models.« less

An observationally constrained estimate of global dust aerosol optical depth

DOE PAGES

Ridley, David A.; Heald, Colette L.; Kok, Jasper F.; ...

2016-12-06

Here, the role of mineral dust in climate and ecosystems has been largely quantified using global climate and chemistry model simulations of dust emission, transport, and deposition. However, differences between these model simulations are substantial, with estimates of global dust aerosol optical depth (AOD) that vary by over a factor of 5. Here we develop an observationally based estimate of the global dust AOD, using multiple satellite platforms, in situ AOD observations and four state-of-the-science global models over 2004–2008. We estimate that the global dust AOD at 550 nm is 0.030 ± 0.005 (1σ), higher than the AeroCom model medianmore » (0.023) and substantially narrowing the uncertainty. The methodology used provides regional, seasonal dust AOD and the associated statistical uncertainty for key dust regions around the globe with which model dust schemes can be evaluated. Exploring the regional and seasonal differences in dust AOD between our observationally based estimate and the four models in this study, we find that emissions in Africa are often overrepresented at the expense of Asian and Middle Eastern emissions and that dust removal appears to be too rapid in most models.« less

Impact of Uncertainties in Meteorological Forcing Data and Land Surface Parameters on Global Estimates of Terrestrial Water Balance Components

NASA Astrophysics Data System (ADS)

Nasonova, O. N.; Gusev, Ye. M.; Kovalev, Ye. E.

2009-04-01

Global estimates of the components of terrestrial water balance depend on a technique of estimation and on the global observational data sets used for this purpose. Land surface modelling is an up-to-date and powerful tool for such estimates. However, the results of modelling are affected by the quality of both a model and input information (including meteorological forcing data and model parameters). The latter is based on available global data sets containing meteorological data, land-use information, and soil and vegetation characteristics. Now there are a lot of global data sets, which differ in spatial and temporal resolution, as well as in accuracy and reliability. Evidently, uncertainties in global data sets will influence the results of model simulations, but to which extent? The present work is an attempt to investigate this issue. The work is based on the land surface model SWAP (Soil Water - Atmosphere - Plants) and global 1-degree data sets on meteorological forcing data and the land surface parameters, provided within the framework of the Second Global Soil Wetness Project (GSWP-2). The 3-hourly near-surface meteorological data (for the period from 1 July 1982 to 31 December 1995) are based on reanalyses and gridded observational data used in the International Satellite Land-Surface Climatology Project (ISLSCP) Initiative II. Following the GSWP-2 strategy, we used a number of alternative global forcing data sets to perform different sensitivity experiments (with six alternative versions of precipitation, four versions of radiation, two pure reanalysis products and two fully hybridized products of meteorological data). To reveal the influence of model parameters on simulations, in addition to GSWP-2 parameter data sets, we produced two alternative global data sets with soil parameters on the basis of their relationships with the content of clay and sand in a soil. After this the sensitivity experiments with three different sets of parameters were performed. As a result, 16 variants of global annual estimates of water balance components were obtained. Application of alternative data sets on radiation, precipitation, and soil parameters allowed us to reveal the influence of uncertainties in input data on global estimates of water balance components.

An Inquiry-Based Approach to Teaching the Spherical Earth Model to Preservice Teachers Using the Global Positioning System

ERIC Educational Resources Information Center

Song, Youngjin; Schwenz, Richard

2013-01-01

This article describes an inquiry-based lesson to deepen preservice teachers' understanding of the spherical Earth model using the Global Positioning System. The lesson was designed with four learning goals: (1) to increase preservice teachers' conceptual knowledge of the spherical Earth model; (2) to develop preservice teachers'…

Assessing the impacts of 1.5°C of global warming - The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) approach

NASA Astrophysics Data System (ADS)

Zhao, F.; Frieler, K.; Warszawski, L.; Lange, S.; Schewe, J.; Reyer, C.; Ostberg, S.; Piontek, F.; Betts, R. A.; Burke, E.; Ciais, P.; Deryng, D.; Ebi, K. L.; Emanuel, K.; Elliott, J. W.; Galbraith, E. D.; Gosling, S.; Hickler, T.; Hinkel, J.; Jones, C.; Krysanova, V.; Lotze-Campen, H.; Mouratiadou, I.; Popp, A.; Tian, H.; Tittensor, D.; Vautard, R.; van Vliet, M. T. H.; Eddy, T.; Hattermann, F.; Huber, V.; Mengel, M.; Stevanovic, M.; Kirsten, T.; Mueller Schmied, H.; Denvil, S.; Halladay, K.; Suzuki, T.; Lotze, H. K.

2016-12-01

In Paris, France, December 2015 the Conference of Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the IPCC to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016 the IPCC panel accepted the invitation. Here we describe the model simulations planned within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to address the request by providing tailored cross-sectoral consistent impacts projections. The protocol is designed to allow for 1) a separation of the impacts of the historical warming starting from pre-industrial conditions from other human drivers such as historical land use changes (based on pre-industrial and historical impact model simulations), 2) a quantification of the effects of an additional warming to 1.5°C including a potential overshoot and long term effects up to 2300 in comparison to a no-mitigation scenario (based on the low emissions Representative Concentration Pathway RCP2.6 and a no-mitigation scenario RCP6.0) keeping socio-economic conditions fixed at year 2005 levels, and 3) an assessment of the climate effects based on the same climate scenarios but accounting for parallel changes in socio-economic conditions following the middle of the road Shared Socioeconomic Pathway (SSP2) and differential bio-energy requirements associated with the transformation of the energy system to reach RCP2.6 compared to RCP6.0. To provide the scientific basis for an aggregation of impacts across sectors and an analysis of cross-sectoral interactions potentially damping or amplifying sectoral impacts the protocol is designed to provide consistent impacts projections across a range of impact models from different sectors (global and regional hydrological models, global gridded crop models, global vegetation models, regional forestry models, global and regional marine ecosystem and fisheries models, global and regional coastal infrastructure models, energy models, health models, and agro-economic models).

Assessing the impacts of 1.5°C of global warming - The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) approach

NASA Astrophysics Data System (ADS)

Frieler, Katja; Warszawski, Lila; Zhao, Fang

2017-04-01

In Paris, France, December 2015 the Conference of Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the IPCC to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016 the IPCC panel accepted the invitation. Here we describe the model simulations planned within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to address the request by providing tailored cross-sectoral consistent impacts projections. The protocol is designed to allow for 1) a separation of the impacts of the historical warming starting from pre-industrial conditions from other human drivers such as historical land use changes (based on pre-industrial and historical impact model simulations), 2) a quantification of the effects of an additional warming to 1.5°C including a potential overshoot and long term effects up to 2300 in comparison to a no-mitigation scenario (based on the low emissions Representative Concentration Pathway RCP2.6 and a no-mitigation scenario RCP6.0) keeping socio-economic conditions fixed at year 2005 levels, and 3) an assessment of the climate effects based on the same climate scenarios but accounting for parallel changes in socio-economic conditions following the middle of the road Shared Socioeconomic Pathway (SSP2) and differential bio-energy requirements associated with the transformation of the energy system to reach RCP2.6 compared to RCP6.0. To provide the scientific basis for an aggregation of impacts across sectors and an analysis of cross-sectoral interactions potentially damping or amplifying sectoral impacts the protocol is designed to provide consistent impacts projections across a range of impact models from different sectors (global and regional hydrological models, global gridded crop models, global vegetation models, regional forestry models, global and regional marine ecosystem and fisheries models, global and regional coastal infrastructure models, energy models, health models, and agro-economic models).

Ionospheric Slant Total Electron Content Analysis Using Global Positioning System Based Estimation

NASA Technical Reports Server (NTRS)

Komjathy, Attila (Inventor); Mannucci, Anthony J. (Inventor); Sparks, Lawrence C. (Inventor)

2017-01-01

A method, system, apparatus, and computer program product provide the ability to analyze ionospheric slant total electron content (TEC) using global navigation satellite systems (GNSS)-based estimation. Slant TEC is estimated for a given set of raypath geometries by fitting historical GNSS data to a specified delay model. The accuracy of the specified delay model is estimated by computing delay estimate residuals and plotting a behavior of the delay estimate residuals. An ionospheric threat model is computed based on the specified delay model. Ionospheric grid delays (IGDs) and grid ionospheric vertical errors (GIVEs) are computed based on the ionospheric threat model.

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; Zhang, Guannan; Ye, Ming; Wu, Jianfeng; Wu, Jichun

2017-12-01

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

Modeling the Earth system in the Mission to Planet Earth era

NASA Technical Reports Server (NTRS)

Unninayar, Sushel; Bergman, Kenneth H.

1993-01-01

A broad overview is made of global earth system modeling in the Mission to Planet Earth (MTPE) era for the multidisciplinary audience encompassed by the Global Change Research Program (GCRP). Time scales of global system fluctuation and change are described in Section 2. Section 3 provides a rubric for modeling the global earth system, as presently understood. The ability of models to predict the future state of the global earth system and the extent to which their predictions are reliable are covered in Sections 4 and 5. The 'engineering' use of global system models (and predictions) is covered in Section 6. Section 7 covers aspects of an increasing need for improved transform algorithms and better methods to assimilate this information into global models. Future monitoring and data requirements are detailed in Section 8. Section 9 covers the NASA-initiated concept 'Mission to Planet Earth,' which employs space and ground based measurement systems to provide the scientific basis for understanding global change. Section 10 concludes this review with general remarks concerning the state of global system modeling and observing technology and the need for future research.

Combining the Old and the New: Designing a Curriculum Based on the Taba Model and the Global Scale of English

ERIC Educational Resources Information Center

Aydin, Belgin; Unver, Meral Melek; Alan, Bülent; Saglam, Sercan

2017-01-01

This paper explains the process of designing a curriculum based on the Taba Model and the Global Scale of English (GSE) in an intensive language education program. The Taba Model emphasizing the involvement of the teachers and the learners in the curriculum development process was combined with the GSE, a psychometric tool measuring language…

Description and Evaluation of IAP-AACM: A Global-regional Aerosol Chemistry Model for the Earth System Model CAS-ESM

NASA Astrophysics Data System (ADS)

Wei, Y.; Chen, X.

2017-12-01

We present a first description and evaluation of the IAP Atmospheric Aerosol Chemistry Model (IAP-AACM) which has been integrated into the earth system model CAS-ESM. In this way it is possible to research into interaction of clouds and aerosol by its two-way coupling with the IAP Atmospheric General Circulation Model (IAP-AGCM). The model has a nested global-regional grid based on the Global Environmental Atmospheric Transport Model (GEATM) and the Nested Air Quality Prediction Modeling System (NAQPMS). The AACM provides two optional gas chemistry schemes, the CBM-Z gas chemistry as well as a sulfur oxidize box designed specifically for the CAS-ESM. Now the model driven by AGCM has been applied to a 1-year simulation of tropospheric chemistry both on global and regional scales for 2014, and been evaluated against various observation datasets, including aerosol precursor gas concentration, aerosol mass and number concentrations. Furthermore, global budgets in AACM are compared with other global aerosol models. Generally, the AACM simulations are within the range of other global aerosol model predictions, and the model has a reasonable agreement with observations of gases and particles concentration both on global and regional scales.

Tree-Based Global Model Tests for Polytomous Rasch Models

ERIC Educational Resources Information Center

Komboz, Basil; Strobl, Carolin; Zeileis, Achim

2018-01-01

Psychometric measurement models are only valid if measurement invariance holds between test takers of different groups. Global model tests, such as the well-established likelihood ratio (LR) test, are sensitive to violations of measurement invariance, such as differential item functioning and differential step functioning. However, these…

Quantifying the Global Nitrous Oxide Emissions Using a Trait-based Biogeochemistry Model

NASA Astrophysics Data System (ADS)

Zhuang, Q.; Yu, T.

2017-12-01

Nitrogen is an essential element for the global biogeochemical cycle. It is a key nutrient for organisms and N compounds including nitrous oxide significantly influence the global climate. The activities of bacteria and archaea are responsible for the nitrification and denitrification in a wide variety of environments, so microbes play an important role in the nitrogen cycle in soils. To date, most existing process-based models treated nitrification and denitrification as chemical reactions driven by soil physical variables including soil temperature and moisture. In general, the effect of microbes on N cycling has not been modeled in sufficient details. Soil organic carbon also affects the N cycle because it supplies energy to microbes. In my study, a trait-based biogeochemistry model quantifying N2O emissions from the terrestrial ecosystems is developed based on an extant process-based model TEM (Terrestrial Ecosystem Model). Specifically, the improvement to TEM includes: 1) Incorporating the N fixation process to account for the inflow of N from the atmosphere to biosphere; 2) Implementing the effects of microbial dynamics on nitrification process; 3) fully considering the effects of carbon cycling on N nitrogen cycling following the principles of stoichiometry of carbon and nitrogen in soils, plants, and microbes. The difference between simulations with and without the consideration of bacterial activity lies between 5% 25% based on climate conditions and vegetation types. The trait based module allows a more detailed estimation of global N2O emissions.

A Global Study of GPP focusing on Light Use Efficiency in a Random Forest Regression Model

NASA Astrophysics Data System (ADS)

Fang, W.; Wei, S.; Yi, C.; Hendrey, G. R.

2016-12-01

Light use efficiency (LUE) is at the core of mechanistic modeling of global gross primary production (GPP). However, most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this paper, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated key environmental and biological variables into the tower-based LUE estimates, at 0.5o x 0.5o grid-cell resolution, using a random forest regression (RFR) approach. We then developed an RFR-LUE-GPP model using the grid-cell LUE data, and compared it to a tower-LUE-GPP model by the conventional way of treating LUE as a series of biome-specific constants. In order to calibrate the LUE models, we developed a data-driven RFR-GPP model using a random forest regression method. Our results showed that LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.21 gC m-2 MJ-1 APAR, which led to an estimated global GPP of 102.9 Gt C /year from 2000 to 2005. The tower-LUE-GPP model tended to overestimate forest GPP in tropical and boreal regions. Large uncertainties exist in GPP estimates over sparsely vegetated areas covered by savannas and woody savannas around the middle to low latitudes (i.g. 20oS to 40oS and 5oN to 15oN) due to lack of available data. Model results were improved by incorporating Köppen climate types to represent climate /meteorological information in machine learning modeling. This shed new light on the recognized issues of climate dependence of spring onset of photosynthesis and the challenges in modeling the biome GPP of evergreen broad leaf forests (EBF) accurately. The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echoed the necessity of modeling GPP separately by latitudes. This work provided a global distribution of LUE estimate, and developed a comprehensive algorithm modeling global terrestrial carbon with high spatial and temporal resolutions.

An Improved Statistical Solution for Global Seismicity by the HIST-ETAS Approach

NASA Astrophysics Data System (ADS)

Chu, A.; Ogata, Y.; Katsura, K.

2010-12-01

For long-term global seismic model fitting, recent work by Chu et al. (2010) applied the spatial-temporal ETAS model (Ogata 1998) and analyzed global data partitioned into tectonic zones based on geophysical characteristics (Bird 2003), and it has shown tremendous improvements of model fitting compared with one overall global model. While the ordinary ETAS model assumes constant parameter values across the complete region analyzed, the hierarchical space-time ETAS model (HIST-ETAS, Ogata 2004) is a newly introduced approach by proposing regional distinctions of the parameters for more accurate seismic prediction. As the HIST-ETAS model has been fit to regional data of Japan (Ogata 2010), our work applies the model to describe global seismicity. Employing the Akaike's Bayesian Information Criterion (ABIC) as an assessment method, we compare the MLE results with zone divisions considered to results obtained by an overall global model. Location dependent parameters of the model and Gutenberg-Richter b-values are optimized, and seismological interpretations are discussed.

Global model of zenith tropospheric delay proposed based on EOF analysis

NASA Astrophysics Data System (ADS)

Sun, Langlang; Chen, Peng; Wei, Erhu; Li, Qinzheng

2017-07-01

Tropospheric delay is one of the main error budgets in Global Navigation Satellite System (GNSS) measurements. Many empirical correction models have been developed to compensate this delay, and models which do not require meteorological parameters have received the most attention. This study established a global troposphere zenith total delay (ZTD) model, called Global Empirical Orthogonal Function Troposphere (GEOFT), based on the empirical orthogonal function (EOF, also known as geographically weighted PCAs) analysis method and the Global Geodetic Observing System (GGOS) Atmosphere data from 2012 to 2015. The results showed that ZTD variation could be well represented by the characteristics of the EOF base function Ek and associated coefficients Pk. Here, E1 mainly signifies the equatorial anomaly; E2 represents north-south asymmetry, and E3 and E4 reflects regional variation. Moreover, P1 mainly reflects annual and semiannual variation components; P2 and P3 mainly contains annual variation components, and P4 displays semiannual variation components. We validated the proposed GEOFT model using tropospheric delay data of GGOS ZTD grid data and the tropospheric product of the International GNSS Service (IGS) over the year 2016. The results showed that GEOFT model has high accuracy with bias and RMS of -0.3 and 3.9 cm, respectively, with respect to the GGOS ZTD data, and of -0.8 and 4.1 cm, respectively, with respect to the global IGS tropospheric product. The accuracy of GEOFT demonstrating that the use of the EOF analysis method to characterize ZTD variation is reasonable.

Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range.

PubMed

Melenteva, Anastasiia; Galyanin, Vladislav; Savenkova, Elena; Bogomolov, Andrey

2016-07-15

A large set of fresh cow milk samples collected from many suppliers over a large geographical area in Russia during a year has been analyzed by optical spectroscopy in the range 400-1100 nm in accordance with previously developed scatter-based technique. The global (i.e. resistant to seasonal, genetic, regional and other variations of the milk composition) models for fat and total protein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory prediction performances enabling their practical application in the dairy. The root mean-square errors of prediction (RMSEP) were 0.09 and 0.10 for fat and total protein content, respectively. The issues of raw milk analysis and multivariate modelling based on the historical spectroscopic data have been considered and approaches to the creation of global models and their transfer between the instruments have been proposed. Availability of global models should significantly facilitate the dissemination of optical spectroscopic methods for the laboratory and in-line quantitative milk analysis. Copyright © 2016. Published by Elsevier Ltd.

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

NASA Astrophysics Data System (ADS)

Engström, Kerstin; Olin, Stefan; Rounsevell, Mark D. A.; Brogaard, Sara; van Vuuren, Detlef P.; Alexander, Peter; Murray-Rust, Dave; Arneth, Almut

2016-11-01

We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) using socio-economic data from the SSPs and climate data from the RCPs (representative concentration pathways). The simulated range of global cropland is 893-2380 Mha in 2100 (± 1 standard deviation), with the main uncertainties arising from differences in the socio-economic conditions prescribed by the SSP scenarios and the assumptions that underpin the translation of qualitative SSP storylines into quantitative model input parameters. Uncertainties in the assumptions for population growth, technological change and cropland degradation were found to be the most important for global cropland, while uncertainty in food consumption had less influence on the results. The uncertainties arising from climate variability and the differences between climate change scenarios do not strongly affect the range of global cropland futures. Some overlap occurred across all of the conditional probabilistic futures, except for those based on SSP3. We conclude that completely different socio-economic and climate change futures, although sharing low to medium population development, can result in very similar cropland areas on the aggregated global scale.

The Global Fund to Fight AIDS, Tuberculosis and Malaria's investments in harm reduction through the rounds-based funding model (2002-2014).

PubMed

Bridge, Jamie; Hunter, Benjamin M; Albers, Eliot; Cook, Catherine; Guarinieri, Mauro; Lazarus, Jeffrey V; MacAllister, Jack; McLean, Susie; Wolfe, Daniel

2016-01-01

Harm reduction is an evidence-based, effective response to HIV transmission and other harms faced by people who inject drugs, and is explicitly supported by the Global Fund to Fight AIDS, Tuberculosis and Malaria. In spite of this, people who inject drugs continue to have poor and inequitable access to these services and face widespread stigma and discrimination. In 2013, the Global Fund launched a new funding model-signalling the end of the previous rounds-based model that had operated since its founding in 2002. This study updates previous analyses to assess Global Fund investments in harm reduction interventions for the duration of the rounds-based model, from 2002 to 2014. Global Fund HIV and TB/HIV grant documents from 2002 to 2014 were reviewed to identify grants that contained activities for people who inject drugs. Data were collected from detailed grant budgets, and relevant budget lines were recorded and analysed to determine the resources allocated to different interventions that were specifically targeted at people who inject drugs. 151 grants for 58 countries, plus one regional proposal, contained activities targeting people who inject drugs-for a total investment of US$ 620 million. Two-thirds of this budgeted amount was for interventions in the "comprehensive package" defined by the United Nations. 91% of the identified amount was for Eastern Europe and Asia. This study represents an updated, comprehensive assessment of Global Fund investments in harm reduction from its founding (2002) until the start of the new funding model (2014). It also highlights the overall shortfall of harm reduction funding, with the estimated global need being US$ 2.3 billion for harm reduction in 2015 alone. Using this baseline, the Global Fund must carefully monitor its new funding model and ensure that investments in harm reduction are maintained or scaled-up. There are widespread concerns regarding the withdrawal from middle-income countries where harm reduction remains essential and unfunded through other sources: for example, 15% of the identified investments were for countries which are now ineligible for Global Fund support. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

3D model retrieval method based on mesh segmentation

NASA Astrophysics Data System (ADS)

Gan, Yuanchao; Tang, Yan; Zhang, Qingchen

2012-04-01

In the process of feature description and extraction, current 3D model retrieval algorithms focus on the global features of 3D models but ignore the combination of global and local features of the model. For this reason, they show less effective performance to the models with similar global shape and different local shape. This paper proposes a novel algorithm for 3D model retrieval based on mesh segmentation. The key idea is to exact the structure feature and the local shape feature of 3D models, and then to compares the similarities of the two characteristics and the total similarity between the models. A system that realizes this approach was built and tested on a database of 200 objects and achieves expected results. The results show that the proposed algorithm improves the precision and the recall rate effectively.

Evaluating carbon fluxes of global forest ecosystems by using an individual tree-based model FORCCHN.

PubMed

Ma, Jianyong; Shugart, Herman H; Yan, Xiaodong; Cao, Cougui; Wu, Shuang; Fang, Jing

2017-05-15

The carbon budget of forest ecosystems, an important component of the terrestrial carbon cycle, needs to be accurately quantified and predicted by ecological models. As a preamble to apply the model to estimate global carbon uptake by forest ecosystems, we used the CO 2 flux measurements from 37 forest eddy-covariance sites to examine the individual tree-based FORCCHN model's performance globally. In these initial tests, the FORCCHN model simulated gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) with correlations of 0.72, 0.70 and 0.53, respectively, across all forest biomes. The model underestimated GPP and slightly overestimated ER across most of the eddy-covariance sites. An underestimation of NEP arose primarily from the lower GPP estimates. Model performance was better in capturing both the temporal changes and magnitude of carbon fluxes in deciduous broadleaf forest than in evergreen broadleaf forest, and it performed less well for sites in Mediterranean climate. We then applied the model to estimate the carbon fluxes of forest ecosystems on global scale over 1982-2011. This application of FORCCHN gave a total GPP of 59.41±5.67 and an ER of 57.21±5.32PgCyr -1 for global forest ecosystems during 1982-2011. The forest ecosystems over this same period contributed a large carbon storage, with total NEP being 2.20±0.64PgCyr -1 . These values are comparable to and reinforce estimates reported in other studies. This analysis highlights individual tree-based model FORCCHN could be used to evaluate carbon fluxes of forest ecosystems on global scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks

NASA Astrophysics Data System (ADS)

Zhelavskaya, I. S.; Shprits, Y.; Spasojevic, M.

2017-12-01

We present a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2 ≤ L ≤ 6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in-situ observations by using machine learning techniques.

The Global Classroom Model Simultaneous Campus-and Home-Based Education Using Videoconferencing

ERIC Educational Resources Information Center

Weitze, Charlotte Laerke; Ørngreen, Rikke

2014-01-01

This paper presents and discusses findings about how students, teachers, and the organization experience a start-up-project applying videoconferences between campus and home. This is new territory for adult learning centers. The research is based on the "Global Classroom Model" as it is implemented and used at an adult learning center in…

Modeling the plasmasphere based on LEO satellites onboard GPS measurements

NASA Astrophysics Data System (ADS)

Chen, Peng; Yao, Yibin; Li, Qinzheng; Yao, Wanqiang

2017-01-01

The plasmasphere, which is located above the ionosphere, is a significant component of Earth's atmosphere. A global plasmaspheric model was constructed using the total electron content (TEC) along the signal propagation path calculated using onboard Global Positioning System observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and MetOp-A, provided by the COSMIC Data Analysis and Archive Center (CDAAC). First, the global plasmaspheric model was established using only COSMIC TEC, and a set of MetOp-A TEC provided by CDAAC served for external evaluation. Results indicated that the established model using only COSMIC data is highly accurate. Then, COSMIC and MetOp-A TEC were combined to produce a new global plasmaspheric model. Finally, the variational characteristics of global plasmaspheric electron content with latitude, local time, and season were investigated using the global plasmaspheric model established in this paper.

Revisiting the global surface energy budgets with maximum-entropy-production model of surface heat fluxes

NASA Astrophysics Data System (ADS)

Huang, Shih-Yu; Deng, Yi; Wang, Jingfeng

2017-09-01

The maximum-entropy-production (MEP) model of surface heat fluxes, based on contemporary non-equilibrium thermodynamics, information theory, and atmospheric turbulence theory, is used to re-estimate the global surface heat fluxes. The MEP model predicted surface fluxes automatically balance the surface energy budgets at all time and space scales without the explicit use of near-surface temperature and moisture gradient, wind speed and surface roughness data. The new MEP-based global annual mean fluxes over the land surface, using input data of surface radiation, temperature data from National Aeronautics and Space Administration-Clouds and the Earth's Radiant Energy System (NASA CERES) supplemented by surface specific humidity data from the Modern-Era Retrospective Analysis for Research and Applications (MERRA), agree closely with previous estimates. The new estimate of ocean evaporation, not using the MERRA reanalysis data as model inputs, is lower than previous estimates, while the new estimate of ocean sensible heat flux is higher than previously reported. The MEP model also produces the first global map of ocean surface heat flux that is not available from existing global reanalysis products.

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE PAGES

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; ...

2017-12-27

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission

PubMed Central

Parker, Jon; Epstein, Joshua M.

2013-01-01

The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM’s speed and scalability. PMID:24465120

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

A spatial socio-ecosystem approach to analyse human-environment interactions on climate change adaptation for water resources management

NASA Astrophysics Data System (ADS)

Giupponi, Carlo; Mojtahed, Vahid

2017-04-01

Global climate and socio-economic drivers determine the future patterns of the allocation and the trade of resources and commodities in all markets. The agricultural sector is an emblematic case in which natural (e.g. climate), social (e.g. demography) and economic (e.g. the market) drivers of change interact, determining the evolution of social and ecological systems (or simply socio-ecosystems; SES) over time. In order to analyse the dynamics and possible future evolutions of SES, the combination of local complex systems and global drivers and trends require the development of multiscale approaches. At global level, climatic general circulation models (CGM) and computable general equilibrium or partial equilibrium models have been used for many years to explore the effects of global trends and generate future climate and socio-economic scenarios. Al local level, the inherent complexity of SESs and their spatial and temporal variabilities require different modelling approaches of physical/environmental sub-systems (e.g. field scale crop modelling, GIS-based models, etc.) and of human agency decision makers (e.g. agent based models). Global and local models have different assumption, limitations, constrains, etc., but in some cases integration is possible and several attempts are in progress to couple different models within the so-called Integrated Assessment Models. This work explores an innovative proposal to integrate the global and local approaches, where agent-based models (ABM) are used to simulate spatial (i.e. grid-based) and temporal dynamics of land and water resource use spatial and temporal dynamics, under the effect of global drivers. We focus in particular on how global change may affect land-use allocation at the local to regional level, under the influence of limited natural resources, land and water in particular. We specifically explore how constrains and competition for natural resources may induce non-linearities and discontinuities in socio-ecosystems behaviour. Our general ambition is to explore the feasibility of an approach that could be implemented worldwide through the identification of representative cases described by means of spatially explicit integrated simulations in communication with global modelling. Our specific objective is to test how ABMs can support scenario analysis at regional scale, and in particular how this can facilitate understanding of the role of human agency and its behavioural characteristics in local to global dynamics. The SES of interest is the agro-ecosystem with its relationships with other land uses. In order to test the feasibility of application at global level, all the information about land uses, natural resources, local climate, crop potential productions, etc. were derived from freely available spatial data sets covering the whole planet, which provided the ABM model with spatial information as matrices of pixels. Input maps were extracted from the Global Agro-Ecological Zone (GAEZ) web site of the Food and Agriculture Organization of the United Nations and compiled in the local GIS from where they were then converted in a format compatible with Matlab. In this initial application, an ABM prototype was developed in three test areas around the Mediterranean Basin, in agricultural regions of Tunisia, Italy and Spain.

A Comparative Study of the Ionospheric TEC Measurements Using Global Ionospheric Maps of GPS, TOPEX Radar and the Bent Model

NASA Technical Reports Server (NTRS)

Ho, C.; Wilson, B.; Mannucci, A.; Lindqwister, U.; Yuan, D.

1997-01-01

Global ionospheric mapping (GIM) is a new, emerging technique for determining global ionospheric TEC (total electron content) based on measurements from a worldwide network of Global Positioning System (GPS) receivers.

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Morelli, Eugene A.

2014-01-01

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA Technical Reports Server (NTRS)

Putman, William M.

2010-01-01

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Selecting global climate models for regional climate change studies

PubMed Central

Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.

2009-01-01

Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652

Quantification of effective plant rooting depth: advancing global hydrological modelling

NASA Astrophysics Data System (ADS)

Yang, Y.; Donohue, R. J.; McVicar, T.

2017-12-01

Plant rooting depth (Zr) is a key parameter in hydrological and biogeochemical models, yet the global spatial distribution of Zr is largely unknown due to the difficulties in its direct measurement. Moreover, Zr observations are usually only representative of a single plant or several plants, which can differ greatly from the effective Zr over a modelling unit (e.g., catchment or grid-box). Here, we provide a global parameterization of an analytical Zr model that balances the marginal carbon cost and benefit of deeper roots, and produce a climatological (i.e., 1982-2010 average) global Zr map. To test the Zr estimates, we apply the estimated Zr in a highly transparent hydrological model (i.e., the Budyko-Choudhury-Porporato (BCP) model) to estimate mean annual actual evapotranspiration (E) across the globe. We then compare the estimated E with both water balance-based E observations at 32 major catchments and satellite grid-box retrievals across the globe. Our results show that the BCP model, when implemented with Zr estimated herein, optimally reproduced the spatial pattern of E at both scales and provides improved model outputs when compared to BCP model results from two already existing global Zr datasets. These results suggest that our Zr estimates can be effectively used in state-of-the-art hydrological models, and potentially biogeochemical models, where the determination of Zr currently largely relies on biome type-based look-up tables.

Surface daytime net radiation estimation using artificial neural networks

DOE PAGES

Jiang, Bo; Zhang, Yi; Liang, Shunlin; ...

2014-11-11

Net all-wave surface radiation (R n) is one of the most important fundamental parameters in various applications. However, conventional R n measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical R n estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate R n globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. R n estimates provided by the two ANNs were tested against in-situ radiation measurements obtained frommore » 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R 2) of 0.92, a root mean square error (RMSE) of 34.27 W·m –2 , and a bias of –0.61 W·m –2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global R n estimation.« less

Global surgery: A view from the south.

PubMed

Roy, Nobhojit

2017-02-01

This article is based on the Hugh Greenwood Lecture delivered at the 2016 Congress of the British Association of Paediatric Surgeons. It presents the view of the global surgery movement from the bottom of the surgical food chain and proposes what HICs (high-income countries) can do for global surgery in a coordinated fashion. From the LMIC (low- and middle-income countries) surgeon perspective, global surgery is transitioning from the charity-based surgery model to codevelopment with multiple stakeholders. The caveats and current opportunities are described using two case studies. Surgeons may not play a pivotal role in the solutions. The future of the surgical workforce, innovation, workarounds, unmet burden of disease, and health metrics are discussed and multidisciplinary solutions proposed for the entire chain of surgical healthcare delivery in LMIC. A new breed of "essential surgeons", technology solutions for intellectual and physical isolation, competency-based credentialing, industry-driven innovation, task sharing over task shifting, prioritizing delivery based on surgical burden, and a rota-based overseas model of help are proposed as solutions for the issues facing global surgery. Level V. Copyright © 2017 Elsevier Inc. All rights reserved.

Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation modelORCHIDEE - Part 1: Simulating historical global burned area and fire regimes

Treesearch

C. Yue; P. Ciais; P. Cadule; K. Thonicke; S. Archibald; B. Poulter; W. M. Hao; S. Hantson; F. Mouillot; P. Friedlingstein; F. Maignan; N. Viovy

2014-01-01

Fire is an important global ecological process that influences the distribution of biomes, with consequences for carbon, water, and energy budgets. Therefore it is impossible to appropriately model the history and future of the terrestrial ecosystems and the climate system without including fire. This study incorporates the process-based prognostic fire module SPITFIRE...

Climate responses to SATIRE and SIM-based spectral solar forcing in a 3D atmosphere-ocean coupled GCM

NASA Astrophysics Data System (ADS)

Wen, Guoyong; Cahalan, Robert F.; Rind, David; Jonas, Jeffrey; Pilewskie, Peter; Wu, Dong L.; Krivova, Natalie A.

2017-03-01

We apply two reconstructed spectral solar forcing scenarios, one SIM (Spectral Irradiance Monitor) based, the other the SATIRE (Spectral And Total Irradiance REconstruction) modeled, as inputs to the GISS (Goddard Institute for Space Studies) GCMAM (Global Climate Middle Atmosphere Model) to examine climate responses on decadal to centennial time scales, focusing on quantifying the difference of climate response between the two solar forcing scenarios. We run the GCMAM for about 400 years with present day trace gas and aerosol for the two solar forcing inputs. We find that the SIM-based solar forcing induces much larger long-term response and 11-year variation in global averaged stratospheric temperature and column ozone. We find significant decreasing trends of planetary albedo for both forcing scenarios in the 400-year model runs. However the mechanisms for the decrease are very different. For SATIRE solar forcing, the decreasing trend of planetary albedo is associated with changes in cloud cover. For SIM-based solar forcing, without significant change in cloud cover on centennial and longer time scales, the apparent decreasing trend of planetary albedo is mainly due to out-of-phase variation in shortwave radiative forcing proxy (downwelling flux for wavelength >330 nm) and total solar irradiance (TSI). From the Maunder Minimum to present, global averaged annual mean surface air temperature has a response of 0.1 °C to SATIRE solar forcing compared to 0.04 °C to SIM-based solar forcing. For 11-year solar cycle, the global surface air temperature response has 3-year lagged response to either forcing scenario. The global surface air 11-year temperature response to SATIRE forcing is about 0.12 °C, similar to recent multi-model estimates, and comparable to the observational-based evidence. However, the global surface air temperature response to 11-year SIM-based solar forcing is insignificant and inconsistent with observation-based evidence.

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

DOE PAGES

Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey; ...

2016-05-17

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundancemore » and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.« less

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundancemore » and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.« less

A Critique and Response to Multicultural Visions of Globalization

ERIC Educational Resources Information Center

Sriraman, Bharath; Adrian, Harry

2008-01-01

The paper by White in this issue of Interchange contains an interesting model for a global educational perspective based on the writings of Aurobindo and Pierre Teilhard de Chardin. White proposes a foundation for this new perspective based on the synthesis of Aurobindo's and de Chardin's theories of global, social, and conscious evolution. In our…

The Global Rise of the U.S. Community College Model

ERIC Educational Resources Information Center

Chase-Mayoral, Audree M.

2017-01-01

This chapter explores the theoretical and conceptual history of the global rise of the U.S. community college model, focusing on the common missing ingredient that remains elusive among the increasing numbers of these community-based institutions.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring.

PubMed

Chen, Jun; Zhu, Yuanli; Wu, Yongsheng; Cui, Tingwei; Ishizaka, Joji; Ju, Yongtao

2015-01-01

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving Kd(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(λ) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(λ) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.

Polar motion excitation analysis due to global continental water redistribution

NASA Astrophysics Data System (ADS)

Fernandez, L.; Schuh, H.

2006-10-01

We present the results obtained when studying the hydrological excitation of the Earth‘s wobble due to global redistribution of continental water storage. This work was performed in two steps. First, we computed the hydrological angular momentum (HAM) time series based on the global hydrological model LaD (Land Dynamics model) for the period 1980 till 2004. Then, we compared the effectiveness of this excitation by analysing the residuals of the geodetic time series after removing atmospheric and oceanic contributions with the respective hydrological ones. The emphasis was put on low frequency variations. We also present a comparison of HAM time series from LaD with respect to that one from a global model based on the assimilated soil moisture and snow accumulation data from NCEP/NCAR (The National Center for Environmental Prediction/The National Center for Atmospheric Research) reanalysis. Finally, we evaluate the performance of LaD model in closing the polar motion budget at seasonal periods in comparison with the NCEP and the Land Data Assimilation System (LDAS) models.

Cross-site comparison of land-use decision-making and its consequences across land systems with a generalized agent-based model.

PubMed

Magliocca, Nicholas R; Brown, Daniel G; Ellis, Erle C

2014-01-01

Local changes in land use result from the decisions and actions of land-users within land systems, which are structured by local and global environmental, economic, political, and cultural contexts. Such cross-scale causation presents a major challenge for developing a general understanding of how local decision-making shapes land-use changes at the global scale. This paper implements a generalized agent-based model (ABM) as a virtual laboratory to explore how global and local processes influence the land-use and livelihood decisions of local land-users, operationalized as settlement-level agents, across the landscapes of six real-world test sites. Test sites were chosen in USA, Laos, and China to capture globally-significant variation in population density, market influence, and environmental conditions, with land systems ranging from swidden to commercial agriculture. Publicly available global data were integrated into the ABM to model cross-scale effects of economic globalization on local land-use decisions. A suite of statistics was developed to assess the accuracy of model-predicted land-use outcomes relative to observed and random (i.e. null model) landscapes. At four of six sites, where environmental and demographic forces were important constraints on land-use choices, modeled land-use outcomes were more similar to those observed across sites than the null model. At the two sites in which market forces significantly influenced land-use and livelihood decisions, the model was a poorer predictor of land-use outcomes than the null model. Model successes and failures in simulating real-world land-use patterns enabled the testing of hypotheses on land-use decision-making and yielded insights on the importance of missing mechanisms. The virtual laboratory approach provides a practical framework for systematic improvement of both theory and predictive skill in land change science based on a continual process of experimentation and model enhancement.

Cross-Site Comparison of Land-Use Decision-Making and Its Consequences across Land Systems with a Generalized Agent-Based Model

PubMed Central

Magliocca, Nicholas R.; Brown, Daniel G.; Ellis, Erle C.

2014-01-01

Local changes in land use result from the decisions and actions of land-users within land systems, which are structured by local and global environmental, economic, political, and cultural contexts. Such cross-scale causation presents a major challenge for developing a general understanding of how local decision-making shapes land-use changes at the global scale. This paper implements a generalized agent-based model (ABM) as a virtual laboratory to explore how global and local processes influence the land-use and livelihood decisions of local land-users, operationalized as settlement-level agents, across the landscapes of six real-world test sites. Test sites were chosen in USA, Laos, and China to capture globally-significant variation in population density, market influence, and environmental conditions, with land systems ranging from swidden to commercial agriculture. Publicly available global data were integrated into the ABM to model cross-scale effects of economic globalization on local land-use decisions. A suite of statistics was developed to assess the accuracy of model-predicted land-use outcomes relative to observed and random (i.e. null model) landscapes. At four of six sites, where environmental and demographic forces were important constraints on land-use choices, modeled land-use outcomes were more similar to those observed across sites than the null model. At the two sites in which market forces significantly influenced land-use and livelihood decisions, the model was a poorer predictor of land-use outcomes than the null model. Model successes and failures in simulating real-world land-use patterns enabled the testing of hypotheses on land-use decision-making and yielded insights on the importance of missing mechanisms. The virtual laboratory approach provides a practical framework for systematic improvement of both theory and predictive skill in land change science based on a continual process of experimentation and model enhancement. PMID:24489696

Extending the reanalysis to the ionosphere based on ground and LEO based GNSS observations

NASA Astrophysics Data System (ADS)

Yue, X.; Schreiner, W. S.; Kuo, Y.

2012-12-01

We report preliminary results of a global 3-D ionospheric electron density reanalysis during 2002-2011 based on multi-source data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the International Reference Ionosphere (IRI) 2007 model and a Kalman filter technique. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and the TerraSAR-X satellites, and Jason-1 and 2 altimeter TEC measurements. The output of the reanalysis are 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 hr in universal time, 5o in latitude, 10o in longitude, and ~ 30 km in altitude. The climatological features of the reanalysis results, such as solar activity dependence, seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content (GEC) derived from the international GNSS service (IGS) global ionospheric maps (GIM), the observed electron density profiles from the Poker Flat Incoherent Scatter Radar (PFISR) during 2007-2010, and foF2 observed by the global ionosonde network during 2002-2011 are used to validate the reanalysis method. All comparisons show that the reanalysis have smaller deviations and biases than the IRI-2007 predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvement over the IRI predictions. The obvious overestimation of the low-latitude ionospheric F-region densities by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

Globalizing Air Pollution

NASA Astrophysics Data System (ADS)

Lin, J.

2017-12-01

Recent studies have revealed the issue of globalizing air pollution through complex coupling of atmospheric transport (physical route) and economic trade (socioeconomic route). Recognition of such globalizing air pollution has important implications for understanding the impacts of regional and global consumption (of goods and services) on air quality, public health, climate and the ecosystems. And addressing these questions often requires improved modeling, measurements and economic-emission statistics. This talk will introduce the concept and mechanism of globalizing air pollution, with following demonstrations based on recent works on modeling, satellite measurement and multi-disciplinary assessment.

Application of a Global-to-Beam Irradiance Model to the NASA GEWEX SRB Dataset: An Extension of the NASA Surface Meteorology and Solar Energy Datasets

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Stackhouse, Paul W., Jr.; Chandler, William S.; Westberg, David J.

2014-01-01

The DIRINDEX model was designed to estimate hourly solar beam irradiances from hourly global horizontal irradiances. This model was applied to the NASA GEWEX SRB(Rel. 3.0) 3-hourly global horizontal irradiance data to derive3-hourly global maps of beam, or direct normal, irradiance for the period from January 2000 to December 2005 at the 1 deg. x 1 deg. resolution. The DIRINDEX model is a combination of the DIRINT model, a quasi-physical global-to-beam irradiance model based on regression of hourly observed data, and a broadband simplified version of the SOLIS clear-sky beam irradiance model. In this study, the input variables of the DIRINDEX model are 3-hourly global horizontal irradiance, solar zenith angle, dew-point temperature, surface elevation, surface pressure, sea-level pressure, aerosol optical depth at 700 nm, and column water vapor. The resulting values of the 3-hourly direct normal irradiance are then used to compute daily and monthly means. The results are validated against the ground-based BSRN data. The monthly means show better agreement with the BSRN data than the results from an earlier endeavor which empirically derived the monthly mean direct normal irradiance from the GEWEX SRB monthly mean global horizontal irradiance. To assimilate the observed information into the final results, the direct normal fluxes from the DIRINDEX model are adjusted according to the comparison statistics in the latitude-longitude-cosine of solar zenith angle phase space, in which the inverse-distance interpolation is used for the adjustment. Since the NASA Surface meteorology and Solar Energy derives its data from the GEWEX SRB datasets, the results discussed herein will serve to extend the former.

Medial-based deformable models in nonconvex shape-spaces for medical image segmentation.

PubMed

McIntosh, Chris; Hamarneh, Ghassan

2012-01-01

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data

PubMed Central

Scanlon, Bridget R.; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y.; van Beek, Ludovicus P. H.; Wiese, David N.; Reedy, Robert C.; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F. P.

2018-01-01

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km3/y) and increasing (≥0.5 km3/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km3/y, whereas most models estimate decreasing trends (−71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km3/y) but negative for models (−450 to −12 km3/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. PMID:29358394

Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data.

PubMed

Scanlon, Bridget R; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y; Müller Schmied, Hannes; van Beek, Ludovicus P H; Wiese, David N; Wada, Yoshihide; Long, Di; Reedy, Robert C; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F P

2018-02-06

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002-2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤-0.5 km 3 /y) and increasing (≥0.5 km 3 /y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km 3 /y, whereas most models estimate decreasing trends (-71 to 11 km 3 /y). Land water storage trends, summed over all basins, are positive for GRACE (∼71-82 km 3 /y) but negative for models (-450 to -12 km 3 /y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. Copyright © 2018 the Author(s). Published by PNAS.

Energy-based and process-based constraints on aerosol-climate interaction

NASA Astrophysics Data System (ADS)

Suzuki, K.; Sato, Y.; Takemura, T.; Michibata, T.; Goto, D.; Oikawa, E.

2017-12-01

Recent advance in both satellite observations and global modeling provides us with a novel opportunity to investigate the long-standing aerosol-climate interaction issue at a fundamental process level, particularly with a combined use of them. In this presentation, we will highlight our recent progress in understanding the aerosol-cloud-precipitation interaction and its implication for global climate with a synergistic use of a state-of-the-art global climate model (MIROC), a global cloud-resolving model (NICAM) and recent satellite observations (A-Train). In particular, we explore two different aspects of the aerosol-climate interaction issue, i.e. (i) the global energy balance perspective with its modulation due to aerosols and (ii) the process-level characteristics of the aerosol-induced perturbations to cloud and precipitation. For the former, climate model simulations are used to quantify how components of global energy budget are modulated by the aerosol forcing. The moist processes are shown to be a critical pathway that links the forcing efficacy and the hydrologic sensitivity arising from aerosol perturbations. Effects of scattering (e.g. sulfate) and absorbing (e.g. black carbon) aerosols are compared in this context to highlight their distinctively different impacts on climate and hydrologic cycle. The aerosol-induced modulation of moist processes is also investigated in the context of the second aspect above to facilitate recent arguments on possible overestimates of the aerosol-cloud interaction in climate models. Our recent simulations with NICAM are shown to highlight how diverse responses of cloud to aerosol perturbation, which have been failed to represent in traditional climate models, are reproduced by the high-resolution global model with sophisticated cloud microphysics. We will discuss implications of these findings for a linkage between the two aspects above to aid advance process-based understandings of the aerosol-climate interaction and also to mitigate a "dichotomy" recently found by the authors between the two aspects in the context of the climate projection.

The NASA/MSFC global reference atmospheric model: 1990 version (GRAM-90). Part 2: Program/data listings

NASA Technical Reports Server (NTRS)

Justus, C. G.; Alyea, F. N.; Cunnold, D. M.; Jeffries, W. R., III; Johnson, D. L.

1991-01-01

A new (1990) version of the NASA/MSFC Global Reference Atmospheric Model (GRAM-90) was completed and the program and key data base listing are presented. GRAM-90 incorporate extensive new data, mostly collected under the Middle Atmosphere Program, to produce a completely revised middle atmosphere model (20 to 120 km). At altitudes greater than 120 km, GRAM-90 uses the NASA Marshall Engineering Thermosphere model. Complete listings of all program and major data bases are presented. Also, a test case is included.

Globally-Applicable Predictive Wildfire Model   a Temporal-Spatial GIS Based Risk Analysis Using Data Driven Fuzzy Logic Functions

NASA Astrophysics Data System (ADS)

van den Dool, G.

2017-11-01

This study (van den Dool, 2017) is a proof of concept for a global predictive wildfire model, in which the temporal-spatial characteristics of wildfires are placed in a Geographical Information System (GIS), and the risk analysis is based on data-driven fuzzy logic functions. The data sources used in this model are available as global datasets, but subdivided into three pilot areas: North America (California/Nevada), Europe (Spain), and Asia (Mongolia), and are downscaled to the highest resolution (3-arc second). The GIS is constructed around three themes: topography, fuel availability and climate. From the topographical data, six derived sub-themes are created and converted to a fuzzy membership based on the catchment area statistics. The fuel availability score is a composite of four data layers: land cover, wood loads, biomass, biovolumes. As input for the climatological sub-model reanalysed daily averaged, weather-related data is used, which is accumulated to a global weekly time-window (to account for the uncertainty within the climatological model) and forms the temporal component of the model. The final product is a wildfire risk score (from 0 to 1) by week, representing the average wildfire risk in an area. To compute the potential wildfire risk the sub-models are combined usinga Multi-Criteria Approach, and the model results are validated against the area under the Receiver Operating Characteristic curve.

Global distribution of carbon turnover times in terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Carvalhais, Nuno; Forkel, Matthias; Khomik, Myroslava; Bellarby, Jessica; Jung, Martin; Migliavacca, Mirco; Mu, Mingquan; Saatchi, Sassan; Santoro, Maurizio; Thurner, Martin; Weber, Ulrich; Ahrens, Bernhard; Beer, Christian; Cescatti, Alessandro; Randerson, James T.; Reichstein, Markus

2015-04-01

The response of the carbon cycle in terrestrial ecosystems to climate variability remains one of the largest uncertainties affecting future projections of climate change. This feedback between the terrestrial carbon cycle and climate is partly determined by the response of carbon uptake and by changes in the residence time of carbon in land ecosystems, which depend on climate, soil, and vegetation type. Thus, it is of foremost importance to quantify the turnover times of carbon in terrestrial ecosystems and its spatial co-variability with climate. Here, we develop a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times (τ) to investigate its co-variation with climate at global scale. Assuming a balance between uptake (gross primary production, GPP) and emission fluxes, τ can be defined as the ratio between the total stock (C_total) and the output or input fluxes (GPP). The estimation of vegetation (C_veg) stocks relies on new remote sensing-based estimates from Saatchi et al (2011) and Thurner et al (2014), while soil carbon stocks (C_soil) are estimated based on state of the art global (Harmonized World Soil Database) and regional (Northern Circumpolar Soil Carbon Database) datasets. The uptake flux estimates are based on global observation-based fields of GPP (Jung et al., 2011). Globally, we find an overall mean global carbon turnover time of 23-4+7 years (95% confidence interval). A strong spatial variability globally is also observed, from shorter residence times in equatorial regions to longer periods at latitudes north of 75°N (mean τ of 15 and 255 years, respectively). The observed latitudinal pattern reflect the clear dependencies on temperature, showing increases from the equator to the poles, which is consistent with our current understanding of temperature controls on ecosystem dynamics. However, long turnover times are also observed in semi-arid and forest-herbaceous transition regions. Furthermore, based on a local correlation analysis, our results reveal a similarly strong association between τ and precipitation. A further analysis of carbon turnover times as simulated by state-of-the-art coupled climate carbon-cycle models from the CMIP5 experiments reveals wide variations between models and a tendency to underestimate the global τ by 36%. The latitudinal patterns correlate significantly with the observation-based patterns. However, the models show stronger associations between τ and temperature than the observation-based estimates. In general, the stronger relationship between τ and precipitation is not reproduced and the modeled turnover times are significantly faster in many semi-arid regions. Ultimately, these results suggest a strong role of the hydrological cycle in the carbon cycle-climate interactions, which is not currently reproduced by Earth system models.

Advances in air quality prediction with the use of integrated systems

NASA Astrophysics Data System (ADS)

Dragani, R.; Benedetti, A.; Engelen, R. J.; Peuch, V. H.

2017-12-01

Recent years have seen the rise of global operational atmospheric composition forecasting systems for several applications including climate monitoring, provision of boundary conditions for regional air quality forecasting, energy sector applications, to mention a few. Typically, global forecasts are provided in the medium-range up to five days ahead and are initialized with an analysis based on satellite data. In this work we present the latest advances in data assimilation using the ECMWF's 4D-Var system extended to atmospheric composition which is currently operational under the Copernicus Atmosphere Monitoring Service of the European Commission. The service is based on acquisition of all relevant data available in near-real-time, the processing of these datasets in the assimilation and the subsequent dissemination of global forecasts at ECMWF. The global forecasts are used by the CAMS regional models as boundary conditions for the European forecasts based on a multi-model ensemble. The global forecasts are also used to provide boundary conditions for other parts of the world (e.g., China) and are freely available to all interested entities. Some of the regional models also perform assimilation of satellite and ground-based observations. All products are assessed, validated and made publicly available on https://atmosphere.copernicus.eu/.

Global 3-D ionospheric electron density reanalysis based on multisource data assimilation

NASA Astrophysics Data System (ADS)

Yue, Xinan; Schreiner, William S.; Kuo, Ying-Hwa; Hunt, Douglas C.; Wang, Wenbin; Solomon, Stanley C.; Burns, Alan G.; Bilitza, Dieter; Liu, Jann-Yenq; Wan, Weixing; Wickert, Jens

2012-09-01

We report preliminary results of a global 3-D ionospheric electron density reanalysis demonstration study during 2002-2011 based on multisource data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the International Reference Ionosphere (IRI) 2007 model and a Kalman filter technique. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground-based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and the TerraSAR-X satellites, and Jason-1 and 2 altimeter TEC measurements. The output of the reanalysis are 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 h in universal time, 5° in latitude, 10° in longitude, and ˜30 km in altitude. The climatological features of the reanalysis results, such as solar activity dependence, seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content derived from the international GNSS service global ionospheric maps, the observed electron density profiles from the Poker Flat Incoherent Scatter Radar during 2007-2010, and foF2 observed by the global ionosonde network during 2002-2011 are used to validate the reanalysis method. All comparisons show that the reanalysis have smaller deviations and biases than the IRI-2007 predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvement over the IRI predictions. The obvious overestimation of the low-latitude ionospheric F region densities by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

Global warming description using Daisyworld model with greenhouse gases.

PubMed

Paiva, Susana L D; Savi, Marcelo A; Viola, Flavio M; Leiroz, Albino J K

2014-11-01

Daisyworld is an archetypal model of the earth that is able to describe the global regulation that can emerge from the interaction between life and environment. This article proposes a model based on the original Daisyworld considering greenhouse gases emission and absorption, allowing the description of the global warming phenomenon. Global and local analyses are discussed evaluating the influence of greenhouse gases in the planet dynamics. Numerical simulations are carried out showing the general qualitative behavior of the Daisyworld for different scenarios that includes solar luminosity variations and greenhouse gases effect. Nonlinear dynamics perspective is of concern discussing a way that helps the comprehension of the global warming phenomenon. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A Microphysics-Based Black Carbon Aging Scheme in a Global Chemical Transport Model: Constraints from HIPPO Observations

NASA Astrophysics Data System (ADS)

He, C.; Li, Q.; Liou, K. N.; Qi, L.; Tao, S.; Schwarz, J. P.

2015-12-01

Black carbon (BC) aging significantly affects its distributions and radiative properties, which is an important uncertainty source in estimating BC climatic effects. Global models often use a fixed aging timescale for the hydrophobic-to-hydrophilic BC conversion or a simple parameterization. We have developed and implemented a microphysics-based BC aging scheme that accounts for condensation and coagulation processes into a global 3-D chemical transport model (GEOS-Chem). Model results are systematically evaluated by comparing with the HIPPO observations across the Pacific (67°S-85°N) during 2009-2011. We find that the microphysics-based scheme substantially increases the BC aging rate over source regions as compared with the fixed aging timescale (1.2 days), due to the condensation of sulfate and secondary organic aerosols (SOA) and coagulation with pre-existing hydrophilic aerosols. However, the microphysics-based scheme slows down BC aging over Polar regions where condensation and coagulation are rather weak. We find that BC aging is primarily dominated by condensation process that accounts for ~75% of global BC aging, while the coagulation process is important over source regions where a large amount of pre-existing aerosols are available. Model results show that the fixed aging scheme tends to overestimate BC concentrations over the Pacific throughout the troposphere by a factor of 2-5 at different latitudes, while the microphysics-based scheme reduces the discrepancies by up to a factor of 2, particularly in the middle troposphere. The microphysics-based scheme developed in this work decreases BC column total concentrations at all latitudes and seasons, especially over tropical regions, leading to large improvement in model simulations. We are presently analyzing the impact of this scheme on global BC budget and lifetime, quantifying its uncertainty associated with key parameters, and investigating the effects of heterogeneous chemical oxidation on BC aging.

Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain

PubMed Central

2017-01-01

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization. PMID:28873432

Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain.

PubMed

Xing, Lizhi

2017-01-01

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization.

A global water scarcity assessment under Shared Socio-economic Pathways - Part 1: Water use

NASA Astrophysics Data System (ADS)

Hanasaki, N.; Fujimori, S.; Yamamoto, T.; Yoshikawa, S.; Masaki, Y.; Hijioka, Y.; Kainuma, M.; Kanamori, Y.; Masui, T.; Takahashi, K.; Kanae, S.

2013-07-01

A novel global water scarcity assessment for the 21st century is presented in a two-part paper. In this first paper, water use scenarios are presented for the latest global hydrological models. The scenarios are compatible with the socio-economic scenarios of the Shared Socio-economic Pathways (SSPs), which are a part of the latest set of scenarios on global change developed by the integrated assessment, the IAV (climate change impact, adaptation, and vulnerability assessment), and the climate modeling community. The SSPs depict five global situations based on substantially different socio-economic conditions during the 21st century. Water use scenarios were developed to reflect not only quantitative socio-economic factors, such as population and electricity production, but also key qualitative concepts such as the degree of technological change and overall environmental consciousness. Each scenario consists of five factors: irrigated area, crop intensity, irrigation efficiency, and withdrawal-based potential industrial and municipal water demands. The first three factors are used to estimate the potential irrigation water demand. All factors were developed using simple models based on a literature review and analysis of historical records. The factors are grid-based at a spatial resolution of 0.5° × 0.5° and cover the whole 21st century in five-year intervals. Each factor shows wide variation among the different global situations depicted: the irrigated area in 2085 varies between 2.7 × 106 and 4.5 × 106 km2, withdrawal-based potential industrial water demand between 246 and 1714 km3 yr-1, and municipal water between 573 and 1280 km3 yr-1. The water use scenarios can be used for global water scarcity assessments that identify the regions vulnerable to water scarcity and analyze the timing and magnitude of scarcity conditions.

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

NASA Technical Reports Server (NTRS)

Rienecker, Michele M.

2012-01-01

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5).

A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China

PubMed Central

Li, Huashan; Cao, Fei; Wang, Xianlong; Ma, Weibin

2014-01-01

Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China. PMID:24605046

Global health governance as shared health governance.

PubMed

Ruger, Jennifer Prah

2012-07-01

With the exception of key 'proven successes' in global health, the current regime of global health governance can be understood as transnational and national actors pursuing their own interests under a rational actor model of international cooperation, which fails to provide sufficient justification for an obligation to assist in meeting the health needs of others. An ethical commitment to providing all with the ability to be healthy is required. This article develops select components of an alternative model of shared health governance (SHG), which aims to provide a 'road map,' 'focal points' and 'the glue' among various global health actors to better effectuate cooperation on universal ethical principles for an alternative global health equilibrium. Key features of SHG include public moral norms as shared authoritative standards; ethical commitments, shared goals and role allocation; shared sovereignty and constitutional commitments; legitimacy and accountability; country-level attention to international health relations. A framework of social agreement based on 'overlapping consensus' is contrasted against one based on self-interested political bargaining. A global health constitution delineating duties and obligations of global health actors and a global institute of health and medicine for holding actors responsible are proposed. Indicators for empirical assessment of select SHG principles are described. Global health actors, including states, must work together to correct and avert global health injustices through a framework of SHG based on shared ethical commitments.

Setting up a hydrological model based on global data for the Ayeyarwady basin in Myanmar

NASA Astrophysics Data System (ADS)

ten Velden, Corine; Sloff, Kees; Nauta, Tjitte

2017-04-01

The use of global datasets in local hydrological modelling can be of great value. It opens up the possibility to include data for areas where local data is not or only sparsely available. In hydrological modelling the existence of both static physical data such as elevation and land use, and dynamic meteorological data such as precipitation and temperature, is essential for setting up a hydrological model, but often such data is difficult to obtain at the local level. For the Ayeyarwady catchment in Myanmar a distributed hydrological model (Wflow: https://github.com/openstreams/wflow) was set up with only global datasets, as part of a water resources study. Myanmar is an emerging economy, which has only recently become more receptive to foreign influences. It has a very limited hydrometeorological measurement network, with large spatial and temporal gaps, and data that are of uncertain quality and difficult to obtain. The hydrological model was thus set up based on resampled versions of the SRTM digital elevation model, the GlobCover land cover dataset and the HWSD soil dataset. Three global meteorological datasets were assessed and compared for use in the hydrological model: TRMM, WFDEI and MSWEP. The meteorological datasets were assessed based on their conformity with several precipitation station measurements, and the overall model performance was assessed by calculating the NSE and RVE based on discharge measurements of several gauging stations. The model was run for the period 1979-2012 on a daily time step, and the results show an acceptable applicability of the used global datasets in the hydrological model. The WFDEI forcing dataset gave the best results, with a NSE of 0.55 at the outlet of the model and a RVE of 8.5%, calculated over the calibration period 2006-2012. As a general trend the modelled discharge at the upstream stations tends to be underestimated, and at the downstream stations slightly overestimated. The quality of the discharge measurements that form the basis for the performance calculations is uncertain; data analysis suggests that rating curves are not frequently updated. The modelling results are not perfect and there is ample room for improvement, but the results are reasonable given the notion that setting up a hydrological model for this area would not have been possible without the use of global datasets due to the lack of available local data. The resulting hydrological model then enabled the set-up of the RIBASIM water allocation model for the Ayeyarwady basin in order to assess its water resources. The study discussed here is a first step; ideally this is followed up by a more thorough calibration and validation with the limited local measurements available, e.g. a precipitation correction based on the available rainfall measurements, to ensure the integration of global and local data.

Optimal design of piezoelectric transformers: a rational approach based on an analytical model and a deterministic global optimization.

PubMed

Pigache, Francois; Messine, Frédéric; Nogarede, Bertrand

2007-07-01

This paper deals with a deterministic and rational way to design piezoelectric transformers in radial mode. The proposed approach is based on the study of the inverse problem of design and on its reformulation as a mixed constrained global optimization problem. The methodology relies on the association of the analytical models for describing the corresponding optimization problem and on an exact global optimization software, named IBBA and developed by the second author to solve it. Numerical experiments are presented and compared in order to validate the proposed approach.

Global Kalman filter approaches to estimate absolute angles of lower limb segments.

PubMed

Nogueira, Samuel L; Lambrecht, Stefan; Inoue, Roberto S; Bortole, Magdo; Montagnoli, Arlindo N; Moreno, Juan C; Rocon, Eduardo; Terra, Marco H; Siqueira, Adriano A G; Pons, Jose L

2017-05-16

In this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF. The results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance. The results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

NASA Astrophysics Data System (ADS)

Crow, W. T.; Bolten, J. D.

2014-12-01

Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.

Developing a global crop model for maize, wheat, and soybean production

NASA Astrophysics Data System (ADS)

Deryng, D.; Ramankutty, N.; Sacks, W. J.

2008-12-01

Recently, the world food supply has faced a crisis due to increasing food prices driven by rising food demand, increasing fuel prices, poor harvests due to climate factors, and the use of crops such as maize and soybean to produce biofuel. In order to assess the future of global food availability, there is a need for understanding the factors underlying food production. Farmer management practices along with climatic conditions are the main elements directly influencing crop yield. As a consequence, estimations of future world food production require the use of a global crop model that simulates reasonably the effect of both climate and management practices on yield. Only a few global crop models have been developed to date, and currently none of them represent management factors adequately, principally due to the lack of spatially explicit datasets at the global scale. In this study, we present a global crop model designed for maize, wheat, and soybean production that incorporates planting and harvest decisions, along with irrigation options based on newly available data. The crop model is built on a simple water-balance algorithm based on the Penman- Monteith equation combined with a light use efficiency approach that calculates biomass production under non-nutrient-limiting conditions. We used a world crop calendar dataset to develop statistical relationships between climate variables and planting dates for different regions of the world. Development stages are defined based on total growing degree days required to reach the beginning of each phase. Irrigation options are considered in regions where water stress occurs and irrigation infrastructures exist. We use a global dataset on irrigated areas for each crop type. The quantity of water applied is then calculated in order to avoid water stress but with an upper threshold derived from total irrigation withdrawal quantity estimated by the global water use model WaterGAP 2. Our analysis will present the model sensitivity to different scenarios of management practices, e.g. planting date and water supply, under non-nutrient limited conditions. With this study, we hope to clarify the importance of planting date and irrigation versus climate for crop yield.

The Mental Health Recovery Measure Can Be Used to Assess Aspects of Both Customer-Based and Service-Based Recovery in the Context of Severe Mental Illness.

PubMed

Oliveira-Maia, Albino J; Mendonça, Carina; Pessoa, Maria J; Camacho, Marta; Gago, Joaquim

2016-01-01

Within clinical psychiatry, recovery from severe mental illness (SMI) has classically been defined according to symptoms and function (service-based recovery). However, service-users have argued that recovery should be defined as the process of overcoming mental illness, regaining self-control and establishing a meaningful life (customer-based recovery). Here, we aimed to compare customer-based and service-based recovery and clarify their differential relationship with other constructs, namely needs and quality of life. The study was conducted in 101 patients suffering from SMI, recruited from a rural community mental health setting in Portugal. Customer-based recovery and function-related service-based recovery were assessed, respectively, using a shortened version of the Mental Health Recovery Measure (MHRM-20) and the Global Assessment of Functioning score. The Camberwell Assessment of Need scale was used to objectively assess needs, while subjective quality of life was measured with the TL-30s scale. Using multiple linear regression models, we found that the Global Assessment of Functioning score was incrementally predictive of the MHRM-20 score, when added to a model including only clinical and demographic factors, and that this model was further incremented by the score for quality of life. However, in an alternate model using the Global Assessment of Functioning score as the dependent variable, while the MHRM-20 score contributed significantly to the model when added to clinical and demographic factors, the model was not incremented by the score for quality of life. These results suggest that, while a more global concept of recovery from SMI may be assessed using measures for service-based and customer-based recovery, the latter, namely the MHRM-20, also provides information about subjective well-being. Pending confirmation of these findings in other populations, this instrument could thus be useful for comprehensive assessment of recovery and subjective well-being in patients suffering from SMI.

The Mental Health Recovery Measure Can Be Used to Assess Aspects of Both Customer-Based and Service-Based Recovery in the Context of Severe Mental Illness

PubMed Central

Oliveira-Maia, Albino J.; Mendonça, Carina; Pessoa, Maria J.; Camacho, Marta; Gago, Joaquim

2016-01-01

Within clinical psychiatry, recovery from severe mental illness (SMI) has classically been defined according to symptoms and function (service-based recovery). However, service-users have argued that recovery should be defined as the process of overcoming mental illness, regaining self-control and establishing a meaningful life (customer-based recovery). Here, we aimed to compare customer-based and service-based recovery and clarify their differential relationship with other constructs, namely needs and quality of life. The study was conducted in 101 patients suffering from SMI, recruited from a rural community mental health setting in Portugal. Customer-based recovery and function-related service-based recovery were assessed, respectively, using a shortened version of the Mental Health Recovery Measure (MHRM-20) and the Global Assessment of Functioning score. The Camberwell Assessment of Need scale was used to objectively assess needs, while subjective quality of life was measured with the TL-30s scale. Using multiple linear regression models, we found that the Global Assessment of Functioning score was incrementally predictive of the MHRM-20 score, when added to a model including only clinical and demographic factors, and that this model was further incremented by the score for quality of life. However, in an alternate model using the Global Assessment of Functioning score as the dependent variable, while the MHRM-20 score contributed significantly to the model when added to clinical and demographic factors, the model was not incremented by the score for quality of life. These results suggest that, while a more global concept of recovery from SMI may be assessed using measures for service-based and customer-based recovery, the latter, namely the MHRM-20, also provides information about subjective well-being. Pending confirmation of these findings in other populations, this instrument could thus be useful for comprehensive assessment of recovery and subjective well-being in patients suffering from SMI. PMID:27857698

An evaluation of 20th century climate for the Southeastern United States as simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models

USGS Publications Warehouse

David E. Rupp,

2016-05-05

The 20th century climate for the Southeastern United States and surrounding areas as simulated by global climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) was evaluated. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. CMIP5 global climate models were ranked by their ability to reproduce the observed climate. Differences in the performance of the models between regions of the United States (the Southeastern and Northwestern United States) warrant a regional-scale assessment of CMIP5 models.

Seasonal Variability in Global Eddy Diffusion and the Effect on Thermospheric Neutral Density

NASA Astrophysics Data System (ADS)

Pilinski, M.; Crowley, G.

2014-12-01

We describe a method for making single-satellite estimates of the seasonal variability in global-average eddy diffusion coefficients. Eddy diffusion values as a function of time between January 2004 and January 2008 were estimated from residuals of neutral density measurements made by the CHallenging Minisatellite Payload (CHAMP) and simulations made using the Thermosphere Ionosphere Mesosphere Electrodynamics - Global Circulation Model (TIME-GCM). The eddy diffusion coefficient results are quantitatively consistent with previous estimates based on satellite drag observations and are qualitatively consistent with other measurement methods such as sodium lidar observations and eddy-diffusivity models. The eddy diffusion coefficient values estimated between January 2004 and January 2008 were then used to generate new TIME-GCM results. Based on these results, the RMS difference between the TIME-GCM model and density data from a variety of satellites is reduced by an average of 5%. This result, indicates that global thermospheric density modeling can be improved by using data from a single satellite like CHAMP. This approach also demonstrates how eddy diffusion could be estimated in near real-time from satellite observations and used to drive a global circulation model like TIME-GCM. Although the use of global values improves modeled neutral densities, there are some limitations of this method, which are discussed, including that the latitude-dependence of the seasonal neutral-density signal is not completely captured by a global variation of eddy diffusion coefficients. This demonstrates the need for a latitude-dependent specification of eddy diffusion consistent with diffusion observations made by other techniques.

Seasonal variability in global eddy diffusion and the effect on neutral density

NASA Astrophysics Data System (ADS)

Pilinski, M. D.; Crowley, G.

2015-04-01

We describe a method for making single-satellite estimates of the seasonal variability in global-average eddy diffusion coefficients. Eddy diffusion values as a function of time were estimated from residuals of neutral density measurements made by the Challenging Minisatellite Payload (CHAMP) and simulations made using the thermosphere-ionosphere-mesosphere electrodynamics global circulation model (TIME-GCM). The eddy diffusion coefficient results are quantitatively consistent with previous estimates based on satellite drag observations and are qualitatively consistent with other measurement methods such as sodium lidar observations and eddy diffusivity models. Eddy diffusion coefficient values estimated between January 2004 and January 2008 were then used to generate new TIME-GCM results. Based on these results, the root-mean-square sum for the TIME-GCM model is reduced by an average of 5% when compared to density data from a variety of satellites, indicating that the fidelity of global density modeling can be improved by using data from a single satellite like CHAMP. This approach also demonstrates that eddy diffusion could be estimated in near real-time from satellite observations and used to drive a global circulation model like TIME-GCM. Although the use of global values improves modeled neutral densities, there are limitations to this method, which are discussed, including that the latitude dependence of the seasonal neutral-density signal is not completely captured by a global variation of eddy diffusion coefficients. This demonstrates the need for a latitude-dependent specification of eddy diffusion which is also consistent with diffusion observations made by other techniques.

Computing diffuse fraction of global horizontal solar radiation: A model comparison.

PubMed

Dervishi, Sokol; Mahdavi, Ardeshir

2012-06-01

For simulation-based prediction of buildings' energy use or expected gains from building-integrated solar energy systems, information on both direct and diffuse component of solar radiation is necessary. Available measured data are, however, typically restricted to global horizontal irradiance. There have been thus many efforts in the past to develop algorithms for the derivation of the diffuse fraction of solar irradiance. In this context, the present paper compares eight models for estimating diffuse fraction of irradiance based on a database of measured irradiance from Vienna, Austria. These models generally involve mathematical formulations with multiple coefficients whose values are typically valid for a specific location. Subsequent to a first comparison of these eight models, three better performing models were selected for a more detailed analysis. Thereby, the coefficients of the models were modified to account for Vienna data. The results suggest that some models can provide relatively reliable estimations of the diffuse fractions of the global irradiance. The calibration procedure could only slightly improve the models' performance.

Local versus Global Information Relevance in Website Use: A Case Study with the Information Literacy Portal AlfinEEES

ERIC Educational Resources Information Center

Marco, Francisco Javier Garcia; Pinto, Maria

2010-01-01

Introduction: A model to explore the relations among local and global relevance-based information behaviour is proposed that is based on objective and subjective measures of the relevance of the Website contents. Method: Global interest for the Website was researched using data on visits, while local use was explored with two surveys on the…

Visualization and dissemination of global crustal models on virtual globes

NASA Astrophysics Data System (ADS)

Zhu, Liang-feng; Pan, Xin; Sun, Jian-zhong

2016-05-01

Global crustal models, such as CRUST 5.1 and its descendants, are very useful in a broad range of geoscience applications. The current method for representing the existing global crustal models relies heavily on dedicated computer programs to read and work with those models. Therefore, it is not suited to visualize and disseminate global crustal information to non-geological users. This shortcoming is becoming obvious as more and more people from both academic and non-academic institutions are interested in understanding the structure and composition of the crust. There is a pressing need to provide a modern, universal and user-friendly method to represent and visualize the existing global crustal models. In this paper, we present a systematic framework to easily visualize and disseminate the global crustal structure on virtual globes. Based on crustal information exported from the existing global crustal models, we first create a variety of KML-formatted crustal models with different levels of detail (LODs). And then the KML-formatted models can be loaded into a virtual globe for 3D visualization and model dissemination. A Keyhole Markup Language (KML) generator (Crust2KML) is developed to automatically convert crustal information obtained from the CRUST 1.0 model into KML-formatted global crustal models, and a web application (VisualCrust) is designed to disseminate and visualize those models over the Internet. The presented framework and associated implementations can be conveniently exported to other applications to support visualizing and analyzing the Earth's internal structure on both regional and global scales in a 3D virtual-globe environment.

How well do we succeed in modeling the global soil carbon pools?

NASA Astrophysics Data System (ADS)

Viskari, T.; Liski, J.

2017-12-01

Terrestrial carbon pools are a crucial part of the global carbon cycle. Carbon from vegetation is deposited to the soil, which in turn releases carbon dioxide back to the atmosphere through heterotrophic respiration. The resulting soil carbon storage in the largest on land. While there are continuous efforts to improve the modeling of global soil carbon and how this storage is affected by climate change, this research requires still a more reliable baseline on how well the models estimate the current global soil carbon pools. Especially such comparisons are important for identifying the major challenges in the current soil carbon models. Here, we used the Yasso soil carbon model to create a global soil carbon map at a 0.5 degree resolution based on the available climate, land cover and vegetation productivity information. Yasso model describes the soil carbon cycling by pools that represent the breaking down of dead organic matter. We compared the model results to a measurement based projection of global soil carbon pools, and we examined the differences and spatial correlations between the two maps. In our findings, the modelled predictions captured the overall soil carbon distributions within 5 kgCm-2 on 63 % of the land area. The spatial distributions fit each other as well. The average soil carbon is smaller with the Yasso prediction ( 8.5 kg m-2) than with the measurement map ( 10 kg m-2) and there are notable areas, such as Siberia and Southern North America, where there are large differences between the model predictions and measurements. These results not only encourage future development of soil carbon models, but also highlight problem areas to focus and improve upon.

Regional Densification of a Global VTEC Model Based on B-Spline Representations

NASA Astrophysics Data System (ADS)

Erdogan, Eren; Schmidt, Michael; Dettmering, Denise; Goss, Andreas; Seitz, Florian; Börger, Klaus; Brandert, Sylvia; Görres, Barbara; Kersten, Wilhelm F.; Bothmer, Volker; Hinrichs, Johannes; Mrotzek, Niclas

2017-04-01

The project OPTIMAP is a joint initiative of the Bundeswehr GeoInformation Centre (BGIC), the German Space Situational Awareness Centre (GSSAC), the German Geodetic Research Institute of the Technical University Munich (DGFI-TUM) and the Institute for Astrophysics at the University of Göttingen (IAG). The main goal of the project is the development of an operational tool for ionospheric mapping and prediction (OPTIMAP). Two key features of the project are the combination of different satellite observation techniques (GNSS, satellite altimetry, radio occultations and DORIS) and the regional densification as a remedy against problems encountered with the inhomogeneous data distribution. Since the data from space-geoscientific mission which can be used for modeling ionospheric parameters, such as the Vertical Total Electron Content (VTEC) or the electron density, are distributed rather unevenly over the globe at different altitudes, appropriate modeling approaches have to be developed to handle this inhomogeneity. Our approach is based on a two-level strategy. To be more specific, in the first level we compute a global VTEC model with a moderate regional and spectral resolution which will be complemented in the second level by a regional model in a densification area. The latter is a region characterized by a dense data distribution to obtain a high spatial and spectral resolution VTEC product. Additionally, the global representation means a background model for the regional one to avoid edge effects at the boundaries of the densification area. The presented approach based on a global and a regional model part, i.e. the consideration of a regional densification is called the Two-Level VTEC Model (TLVM). The global VTEC model part is based on a series expansion in terms of polynomial B-Splines in latitude direction and trigonometric B-Splines in longitude direction. The additional regional model part is set up by a series expansion in terms of polynomial B-splines for both directions. The spectral resolution of both model parts is defined by the number of B-spline basis functions introduced for longitude and latitude directions related to appropriate coordinate systems. Furthermore, the TLVM has to be developed under the postulation that the global model part will be computed continuously in near real-time (NRT) and routinely predicted into the future by an algorithm based on deterministic and statistical forecast models. Thus, the additional regional densification model part, which will be computed also in NRT, but possibly only for a specified time duration, must be estimated independently from the global one. For that purpose a data separation procedure has to be developed in order to estimate the unknown series coefficients of both model parts independently. This procedure must also consider additional technique-dependent unknowns such as the Differential Code Biases (DCBs) within GNSS and intersystem biases. In this contribution we will present the concept to set up the TLVM including the data combination and the Kalman filtering procedure; first numerical results will be presented.

Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Consequences of increasing bioenergy demand on wood and forests: an application of the global forest products model

Treesearch

Joseph Buongiorno; Ronald Raunikar; Shushuai Zhu

2011-01-01

The Global Forest Products Model (GFPM) was applied to project the consequences for the global forest sector of doubling the rate of growth of bioenergy demand relative to a base scenario, other drivers being maintained constant. The results showed that this would lead to the convergence of the price of fuelwood and industrial roundwood, raising the price of industrial...

Exploring the role of movement in determining the global distribution of marine biomass using a coupled hydrodynamic - Size-based ecosystem model

NASA Astrophysics Data System (ADS)

Watson, James R.; Stock, Charles A.; Sarmiento, Jorge L.

2015-11-01

Modeling the dynamics of marine populations at a global scale - from phytoplankton to fish - is necessary if we are to quantify how climate change and other broad-scale anthropogenic actions affect the supply of marine-based food. Here, we estimate the abundance and distribution of fish biomass using a simple size-based food web model coupled to simulations of global ocean physics and biogeochemistry. We focus on the spatial distribution of biomass, identifying highly productive regions - shelf seas, western boundary currents and major upwelling zones. In the absence of fishing, we estimate the total ocean fish biomass to be ∼ 2.84 ×109 tonnes, similar to previous estimates. However, this value is sensitive to the choice of parameters, and further, allowing fish to move had a profound impact on the spatial distribution of fish biomass and the structure of marine communities. In particular, when movement is implemented the viable range of large predators is greatly increased, and stunted biomass spectra characterizing large ocean regions in simulations without movement, are replaced with expanded spectra that include large predators. These results highlight the importance of considering movement in global-scale ecological models.

Process-based upscaling of surface-atmosphere exchange

NASA Astrophysics Data System (ADS)

Keenan, T. F.; Prentice, I. C.; Canadell, J.; Williams, C. A.; Wang, H.; Raupach, M. R.; Collatz, G. J.; Davis, T.; Stocker, B.; Evans, B. J.

2015-12-01

Empirical upscaling techniques such as machine learning and data-mining have proven invaluable tools for the global scaling of disparate observations of surface-atmosphere exchange, but are not based on a theoretical understanding of the key processes involved. This makes spatial and temporal extrapolation outside of the training domain difficult at best. There is therefore a clear need for the incorporation of knowledge of ecosystem function, in combination with the strength of data mining. Here, we present such an approach. We describe a novel diagnostic process-based model of global photosynthesis and ecosystem respiration, which is directly informed by a variety of global datasets relevant to ecosystem state and function. We use the model framework to estimate global carbon cycling both spatially and temporally, with a specific focus on the mechanisms responsible for long-term change. Our results show the importance of incorporating process knowledge into upscaling approaches, and highlight the effect of key processes on the terrestrial carbon cycle.

A Neural Network Model for K(λ) Retrieval and Application to Global K par Monitoring

PubMed Central

Chen, Jun; Zhu, Yuanli; Wu, Yongsheng; Cui, Tingwei; Ishizaka, Joji; Ju, Yongtao

2015-01-01

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths K d(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical K d(λ) retrieval model (SAKM) and Jamet’s neural network model (JNNM), and then develop a new neural network K d(λ) retrieval model (NNKM). Based on the comparison of K d(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in K d(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The K d(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving K d(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate K par from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving K par from the global oceanic and coastal waters with 20.2% uncertainty. The K par are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving K par from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high K d(λ) and K par values are usually found around the coastal zones in the high latitude regions, while low K d(λ) and K par values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean. PMID:26083341

The Global Internet Pandemic

ERIC Educational Resources Information Center

Carter, Deborah Joy

2009-01-01

The global rise of Internet-based education is discussed in relation to models drawn from social studies and epidemiology. Experiential and data density models are highlighted, also the capacity for technological change, and phenomena observed in the spread of disease. The lesson of these illustrations is that even apparently permanent phenomena…

Conceptual model for partnership and sustainability in global health.

PubMed

Leffers, Jeanne; Mitchell, Emma

2011-01-01

Although nursing has a long history of service to the global community, the profession lacks a theoretical and empirical base for nurses to frame their global practice. A study using grounded theory methodology to investigate partnership and sustainability for global health led to the development of a conceptual model. Interviews were conducted with 13 global health nurse experts. Themes from the interviews were: components for engagement, mutual goal setting, cultural bridging, collaboration, capacity building, leadership, partnership, ownership, and sustainability. Next, the identified themes were reviewed in the literature in order to evaluate their conceptual relationships. Finally, careful comparison of the interview transcripts and the supporting literature led to the Conceptual Framework for Partnership and Sustainability in Global Health Nursing. The model posits that engagement and partnership must precede any planning and intervention in order to create sustainable interventions. This conceptual framework will offer nurses important guidance for global health nursing practice. © 2010 Wiley Periodicals, Inc.

AgMIP Coordinated Global and Regional Assessments for 1.5°C and 2.0°C

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2017-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study performs a proof-of-concept of the CGRA to demonstrate advantages and challenges of the framework. This effort responds to the request by UNFCCC for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), HAPPI and CMIP5 ensemble scenarios, global gridded crop models, global agricultural economic models, site-based crop models, and within-country regional economic models. CGRA results show that at the global scale, mixed areas of positive and negative simulated yield changes, with declines in some breadbasket regions led to overall declines in productivity at both 1.5°C and 2.0°C. These projected global yield changes resulted in increases in prices of major commodities in a global economic model. Simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on region and crop, but with more negative effects on productivity at 2.0°C than at 1.5°C for the most part. In conjunction with price changes from the global economics models, these productivity declines resulted generally in small positive effects on regional farm livelihoods, showing that farming systems should continue to be viable under high mitigation scenarios. CGRA protocols focus on how mitigation actions and effects differ across scales, with main mechanisms studied in the integrated assessment models being policies and technologies that reduce direct non-CO2 emissions from agriculture, reduce CO2 emissions from land use change and forest sink enhancement, and utilize biomass for energy production. At regional scales, increasing soil organic carbon (SOC) is of active interest.

Sixth-Grade Students' Progress in Understanding the Mechanisms of Global Climate Change

NASA Astrophysics Data System (ADS)

Visintainer, Tammie; Linn, Marcia

2015-04-01

Developing solutions for complex issues such as global climate change requires an understanding of the mechanisms involved. This study reports on the impact of a technology-enhanced unit designed to improve understanding of global climate change, its mechanisms, and their relationship to everyday energy use. Global Climate Change, implemented in the Web-based Inquiry Science Environment (WISE), engages sixth-grade students in conducting virtual investigations using NetLogo models to foster an understanding of core mechanisms including the greenhouse effect. Students then test how the greenhouse effect is enhanced by everyday energy use. This study draws on three data sources: (1) pre- and post-unit interviews, (2) analysis of embedded assessments following virtual investigations, and (3) contrasting cases of two students (normative vs. non-normative understanding of the greenhouse effect). Results show the value of using virtual investigations for teaching the mechanisms associated with global climate change. Interviews document that students hold a wide range of ideas about the mechanisms driving global climate change. Investigations with models help students use evidence-based reasoning to distinguish their ideas. Results show that understanding the greenhouse effect offers a foundation for building connections between everyday energy use and increases in global temperature. An impediment to establishing coherent understanding was the persistence of an alternative conception about ozone as an explanation for climate change. These findings illustrate the need for regular revision of curriculum based on classroom trials. We discuss key design features of models and instructional revisions that can transform the teaching and learning of global climate change.

Simulations of Seismic Wave Propagation on Mars

DOE PAGES

Bozdağ, Ebru; Ruan, Youyi; Metthez, Nathan; ...

2017-03-23

In this paper, we present global and regional synthetic seismograms computed for 1D and 3D Mars models based on the spectral-element method. For global simulations, we implemented a radially-symmetric Mars model with a 110 km thick crust. For this 1D model, we successfully benchmarked the 3D seismic wave propagation solver SPECFEM3D_GLOBE against the 2D axisymmetric wave propagation solver AxiSEM at periods down to 10 s. We also present higher-resolution body-wave simulations with AxiSEM down to 1 s in a model with a more complex 1D crust, revealing wave propagation effects that would have been difficult to interpret based on raymore » theory. For 3D global simulations based on SPECFEM3D_GLOBE, we superimposed 3D crustal thickness variations capturing the distinct crustal dichotomy between Mars’ northern and southern hemispheres, as well as topography, ellipticity, gravity, and rotation. The global simulations clearly indicate that the 3D crust speeds up body waves compared to the reference 1D model, whereas it significantly changes surface waveforms and their dispersive character depending on its thickness. We also perform regional simulations with the solver SES3D based on 3D crustal models derived from surface composition, thereby addressing the effects of various distinct crustal features down to 2 s. The regional simulations confirm the strong effects of crustal variations on waveforms. Finally, we conclude that the numerical tools are ready for examining more scenarios, including various other seismic models and sources.« less

Simulations of Seismic Wave Propagation on Mars

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bozdağ, Ebru; Ruan, Youyi; Metthez, Nathan

In this paper, we present global and regional synthetic seismograms computed for 1D and 3D Mars models based on the spectral-element method. For global simulations, we implemented a radially-symmetric Mars model with a 110 km thick crust. For this 1D model, we successfully benchmarked the 3D seismic wave propagation solver SPECFEM3D_GLOBE against the 2D axisymmetric wave propagation solver AxiSEM at periods down to 10 s. We also present higher-resolution body-wave simulations with AxiSEM down to 1 s in a model with a more complex 1D crust, revealing wave propagation effects that would have been difficult to interpret based on raymore » theory. For 3D global simulations based on SPECFEM3D_GLOBE, we superimposed 3D crustal thickness variations capturing the distinct crustal dichotomy between Mars’ northern and southern hemispheres, as well as topography, ellipticity, gravity, and rotation. The global simulations clearly indicate that the 3D crust speeds up body waves compared to the reference 1D model, whereas it significantly changes surface waveforms and their dispersive character depending on its thickness. We also perform regional simulations with the solver SES3D based on 3D crustal models derived from surface composition, thereby addressing the effects of various distinct crustal features down to 2 s. The regional simulations confirm the strong effects of crustal variations on waveforms. Finally, we conclude that the numerical tools are ready for examining more scenarios, including various other seismic models and sources.« less

Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Rudolf, Bruno; Schneider, Udo; Keehn, Peter R.

1995-01-01

The 'satellite-gauge model' (SGM) technique is described for combining precipitation estimates from microwave satellite data, infrared satellite data, rain gauge analyses, and numerical weather prediction models into improved estimates of global precipitation. Throughout, monthly estimates on a 2.5 degrees x 2.5 degrees lat-long grid are employed. First, a multisatellite product is developed using a combination of low-orbit microwave and geosynchronous-orbit infrared data in the latitude range 40 degrees N - 40 degrees S (the adjusted geosynchronous precipitation index) and low-orbit microwave data alone at higher latitudes. Then the rain gauge analysis is brougth in, weighting each field by its inverse relative error variance to produce a nearly global, observationally based precipitation estimate. To produce a complete global estimate, the numerical model results are used to fill data voids in the combined satellite-gauge estimate. Our sequential approach to combining estimates allows a user to select the multisatellite estimate, the satellite-gauge estimate, or the full SGM estimate (observationally based estimates plus the model information). The primary limitation in the method is imperfections in the estimation of relative error for the individual fields. The SGM results for one year of data (July 1987 to June 1988) show important differences from the individual estimates, including model estimates as well as climatological estimates. In general, the SGM results are drier in the subtropics than the model and climatological results, reflecting the relatively dry microwave estimates that dominate the SGM in oceanic regions.

APOLLO: a quality assessment service for single and multiple protein models.

PubMed

Wang, Zheng; Eickholt, Jesse; Cheng, Jianlin

2011-06-15

We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure. http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm

NASA Astrophysics Data System (ADS)

Yi, Jin; Li, Xinyu; Xiao, Mi; Xu, Junnan; Zhang, Lin

2017-01-01

Engineering design often involves different types of simulation, which results in expensive computational costs. Variable fidelity approximation-based design optimization approaches can realize effective simulation and efficiency optimization of the design space using approximation models with different levels of fidelity and have been widely used in different fields. As the foundations of variable fidelity approximation models, the selection of sample points of variable-fidelity approximation, called nested designs, is essential. In this article a novel nested maximin Latin hypercube design is constructed based on successive local enumeration and a modified novel global harmony search algorithm. In the proposed nested designs, successive local enumeration is employed to select sample points for a low-fidelity model, whereas the modified novel global harmony search algorithm is employed to select sample points for a high-fidelity model. A comparative study with multiple criteria and an engineering application are employed to verify the efficiency of the proposed nested designs approach.

Combining global and local approximations

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.

1991-01-01

A method based on a linear approximation to a scaling factor, designated the 'global-local approximation' (GLA) method, is presented and shown capable of extending the range of usefulness of derivative-based approximations to a more refined model. The GLA approach refines the conventional scaling factor by means of a linearly varying, rather than constant, scaling factor. The capabilities of the method are demonstrated for a simple beam example with a crude and more refined FEM model.

A New Synthetic Global Biomass Carbon Map for the year 2010

NASA Astrophysics Data System (ADS)

Spawn, S.; Lark, T.; Gibbs, H.

2017-12-01

Satellite technologies have facilitated a recent boom in high resolution, large-scale biomass estimation and mapping. These data are the input into a wide range of global models and are becoming the gold standard for required national carbon (C) emissions reporting. Yet their geographical and/or thematic scope may exclude some or all parts of a given country or region. Most datasets tend to focus exclusively on forest biomass. Grasslands and shrublands generally store less C than forests but cover nearly twice as much global land area and may represent a significant portion of a given country's biomass C stock. To address these shortcomings, we set out to create synthetic, global above- and below-ground biomass maps that combine recently-released satellite based data of standing forest biomass with novel estimates for non-forest biomass stocks that are typically neglected. For forests we integrated existing publicly available regional, global and biome-specific biomass maps and modeled below ground biomass using empirical relationships described in the literature. For grasslands, we developed models for both above- and below-ground biomass based on NPP, mean annual temperature and precipitation to extrapolate field measurements across the globe. Shrubland biomass was extrapolated from existing regional biomass maps using environmental factors to generate the first global estimate of shrub biomass. Our new synthetic map of global biomass carbon circa 2010 represents an update to the IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 (Ruesch and Gibbs, 2008) using the best data currently available. In the absence of a single seamless remotely sensed map of global biomass, our synthetic map provides the only globally-consistent source of comprehensive biomass C data and is valuable for land change analyses, carbon accounting, and emissions modeling.

The AgMIP Coordinated Global and Regional Assessments (CGRA) of Climate Change Impacts on Agriculture and Food Security

NASA Technical Reports Server (NTRS)

Ruane, Alex; Rosenzweig, Cynthia; Elliott, Joshua; Antle, John

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has been working since 2010 to construct a protocol-based framework enabling regional assessments (led by regional experts and modelers) that can provide consistent inputs to global economic and integrated assessment models. These global models can then relay important global-level information that drive regional decision-making and outcomes throughout an interconnected agricultural system. AgMIPs community of nearly 800 climate, crop, livestock, economics, and IT experts has improved the state-of-the-art through model intercomparisons, validation exercises, regional integrated assessments, and the launch of AgMIP programs on all six arable continents. AgMIP is now launching Coordinated Global and Regional Assessments (CGRA) of climate change impacts on agriculture and food security to link global and regional crop and economic models using a protocol-based framework. The CGRA protocols are being developed to utilize historical observations, climate projections, and RCPsSSPs from CMIP5 (and potentially CMIP6), and will examine stakeholder-driven agricultural development and adaptation scenarios to provide cutting-edge assessments of climate changes impact on agriculture and food security. These protocols will build on the foundation of established protocols from AgMIPs 30+ activities, and will emphasize the use of multiple models, scenarios, and scales to enable an accurate assessment of related uncertainties. The CGRA is also designed to provide the outputs necessary to feed into integrated assessment models (IAMs), nutrition and food security assessments, nitrogen and carbon cycle models, and additional impact-sector assessments (e.g., water resources, land-use, biomes, urban areas). This presentation will describe the current status of CGRA planning and initial prototype experiments to demonstrate key aspects of the protocols before wider implementation ahead of the IPCC Sixth Assessment Report.

The AgMIP Coordinated Global and Regional Assessments (CGRA) of Climate Change Impacts on Agriculture and Food Security

NASA Astrophysics Data System (ADS)

Ruane, A. C.; Rosenzweig, C.; Antle, J. M.; Elliott, J. W.

2015-12-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has been working since 2010 to construct a protocol-based framework enabling regional assessments (led by regional experts and modelers) that can provide consistent inputs to global economic and integrated assessment models. These global models can then relay important global-level information that drive regional decision-making and outcomes throughout an interconnected agricultural system. AgMIP's community of nearly 800 climate, crop, livestock, economics, and IT experts has improved the state-of-the-art through model intercomparisons, validation exercises, regional integrated assessments, and the launch of AgMIP programs on all six arable continents. AgMIP is now launching Coordinated Global and Regional Assessments (CGRA) of climate change impacts on agriculture and food security to link global and regional crop and economic models using a protocol-based framework. The CGRA protocols are being developed to utilize historical observations, climate projections, and RCPs/SSPs from CMIP5 (and potentially CMIP6), and will examine stakeholder-driven agricultural development and adaptation scenarios to provide cutting-edge assessments of climate change's impact on agriculture and food security. These protocols will build on the foundation of established protocols from AgMIP's 30+ activities, and will emphasize the use of multiple models, scenarios, and scales to enable an accurate assessment of related uncertainties. The CGRA is also designed to provide the outputs necessary to feed into integrated assessment models (IAMs), nutrition and food security assessments, nitrogen and carbon cycle models, and additional impact-sector assessments (e.g., water resources, land-use, biomes, urban areas). This presentation will describe the current status of CGRA planning and initial prototype experiments to demonstrate key aspects of the protocols before wider implementation ahead of the IPCC Sixth Assessment Report.

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available management options to meet demands for food and energy over the next century. The coupled models provide a new platform for evaluating future changes in agricultural management based on food demand, bioenergy demand, and changes in crop yield and soil C under a changing climate. This framework can be applied to evaluate the economically and biophysically optimal distribution of management under future climates.

Global optimization of multicomponent distillation configurations: 2. Enumeration based global minimization algorithm

DOE PAGES

Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.; ...

2016-02-10

We present a general Global Minimization Algorithm (GMA) to identify basic or thermally coupled distillation configurations that require the least vapor duty under minimum reflux conditions for separating any ideal or near-ideal multicomponent mixture into a desired number of product streams. In this algorithm, global optimality is guaranteed by modeling the system using Underwood equations and reformulating the resulting constraints to bilinear inequalities. The speed of convergence to the globally optimal solution is increased by using appropriate feasibility and optimality based variable-range reduction techniques and by developing valid inequalities. As a result, the GMA can be coupled with already developedmore » techniques that enumerate basic and thermally coupled distillation configurations, to provide for the first time, a global optimization based rank-list of distillation configurations.« less

Evaluation of Global Observations-Based Evapotranspiration Datasets and IPCC AR4 Simulations

NASA Technical Reports Server (NTRS)

Mueller, B.; Seneviratne, S. I.; Jimenez, C.; Corti, T.; Hirschi, M.; Balsamo, G.; Ciais, P.; Dirmeyer, P.; Fisher, J. B.; Guo, Z.;

A fully traits-based approach to modeling global vegetation distribution.

PubMed

van Bodegom, Peter M; Douma, Jacob C; Verheijen, Lieneke M

2014-09-23

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

Revising a conceptual model of partnership and sustainability in global health.

PubMed

Upvall, Michele J; Leffers, Jeanne M

2018-05-01

Models to guide global health partnerships are rare in the nursing literature. The Conceptual Model for Partnership and Sustainability in Global Health while significant was based on Western perspectives. The purpose of this study was to revise the model to include the voice of nurses from low- and middle-resource countries. Grounded theory was used to maintain fidelity with the design in the original model. A purposive sample of 15 participants from a variety of countries in Africa, the Caribbean, and Southeast Asia and having extensive experience in global health partnerships were interviewed. Skype recordings and in-person interviews were audiotaped using the same questions as the original study. Theoretical coding and a comparison of results with the original study was completed independently by the researchers. The process of global health partnerships was expanded from the original model to include engagement processes and processes for ongoing partnership development. New concepts of Transparency, Expanded World View, and Accompaniment were included as well as three broad themes: Geopolitical Influence, Power differential/Inequities, and Collegial Friendships. The revised conceptual model embodies a more comprehensive model of global health partnerships with representation of nurses from low- and middle-resource countries. © 2018 Wiley Periodicals, Inc.

Dynamic Analysis of the Melanoma Model: From Cancer Persistence to Its Eradication

NASA Astrophysics Data System (ADS)

Starkov, Konstantin E.; Jimenez Beristain, Laura

In this paper, we study the global dynamics of the five-dimensional melanoma model developed by Kronik et al. This model describes interactions of tumor cells with cytotoxic T cells and respective cytokines under cellular immunotherapy. We get the ultimate upper and lower bounds for variables of this model, provide formulas for equilibrium points and present local asymptotic stability/hyperbolic instability conditions. Next, we prove the existence of the attracting set. Based on these results we come to global asymptotic melanoma eradication conditions via global stability analysis. Finally, we provide bounds for a locus of the melanoma persistence equilibrium point, study the case of melanoma persistence and describe conditions under which we observe global attractivity to the unique melanoma persistence equilibrium point.

Residue-level global and local ensemble-ensemble comparisons of protein domains.

PubMed

Clark, Sarah A; Tronrud, Dale E; Karplus, P Andrew

2015-09-01

Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an ensemble of models consistent with the available information. Current strategies for comparing ensembles lose information because they use only a single representative structure. Here, we describe the ENSEMBLATOR and its novel strategy to directly compare two ensembles containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The ENSEMBLATOR has four components: eePREP (ee for ensemble-ensemble), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two ensembles; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between ensembles. We illustrate the ENSEMBLATOR's capabilities by showing how using it to analyze NMR ensembles and to compare NMR ensembles with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a "consistency check" of NMR-derived ensembles may be a useful analysis step for NMR-based structure determinations in general. The ENSEMBLATOR 1.0 is available as a first generation tool to carry out ensemble-ensemble comparisons. © 2015 The Protein Society.

Residue-level global and local ensemble-ensemble comparisons of protein domains

PubMed Central

Clark, Sarah A; Tronrud, Dale E; Andrew Karplus, P

2015-01-01

Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an ensemble of models consistent with the available information. Current strategies for comparing ensembles lose information because they use only a single representative structure. Here, we describe the ENSEMBLATOR and its novel strategy to directly compare two ensembles containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The ENSEMBLATOR has four components: eePREP (ee for ensemble-ensemble), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two ensembles; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between ensembles. We illustrate the ENSEMBLATOR's capabilities by showing how using it to analyze NMR ensembles and to compare NMR ensembles with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a “consistency check” of NMR-derived ensembles may be a useful analysis step for NMR-based structure determinations in general. The ENSEMBLATOR 1.0 is available as a first generation tool to carry out ensemble-ensemble comparisons. PMID:26032515

aerosol radiative effects and forcing: spatial and temporal distributions

NASA Astrophysics Data System (ADS)

Kinne, Stefan

2014-05-01

A monthly climatology for aerosol optical properties based on a synthesis from global modeling and observational data has been applied to illustrate spatial distributions and global averages of aerosol radiative impacts. With the help of a pre-industrial reference for aerosol optical properties from global modeling, also the aerosol direct forcing (ca -0.35W/m2 globally and annual averaged) and their spatial and seasonal distributions and contributions by individual aerosol components are estimated. Finally, CCN and IN concentrations associated with this climatology are applied to estimate aerosol indirect effects and forcing.

A physically based model of global freshwater surface temperature

NASA Astrophysics Data System (ADS)

Beek, Ludovicus P. H.; Eikelboom, Tessa; Vliet, Michelle T. H.; Bierkens, Marc F. P.

2012-09-01

Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5° on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for the Arctic rivers because the timing of ice breakup is predicted too late in the year due to the lack of including a mechanical breakup mechanism. Moreover, surface water temperatures for tropical rivers were overestimated, most likely due to an overestimation of rainfall temperature and incoming shortwave radiation. The spatiotemporal variation of water temperature reveals large temperature differences between water and atmosphere for the higher latitudes, while considerable lateral transport of heat can be observed for rivers crossing hydroclimatic zones, such as the Nile, the Mississippi, and the large rivers flowing to the Arctic. Overall, our model results show promise for future projection of global surface freshwater temperature under global change.

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception

PubMed Central

Clarke, Aaron M.; Herzog, Michael H.; Francis, Gregory

2014-01-01

Experimentalists tend to classify models of visual perception as being either local or global, and involving either feedforward or feedback processing. We argue that these distinctions are not as helpful as they might appear, and we illustrate these issues by analyzing models of visual crowding as an example. Recent studies have argued that crowding cannot be explained by purely local processing, but that instead, global factors such as perceptual grouping are crucial. Theories of perceptual grouping, in turn, often invoke feedback connections as a way to account for their global properties. We examined three types of crowding models that are representative of global processing models, and two of which employ feedback processing: a model based on Fourier filtering, a feedback neural network, and a specific feedback neural architecture that explicitly models perceptual grouping. Simulations demonstrate that crucial empirical findings are not accounted for by any of the models. We conclude that empirical investigations that reject a local or feedforward architecture offer almost no constraints for model construction, as there are an uncountable number of global and feedback systems. We propose that the identification of a system as being local or global and feedforward or feedback is less important than the identification of a system's computational details. Only the latter information can provide constraints on model development and promote quantitative explanations of complex phenomena. PMID:25374554

The changing global carbon cycle: linking local plant-soil carbon dynamics to global consequences

Treesearch

F. Stuart Chapin; Jack McFarland; A. David McGuire; Eugenie S. Euskirchen; Roger W. Ruess; Knut Kielland

2009-01-01

Most current climate-carbon cycle models that include the terrestrial carbon (C) cycle are based on a model developed 40 years ago by Woodwell & Whittaker (1968) and omit advances in biogeochemical understanding since that time. Their model treats net C emissions from ecosystems as the balance between net primary production (NPP) and heterotrophic respiration (HR,...

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison.

PubMed

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D A; Arneth, Almut; Calvin, Katherine; Doelman, Jonathan; Eitelberg, David A; Engström, Kerstin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Humpenöder, Florian; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Meiyappan, Prasanth; Popp, Alexander; Sands, Ronald D; Schaldach, Rüdiger; Schüngel, Jan; Stehfest, Elke; Tabeau, Andrzej; Van Meijl, Hans; Van Vliet, Jasper; Verburg, Peter H

2016-12-01

Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Rapid Processing of a Global Feature in the ON Visual Pathways of Behaving Monkeys.

PubMed

Huang, Jun; Yang, Yan; Zhou, Ke; Zhao, Xudong; Zhou, Quan; Zhu, Hong; Yang, Yingshan; Zhang, Chunming; Zhou, Yifeng; Zhou, Wu

2017-01-01

Visual objects are recognized by their features. Whereas, some features are based on simple components (i.e., local features, such as orientation of line segments), some features are based on the whole object (i.e., global features, such as an object having a hole in it). Over the past five decades, behavioral, physiological, anatomical, and computational studies have established a general model of vision, which starts from extracting local features in the lower visual pathways followed by a feature integration process that extracts global features in the higher visual pathways. This local-to-global model is successful in providing a unified account for a vast sets of perception experiments, but it fails to account for a set of experiments showing human visual systems' superior sensitivity to global features. Understanding the neural mechanisms underlying the "global-first" process will offer critical insights into new models of vision. The goal of the present study was to establish a non-human primate model of rapid processing of global features for elucidating the neural mechanisms underlying differential processing of global and local features. Monkeys were trained to make a saccade to a target in the black background, which was different from the distractors (white circle) in color (e.g., red circle target), local features (e.g., white square target), a global feature (e.g., white ring with a hole target) or their combinations (e.g., red square target). Contrary to the predictions of the prevailing local-to-global model, we found that (1) detecting a distinction or a change in the global feature was faster than detecting a distinction or a change in color or local features; (2) detecting a distinction in color was facilitated by a distinction in the global feature, but not in the local features; and (3) detecting the hole was interfered by the local features of the hole (e.g., white ring with a squared hole). These results suggest that monkey ON visual systems have a subsystem that is more sensitive to distinctions in the global feature than local features. They also provide the behavioral constraints for identifying the underlying neural substrates.

Dynamic Modeling of the Human Coagulation Cascade Using Reduced Order Effective Kinetic Models (Open Access)

DTIC Science & Technology

2015-03-16

shaded region around each total sensitivity value was the maximum uncertainty in that value estimated by the Sobol method. 2.4. Global Sensitivity...Performance We conducted a global sensitivity analysis, using the variance-based method of Sobol , to estimate which parameters controlled the...Hunter, J.D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, 9, 90–95. 69. Sobol , I. Global sensitivity indices for nonlinear

Mapping local and global variability in plant trait distributions

DOE PAGES

Butler, Ethan E.; Datta, Abhirup; Flores-Moreno, Habacuc; ...

2017-12-01

Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrencemore » ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates of functional diversity.« less

Mapping local and global variability in plant trait distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, Ethan E.; Datta, Abhirup; Flores-Moreno, Habacuc

Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrencemore » ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates of functional diversity.« less

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

PubMed Central

2011-01-01

Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task. PMID:21867520

Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

NASA Astrophysics Data System (ADS)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

Forecasting the global shortage of physicians: an economic- and needs-based approach

PubMed Central

Liu, Jenny X; Kinfu, Yohannes; Dal Poz, Mario R

2008-01-01

Abstract Objective Global achievements in health may be limited by critical shortages of health-care workers. To help guide workforce policy, we estimate the future demand for, need for and supply of physicians, by WHO region, to determine where likely shortages will occur by 2015, the target date of the Millennium Development Goals. Methods Using World Bank and WHO data on physicians per capita from 1980 to 2001 for 158 countries, we employ two modelling approaches for estimating the future global requirement for physicians. A needs-based model determines the number of physicians per capita required to achieve 80% coverage of live births by a skilled health-care attendant. In contrast, our economic model identifies the number of physicians per capita that are likely to be demanded, given each country’s economic growth. These estimates are compared to the future supply of physicians projected by extrapolating the historical rate of increase in physicians per capita for each country. Findings By 2015, the global supply of physicians appears to be in balance with projected economic demand. Because our measure of need reflects the minimum level of workforce density required to provide a basic health service that is met in all but the least developed countries, the needs-based estimates predict a global surplus of physicians. However, on a regional basis, both models predict shortages for many countries in the WHO African Region in 2015, with some countries experiencing a needs-based shortage, a demand-based shortage, or both. Conclusion The type of policy intervention needed to alleviate projected shortages, such as increasing health-care training or adopting measures to discourage migration, depends on the type of shortage projected. PMID:18670663

Assessing water resources in Azerbaijan using a local distributed model forced and constrained with global data

NASA Astrophysics Data System (ADS)

Bouaziz, Laurène; Hegnauer, Mark; Schellekens, Jaap; Sperna Weiland, Frederiek; ten Velden, Corine

2017-04-01

In many countries, data is scarce, incomplete and often not easily shared. In these cases, global satellite and reanalysis data provide an alternative to assess water resources. To assess water resources in Azerbaijan, a completely distributed and physically based hydrological wflow-sbm model was set-up for the entire Kura basin. We used SRTM elevation data, a locally available river map and one from OpenStreetMap to derive the drainage direction network at the model resolution of approximately 1x1 km. OpenStreetMap data was also used to derive the fraction of paved area per cell to account for the reduced infiltration capacity (c.f. Schellekens et al. 2014). We used the results of a global study to derive root zone capacity based on climate data (Wang-Erlandsson et al., 2016). To account for the variation in vegetation cover over the year, monthly averages of Leaf Area Index, based on MODIS data, were used. For the soil-related parameters, we used global estimates as provided by Dai et al. (2013). This enabled the rapid derivation of a first estimate of parameter values for our hydrological model. Digitized local meteorological observations were scarce and available only for limited time period. Therefore several sources of global meteorological data were evaluated: (1) EU-WATCH global precipitation, temperature and derived potential evaporation for the period 1958-2001 (Harding et al., 2011), (2) WFDEI precipitation, temperature and derived potential evaporation for the period 1979-2014 (by Weedon et al., 2014), (3) MSWEP precipitation (Beck et al., 2016) and (4) local precipitation data from more than 200 stations in the Kura basin were available from the NOAA website for a period up to 1991. The latter, together with data archives from Azerbaijan, were used as a benchmark to evaluate the global precipitation datasets for the overlapping period 1958-1991. By comparing the datasets, we found that monthly mean precipitation of EU-WATCH and WFDEI coincided well with NOAA stations and that MSWEP slightly overestimated precipitation amounts. On a daily basis, there were discrepancies in the peak timing and magnitude between measured precipitation and the global products. A bias between EU-WATCH and WFDEI temperature and potential evaporation was observed and to model the water balance correctly, it was needed to correct EU-WATCH to WFDEI mean monthly values. Overall, the available sources enabled rapid set-up of a hydrological model including the forcing of the model with a relatively good performance to assess water resources in Azerbaijan with a limited calibration effort and allow for a similar set-up anywhere in the world. Timing and quantification of peak volume remains a weakness in global data, making it difficult to be used for some applications (flooding) and for detailed calibration. Selecting and comparing different sources of global meteorological data is important to have a reliable set which improves model performance. - Beck et al., 2016. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2014) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. Discuss. - Dai Y. et al. ,2013. Development of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions for Land Surface Modeling. Journal of Hydrometeorology - Harding, R. et al., 2011., WATCH: Current knowledge of the Terrestrial global water cycle, J. Hydrometeorol. - Schellekens, J. et al., 2014. Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model. Environmental Modelling&Software - Wang-Erlandsson L. et al., 2016. Global Root Zone Storage Capacity from Satellite-Based Evaporation. Hydrology and Earth System Sciences - Weedon, G. et al., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resources Research.

Hydrologic Derivatives for Modeling and Analysis—A new global high-resolution database

USGS Publications Warehouse

Verdin, Kristine L.

2017-07-17

The U.S. Geological Survey has developed a new global high-resolution hydrologic derivative database. Loosely modeled on the HYDRO1k database, this new database, entitled Hydrologic Derivatives for Modeling and Analysis, provides comprehensive and consistent global coverage of topographically derived raster layers (digital elevation model data, flow direction, flow accumulation, slope, and compound topographic index) and vector layers (streams and catchment boundaries). The coverage of the data is global, and the underlying digital elevation model is a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), GMTED2010 (Global Multi-resolution Terrain Elevation Data 2010), and the SRTM (Shuttle Radar Topography Mission). For most of the globe south of 60°N., the raster resolution of the data is 3 arc-seconds, corresponding to the resolution of the SRTM. For the areas north of 60°N., the resolution is 7.5 arc-seconds (the highest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30 arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information. This database is appropriate for use in continental-scale modeling efforts. The work described in this report was conducted by the U.S. Geological Survey in cooperation with the National Aeronautics and Space Administration Goddard Space Flight Center.

Analysis of inter-country input-output table based on bibliographic coupling network: How industrial sectors on the GVC compete for production resources

NASA Astrophysics Data System (ADS)

Guan, Jun; Xu, Xiaoyu; Xing, Lizhi

2018-03-01

The input-output table is comprehensive and detailed in describing national economic systems with abundance of economic relationships depicting information of supply and demand among industrial sectors. This paper focuses on how to quantify the degree of competition on the global value chain (GVC) from the perspective of econophysics. Global Industrial Strongest Relevant Network models are established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output (ICIO) tables and then have them transformed into Global Industrial Resource Competition Network models to analyze the competitive relationships based on bibliographic coupling approach. Three indicators well suited for the weighted and undirected networks with self-loops are introduced here, including unit weight for competitive power, disparity in the weight for competitive amplitude and weighted clustering coefficient for competitive intensity. Finally, these models and indicators were further applied empirically to analyze the function of industrial sectors on the basis of the latest World Input-Output Database (WIOD) in order to reveal inter-sector competitive status during the economic globalization.

Robust Kalman filtering cooperated Elman neural network learning for vision-sensing-based robotic manipulation with global stability.

PubMed

Zhong, Xungao; Zhong, Xunyu; Peng, Xiafu

2013-10-08

In this paper, a global-state-space visual servoing scheme is proposed for uncalibrated model-independent robotic manipulation. The scheme is based on robust Kalman filtering (KF), in conjunction with Elman neural network (ENN) learning techniques. The global map relationship between the vision space and the robotic workspace is learned using an ENN. This learned mapping is shown to be an approximate estimate of the Jacobian in global space. In the testing phase, the desired Jacobian is arrived at using a robust KF to improve the ENN learning result so as to achieve robotic precise convergence of the desired pose. Meanwhile, the ENN weights are updated (re-trained) using a new input-output data pair vector (obtained from the KF cycle) to ensure robot global stability manipulation. Thus, our method, without requiring either camera or model parameters, avoids the corrupted performances caused by camera calibration and modeling errors. To demonstrate the proposed scheme's performance, various simulation and experimental results have been presented using a six-degree-of-freedom robotic manipulator with eye-in-hand configurations.

The status and challenge of global fire modelling

DOE PAGES

Hantson, Stijn; Arneth, Almut; Harrison, Sandy P.; ...

2016-06-09

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central questionmore » underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. In conclusion, we indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.« less

The status and challenge of global fire modelling

NASA Astrophysics Data System (ADS)

Hantson, Stijn; Arneth, Almut; Harrison, Sandy P.; Kelley, Douglas I.; Prentice, I. Colin; Rabin, Sam S.; Archibald, Sally; Mouillot, Florent; Arnold, Steve R.; Artaxo, Paulo; Bachelet, Dominique; Ciais, Philippe; Forrest, Matthew; Friedlingstein, Pierre; Hickler, Thomas; Kaplan, Jed O.; Kloster, Silvia; Knorr, Wolfgang; Lasslop, Gitta; Li, Fang; Mangeon, Stephane; Melton, Joe R.; Meyn, Andrea; Sitch, Stephen; Spessa, Allan; van der Werf, Guido R.; Voulgarakis, Apostolos; Yue, Chao

2016-06-01

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.

The status and challenge of global fire modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hantson, Stijn; Arneth, Almut; Harrison, Sandy P.

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central questionmore » underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. In conclusion, we indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.« less

A Land System representation for global assessments and land-use modeling.

PubMed

van Asselen, Sanneke; Verburg, Peter H

2012-10-01

Current global scale land-change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land-use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human-environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi-)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land-use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land-change models that include the human drivers of land change are best parameterized at sub-global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions. © 2012 Blackwell Publishing Ltd.

Global Trends and Variability in Integrated Water Vapor from Ground-Based GPS Data and Climate Models

NASA Astrophysics Data System (ADS)

Bock, O.; Parracho, A. C.; Bastin, S.; Hourdin, F.

2016-12-01

A high-quality, consistent, global, long-term dataset of integrated water vapor (IWV) was produced from Global Positioning System (GPS) measurements at more than 400 sites over the globe among which 120 sites have more than 15 years of data. The GPS delay data were converted to IWV using surface pressure and weighted mean temperature estimates from ERA-Interim reanalysis. A two-step screening method was developed to detect and remove outliers in the IWV data. It is based on: 1) GPS data processing information and delay formal errors, and 2) inter-comparison with ERA-Interim reanalysis data. The GPS IWV data are also homogenized to correct for offsets due to instrumental changes and other unknown factors. The differential homogenization method uses ERA-Interim IWV as a reference. The resulting GPS data are used to document the mean distribution, the global trends and the variability of IWV over the period 1995-2010, and to assess global climate model simulations extracted from the IPCC AR5 archive. Large coherent spatial patterns of moistening and drying are evidenced but significant discrepancies are also seen between GPS measurements, reanalysis and climate models in various regions. In terms of variability, the monthly mean anomalies are inter-compared. The temporal correlation between GPS and the climate model simulations is overall quite small but the spatial variation of the magnitude of the anomalies is globally well simulated. GPS IWV data prove to be useful to validate global climate model simulations and highlight deficiencies in their representation of the water cycle.

Similar Estimates of Temperature Impacts on Global Wheat Yield by Three Independent Methods

NASA Technical Reports Server (NTRS)

Liu, Bing; Asseng, Senthold; Muller, Christoph; Ewart, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.;

Similar estimates of temperature impacts on global wheat yield by three independent methods

NASA Astrophysics Data System (ADS)

Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Alderman, Phillip D.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andy; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fereres, Elias; Folberth, Christian; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A.; Izaurralde, Roberto C.; Jabloun, Mohamed; Jones, Curtis D.; Kersebaum, Kurt C.; Kimball, Bruce A.; Koehler, Ann-Kristin; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry J.; Olesen, Jørgen E.; Ottman, Michael J.; Palosuo, Taru; Prasad, P. V. Vara; Priesack, Eckart; Pugh, Thomas A. M.; Reynolds, Matthew; Rezaei, Ehsan E.; Rötter, Reimund P.; Schmid, Erwin; Semenov, Mikhail A.; Shcherbak, Iurii; Stehfest, Elke; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wall, Gerard W.; Wang, Enli; White, Jeffrey W.; Wolf, Joost; Zhao, Zhigan; Zhu, Yan

2016-12-01

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify `method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

NASA Astrophysics Data System (ADS)

Rosenzweig, Cynthia; Ruane, Alex C.; Antle, John; Elliott, Joshua; Ashfaq, Muhammad; Chatta, Ashfaq Ahmad; Ewert, Frank; Folberth, Christian; Hathie, Ibrahima; Havlik, Petr; Hoogenboom, Gerrit; Lotze-Campen, Hermann; MacCarthy, Dilys S.; Mason-D'Croz, Daniel; Contreras, Erik Mencos; Müller, Christoph; Perez-Dominguez, Ignacio; Phillips, Meridel; Porter, Cheryl; Raymundo, Rubi M.; Sands, Ronald D.; Schleussner, Carl-Friedrich; Valdivia, Roberto O.; Valin, Hugo; Wiebe, Keith

2018-05-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate. This article is part of the theme issue `The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments.

PubMed

Rosenzweig, Cynthia; Ruane, Alex C; Antle, John; Elliott, Joshua; Ashfaq, Muhammad; Chatta, Ashfaq Ahmad; Ewert, Frank; Folberth, Christian; Hathie, Ibrahima; Havlik, Petr; Hoogenboom, Gerrit; Lotze-Campen, Hermann; MacCarthy, Dilys S; Mason-D'Croz, Daniel; Contreras, Erik Mencos; Müller, Christoph; Perez-Dominguez, Ignacio; Phillips, Meridel; Porter, Cheryl; Raymundo, Rubi M; Sands, Ronald D; Schleussner, Carl-Friedrich; Valdivia, Roberto O; Valin, Hugo; Wiebe, Keith

2018-05-13

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO 2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'. © 2018 The Authors.

Ensemble Downscaling of Winter Seasonal Forecasts: The MRED Project

NASA Astrophysics Data System (ADS)

Arritt, R. W.; Mred Team

2010-12-01

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional project that is producing large ensembles of downscaled winter seasonal forecasts from coupled atmosphere-ocean seasonal prediction models. Eight regional climate models each are downscaling 15-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the new NASA seasonal forecast system based on the GEOS5 atmospheric model coupled with the MOM4 ocean model. This produces 240-member ensembles, i.e., 8 regional models x 15 global ensemble members x 2 global models, for each winter season (December-April) of 1982-2003. Results to date show that combined global-regional downscaled forecasts have greatest skill for seasonal precipitation anomalies during strong El Niño events such as 1982-83 and 1997-98. Ensemble means of area-averaged seasonal precipitation for the regional models generally track the corresponding results for the global model, though there is considerable inter-model variability amongst the regional models. For seasons and regions where area mean precipitation is accurately simulated the regional models bring added value by extracting greater spatial detail from the global forecasts, mainly due to better resolution of terrain in the regional models. Our results also emphasize that an ensemble approach is essential to realizing the added value from the combined global-regional modeling system.

Assessing the vertical structure of baroclinic tidal currents in a global model

NASA Astrophysics Data System (ADS)

Timko, Patrick; Arbic, Brian; Scott, Robert

2010-05-01

Tidal forcing plays an important role in many aspects of oceanography. Mixing, transport of particulates and internal wave generation are just three examples of local phenomena that may depend on the strength of local tidal currents. Advances in satellite altimetry have made an assessment of the global barotropic tide possible. However, the vertical structure of the tide may only be observed by deployment of instruments throughout the water column. Typically these observations are conducted at pre-determined depths based upon the interest of the observer. The high cost of such observations often limits both the number and the length of the observations resulting in a limit to our knowledge of the vertical structure of tidal currents. One way to expand our insight into the baroclinic structure of the ocean is through the use of numerical models. We compare the vertical structure of the global baroclinic tidal velocities in 1/12 degree HYCOM (HYbrid Coordinate Ocean Model) to a global database of current meter records. The model output is a subset of a 5 year global simulation that resolves the eddying general circulation, barotropic tides and baroclinic tides using 32 vertical layers. The density structure within the simulation is both vertically and horizontally non-uniform. In addition to buoyancy forcing the model is forced by astronomical tides and winds. We estimate the dominant semi-diurnal (M2), and diurnal (K1) tidal constituents of the model data using classical harmonic analysis. In regions where current meter record coverage is adequate, the model skill in replicating the vertical structure of the dominant diurnal and semi-diurnal tidal currents is assessed based upon the strength, orientation and phase of the tidal ellipses. We also present a global estimate of the baroclinic tidal energy at fixed depths estimated from the model output.

Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

PubMed Central

Feng, Fei; Yao, Yunjun; Liu, Meng

2017-01-01

Estimating cropland latent heat flux (LE) from continental to global scales is vital to modeling crop production and managing water resources. Over the past several decades, numerous LE models were developed, such as the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing-based Penman–Monteith LE algorithm (RRS), the Priestley–Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL) and the modified satellite-based Priestley-Taylor LE algorithm (MS-PT). However, these LE models have not been directly compared over the global cropland ecosystem using various algorithms. In this study, we evaluated the performances of these four LE models using 34 eddy covariance (EC) sites. The results showed that mean annual LE for cropland varied from 33.49 to 58.97 W/m2 among the four models. The interannual LE slightly increased during 1982–2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R2) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m2. MS-PT had good overall performance across the cropland ecosystem with the highest R2, lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R2 ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m2, might be attributed to empirical parameters in the structure algorithms and calibrated coefficients. PMID:28837704

A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0)

NASA Astrophysics Data System (ADS)

Bloom, A. Anthony; Bowman, Kevin W.; Lee, Meemong; Turner, Alexander J.; Schroeder, Ronny; Worden, John R.; Weidner, Richard; McDonald, Kyle C.; Jacob, Daniel J.

2017-06-01

Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009-2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001-2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.

Projection of climatic suitability for Aedes albopictus Skuse (Culicidae) in Europe under climate change conditions

NASA Astrophysics Data System (ADS)

Fischer, Dominik; Thomas, Stephanie Margarete; Niemitz, Franziska; Reineking, Björn; Beierkuhnlein, Carl

2011-07-01

During the last decades the disease vector Aedes albopictus ( Ae. albopictus) has rapidly spread around the globe. The spread of this species raises serious public health concerns. Here, we model the present distribution and the future climatic suitability of Europe for this vector in the face of climate change. In order to achieve the most realistic current prediction and future projection, we compare the performance of four different modelling approaches, differentiated by the selection of climate variables (based on expert knowledge vs. statistical criteria) and by the geographical range of presence records (native range vs. global range). First, models of the native and global range were built with MaxEnt and were either based on (1) statistically selected climatic input variables or (2) input variables selected with expert knowledge from the literature. Native models show high model performance (AUC: 0.91-0.94) for the native range, but do not predict the European distribution well (AUC: 0.70-0.72). Models based on the global distribution of the species, however, were able to identify all regions where Ae. albopictus is currently established, including Europe (AUC: 0.89-0.91). In a second step, the modelled bioclimatic envelope of the global range was projected to future climatic conditions in Europe using two emission scenarios implemented in the regional climate model COSMO-CLM for three time periods 2011-2040, 2041-2070, and 2071-2100. For both global-driven models, the results indicate that climatically suitable areas for the establishment of Ae. albopictus will increase in western and central Europe already in 2011-2040 and with a temporal delay in eastern Europe. On the other hand, a decline in climatically suitable areas in southern Europe is pronounced in the Expert knowledge based model. Our projections appear unaffected by non-analogue climate, as this is not detected by Multivariate Environmental Similarity Surface analysis. The generated risk maps can aid in identifying suitable habitats for Ae. albopictus and hence support monitoring and control activities to avoid disease vector establishment.

Utah State University Global Assimilation of Ionospheric Measurements Gauss-Markov Kalman filter model of the ionosphere: Model description and validation

NASA Astrophysics Data System (ADS)

Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Thompson, D. C.; Zhu, L.

2006-11-01

The Utah State University Gauss-Markov Kalman Filter (GMKF) was developed as part of the Global Assimilation of Ionospheric Measurements (GAIM) program. The GMKF uses a physics-based model of the ionosphere and a Gauss-Markov Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) observations. The physics-based model is the Ionospheric Forecast Model (IFM), which accounts for five ion species and covers the E region, F region, and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. In the current configuration, the GMKF assimilates slant total electron content (TEC) from a variable number of global positioning satellite (GPS) ground sites, bottomside electron density (Ne) profiles from a variable number of ionosondes, in situ Ne from four Defense Meteorological Satellite Program (DMSP) satellites, and nighttime line-of-sight ultraviolet (UV) radiances measured by satellites. To test the GMKF for real-time operations and to validate its ionospheric density specifications, we have tested the model performance for a variety of geophysical conditions. During these model runs various combination of data types and data quantities were assimilated. To simulate real-time operations, the model ran continuously and automatically and produced three-dimensional global electron density distributions in 15 min increments. In this paper we will describe the Gauss-Markov Kalman filter model and present results of our validation study, with an emphasis on comparisons with independent observations.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

PubMed

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Global assessment of ocean carbon export by combining satellite observations and food-web models

NASA Astrophysics Data System (ADS)

Siegel, D. A.; Buesseler, K. O.; Doney, S. C.; Sailley, S. F.; Behrenfeld, M. J.; Boyd, P. W.

2014-03-01

The export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food-web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional-scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional-scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of 6 Pg C yr-1. Global export estimates show small variation (typically < 10%) to factor of 2 changes in model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump.

Application of Observed Precipitation in NCEP Global and Regional Data Assimilation Systems, Including Reanalysis and Land Data Assimilation

NASA Astrophysics Data System (ADS)

Mitchell, K. E.

2006-12-01

The Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) applies several different analyses of observed precipitation in both the data assimilation and validation components of NCEP's global and regional numerical weather and climate prediction/analysis systems (including in NCEP global and regional reanalysis). This invited talk will survey these data assimilation and validation applications and methodologies, as well as the temporal frequency, spatial domains, spatial resolution, data sources, data density and data quality control in the precipitation analyses that are applied. Some of the precipitation analyses applied by EMC are produced by NCEP's Climate Prediction Center (CPC), while others are produced by the River Forecast Centers (RFCs) of the National Weather Service (NWS), or by automated algorithms of the NWS WSR-88D Radar Product Generator (RPG). Depending on the specific type of application in data assimilation or model forecast validation, the temporal resolution of the precipitation analyses may be hourly, daily, or pentad (5-day) and the domain may be global, continental U.S. (CONUS), or Mexico. The data sources for precipitation include ground-based gauge observations, radar-based estimates, and satellite-based estimates. The precipitation analyses over the CONUS are analyses of either hourly, daily or monthly totals of precipitation, and they are of two distinct types: gauge-only or primarily radar-estimated. The gauge-only CONUS analysis of daily precipitation utilizes an orographic-adjustment technique (based on the well-known PRISM precipitation climatology of Oregon State University) developed by the NWS Office of Hydrologic Development (OHD). The primary NCEP global precipitation analysis is the pentad CPC Merged Analysis of Precipitation (CMAP), which blends both gauge observations and satellite estimates. The presentation will include a brief comparison between the CMAP analysis and other global precipitation analyses by other institutions. Other global precipitation analyses produced by other methodologies are also used by EMC in certain applications, such as CPC's well-known satellite-IR based technique known as "GPI", and satellite-microwave based estimates from NESDIS or NASA. Finally, the presentation will cover the three assimilation methods used by EMC to assimilate precipitation data, including 1) 3D-VAR variational assimilation in NCEP's Global Data Assimilation System (GDAS), 2) direct insertion of precipitation-inferred vertical latent heating profiles in NCEP's N. American Data Assimilation System (NDAS) and its N. American Regional Reanalysis (NARR) counterpart, and 3) direct use of observed precipitation to drive the Noah land model component of NCEP's Global and N. American Land Data Assimilation Systems (GLDAS and NLDAS). In the applications of precipitation analyses in data assimilation at NCEP, the analyses are temporally disaggregated to hourly or less using time-weights calculated from A) either radar-based estimates or an analysis of hourly gauge-observations for the CONUS-domain daily precipitation analyses, or B) global model forecasts of 6-hourly precipitation (followed by linear interpolation to hourly or less) for the global CMAP precipitation analysis.

Three dimensional global modeling of atmospheric CO2

NASA Technical Reports Server (NTRS)

Fung, I.; Hansen, J.; Rind, D.

1983-01-01

A model was developed to study the prospects of extracting information on carbon dioxide sources and sinks from observed CO2 variations. The approach uses a three dimensional global transport model, based on winds from a 3-D general circulation model (GCM), to advect CO2 noninteractively, i.e., as a tracer, with specified sources and sinks of CO2 at the surface. The 3-D model employed is identified and biosphere, ocean and fossil fuel sources and sinks are discussed. Some preliminary model results are presented.

Assessing the impacts of 1.5 °C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

NASA Astrophysics Data System (ADS)

Frieler, Katja; Lange, Stefan; Piontek, Franziska; Reyer, Christopher P. O.; Schewe, Jacob; Warszawski, Lila; Zhao, Fang; Chini, Louise; Denvil, Sebastien; Emanuel, Kerry; Geiger, Tobias; Halladay, Kate; Hurtt, George; Mengel, Matthias; Murakami, Daisuke; Ostberg, Sebastian; Popp, Alexander; Riva, Riccardo; Stevanovic, Miodrag; Suzuki, Tatsuo; Volkholz, Jan; Burke, Eleanor; Ciais, Philippe; Ebi, Kristie; Eddy, Tyler D.; Elliott, Joshua; Galbraith, Eric; Gosling, Simon N.; Hattermann, Fred; Hickler, Thomas; Hinkel, Jochen; Hof, Christian; Huber, Veronika; Jägermeyr, Jonas; Krysanova, Valentina; Marcé, Rafael; Müller Schmied, Hannes; Mouratiadou, Ioanna; Pierson, Don; Tittensor, Derek P.; Vautard, Robert; van Vliet, Michelle; Biber, Matthias F.; Betts, Richard A.; Bodirsky, Benjamin Leon; Deryng, Delphine; Frolking, Steve; Jones, Chris D.; Lotze, Heike K.; Lotze-Campen, Hermann; Sahajpal, Ritvik; Thonicke, Kirsten; Tian, Hanqin; Yamagata, Yoshiki

2017-11-01

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).

SoilGrids1km — Global Soil Information Based on Automated Mapping

PubMed Central

Hengl, Tomislav; de Jesus, Jorge Mendes; MacMillan, Robert A.; Batjes, Niels H.; Heuvelink, Gerard B. M.; Ribeiro, Eloi; Samuel-Rosa, Alessandro; Kempen, Bas; Leenaars, Johan G. B.; Walsh, Markus G.; Gonzalez, Maria Ruiperez

2014-01-01

Background Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license. PMID:25171179

Five-minute, 1/2°, and 1° data sets of continental watersheds and river networks for use in regional and global hydrologic and climate system modeling studies

NASA Astrophysics Data System (ADS)

Graham, S. T.; Famiglietti, J. S.; Maidment, D. R.

1999-02-01

A major shortcoming of the land surface component in climate models is the absence of a river transport algorithm. This issue becomes particularly important in fully coupled climate system models (CSMs), where river transport is required to close and realistically represent the global water cycle. The development of a river transport algorithm requires knowledge of watersheds and river networks at a scale that is appropriate for use in CSMs. These data must be derived largely from global digital topographic information. The purpose of this paper is to describe a new data set of watersheds and river networks, which is derived primarily from the TerrainBase 5' Global DTM (digital terrain model) and the CIA World Data Bank II. These data serve as a base map for routing continental runoff to the appropriate coast and therefore into the appropriate ocean or inland sea. Using this data set, the runoff produced in any grid cell, when coupled with a routing algorithm, can easily be transported to the appropriate water body and distributed across that water body as desired. The data set includes watershed and flow direction information, as well as supporting hydrologic data at 5', 1/2°, and 1° resolutions globally. It will be useful in fully coupled land-ocean-atmosphere models, in terrestrial ecosystem models, or in stand-alone macroscale hydrologic-modeling studies.

The McLuhan Global Classroom: A Singapore-U.S. One-Year Instructional Interaction.

ERIC Educational Resources Information Center

Aune, Adonica Schultz; Lim, Dan

WebCT was integrated and modeled in a global Instructional Technology (IT) Certification Summer Institute offered through the University of Minnesota. Courses were first introduced with an on-site certification where technology integration was modeled in each course through the use of highly interactive web-based learning applications and games…

A Global Landslide Nowcasting System using Remotely Sensed Information

NASA Astrophysics Data System (ADS)

Kirschbaum, Dalia; Stanely, Thomas

2017-04-01

A global Landslide Hazard Assessment model for Situational Awareness (LHASA) has been developed that combines susceptibility information with satellite-based precipitation to provide an indication of potential landslide activity at the global scale every 30 minutes. This model utilizes a 1-km global susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. A multi-satellite dataset from the Global Precipitation Measurement (GPM) mission is used to identify the current and antecedent rainfall conditions from the past 7 days. When both rainfall and susceptibility are high, a "nowcast" is issued to indicate areas where a landslide may be likely. The global LHASA model is currently being run in near real-time every 30 minutes and the outputs are available in several different formats at https://pmm.nasa.gov/precip-apps. This talk outlines the LHASA system, discusses the performance metrics and potential applications of the LHASA system.

Data assimilation of ground GPG total electron content into a physics-based ionosheric model by use of the Kalman filter

NASA Technical Reports Server (NTRS)

Hajj, G. A.; Wilson, B. D.; Wang, C.; Pi, X.; Rosen, I. G.

2004-01-01

A three-dimensional (3-D) Global Assimilative Ionospheric Model (GAIM) is currently being developed by a joint University of Southern California and Jet Propulsion Laboratory (JPL) team. To estimate the electron density on a global grid, GAIM uses a first-principles ionospheric physics model and the Kalman filter as one of its possible estimation techniques.

The Precedence of Global Features in the Perception of Map Symbols

DTIC Science & Technology

1988-06-01

be continually updated. The present study evaluated the feasibility of a serial model of visual processing. By comparing performance between a symbol...symbols, is based on a " filter - ing" procedure, consisting of a series of passive-to-active or global- to-local stages. Navon (1977, 1981a) has proposed a...packages or segments. This advances the earlier, static feature aggregation ap- proaches to comprise a "figure." According to the global precedence model

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

DTIC Science & Technology

2015-03-16

sensitivity value was the maximum uncertainty in that value estimated by the Sobol method. 2.4. Global Sensitivity Analysis of the Reduced Order Coagulation...sensitivity analysis, using the variance-based method of Sobol , to estimate which parameters controlled the performance of the reduced order model [69]. We...Environment. Comput. Sci. Eng. 2007, 9, 90–95. 69. Sobol , I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

Global baseline data on phosphorus pollution of large lakes

NASA Astrophysics Data System (ADS)

Fink, Gabriel; Flörke, Martina; Alcamo, Joseph

2016-04-01

Lakes are exposed to harmful eutrophication which is the most concerning water quality issue on global scale. Eutrophication is caused by phosphorous pollution in most lakes. Hence, global consistent base line data on phosphorus loadings are needed to assess future sustainable development. We used the modeling framework WaterGAP3 to calculate present total phosphorus loadings to the world's largest lakes. Estimates of modeled total phosphorus (TP) loadings as well as the contributions of different sectors were successfully validated against measured data. Based on these findings, annual total phosphorus loadings to lakes were calculated for diffuse and point sources according to the different sectors domestic, manufacturing, urban surface runoff, agriculture and background for the time period 1990 to 2010. Our results show high phosphorus loadings into lakes in southern latitudes. On global average, industrial fertilizer is the main anthropogenic source while background loadings are low in comparison. Nevertheless, both features indicate a high potential to reduce the exposure to eutrophication in lakes which are faced with high phosphor inputs. The global average of TP loadings was 7% higher in the time period 2005-2010 than in the period 1990-1995. The global average in 2005-2010 results from an increase in TP loadings of 79% in South America, which was dampened by a decrease in Europe, North America, and Asia. Chinese lakes were exposed to massive increasing phosphorus loadings, too. Both increasing and decreasing trends are caused primarily by changing industrial fertilizer application rates. In conclusion, this study provides a consistent and model based synopsis of global trends and sources of phosphorus loadings to large lakes. The estimates of phosphorus pollution of lakes present a basis for assessing and managing the global eutrophication problem.

Prediction of global and local model quality in CASP8 using the ModFOLD server.

PubMed

McGuffin, Liam J

2009-01-01

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0--an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/. Copyright 2009 Wiley-Liss, Inc.

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

Safta, C.; Ricciuto, D. M.; Sargsyan, K.; Najm, H. N.; Debusschere, B.; Thornton, P. E.

2013-12-01

We present uncertainty quantification results for a process-based ecosystem carbon model. The model employs 18 parameters and is driven by meteorological data corresponding to years 1992-2006 at the Harvard Forest site. Daily Net Ecosystem Exchange (NEE) observations were available to calibrate the model parameters and test the performance of the model. Posterior distributions show good predictive capabilities for the calibrated model. A global sensitivity analysis was first performed to determine the important model parameters based on their contribution to the variance of NEE. We then proceed to calibrate the model parameters in a Bayesian framework. The daily discrepancies between measured and predicted NEE values were modeled as independent and identically distributed Gaussians with prescribed daily variance according to the recorded instrument error. All model parameters were assumed to have uninformative priors with bounds set according to expert opinion. The global sensitivity results show that the rate of leaf fall (LEAFALL) is responsible for approximately 25% of the total variance in the average NEE for 1992-2005. A set of 4 other parameters, Nitrogen use efficiency (NUE), base rate for maintenance respiration (BR_MR), growth respiration fraction (RG_FRAC), and allocation to plant stem pool (ASTEM) contribute between 5% and 12% to the variance in average NEE, while the rest of the parameters have smaller contributions. The posterior distributions, sampled with a Markov Chain Monte Carlo algorithm, exhibit significant correlations between model parameters. However LEAFALL, the most important parameter for the average NEE, is not informed by the observational data, while less important parameters show significant updates between their prior and posterior densities. The Fisher information matrix values, indicating which parameters are most informed by the experimental observations, are examined to augment the comparison between the calibration and global sensitivity analysis results.

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.

PubMed

Corzo, Gerald; Solomatine, Dimitri

2007-05-01

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.

E-Global Library: The Academic Campus Library Meets the Internet.

ERIC Educational Resources Information Center

Heilig, Jean M.

2001-01-01

Describes e-global library, the first Internet-based virtual library designed for online students at Jones International University and that has grown into a separately licensable product. Highlights include marketing to other academic libraries, both online and traditional; fees; the e-global library model; collection development policies;…

Grid Transmission Expansion Planning Model Based on Grid Vulnerability

NASA Astrophysics Data System (ADS)

Tang, Quan; Wang, Xi; Li, Ting; Zhang, Quanming; Zhang, Hongli; Li, Huaqiang

2018-03-01

Based on grid vulnerability and uniformity theory, proposed global network structure and state vulnerability factor model used to measure different grid models. established a multi-objective power grid planning model which considering the global power network vulnerability, economy and grid security constraint. Using improved chaos crossover and mutation genetic algorithm to optimize the optimal plan. For the problem of multi-objective optimization, dimension is not uniform, the weight is not easy given. Using principal component analysis (PCA) method to comprehensive assessment of the population every generation, make the results more objective and credible assessment. the feasibility and effectiveness of the proposed model are validated by simulation results of Garver-6 bus system and Garver-18 bus.

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

NASA Astrophysics Data System (ADS)

Qi, W.; Zhang, C.; Fu, G.; Sweetapple, C.; Zhou, H.

2016-02-01

The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

Magnetospheric Substorm Evolution in the Magnetotail: Challenge to Global MHD Modeling.

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.; Hesse, M.; Dorelli, J.; Rastaetter, L.

2003-12-01

Testing the ability of global MHD models to describe magnetotail evolution during substroms is one of the elements of science based validation efforts at CCMC. We perform simulations of magnetotail dynamics using global MHD models residing at CCMC. We select solar wind conditions which drive the accumulation of magnetic field in the tail lobes and subsequent magnetic reconnection and energy release. We will analyze the effects of spatial resolution in the plasma sheet on modeled expansion phase evolution, maximum energy stored in the tail, and details of magnetotail reconnection. We will pay special attention to current sheet thinning and multiple plasmoid formation.

Large historical growth in global terrestrial gross primary production

DOE PAGES

Campbell, J. E.; Berry, J. A.; Seibt, U.; ...

2017-04-05

Growth in terrestrial gross primary production (GPP) may provide a negative feedback for climate change. It remains uncertain, however, to what extent biogeochemical processes can suppress global GPP growth. In consequence, model estimates of terrestrial carbon storage and carbon cycle –climate feedbacks remain poorly constrained. Here we present a global, measurement-based estimate of GPP growth during the twentieth century based on long-term atmospheric carbonyl sulphide (COS) records derived from ice core, firn, and ambient air samples. Here, we interpret these records using a model that simulates changes in COS concentration due to changes in its sources and sinks, including amore » large sink that is related to GPP. We find that the COS record is most consistent with climate-carbon cycle model simulations that assume large GPP growth during the twentieth century (31% ± 5%; mean ± 95% confidence interval). Finally, while this COS analysis does not directly constrain estimates of future GPP growth it provides a global-scale benchmark for historical carbon cycle simulations.« less

Large historical growth in global terrestrial gross primary production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, J. E.; Berry, J. A.; Seibt, U.

Growth in terrestrial gross primary production (GPP) may provide a negative feedback for climate change. It remains uncertain, however, to what extent biogeochemical processes can suppress global GPP growth. In consequence, model estimates of terrestrial carbon storage and carbon cycle –climate feedbacks remain poorly constrained. Here we present a global, measurement-based estimate of GPP growth during the twentieth century based on long-term atmospheric carbonyl sulphide (COS) records derived from ice core, firn, and ambient air samples. Here, we interpret these records using a model that simulates changes in COS concentration due to changes in its sources and sinks, including amore » large sink that is related to GPP. We find that the COS record is most consistent with climate-carbon cycle model simulations that assume large GPP growth during the twentieth century (31% ± 5%; mean ± 95% confidence interval). Finally, while this COS analysis does not directly constrain estimates of future GPP growth it provides a global-scale benchmark for historical carbon cycle simulations.« less

Evaluating the effects of terrestrial ecosystems, climate and carbon dioxide on weathering over geological time: a global-scale process-based approach.

PubMed

Taylor, Lyla L; Banwart, Steve A; Valdes, Paul J; Leake, Jonathan R; Beerling, David J

2012-02-19

Global weathering of calcium and magnesium silicate rocks provides the long-term sink for atmospheric carbon dioxide (CO(2)) on a timescale of millions of years by causing precipitation of calcium carbonates on the seafloor. Catchment-scale field studies consistently indicate that vegetation increases silicate rock weathering, but incorporating the effects of trees and fungal symbionts into geochemical carbon cycle models has relied upon simple empirical scaling functions. Here, we describe the development and application of a process-based approach to deriving quantitative estimates of weathering by plant roots, associated symbiotic mycorrhizal fungi and climate. Our approach accounts for the influence of terrestrial primary productivity via nutrient uptake on soil chemistry and mineral weathering, driven by simulations using a dynamic global vegetation model coupled to an ocean-atmosphere general circulation model of the Earth's climate. The strategy is successfully validated against observations of weathering in watersheds around the world, indicating that it may have some utility when extrapolated into the past. When applied to a suite of six global simulations from 215 to 50 Ma, we find significantly larger effects over the past 220 Myr relative to the present day. Vegetation and mycorrhizal fungi enhanced climate-driven weathering by a factor of up to 2. Overall, we demonstrate a more realistic process-based treatment of plant fungal-geosphere interactions at the global scale, which constitutes a first step towards developing 'next-generation' geochemical models.

Evaluating the effects of terrestrial ecosystems, climate and carbon dioxide on weathering over geological time: a global-scale process-based approach

PubMed Central

Taylor, Lyla L.; Banwart, Steve A.; Valdes, Paul J.; Leake, Jonathan R.; Beerling, David J.

2012-01-01

Global weathering of calcium and magnesium silicate rocks provides the long-term sink for atmospheric carbon dioxide (CO2) on a timescale of millions of years by causing precipitation of calcium carbonates on the seafloor. Catchment-scale field studies consistently indicate that vegetation increases silicate rock weathering, but incorporating the effects of trees and fungal symbionts into geochemical carbon cycle models has relied upon simple empirical scaling functions. Here, we describe the development and application of a process-based approach to deriving quantitative estimates of weathering by plant roots, associated symbiotic mycorrhizal fungi and climate. Our approach accounts for the influence of terrestrial primary productivity via nutrient uptake on soil chemistry and mineral weathering, driven by simulations using a dynamic global vegetation model coupled to an ocean–atmosphere general circulation model of the Earth's climate. The strategy is successfully validated against observations of weathering in watersheds around the world, indicating that it may have some utility when extrapolated into the past. When applied to a suite of six global simulations from 215 to 50 Ma, we find significantly larger effects over the past 220 Myr relative to the present day. Vegetation and mycorrhizal fungi enhanced climate-driven weathering by a factor of up to 2. Overall, we demonstrate a more realistic process-based treatment of plant fungal–geosphere interactions at the global scale, which constitutes a first step towards developing ‘next-generation’ geochemical models. PMID:22232768

Modeling and Managing the Risks of Measles and Rubella: A Global Perspective, Part I.

PubMed

Thompson, Kimberly M; Cochi, Stephen L

2016-07-01

Over the past 50 years, the use of vaccines led to significant decreases in the global burdens of measles and rubella, motivated at least in part by the successive development of global control and elimination targets. The Global Vaccine Action Plan (GVAP) includes specific targets for regional elimination of measles and rubella in five of six regions of the World Health Organization by 2020. Achieving the GVAP measles and rubella goals will require significant immunization efforts and associated financial investments and political commitments. Planning and budgeting for these efforts can benefit from learning some important lessons from the Global Polio Eradication Initiative (GPEI). Following an overview of the global context of measles and rubella risks and discussion of lessons learned from the GPEI, we introduce the contents of the special issue on modeling and managing the risks of measles and rubella. This introduction describes the synthesis of the literature available to support evidence-based model inputs to support the development of an integrated economic and dynamic disease transmission model to support global efforts to optimally manage these diseases globally using vaccines. © 2016 Society for Risk Analysis.

A Simple Model of Global Aerosol Indirect Effects

NASA Technical Reports Server (NTRS)

Ghan, Steven J.; Smith, Steven J.; Wang, Minghuai; Zhang, Kai; Pringle, Kirsty; Carslaw, Kenneth; Pierce, Jeffrey; Bauer, Susanne; Adams, Peter

2013-01-01

Most estimates of the global mean indirect effect of anthropogenic aerosol on the Earth's energy balance are from simulations by global models of the aerosol lifecycle coupled with global models of clouds and the hydrologic cycle. Extremely simple models have been developed for integrated assessment models, but lack the flexibility to distinguish between primary and secondary sources of aerosol. Here a simple but more physically based model expresses the aerosol indirect effect (AIE) using analytic representations of cloud and aerosol distributions and processes. Although the simple model is able to produce estimates of AIEs that are comparable to those from some global aerosol models using the same global mean aerosol properties, the estimates by the simple model are sensitive to preindustrial cloud condensation nuclei concentration, preindustrial accumulation mode radius, width of the accumulation mode, size of primary particles, cloud thickness, primary and secondary anthropogenic emissions, the fraction of the secondary anthropogenic emissions that accumulates on the coarse mode, the fraction of the secondary mass that forms new particles, and the sensitivity of liquid water path to droplet number concentration. Estimates of present-day AIEs as low as 5 W/sq m and as high as 0.3 W/sq m are obtained for plausible sets of parameter values. Estimates are surprisingly linear in emissions. The estimates depend on parameter values in ways that are consistent with results from detailed global aerosol-climate simulation models, which adds to understanding of the dependence on AIE uncertainty on uncertainty in parameter values.

Ability of the current global observing network to constrain N2O sources and sinks

NASA Astrophysics Data System (ADS)

Millet, D. B.; Wells, K. C.; Chaliyakunnel, S.; Griffis, T. J.; Henze, D. K.; Bousserez, N.

2014-12-01

The global observing network for atmospheric N2O combines flask and in-situ measurements at ground stations with sustained and campaign-based aircraft observations. In this talk we apply a new global model of N2O (based on GEOS-Chem) and its adjoint to assess the strengths and weaknesses of this network for quantifying N2O emissions. We employ an ensemble of pseudo-observation analyses to evaluate the relative constraints provided by ground-based (surface, tall tower) and airborne (HIPPO, CARIBIC) observations, and the extent to which variability (e.g. associated with pulsing or seasonality of emissions) not captured by the a priori inventory can bias the inferred fluxes. We find that the ground-based and HIPPO datasets each provide a stronger constraint on the distribution of global emissions than does the CARIBIC dataset on its own. Given appropriate initial conditions, we find that our inferred surface fluxes are insensitive to model errors in the stratospheric loss rate of N2O over the timescale of our analysis (2 years); however, the same is not necessarily true for model errors in stratosphere-troposphere exchange. Finally, we examine the a posteriori error reduction distribution to identify priority locations for future N2O measurements.

Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

PubMed Central

Carbonell, Felix; Bellec, Pierre

2011-01-01

Abstract The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)–based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations. PMID:22444074

Modeling Global Urbanization Supported by Nighttime Light Remote Sensing

NASA Astrophysics Data System (ADS)

Zhou, Y.

2015-12-01

Urbanization, a major driver of global change, profoundly impacts our physical and social world, for example, altering carbon cycling and climate. Understanding these consequences for better scientific insights and effective decision-making unarguably requires accurate information on urban extent and its spatial distributions. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the nighttime light remote sensing data, extended this method to the global domain by developing a computational method (parameterization) to estimate the key parameters in the cluster-based method, and built a consistent 20-year global urban map series to evaluate the time-reactive nature of global urbanization (e.g. 2000 in Fig. 1). Supported by urban maps derived from nightlights remote sensing data and socio-economic drivers, we developed an integrated modeling framework to project future urban expansion by integrating a top-down macro-scale statistical model with a bottom-up urban growth model. With the models calibrated and validated using historical data, we explored urban growth at the grid level (1-km) over the next two decades under a number of socio-economic scenarios. The derived spatiotemporal information of historical and potential future urbanization will be of great value with practical implications for developing adaptation and risk management measures for urban infrastructure, transportation, energy, and water systems when considered together with other factors such as climate variability and change, and high impact weather events.

Global mean dynamic topography based on GOCE data and Wiener filters

NASA Astrophysics Data System (ADS)

Gilardoni, Maddalena; Reguzzoni, Mirko; Albertella, Alberta

2015-04-01

A mean dynamic ocean topography (MDT) has been computed by using a GOCE-only gravity model and a given mean sea surface (MSS) obtained from satellite altimetry. Since the used gravity model, i.e. the fifth release of the time-wise solution covering the full mission lifetime, is truncated at a maximum harmonic degree of 280, the obtained MDT has to be consistently filtered. This has been done globally by using the spherical harmonic representation and following a Wiener minimization principle. This global filtering approach is convenient from the computational point of view but requires to have MDT values all over the Earth surface and therefore to fill the continents with fictitious data. The main improvements with respect to the already presented results are in the MDT filling procedure (to guarantee that the global signal has the same covariance of the one over the oceans), in the error modelling of the input MSS and in the error estimation of the filtered MDT and of the corresponding geostrophic velocities. The impact of GOCE data in the ocean circulation global modelling has been assessed by comparing the pattern of the obtained geostrophic currents with those computed by using EGM2008. Comparisons with independent circulation data based on drifters and other MDT models have been also performed with the aim of evaluating the accuracy of the obtained results.

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

NASA Astrophysics Data System (ADS)

Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro

2018-06-01

A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.

Study on generation investment decision-making considering multi-agent benefit for global energy internet

NASA Astrophysics Data System (ADS)

Li, Pai; Huang, Yuehui; Jia, Yanbing; Liu, Jichun; Niu, Yi

2018-02-01

Abstract . This article has studies on the generation investment decision in the background of global energy interconnection. Generation investment decision model considering the multiagent benefit is proposed. Under the back-ground of global energy Interconnection, generation investors in different clean energy base not only compete with other investors, but also facing being chosen by the power of the central area, therefor, constructing generation investment decision model considering multiagent benefit can be close to meet the interests demands. Using game theory, the complete information game model is adopted to solve the strategies of different subjects in equilibrium state.

Observations of geographically correlated orbit errors for TOPEX/Poseidon using the global positioning system

NASA Technical Reports Server (NTRS)

Christensen, E. J.; Haines, B. J.; Mccoll, K. C.; Nerem, R. S.

1994-01-01

We have compared Global Positioning System (GPS)-based dynamic and reduced-dynamic TOPEX/Poseidon orbits over three 10-day repeat cycles of the ground-track. The results suggest that the prelaunch joint gravity model (JGM-1) introduces geographically correlated errors (GCEs) which have a strong meridional dependence. The global distribution and magnitude of these GCEs are consistent with a prelaunch covariance analysis, with estimated and predicted global rms error statistics of 2.3 and 2.4 cm rms, respectively. Repeating the analysis with the post-launch joint gravity model (JGM-2) suggests that a portion of the meridional dependence observed in JGM-1 still remains, with global rms error of 1.2 cm.

A storm-time plasmasphere evolution study using data assimilation

NASA Astrophysics Data System (ADS)

Nikoukar, R.; Bust, G. S.; Bishop, R. L.; Coster, A. J.; Lemon, C.; Turner, D. L.; Roeder, J. L.

2017-12-01

In this work, we study the evolution of the Earth's plasmasphere during geomagnetic active periods using the Plasmasphere Data Assimilation (PDA) model. The total electron content (TEC) measurements from an extensive network of global ground-based GPS receivers as well as GPS receivers on-board Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellites and Communications/Navigation Outage Forecasting System (C/NOFS) satellite are ingested into the model. Global Core Plasma model, which is an empirical plasmasphere model, is utilized as the background model. Based on the 3D-VAR optimization, the PDA assimilative model benefits from incorporation of regularization techniques to prevent non-physical altitudinal variation in density estimates due to the limited-angle observational geometry. This work focuses on the plasmapause location, plasmasphere erosion time scales and refilling rates during the main and recovery phases of geomagnetic storms as estimated from the PDA 3-dimensional global maps of electron density in the ionosphere/plasmasphere. The comparison between the PDA results with in-situ density measurements from THEMIS and Van Allen Probes, and the RCM-E first-principle model will be also presented.

Assessment of the suitability of GOCE-based geoid models for the unification of the North American vertical datums

NASA Astrophysics Data System (ADS)

Amjadiparvar, Babak; Sideris, Michael

2015-04-01

Precise gravimetric geoid heights are required when the unification of vertical datums is performed using the Geodetic Boundary Value Problem (GBVP) approach. Five generations of Global Geopotential Models (GGMs) derived from Gravity field and steady-state Ocean Circulation Explorer (GOCE) observations have been computed and released so far (available via IAG's International Centre for Global Earth Models, ICGEM, http://icgem.gfz-potsdam.de/ICGEM/). The performance of many of these models with respect to geoid determination has been studied in order to select the best performing model to be used in height datum unification in North America. More specifically, Release-3, 4 and 5 of the GOCE-based global geopotential models have been evaluated using GNSS-levelling data as independent control values. Comparisons against EGM2008 show that each successive release improves upon the previous one, with Release-5 models showing an improvement over EGM2008 in Canada and CONUS between spherical harmonic degrees 100 and 210. In Alaska and Mexico, a considerable improvement over EGM2008 was brought by the Release-5 models when used up to spherical harmonic degrees of 250 and 280, respectively. The positive impact of the Release-5 models was also felt when a gravimetric geoid was computed using the GOCE-based GGMs together with gravity and topography data in Canada. This geoid model, with appropriately modified Stokes kernel between spherical harmonic degrees 190 and 260, performed better than the official Canadian gravimetric geoid model CGG2013, thus illustrating the advantages of using the latest release GOCE-based models for vertical datum unification in North America.

Teaching corner: child family health international : the ethics of asset-based global health education programs.

PubMed

Evert, Jessica

2015-03-01

Child Family Health International (CFHI) is a U.S.-based nonprofit, nongovernmental organization (NGO) that has more than 25 global health education programs in seven countries annually serving more than 600 interprofessional undergraduate, graduate, and postgraduate participants in programs geared toward individual students and university partners. Recognized by Special Consultative Status with the United Nations Economic and Social Council (ECOSOC), CFHI utilizes an asset-based community engagement model to ensure that CFHI's programs challenge, rather than reinforce, historical power imbalances between the "Global North" and "Global South." CFHI's programs are predicated on ethical principles including reciprocity, sustainability, humility, transparency, nonmaleficence, respect for persons, and social justice.

Magnetosphere Modeling: From Cartoons to Simulations

NASA Astrophysics Data System (ADS)

Gombosi, T. I.

2017-12-01

Over the last half a century physics-based global computer simulations became a bridge between experiment and basic theory and now it represents the "third pillar" of geospace research. Today, many of our scientific publications utilize large-scale simulations to interpret observations, test new ideas, plan campaigns, or design new instruments. Realistic simulations of the complex Sun-Earth system have been made possible by the dramatically increased power of both computing hardware and numerical algorithms. Early magnetosphere models were based on simple E&M concepts (like the Chapman-Ferraro cavity) and hydrodynamic analogies (bow shock). At the beginning of the space age current system models were developed culminating in the sophisticated Tsyganenko-type description of the magnetic configuration. The first 3D MHD simulations of the magnetosphere were published in the early 1980s. A decade later there were several competing global models that were able to reproduce many fundamental properties of the magnetosphere. The leading models included the impact of the ionosphere by using a height-integrated electric potential description. Dynamic coupling of global and regional models started in the early 2000s by integrating a ring current and a global magnetosphere model. It has been recognized for quite some time that plasma kinetic effects play an important role. Presently, global hybrid simulations of the dynamic magnetosphere are expected to be possible on exascale supercomputers, while fully kinetic simulations with realistic mass ratios are still decades away. In the 2010s several groups started to experiment with PIC simulations embedded in large-scale 3D MHD models. Presently this integrated MHD-PIC approach is at the forefront of magnetosphere simulations and this technique is expected to lead to some important advances in our understanding of magnetosheric physics. This talk will review the evolution of magnetosphere modeling from cartoons to current systems, to global MHD to MHD-PIC and discuss the role of state-of-the-art models in forecasting space weather.

Development and Evaluation of an Integrated Hydrological Modeling Framework for Monitoring and Understanding Floods and Droughts

NASA Astrophysics Data System (ADS)

Yang, Z. L.; Wu, W. Y.; Lin, P.; Maidment, D. R.

2017-12-01

Extreme water events such as catastrophic floods and severe droughts have increased in recent decades. Mitigating the risk to lives, food security, infrastructure, energy supplies, as well as numerous other industries posed by these extreme events requires informed decision-making and planning based on sound science. We are developing a global water modeling capability by building models that will provide total operational water predictions (evapotranspiration, soil moisture, groundwater, channel flow, inundation, snow) at unprecedented spatial resolutions and updated frequencies. Toward this goal, this talk presents an integrated global hydrological modeling framework that takes advantage of gridded meteorological forcing, land surface modeling, channeled flow modeling, ground observations, and satellite remote sensing. Launched in August 2016, the National Water Model successfully incorporates weather forecasts to predict river flows for more than 2.7 million rivers across the continental United States, which transfers a "synoptic weather map" to a "synoptic river flow map" operationally. In this study, we apply a similar framework to a high-resolution global river network database, which is developed from a hierarchical Dominant River Tracing (DRT) algorithm, and runoff output from the Global Land Data Assimilation System (GLDAS) to a vector-based river routing model (The Routing Application for Parallel Computation of Discharge, RAPID) to produce river flows from 2001 to 2016 using Message Passing Interface (MPI) on Texas Advanced Computer Center's Stampede system. In this simulation, global river discharges for more than 177,000 rivers are computed every 30 minutes. The modeling framework's performance is evaluated with various observations including river flows at more than 400 gauge stations globally. Overall, the model exhibits a reasonably good performance in simulating the averaged patterns of terrestrial water storage, evapotranspiration and runoff. The system is appropriate for monitoring and studying floods and droughts. Directions for future research will be outlined and discussed.

Global trends and variability in integrated water vapour from ground-based GPS data and atmospheric models

NASA Astrophysics Data System (ADS)

Bock, Olivier; Parracho, Ana; Bastin, Sophie; Hourdin, Frededic; Mellul, Lidia

2016-04-01

A high-quality, consistent, global, long-term dataset of integrated water vapour (IWV) was produced from Global Positioning System (GPS) measurements at more than 400 sites over the globe among which 120 sites have more than 15 years of data. The GPS delay data were converted to IWV using surface pressure and weighted mean temperature estimates from ERA-Interim reanalysis. A two-step screening method was developed to detect and remove outliers in the IWV data. It is based on: 1) GPS data processing information and delay formal errors, and 2) intercomparison with ERA-Interim reanalysis data. The GPS IWV data are also homogenized to correct for offsets due to instrumental changes and other unknown factors. The differential homogenization method uses ERA-Interim IWV as a reference. The resulting GPS data are used to document the mean distribution, the global trends and the variability of IWV over the period 1995-2010, and are analysed in coherence with precipitation and surface temperature data (from observations and ERA-Interim reanalysis). These data are also used to assess global climate model simulations extracted from the IPCC AR5 archive. Large coherent spatial patterns of moistening and drying are evidenced but significant discrepancies are also seen between GPS measurements, reanalysis and climate models in various regions. In terms of variability, the monthly mean anomalies are intercompared. The temporal correlation between GPS and the climate model simulations is overall quite small but the spatial variation of the magnitude of the anomalies is globally well simulated. GPS IWV data prove to be useful to validate global climate model simulations and highlight deficiencies in their representation of the water cycle.

Global carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models

NASA Astrophysics Data System (ADS)

Zhang, Shupeng; Yi, Xue; Zheng, Xiaogu; Chen, Zhuoqi; Dan, Bo; Zhang, Xuanze

2014-11-01

In this paper, a global carbon assimilation system (GCAS) is developed for optimizing the global land surface carbon flux at 1° resolution using multiple ecosystem models. In GCAS, three ecosystem models, Boreal Ecosystem Productivity Simulator, Carnegie-Ames-Stanford Approach, and Community Atmosphere Biosphere Land Exchange, produce the prior fluxes, and an atmospheric transport model, Model for OZone And Related chemical Tracers, is used to calculate atmospheric CO2 concentrations resulting from these prior fluxes. A local ensemble Kalman filter is developed to assimilate atmospheric CO2 data observed at 92 stations to optimize the carbon flux for six land regions, and the Bayesian model averaging method is implemented in GCAS to calculate the weighted average of the optimized fluxes based on individual ecosystem models. The weights for the models are found according to the closeness of their forecasted CO2 concentration to observation. Results of this study show that the model weights vary in time and space, allowing for an optimum utilization of different strengths of different ecosystem models. It is also demonstrated that spatial localization is an effective technique to avoid spurious optimization results for regions that are not well constrained by the atmospheric data. Based on the multimodel optimized flux from GCAS, we found that the average global terrestrial carbon sink over the 2002-2008 period is 2.97 ± 1.1 PgC yr-1, and the sinks are 0.88 ± 0.52, 0.27 ± 0.33, 0.67 ± 0.39, 0.90 ± 0.68, 0.21 ± 0.31, and 0.04 ± 0.08 PgC yr-1 for the North America, South America, Africa, Eurasia, Tropical Asia, and Australia, respectively. This multimodel GCAS can be used to improve global carbon cycle estimation.

Tomographic Validation of the AWSoM Model of the Inner Corona During Solar Minima

NASA Astrophysics Data System (ADS)

Manchester, W.; Vásquez, A. M.; Lloveras, D. G.; Mac Cormack, C.; Nuevo, F.; Lopez-Fuentes, M.; Frazin, R. A.; van der Holst, B.; Landi, E.; Gombosi, T. I.

2017-12-01

Continuous improvement of MHD three-dimensional (3D) models of the global solar corona, such as the Alfven Wave Solar Model (AWSoM) of the Space Weather Modeling Framework (SWMF), requires testing their ability to reproduce observational constraints at a global scale. To that end, solar rotational tomography based on EUV image time-series can be used to reconstruct the 3D distribution of the electron density and temperature in the inner solar corona (r < 1.25 Rsun). The tomographic results, combined with a global coronal magnetic model, can further provide constraints on the energy input flux required at the coronal base to maintain stable structures. In this work, tomographic reconstructions are used to validate steady-state 3D MHD simulations of the inner corona using the latest version of the AWSoM model. We perform the study for selected rotations representative of solar minimum conditions, when the global structure of the corona is more axisymmetric. We analyse in particular the ability of the MHD simulation to match the tomographic results across the boundary region between the equatorial streamer belt and the surrounding coronal holes. The region is of particular interest as the plasma flow from that zone is thought to be related to the origin of the slow component of the solar wind.

Team Investment and Longitudinal Relationships: An Innovative Global Health Education Model.

PubMed

Myers, Kimberly R; Fredrick, N Benjamin

2017-12-01

Increasing student interest in global health has resulted in medical schools offering more global health opportunities. However, concerns have been raised, particularly about one-time, short-term experiences, including lack of follow-through for students and perpetuation of unintentional messages of global health heroism, neocolonialism, and disregard for existing systems and communities of care. Medical schools must develop global health programs that address these issues. The Global Health Scholars Program (GHSP) was created in 2008-2009 at Penn State College of Medicine. This four-year program is based on values of team investment and longitudinal relationships and uses the service-learning framework of preparation, service, and reflection. Teams of approximately five students, with faculty oversight, participate in two separate monthlong trips abroad to the same host community in years 1 and 4, and in campus- and Web-based activities in years 2 and 3. As of December 2016, 191 students have been accepted into the GHSP. Since inception, applications have grown by 475% and program sites have expanded from one to seven sites on four continents. The response from students has been positive, but logistical challenges persist in sustaining team investment and maintaining longitudinal relationships between student teams and host communities. Formal methods of assessment should be used to compare the GHSP model with more traditional approaches to global health education. Other medical schools with similar aims can adapt the GHSP model to expand their global health programming.

GRACILE: a comprehensive climatology of atmospheric gravity wave parameters based on satellite limb soundings

NASA Astrophysics Data System (ADS)

Ern, Manfred; Trinh, Quang Thai; Preusse, Peter; Gille, John C.; Mlynczak, Martin G.; Russell, James M., III; Riese, Martin

2018-04-01

Gravity waves are one of the main drivers of atmospheric dynamics. The spatial resolution of most global atmospheric models, however, is too coarse to properly resolve the small scales of gravity waves, which range from tens to a few thousand kilometers horizontally, and from below 1 km to tens of kilometers vertically. Gravity wave source processes involve even smaller scales. Therefore, general circulation models (GCMs) and chemistry climate models (CCMs) usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified. For this reason, comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. We present a gravity wave climatology based on atmospheric infrared limb emissions observed by satellite (GRACILE). GRACILE is a global data set of gravity wave distributions observed in the stratosphere and the mesosphere by the infrared limb sounding satellite instruments High Resolution Dynamics Limb Sounder (HIRDLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). Typical distributions (zonal averages and global maps) of gravity wave vertical wavelengths and along-track horizontal wavenumbers are provided, as well as gravity wave temperature variances, potential energies and absolute momentum fluxes. This global data set captures the typical seasonal variations of these parameters, as well as their spatial variations. The GRACILE data set is suitable for scientific studies, and it can serve for comparison with other instruments (ground-based, airborne, or other satellite instruments) and for comparison with gravity wave distributions, both resolved and parametrized, in GCMs and CCMs. The GRACILE data set is available as supplementary data at https://doi.org/10.1594/PANGAEA.879658.

Improving Distributed Diagnosis Through Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew John; Roychoudhury, Indranil; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

2011-01-01

Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

NASA Astrophysics Data System (ADS)

López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

2015-04-01

The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with downscaled global climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with downscaled satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.

Statistical prediction of September Arctic Sea Ice minimum based on stable teleconnections with global climate and oceanic patterns

NASA Astrophysics Data System (ADS)

Ionita, M.; Grosfeld, K.; Scholz, P.; Lohmann, G.

2016-12-01

Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad information interest exists on sea ice, its coverage, variability and long term change. Knowledge on sea ice requires high quality data on ice extent, thickness and its dynamics. However, its predictability depends on various climate parameters and conditions. In order to provide insights into the potential development of a monthly/seasonal signal, we developed a robust statistical model based on ocean heat content, sea surface temperature and atmospheric variables to calculate an estimate of the September minimum sea ice extent for every year. Although previous statistical attempts at monthly/seasonal forecasts of September sea ice minimum show a relatively reduced skill, here it is shown that more than 97% (r = 0.98) of the September sea ice extent can predicted three months in advance by using previous months conditions via a multiple linear regression model based on global sea surface temperature (SST), mean sea level pressure (SLP), air temperature at 850hPa (TT850), surface winds and sea ice extent persistence. The statistical model is based on the identification of regions with stable teleconnections between the predictors (climatological parameters) and the predictand (here sea ice extent). The results based on our statistical model contribute to the sea ice prediction network for the sea ice outlook report (https://www.arcus.org/sipn) and could provide a tool for identifying relevant regions and climate parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

Potential change in lodgepole pine site index and distribution under climatic change in Alberta.

Treesearch

Robert A. Monserud; Yuqing Yang; Shongming Huang; Nadja Tchebakova

2008-01-01

We estimated the impact of global climate change on lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm.) site productivity in Alberta based on the Alberta Climate Model and the A2 SRES climate change scenario projections from three global circulation models (CGCM2, HADCM3, and ECHAM4). Considerable warming is...

Evaluation of gravitational gradients generated by Earth's crustal structures

NASA Astrophysics Data System (ADS)

Novák, Pavel; Tenzer, Robert; Eshagh, Mehdi; Bagherbandi, Mohammad

2013-02-01

Spectral formulas for the evaluation of gravitational gradients generated by upper Earth's mass components are presented in the manuscript. The spectral approach allows for numerical evaluation of global gravitational gradient fields that can be used to constrain gravitational gradients either synthesised from global gravitational models or directly measured by the spaceborne gradiometer on board of the GOCE satellite mission. Gravitational gradients generated by static atmospheric, topographic and continental ice masses are evaluated numerically based on available global models of Earth's topography, bathymetry and continental ice sheets. CRUST2.0 data are then applied for the numerical evaluation of gravitational gradients generated by mass density contrasts within soft and hard sediments, upper, middle and lower crust layers. Combined gravitational gradients are compared to disturbing gravitational gradients derived from a global gravitational model and an idealised Earth's model represented by the geocentric homogeneous biaxial ellipsoid GRS80. The methodology could be used for improved modelling of the Earth's inner structure.

A stochastic global identification framework for aerospace structures operating under varying flight states

NASA Astrophysics Data System (ADS)

Kopsaftopoulos, Fotis; Nardari, Raphael; Li, Yu-Hung; Chang, Fu-Kuo

2018-01-01

In this work, a novel data-based stochastic "global" identification framework is introduced for aerospace structures operating under varying flight states and uncertainty. In this context, the term "global" refers to the identification of a model that is capable of representing the structure under any admissible flight state based on data recorded from a sample of these states. The proposed framework is based on stochastic time-series models for representing the structural dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed, angle of attack, altitude and temperature, forming a flight state vector. The method's cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. This is achieved via the use of functional data pooling techniques for optimally treating - as a single entity - the data records corresponding to the various flight states. In this proof-of-concept study the flight state vector is defined by two variables, namely the airspeed and angle of attack of the vehicle. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the identification of a stochastic global VFP model via appropriate parameter estimation and model structure selection methods. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the wing's aeroelastic response under the admissible flight states via a minimum number of estimated parameters compared to standard identification approaches. The obtained results demonstrate the high accuracy and effectiveness of the proposed global identification framework, thus constituting a first step towards the next generation of "fly-by-feel" aerospace vehicles with state awareness capabilities.

ALM-FATES: Using dynamic vegetation and demography to capture changes in forest carbon cycling and competition at the global scale

NASA Astrophysics Data System (ADS)

Holm, J. A.; Knox, R. G.; Koven, C.; Riley, W. J.; Bisht, G.; Fisher, R.; Christoffersen, B. O.; Dietze, M.; Chambers, J. Q.

2017-12-01

The inclusion of dynamic vegetation demography in Earth System Models (ESMs) has been identified as a critical step in moving ESMs towards more realistic representations of plant ecology and the processes that govern climatically important fluxes of carbon, energy, and water. Successful application of dynamic vegetation models, and process-based approaches to simulate plant demography, succession, and response to disturbances without climate envelopes at the global scale is a challenging endeavor. We integrated demographic processes using the Functionally-Assembled Terrestrial Ecosystem Simulator (FATES) in the newly developed ACME Land Model (ALM). We then use an ALM-FATES globally gridded simulation for the first time to investigate plant functional type (PFT) distributions and dynamic turnover rates. Initial global simulations successfully include six interacting and competing PFTs (ranging from tropical to boreal, evergreen, deciduous, needleleaf and broadleaf); including more PFTs is planned. Global maps of net primary productivity, leaf area index, and total vegetation biomass by ALM-FATES matched patterns and values when compared to CLM4.5-BGC and MODIS estimates. We also present techniques for PFT parameterization based on the Predictive Ecosystem Analyzer (PEcAn), field based turnover rates, improved PFT groupings based on trait-tradeoffs, and improved representation of multiple canopy positions. Finally, we applied the improved ALM-FATES model at a central Amazon tropical and western U.S. temperate sites and demonstrate improvements in predicted PFT size- and age-structure and regional distribution. Results from the Amazon tropical site investigate the ability and magnitude of a tropical forest to act as a carbon sink by 2100 with a doubling of CO2, while results from the temperate sites investigate the response of forest mortality with increasing droughts.

Global radiation maps and their modulation by clouds. - An assessment of limitations and deficiencies in global modelling

NASA Astrophysics Data System (ADS)

Kinne, Stefan; Stubenrauch, Claudia; Raschke, Erhard

2010-05-01

Satellite sensed solar and infrared broadband radiation maps at the top of the atmosphere (ToA) usually serve as reference and constrains to global modelling. Complimentary radiation maps at the surface are less certain, as they require accurate knowledge about atmospheric and environmental properties. Despite differences among multi-decadal data-projects of ISCCP, the SRB and the CERES, their diversity is small in comparison to efforts in global modelling. Based on simulations for the IPCC fourth assessment, clear biases on a regional and seasonal basis are identified and illustrate deficiencies in the representation of clouds. These deficiencies are explored in the context of available cloud data from passive and active remote sensing from space.

Global Aerosol Observations

Atmospheric Science Data Center

2013-04-19

... atmosphere, directly influencing global climate and human health. Ground-based networks that accurately measure column aerosol amount and ... being used to improve Air Quality Models and for regional health studies. To assess the human-health impact of chronic aerosol exposure, ...

A satellite and model based flood inundation climatology of Australia

NASA Astrophysics Data System (ADS)

Schumann, G.; Andreadis, K.; Castillo, C. J.

2013-12-01

To date there is no coherent and consistent database on observed or simulated flood event inundation and magnitude at large scales (continental to global). The only compiled data set showing a consistent history of flood inundation area and extent at a near global scale is provided by the MODIS-based Dartmouth Flood Observatory. However, MODIS satellite imagery is only available from 2000 and is hampered by a number of issues associated with flood mapping using optical images (e.g. classification algorithms, cloud cover, vegetation). Here, we present for the first time a proof-of-concept study in which we employ a computationally efficient 2-D hydrodynamic model (LISFLOOD-FP) complemented with a sub-grid channel formulation to generate a complete flood inundation climatology of the past 40 years (1973-2012) for the entire Australian continent. The model was built completely from freely available SRTM-derived data, including channel widths, bank heights and floodplain topography, which was corrected for vegetation canopy height using a global ICESat canopy dataset. Channel hydraulics were resolved using actual channel data and bathymetry was estimated within the model using hydraulic geometry. On the floodplain, the model simulated the flow paths and inundation variables at a 1 km resolution. The developed model was run over a period of 40 years and a floodplain inundation climatology was generated and compared to satellite flood event observations. Our proof-of-concept study demonstrates that this type of model can reliably simulate past flood events with reasonable accuracies both in time and space. The Australian model was forced with both observed flow climatology and VIC-simulated flows in order to assess the feasibility of a model-based flood inundation climatology at the global scale.

One technique for refining the global Earth gravity models

NASA Astrophysics Data System (ADS)

Koneshov, V. N.; Nepoklonov, V. B.; Polovnev, O. V.

2017-01-01

The results of the theoretical and experimental research on the technique for refining the global Earth geopotential models such as EGM2008 in the continental regions are presented. The discussed technique is based on the high-resolution satellite data for the Earth's surface topography which enables the allowance for the fine structure of the Earth's gravitational field without the additional gravimetry data. The experimental studies are conducted by the example of the new GGMplus global gravity model of the Earth with a resolution about 0.5 km, which is obtained by expanding the EGM2008 model to degree 2190 with the corrections for the topograohy calculated from the SRTM data. The GGMplus and EGM2008 models are compared with the regional geoid models in 21 regions of North America, Australia, Africa, and Europe. The obtained estimates largely support the possibility of refining the global geopotential models such as EGM2008 by the procedure implemented in GGMplus, particularly in the regions with relatively high elevation difference.

Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

NASA Astrophysics Data System (ADS)

Ye, Liming; Yang, Guixia; Van Ranst, Eric; Tang, Huajun

2013-03-01

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (˜10-year) environmental planning and decision making.

Global evaluation of biofuel potential from microalgae

PubMed Central

Moody, Jeffrey W.; McGinty, Christopher M.; Quinn, Jason C.

2014-01-01

In the current literature, the life cycle, technoeconomic, and resource assessments of microalgae-based biofuel production systems have relied on growth models extrapolated from laboratory-scale data, leading to a large uncertainty in results. This type of simplistic growth modeling overestimates productivity potential and fails to incorporate biological effects, geographical location, or cultivation architecture. This study uses a large-scale, validated, outdoor photobioreactor microalgae growth model based on 21 reactor- and species-specific inputs to model the growth of Nannochloropsis. This model accurately accounts for biological effects such as nutrient uptake, respiration, and temperature and uses hourly historical meteorological data to determine the current global productivity potential. Global maps of the current near-term microalgae lipid and biomass productivity were generated based on the results of annual simulations at 4,388 global locations. Maximum annual average lipid yields between 24 and 27 m3·ha−1·y−1, corresponding to biomass yields of 13 to 15 g·m−2·d−1, are possible in Australia, Brazil, Colombia, Egypt, Ethiopia, India, Kenya, and Saudi Arabia. The microalgae lipid productivity results of this study were integrated with geography-specific fuel consumption and land availability data to perform a scalability assessment. Results highlight the promising potential of microalgae-based biofuels compared with traditional terrestrial feedstocks. When water, nutrients, and CO2 are not limiting, many regions can potentially meet significant fractions of their transportation fuel requirements through microalgae production, without land resource restriction. Discussion focuses on sensitivity of monthly variability in lipid production compared with annual average yields, effects of temperature on productivity, and a comparison of results with previous published modeling assumptions. PMID:24912176

Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

NASA Astrophysics Data System (ADS)

Zoffoli, M. Laura; Lee, Zhongping; Ondrusek, Michael; Lin, Junfang; Kovach, Charles; Wei, Jianwei; Lewis, Marlon

2017-11-01

The transmittance of solar radiation in the oceanic water column plays an important role in heat transfer and photosynthesis, with implications for the global carbon cycle, global circulation, and climate. Globally, the transmittance of solar radiation in the visible domain (˜400-700 nm) (TRVIS) through the water column, which determines the vertical distribution of visible light, has to be based on remote sensing products. There are models centered on chlorophyll-a (Chl) concentration or Inherent Optical Properties (IOPs) as both can be derived from ocean color measurements. We present evaluations of both schemes with field data from clear oceanic and from coastal waters. Here five models were evaluated: (1) Morel and Antoine (1994) (MA94), (2) Ohlmann and Siegel (2000) (OS00), (3) Murtugudde et al. (2002) (MU02), (4) Manizza et al. (2005) (MA05), and (5) Lee et al. ([Lee, Z., 2005]) (IOPs05), where the first four are Chl-based and the last one is IOPs-based, with all inputs derived from remote sensing reflectance. It is found that the best performing model is the IOPs05, with Unbiased Absolute Percent Difference (UAPD) ˜23%, while Chl-based models show higher uncertainties (UAPD for MA94: ˜54%, OS00: ˜133%, MU02: ˜56%, and MA05: ˜39%). The IOPs-based model was insensitive to the type of water, allowing it to be applied in most marine environments; whereas some of the Chl-based models (MU02 and MA05) show much higher sensitivities in coastal turbid waters (higher Chl waters). These results highlight the applicablity of using IOPs products for such applications.

Comparison of global and regional ionospheric models

NASA Astrophysics Data System (ADS)

Ranner, H.-P.; Krauss, S.; Stangl, G.

2012-04-01

Modelling of the Earth's ionosphere means the description of the variability of the vertical TEC (Total Electron Content) in dependence of geographic latitude and longitude, height, diurnal and seasonal variation as well as solar activity. Within the project GIOMO (next Generation near real-time IOnospheric MOdels) the objectives are the identification and consolidation of improved ionospheric modelling technologies. The global models Klobuchar (GPS) and NeQuick (currently in use by EGNOS, in future used by Galileo) are compared to the IGS (International GNSS Service) Final GIM (Global Ionospheric Map). Additionally a RIM (Regional Ionospheric Map) for Europe provided by CODE (Center for Orbit Determination in Europe) is investigated. Furthermore the OLG (Observatorium Lustbühel Graz) regional models are calculated for two test beds with different latitudes and extensions (Western Austria and the Aegean region). There are three different approaches, two RIMs are based on spherical harmonics calculated either from code or phase measurements and one RIM is based on a Taylor series expansion around a central point estimated from zero-difference observations. The benefits of regional models are the local flexibility using a dense network of GNSS stations. Near real-time parameters are provided within ten minutes after every clock hour. All models have been compared according to their general behavior, the ability to react upon extreme solar events and the robustness of estimation. A ranking of the different models showed a preference for the RIMs while the global models should be used within a fall-back strategy.

Base flow calibration in a global hydrological model

NASA Astrophysics Data System (ADS)

van Beek, L. P.; Bierkens, M. F.

2006-12-01

Base flow constitutes an important water resource in many parts of the world. Its provenance and yield over time are governed by the storage capacity of local aquifers and the internal drainage paths, which are difficult to capture at the global scale. To represent the spatial and temporal variability in base flow adequately in a distributed global model at 0.5 degree resolution, we resorted to the conceptual model of aquifer storage of Kraaijenhoff- van de Leur (1958) that yields the reservoir coefficient for a linear groundwater store. This model was parameterised using global information on drainage density, climatology and lithology. Initial estimates of aquifer thickness, permeability and specific porosity from literature were linked to the latter two categories and calibrated to low flow data by means of simulated annealing so as to conserve the ordinal information contained by them. The observations used stem from the RivDis dataset of monthly discharge. From this dataset 324 stations were selected with at least 10 years of observations in the period 1958-1991 and an areal coverage of at least 10 cells of 0.5 degree. The dataset was split between basins into a calibration and validation set whilst preserving a representative distribution of lithology types and climate zones. Optimisation involved minimising the absolute differences between the simulated base flow and the lowest 10% of the observed monthly discharge. Subsequently, the reliability of the calibrated parameters was tested by reversing the calibration and validation sets.

Sea-Level Allowances along the World Coastlines

NASA Astrophysics Data System (ADS)

Vandewal, R.; Tsitsikas, C.; Reerink, T.; Slangen, A.; de Winter, R.; Muis, S.; Hunter, J. R.

2017-12-01

Sea level changes as a result of climate change. For projections we take ocean mass changes and volume changes into account. Including gravitational and rotational fingerprints this provide regional sea level changes. Hence we can calculate sea-level rise patterns based on CMIP5 projections. In order to take the variability around the mean state, which follows from the climate models, into account we use the concept of allowances. The allowance indicates the height a coastal structure needs to be increased to maintain the likelihood of sea-level extremes. Here we use a global reanalysis of storm surges and extreme sea levels based on a global hydrodynamic model in order to calculate allowances. It is shown that the model compares in most regions favourably with tide gauge records from the GESLA data set. Combining the CMIP5 projections and the global hydrodynamical model we calculate sea-level allowances along the global coastlines and expand the number of points with a factor 50 relative to tide gauge based results. Results show that allowances increase gradually along continental margins with largest values near the equator. In general values are lower at midlatitudes both in Northern and Southern Hemisphere. Increased risk for extremes are typically 103-104 for the majority of the coastline under the RCP8.5 scenario at the end of the century. Finally we will show preliminary results of the effect of changing wave heights based on the coordinated ocean wave project.

Providing Context for Complexity: Using Infographics and Conceptual Models to Teach Global Change Processes

NASA Astrophysics Data System (ADS)

Bean, J. R.; White, L. D.

2015-12-01

Understanding modern and historical global changes requires interdisciplinary knowledge of the physical and life sciences. The Understanding Global Change website from the UC Museum of Paleontology will use a focal infographic that unifies diverse content often taught in separate K-12 science units. This visualization tool provides scientists with a structure for presenting research within the broad context of global change, and supports educators with a framework for teaching and assessing student understanding of complex global change processes. This new approach to teaching the science of global change is currently being piloted and refined based on feedback from educators and scientists in anticipation of a 2016 website launch. Global change concepts are categorized within the infographic as causes of global change (e.g., burning of fossil fuels, volcanism), ongoing Earth system processes (e.g., ocean circulation, the greenhouse effect), and the changes scientists measure in Earth's physical and biological systems (e.g., temperature, extinctions/radiations). The infographic will appear on all website content pages and provides a template for the creation of flowcharts, which are conceptual models that allow teachers and students to visualize the interdependencies and feedbacks among processes in the atmosphere, hydrosphere, biosphere, and geosphere. The development of this resource is timely given that the newly adopted Next Generation Science Standards emphasize cross-cutting concepts, including model building, and Earth system science. Flowchart activities will be available on the website to scaffold inquiry-based lessons, determine student preconceptions, and assess student content knowledge. The infographic has already served as a learning and evaluation tool during professional development workshops at UC Berkeley, Stanford University, and the Smithsonian National Museum of Natural History. At these workshops, scientists and educators used the infographic to highlight how their research and activities reinforce conceptual links among global change topics. Pre- and post-workshop assessment results and responses to questionnaires have guided the refinement of classroom activities and assessment tools utilizing flowcharts as models for global change processes.

Hybrid-view programming of nuclear fusion simulation code in the PGAS parallel programming language XcalableMP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsugane, Keisuke; Boku, Taisuke; Murai, Hitoshi

Recently, the Partitioned Global Address Space (PGAS) parallel programming model has emerged as a usable distributed memory programming model. XcalableMP (XMP) is a PGAS parallel programming language that extends base languages such as C and Fortran with directives in OpenMP-like style. XMP supports a global-view model that allows programmers to define global data and to map them to a set of processors, which execute the distributed global data as a single thread. In XMP, the concept of a coarray is also employed for local-view programming. In this study, we port Gyrokinetic Toroidal Code - Princeton (GTC-P), which is a three-dimensionalmore » gyrokinetic PIC code developed at Princeton University to study the microturbulence phenomenon in magnetically confined fusion plasmas, to XMP as an example of hybrid memory model coding with the global-view and local-view programming models. In local-view programming, the coarray notation is simple and intuitive compared with Message Passing Interface (MPI) programming while the performance is comparable to that of the MPI version. Thus, because the global-view programming model is suitable for expressing the data parallelism for a field of grid space data, we implement a hybrid-view version using a global-view programming model to compute the field and a local-view programming model to compute the movement of particles. Finally, the performance is degraded by 20% compared with the original MPI version, but the hybrid-view version facilitates more natural data expression for static grid space data (in the global-view model) and dynamic particle data (in the local-view model), and it also increases the readability of the code for higher productivity.« less

Hybrid-view programming of nuclear fusion simulation code in the PGAS parallel programming language XcalableMP

DOE PAGES

Tsugane, Keisuke; Boku, Taisuke; Murai, Hitoshi; ...

2016-06-01

Recently, the Partitioned Global Address Space (PGAS) parallel programming model has emerged as a usable distributed memory programming model. XcalableMP (XMP) is a PGAS parallel programming language that extends base languages such as C and Fortran with directives in OpenMP-like style. XMP supports a global-view model that allows programmers to define global data and to map them to a set of processors, which execute the distributed global data as a single thread. In XMP, the concept of a coarray is also employed for local-view programming. In this study, we port Gyrokinetic Toroidal Code - Princeton (GTC-P), which is a three-dimensionalmore » gyrokinetic PIC code developed at Princeton University to study the microturbulence phenomenon in magnetically confined fusion plasmas, to XMP as an example of hybrid memory model coding with the global-view and local-view programming models. In local-view programming, the coarray notation is simple and intuitive compared with Message Passing Interface (MPI) programming while the performance is comparable to that of the MPI version. Thus, because the global-view programming model is suitable for expressing the data parallelism for a field of grid space data, we implement a hybrid-view version using a global-view programming model to compute the field and a local-view programming model to compute the movement of particles. Finally, the performance is degraded by 20% compared with the original MPI version, but the hybrid-view version facilitates more natural data expression for static grid space data (in the global-view model) and dynamic particle data (in the local-view model), and it also increases the readability of the code for higher productivity.« less

An energy balance climate model with cloud feedbacks

NASA Technical Reports Server (NTRS)

Roads, J. O.; Vallis, G. K.

1984-01-01

The present two-level global climate model, which is based on the atmosphere-surface energy balance, includes physically based parameterizations for the exchange of heat and moisture across latitude belts and between the surface and the atmosphere, precipitation and cloud formation, and solar and IR radiation. The model field predictions obtained encompass surface and atmospheric temperature, precipitation, relative humidity, and cloudiness. In the model integrations presented, it is noted that cloudiness is generally constant with changing temperature at low latitudes. High altitude cloudiness increases with temperature, although the cloud feedback effect on the radiation field remains small because of compensating effects on thermal and solar radiation. The net global feedback by the cloud field is negative, but small.

Tropospheric ozone simulated by a global-multi-regional two-way coupling model system

NASA Astrophysics Data System (ADS)

Yan, Y.; Lin, J.; Chen, J.; Hu, L.

2015-12-01

Current global chemical transport models are limited by horizontal resolutions (100-500 km), and they cannot capture small-scale processes affecting tropospheric ozone (O3). Here we use a recently built two-way coupling system of GEOS-Chem to simulate the global tropospheric O3 in 2009. The system couples the global model (~ 200 km) and its three nested models (~ 50 km) covering Asia, North America and Europe, respectively. Benefiting from the high resolution, the nested models better capture small-scale processes than the global model alone. In the coupling system, the nested models provide results to modify the global model simulation within respective nested domains while taking the lateral boundary conditions from the global model. Due to the "coupling" effects, the two-way system significantly improves the tropospheric O3 simulation upon the global model alone, as found by comparisons with a suite of ground (1420 sites from WDCGG, GMD, EMEP, and AQS), aircraft (HIPPO and MOZAIC), and satellite measurements (two OMI products). Compared to the global model alone, the two-way coupled simulation enhances the correlation in day-to-day variation of afternoon mean O3 with the ground measurements from 0.53 to 0.68 and reduces the mean model bias from 10.8 to 6.7 ppb. Regionally, the coupled model reduces the bias by 4.6 ppb over Europe, 3.9 ppb over North America, and 3.1 ppb over other regions. The two-way coupling brings O3 vertical profiles much closer to the HIPPO and MOZAIC data, reducing the tropospheric (0-9 km) mean bias by 3-10 ppb at most MOZAIC sites and by 5.3 ppb for HIPPO profiles. The two-way coupled simulation also reduces the global tropospheric column ozone by 3.0 DU (9.5%), bringing them closer to the OMI data in all seasons. Simulation improvements are more significant in the northern hemisphere, and are primarily a result of improved representation of the nonlinear ozone chemistry, including but not limited to urban-rural contrast. The two-way coupled simulation also reduces the global tropospheric mean hydroxyl radical by 5% with enhancements by 5% in lifetimes of methyl chloroform and methane, bringing them closer to observation-based estimates. Therefore improving model representations of small-scale processes are a critical step forward to understanding the global tropospheric chemistry.

Advection modes by optimal mass transfer

NASA Astrophysics Data System (ADS)

Iollo, Angelo; Lombardi, Damiano

2014-02-01

Classical model reduction techniques approximate the solution of a physical model by a limited number of global modes. These modes are usually determined by variants of principal component analysis. Global modes can lead to reduced models that perform well in terms of stability and accuracy. However, when the physics of the model is mainly characterized by advection, the nonlocal representation of the solution by global modes essentially reduces to a Fourier expansion. In this paper we describe a method to determine a low-order representation of advection. This method is based on the solution of Monge-Kantorovich mass transfer problems. Examples of application to point vortex scattering, Korteweg-de Vries equation, and hurricane Dean advection are discussed.

An ensemble-based dynamic Bayesian averaging approach for discharge simulations using multiple global precipitation products and hydrological models

NASA Astrophysics Data System (ADS)

Qi, Wei; Liu, Junguo; Yang, Hong; Sweetapple, Chris

2018-03-01

Global precipitation products are very important datasets in flow simulations, especially in poorly gauged regions. Uncertainties resulting from precipitation products, hydrological models and their combinations vary with time and data magnitude, and undermine their application to flow simulations. However, previous studies have not quantified these uncertainties individually and explicitly. This study developed an ensemble-based dynamic Bayesian averaging approach (e-Bay) for deterministic discharge simulations using multiple global precipitation products and hydrological models. In this approach, the joint probability of precipitation products and hydrological models being correct is quantified based on uncertainties in maximum and mean estimation, posterior probability is quantified as functions of the magnitude and timing of discharges, and the law of total probability is implemented to calculate expected discharges. Six global fine-resolution precipitation products and two hydrological models of different complexities are included in an illustrative application. e-Bay can effectively quantify uncertainties and therefore generate better deterministic discharges than traditional approaches (weighted average methods with equal and varying weights and maximum likelihood approach). The mean Nash-Sutcliffe Efficiency values of e-Bay are up to 0.97 and 0.85 in training and validation periods respectively, which are at least 0.06 and 0.13 higher than traditional approaches. In addition, with increased training data, assessment criteria values of e-Bay show smaller fluctuations than traditional approaches and its performance becomes outstanding. The proposed e-Bay approach bridges the gap between global precipitation products and their pragmatic applications to discharge simulations, and is beneficial to water resources management in ungauged or poorly gauged regions across the world.

Baldrige Theory into Practice: A Generic Model

ERIC Educational Resources Information Center

Arif, Mohammed

2007-01-01

Purpose: The education system globally has moved from a push-based or producer-centric system to a pull-based or customer centric system. Malcolm Baldrige Quality Award (MBQA) model happens to be one of the latest additions to the pull based models. The purpose of this paper is to develop a generic framework for MBQA that can be used by…

Process-based models are required to manage ecological systems in a changing world

Treesearch

K. Cuddington; M.-J. Fortin; L.R. Gerber; A. Hastings; A. Liebhold; M. OConnor; C. Ray

2013-01-01

Several modeling approaches can be used to guide management decisions. However, some approaches are better fitted than others to address the problem of prediction under global change. Process-based models, which are based on a theoretical understanding of relevant ecological processes, provide a useful framework to incorporate specific responses to altered...

Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate

PubMed Central

Su, Hui; Jiang, Jonathan H.; Neelin, J. David; Shen, T. Janice; Zhai, Chengxing; Yue, Qing; Wang, Zhien; Huang, Lei; Choi, Yong-Sang; Stephens, Graeme L.; Yung, Yuk L.

2017-01-01

The change of global-mean precipitation under global warming and interannual variability is predominantly controlled by the change of atmospheric longwave radiative cooling. Here we show that tightening of the ascending branch of the Hadley Circulation coupled with a decrease in tropical high cloud fraction is key in modulating precipitation response to surface warming. The magnitude of high cloud shrinkage is a primary contributor to the intermodel spread in the changes of tropical-mean outgoing longwave radiation (OLR) and global-mean precipitation per unit surface warming (dP/dTs) for both interannual variability and global warming. Compared to observations, most Coupled Model Inter-comparison Project Phase 5 models underestimate the rates of interannual tropical-mean dOLR/dTs and global-mean dP/dTs, consistent with the muted tropical high cloud shrinkage. We find that the five models that agree with the observation-based interannual dP/dTs all predict dP/dTs under global warming higher than the ensemble mean dP/dTs from the ∼20 models analysed in this study. PMID:28589940

Short-Range Prediction of Monsoon Precipitation by NCMRWF Regional Unified Model with Explicit Convection

NASA Astrophysics Data System (ADS)

Mamgain, Ashu; Rajagopal, E. N.; Mitra, A. K.; Webster, S.

2018-03-01

There are increasing efforts towards the prediction of high-impact weather systems and understanding of related dynamical and physical processes. High-resolution numerical model simulations can be used directly to model the impact at fine-scale details. Improvement in forecast accuracy can help in disaster management planning and execution. National Centre for Medium Range Weather Forecasting (NCMRWF) has implemented high-resolution regional unified modeling system with explicit convection embedded within coarser resolution global model with parameterized convection. The models configurations are based on UK Met Office unified seamless modeling system. Recent land use/land cover data (2012-2013) obtained from Indian Space Research Organisation (ISRO) are also used in model simulations. Results based on short-range forecast of both the global and regional models over India for a month indicate that convection-permitting simulations by the high-resolution regional model is able to reduce the dry bias over southern parts of West Coast and monsoon trough zone with more intense rainfall mainly towards northern parts of monsoon trough zone. Regional model with explicit convection has significantly improved the phase of the diurnal cycle of rainfall as compared to the global model. Results from two monsoon depression cases during study period show substantial improvement in details of rainfall pattern. Many categories in rainfall defined for operational forecast purposes by Indian forecasters are also well represented in case of convection-permitting high-resolution simulations. For the statistics of number of days within a range of rain categories between `No-Rain' and `Heavy Rain', the regional model is outperforming the global model in all the ranges. In the very heavy and extremely heavy categories, the regional simulations show overestimation of rainfall days. Global model with parameterized convection have tendency to overestimate the light rainfall days and underestimate the heavy rain days compared to the observation data.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

PubMed Central

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

Global Distribution of Outbreaks of Water-Associated Infectious Diseases

PubMed Central

Yang, Kun; LeJeune, Jeffrey; Alsdorf, Doug; Lu, Bo; Shum, C. K.; Liang, Song

2012-01-01

Background Water plays an important role in the transmission of many infectious diseases, which pose a great burden on global public health. However, the global distribution of these water-associated infectious diseases and underlying factors remain largely unexplored. Methods and Findings Based on the Global Infectious Disease and Epidemiology Network (GIDEON), a global database including water-associated pathogens and diseases was developed. In this study, reported outbreak events associated with corresponding water-associated infectious diseases from 1991 to 2008 were extracted from the database. The location of each reported outbreak event was identified and geocoded into a GIS database. Also collected in the GIS database included geo-referenced socio-environmental information including population density (2000), annual accumulated temperature, surface water area, and average annual precipitation. Poisson models with Bayesian inference were developed to explore the association between these socio-environmental factors and distribution of the reported outbreak events. Based on model predictions a global relative risk map was generated. A total of 1,428 reported outbreak events were retrieved from the database. The analysis suggested that outbreaks of water-associated diseases are significantly correlated with socio-environmental factors. Population density is a significant risk factor for all categories of reported outbreaks of water-associated diseases; water-related diseases (e.g., vector-borne diseases) are associated with accumulated temperature; water-washed diseases (e.g., conjunctivitis) are inversely related to surface water area; both water-borne and water-related diseases are inversely related to average annual rainfall. Based on the model predictions, “hotspots” of risks for all categories of water-associated diseases were explored. Conclusions At the global scale, water-associated infectious diseases are significantly correlated with socio-environmental factors, impacting all regions which are affected disproportionately by different categories of water-associated infectious diseases. PMID:22348158

Regional contribution to variability and trends of global gross primary productivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Min; Rafique, Rashid; Asrar, Ghassem R.

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000-2010 total global GPP estimated from the model ensemble to be 117±13 Pg C yr-1 (mean ± 1 standard deviation), whichmore » was higher than MODIS (112 Pg C yr-1), and close to the MTE (120 Pg C yr-1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.« less

Changes in yields and their variability at different levels of global warming

NASA Astrophysics Data System (ADS)

Childers, Katelin

2015-04-01

An assessment of climate change impacts at different levels of global warming is crucial to inform the political discussion about mitigation targets as well as for the inclusion of climate change impacts in Integrated Assessment Models (IAMs) that generally only provide global mean temperature change as an indicator of climate change. While there is a well-established framework for the scalability of regional temperature and precipitation changes with global mean temperature change we provide an assessment of the extent to which impacts such as crop yield changes can also be described in terms of global mean temperature changes without accounting for the specific underlying emissions scenario. Based on multi-crop-model simulations of the four major cereal crops (maize, rice, soy, and wheat) on a 0.5 x 0.5 degree global grid generated within ISI-MIP, we show the average spatial patterns of projected crop yield changes at one half degree warming steps. We find that emissions scenario dependence is a minor component of the overall variance of projected yield changes at different levels of global warming. Furthermore, scenario dependence can be reduced by accounting for the direct effects of CO2 fertilization in each global climate model (GCM)/impact model combination through an inclusion of the global atmospheric CO2 concentration as a second predictor. The choice of GCM output used to force the crop model simulations accounts for a slightly larger portion of the total yield variance, but the greatest contributor to variance in both global and regional crop yields and at all levels of warming, is the inter-crop-model spread. The unique multi impact model ensemble available with ISI-MIP data also indicates that the overall variability of crop yields is projected to increase in conjunction with increasing global mean temperature. This result is consistent throughout the ensemble of impact models and across many world regions. Such a hike in yield volatility could have significant policy implications by affecting food prices and supplies.

Regional contribution to variability and trends of global gross primary productivity

NASA Astrophysics Data System (ADS)

Chen, Min; Rafique, Rashid; Asrar, Ghassem R.; Bond-Lamberty, Ben; Ciais, Philippe; Zhao, Fang; Reyer, Christopher P. O.; Ostberg, Sebastian; Chang, Jinfeng; Ito, Akihiko; Yang, Jia; Zeng, Ning; Kalnay, Eugenia; West, Tristram; Leng, Guoyong; Francois, Louis; Munhoven, Guy; Henrot, Alexandra; Tian, Hanqin; Pan, Shufen; Nishina, Kazuya; Viovy, Nicolas; Morfopoulos, Catherine; Betts, Richard; Schaphoff, Sibyll; Steinkamp, Jörg; Hickler, Thomas

2017-10-01

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000-2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr-1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr-1), and close to the MTE (120 Pg C yr-1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.

Uncertainty associated with convective wet removal of entrained aerosols in a global climate model

NASA Astrophysics Data System (ADS)

Croft, B.; Pierce, J. R.; Martin, R. V.; Hoose, C.; Lohmann, U.

2012-11-01

The uncertainties associated with the wet removal of aerosols entrained above convective cloud bases are investigated in a global aerosol-climate model (ECHAM5-HAM) under a set of limiting assumptions for the wet removal of the entrained aerosols. The limiting assumptions for the wet removal of entrained aerosols are negligible scavenging and vigorous scavenging (either through activation, with size-dependent impaction scavenging, or with the prescribed fractions of the standard model). To facilitate this process-based study, an explicit representation of cloud-droplet-borne and ice-crystal-borne aerosol mass and number, for the purpose of wet removal, is introduced into the ECHAM5-HAM model. This replaces and is compared with the prescribed cloud-droplet-borne and ice-crystal-borne aerosol fraction scavenging scheme of the standard model. A 20% to 35% uncertainty in simulated global, annual mean aerosol mass burdens and optical depth (AOD) is attributed to different assumptions for the wet removal of aerosols entrained above convective cloud bases. Assumptions about the removal of aerosols entrained above convective cloud bases control modeled upper tropospheric aerosol concentrations by as much as one order of magnitude. Simulated aerosols entrained above convective cloud bases contribute 20% to 50% of modeled global, annual mean aerosol mass convective wet deposition (about 5% to 10% of the total dry and wet deposition), depending on the aerosol species, when including wet scavenging of those entrained aerosols (either by activation, size-dependent impaction, or with the prescribed fraction scheme). Among the simulations, the prescribed fraction and size-dependent impaction schemes yield the largest global, annual mean aerosol mass convective wet deposition (by about two-fold). However, the prescribed fraction scheme has more vigorous convective mixed-phase wet removal (by two to five-fold relative to the size-dependent impaction scheme) since nearly all entrained accumulation and coarse mode aerosols are assumed to be cloud-droplet borne or ice-crystal borne, and evaporation due to the Bergeron-Findeisen process is neglected. The simulated convective wet scavenging of entrained accumulation and coarse mode aerosols has feedbacks on new particle formation and the number of Aitken mode aerosols, which control stratiform and convective cloud droplet number concentrations and yield precipitation changes in the ECHAM5-HAM model. However, the geographic distribution of aerosol annual mean convective wet deposition change in the model is driven by changes to the assumptions regarding the scavenging of aerosols entrained above cloud bases rather than by precipitation changes, except for sea salt deposition in the tropics. Uncertainty in the seasonal, regional cycles of AOD due to assumptions about entrained aerosol wet scavenging is similar in magnitude to the estimated error in the AOD retrievals. The uncertainty in aerosol concentrations, burdens, and AOD attributed to different assumptions for the wet scavenging of aerosols entrained above convective cloud bases in a global model motivates the ongoing need to better understand and model the activation and impaction processes that aerosols undergo after entrainment into convective updrafts.

A Fuzzy mathematical model to estimate the effects of global warming on the vitality of Laelia purpurata orchids.

PubMed

Putti, Fernando Ferrari; Filho, Luis Roberto Almeida Gabriel; Gabriel, Camila Pires Cremasco; Neto, Alfredo Bonini; Bonini, Carolina Dos Santos Batista; Rodrigues Dos Reis, André

2017-06-01

This study aimed to develop a fuzzy mathematical model to estimate the impacts of global warming on the vitality of Laelia purpurata growing in different Brazilian environmental conditions. In order to develop the mathematical model was considered as intrinsic factors the parameters: temperature, humidity and shade conditions to determine the vitality of plants. Fuzzy model results could accurately predict the optimal conditions for cultivation of Laelia purpurata in several sites of Brazil. Based on fuzzy model results, we found that higher temperatures and lacking of properly shading can reduce the vitality of orchids. Fuzzy mathematical model could precisely detect the effect of higher temperatures causing damages on vitality of plants as a consequence of global warming. Copyright © 2017 Elsevier Inc. All rights reserved.

The Mpi-M Aerosol Climatology (MAC)

NASA Astrophysics Data System (ADS)

Kinne, S.

2014-12-01

Monthly gridded global data-sets for aerosol optical properties (AOD, SSA and g) and for aerosol microphysical properties (CCN and IN) offer a (less complex) alternate path to include aerosol radiative effects and aerosol impacts on cloud-microphysics in global simulations. Based on merging AERONET sun-/sky-photometer data onto background maps provided by AeroCom phase 1 modeling output and AERONET sun-/the MPI-M Aerosol Climatology (MAC) version 1 was developed and applied in IPCC simulations with ECHAM and as ancillary data-set in satellite-based global data-sets. An updated version 2 of this climatology will be presented now applying central values from the more recent AeroCom phase 2 modeling and utilizing the better global coverage of trusted sun-photometer data - including statistics from the Marine Aerosol network (MAN). Applications include spatial distributions of estimates for aerosol direct and aerosol indirect radiative effects.

How Stationary Are the Internal Tides in a High-Resolution Global Ocean Circulation Model?

DTIC Science & Technology

2014-05-12

Egbert et al., 1994] and that the model global internal tide amplitudes compare well with an altimetric-based tidal analysis [Ray and Byrne, 2010]. The... analysis [Foreman, 1977] applied to the HYCOM total SSH. We will follow Shriver et al. [2012], analyzing the tides along satellite altimeter tracks...spots,’’ the comparison between the model and altimetric analysis is not as good due, in part, to two prob- lems, errors in the model barotropic tides and

The Nature of Global Large-scale Sea Level Variability in Relation to Atmospheric Forcing: A Modeling Study

NASA Technical Reports Server (NTRS)

Fukumori, I.; Raghunath, R.; Fu, L. L.

1996-01-01

The relation between large-scale sea level variability and ocean circulation is studied using a numerical model. A global primitive equaiton model of the ocean is forced by daily winds and climatological heat fluxes corresponding to the period from January 1992 to February 1996. The physical nature of the temporal variability from periods of days to a year, are examined based on spectral analyses of model results and comparisons with satellite altimetry and tide gauge measurements.

Test of High-resolution Global and Regional Climate Model Projections

NASA Astrophysics Data System (ADS)

Stenchikov, Georgiy; Nikulin, Grigory; Hansson, Ulf; Kjellström, Erik; Raj, Jerry; Bangalath, Hamza; Osipov, Sergey

2014-05-01

In scope of CORDEX project we have simulated the past (1975-2005) and future (2006-2050) climates using the GFDL global high-resolution atmospheric model (HIRAM) and the Rossby Center nested regional model RCA4 for the Middle East and North Africa (MENA) region. Both global and nested runs were performed with roughly the same spatial resolution of 25 km in latitude and longitude, and were driven by the 2°x2.5°-resolution fields from GFDL ESM2M IPCC AR5 runs. The global HIRAM simulations could naturally account for interaction of regional processes with the larger-scale circulation features like Indian Summer Monsoon, which is lacking from regional model setup. Therefore in this study we specifically address the consistency of "global" and "regional" downscalings. The performance of RCA4, HIRAM, and ESM2M is tested based on mean, extreme, trends, seasonal and inter-annual variability of surface temperature, precipitation, and winds. The impact of climate change on dust storm activity, extreme precipitation and water resources is specifically addressed. We found that the global and regional climate projections appear to be quite consistent for the modeled period and differ more significantly from ESM2M than between each other.

[Global Atmospheric Chemistry/Transport Modeling and Data-Analysis

NASA Technical Reports Server (NTRS)

Prinn, Ronald G.

1999-01-01

This grant supported a global atmospheric chemistry/transport modeling and data- analysis project devoted to: (a) development, testing, and refining of inverse methods for determining regional and global transient source and sink strengths for trace gases; (b) utilization of these inverse methods which use either the Model for Atmospheric Chemistry and Transport (MATCH) which is based on analyzed observed winds or back- trajectories calculated from these same winds for determining regional and global source and sink strengths for long-lived trace gases important in ozone depletion and the greenhouse effect; (c) determination of global (and perhaps regional) average hydroxyl radical concentrations using inverse methods with multiple "titrating" gases; and (d) computation of the lifetimes and spatially resolved destruction rates of trace gases using 3D models. Important ultimate goals included determination of regional source strengths of important biogenic/anthropogenic trace gases and also of halocarbons restricted by the Montreal Protocol and its follow-on agreements, and hydrohalocarbons now used as alternatives to the above restricted halocarbons.

Global Urban Mapping and Modeling for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Zhou, Y.; Li, X.; Asrar, G.; Yu, S.; Smith, S.; Eom, J.; Imhoff, M. L.

2016-12-01

In the past several decades, the world has experienced fast urbanization, and this trend is expected to continue for decades to come. Urbanization, one of the major land cover and land use changes (LCLUC), is becoming increasingly important in global environmental changes, such as urban heat island (UHI) growth and vegetation phenology change. Better scientific insights and effective decision-making unarguably require reliable science-based information on spatiotemporal changes in urban extent and their environmental impacts. In this study, we developed a globally consistent 20-year urban map series to evaluate the time-reactive nature of global urbanization from the nighttime lights remote sensing data, and projected future urban expansion in the 21st century by employing an integrated modeling framework (Zhou et al. 2014, Zhou et al. 2015). We then evaluated the impacts of urbanization on building energy use and vegetation phenology that affect both ecosystem services and human health. We extended the modeling capability of building energy use in the Global Change Assessment Model (GCAM) with consideration of UHI effects by coupling the remote sensing based urbanization modeling and explored the impact of UHI on building energy use. We also investigated the impact of urbanization on vegetation phenology by using an improved phenology detection algorithm. The derived spatiotemporal information on historical and potential future urbanization and its implications in building energy use and vegetation phenology will be of great value in sustainable urban design and development for building energy use and human health (e.g., pollen allergy), especially when considered together with other factors such as climate variability and change. Zhou, Y., S. J. Smith, C. D. Elvidge, K. Zhao, A. Thomson & M. Imhoff (2014) A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147, 173-185. Zhou, Y., S. J. Smith, K. Zhao, M. Imhoff, A. Thomson, B. Bond-Lamberty, G. R. Asrar, X. Zhang, C. He & C. D. Elvidge (2015) A global map of urban extent from nightlights. Environmental Research Letters, 10, 054011.

Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations

NASA Astrophysics Data System (ADS)

Bhuiyan, M. A. E.; Nikolopoulos, E. I.; Anagnostou, E. N.

2017-12-01

Quantifying the uncertainty of global precipitation datasets is beneficial when using these precipitation products in hydrological applications, because precipitation uncertainty propagation through hydrologic modeling can significantly affect the accuracy of the simulated hydrologic variables. In this research the Iberian Peninsula has been used as the study area with a study period spanning eleven years (2000-2010). This study evaluates the performance of multiple hydrologic models forced with combined global rainfall estimates derived based on a Quantile Regression Forests (QRF) technique. In QRF technique three satellite precipitation products (CMORPH, PERSIANN, and 3B42 (V7)); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset are being utilized in this study. A high-resolution, ground-based observations driven precipitation dataset (named SAFRAN) available at 5 km/1 h resolution is used as reference. Through the QRF blending framework the stochastic error model produces error-adjusted ensemble precipitation realizations, which are used to force four global hydrological models (JULES (Joint UK Land Environment Simulator), WaterGAP3 (Water-Global Assessment and Prognosis), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) and SURFEX (Stands for Surface Externalisée) ) to simulate three hydrologic variables (surface runoff, subsurface runoff and evapotranspiration). The models are forced with the reference precipitation to generate reference-based hydrologic simulations. This study presents a comparative analysis of multiple hydrologic model simulations for different hydrologic variables and the impact of the blending algorithm on the simulated hydrologic variables. Results show how precipitation uncertainty propagates through the different hydrologic model structures to manifest in reduction of error in hydrologic variables.

The challenges associated with applying global models in heterogeneous landscapes: A case study using MOD17 GPP estimates in Hawaii

NASA Astrophysics Data System (ADS)

Kimball, H.; Selmants, P. C.; Running, S. W.; Moreno, A.; Giardina, C. P.

2016-12-01

In this study we evaluate the influence of spatial data product accuracy and resolution on the application of global models for smaller scale heterogeneous landscapes. In particular, we assess the influence of locally specific land cover and high-resolution climate data products on estimates of Gross Primary Production (GPP) for the Hawaiian Islands using the MOD17 model. The MOD17 GPP algorithm uses a measure of the fraction of absorbed photosynthetically active radiation from the National Aeronautics and Space Administration's Earth Observation System. This direct measurement is combined with global land cover (500-m resolution) and climate models ( 1/2-degree resolution) to estimate GPP. We first compared the alignment between the global land cover model used in MOD17 with a Hawaii specific land cover data product. We found that there was a 51.6% overall agreement between the two land cover products. We then compared four MOD17 GPP models: A global model that used the global land cover and low-resolution global climate data products, a model produced using the Hawaii specific land cover and low-resolution global climate data products, a model with global land cover and high-resolution climate data products, and finally, a model using both Hawaii specific land cover and high-resolution climate data products. We found that including either the Hawaii specific land cover or the high-resolution Hawaii climate data products with MOD17 reduced overall estimates of GPP by 8%. When both were used, GPP estimates were reduced by 16%. The reduction associated with land cover is explained by a reduction of the total area designated as evergreen broad leaf forest and an increase in the area designated as barren or sparsely vegetated in the Hawaii land cover product as compared to the global product. The climate based reduction is explained primarily by the spatial resolution and distribution of solar radiation in the Hawaiian Islands. This study highlights the importance of accuracy and resolution when applying global models to highly variable landscapes and provides an estimate of the influence of land cover and climate data products on estimates of GPP using MOD17.

Downscaling global precipitation for local applications - a case for the Rhine basin

NASA Astrophysics Data System (ADS)

Sperna Weiland, Frederiek; van Verseveld, Willem; Schellekens, Jaap

2017-04-01

Within the EU FP7 project eartH2Observe a global Water Resources Re-analysis (WRR) is being developed. This re-analysis consists of meteorological and hydrological water balance variables with global coverage, spanning the period 1979-2014 at 0.25 degrees resolution (Schellekens et al., 2016). The dataset can be of special interest in regions with limited in-situ data availability, yet for local scale analysis particularly in mountainous regions, a resolution of 0.25 degrees may be too coarse and downscaling the data to a higher resolution may be required. A downscaling toolbox has been made that includes spatial downscaling of precipitation based on the global WorldClim dataset that is available at 1 km resolution as a monthly climatology (Hijmans et al., 2005). The input of the down-scaling tool are either the global eartH2Observe WRR1 and WRR2 datasets based on the WFDEI correction methodology (Weedon et al., 2014) or the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2016). Here we present a validation of the datasets over the Rhine catchment by means of a distributed hydrological model (wflow, Schellekens et al., 2014) using a number of precipitation scenarios. (1) We start by running the model using the local reference dataset derived by spatial interpolation of gauge observations. Furthermore we use (2) the MSWEP dataset at the native 0.25-degree resolution followed by (3) MSWEP downscaled with the WorldClim dataset and final (4) MSWEP downscaled with the local reference dataset. The validation will be based on comparison of the modeled river discharges as well as rainfall statistics. We expect that down-scaling the MSWEP dataset with the WorldClim data to higher resolution will increase its performance. To test the performance of the down-scaling routine we have added a run with MSWEP data down-scaled with the local dataset and compare this with the run based on the local dataset itself. - Beck, H. E. et al., 2016. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-236, accepted for final publication. - Hijmans, R.J. et al., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. - Schellekens, J. et al., 2016. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2016-55, under review. - Schellekens, J. et al., 2014. Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model. Environmental Modelling&Software - Weedon, G.P. et al., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resources Research, 50, doi:10.1002/2014WR015638.

Assessing the Influence of Human Activities on Global Water Resources Using an Advanced Land Surface Model

NASA Astrophysics Data System (ADS)

Pokhrel, Y.; Hanasaki, N.; Koirala, S.; Kanae, S.; Oki, T.

2010-12-01

In order to examine the impact of human intervention on the global hydrological cycle, a Land Surface Model was enhanced with schemes to assess the anthropogenic disturbance on the natural water flow at the global scale. Four different schemes namely; reservoir operation, crop growth, environmental flow, and anthropogenic water withdrawal modules from a state-of-the-art global water resources assessment model called H08 were integrated into an offline version of LSM, Minimal Advance Treatment of Surface Interaction and Runoff (MATSIRO). MATSIRO represents majority of the hydrological processes of water and energy exchange between the land surface and the atmosphere on a physical basis and is designed to be coupled with GCM. The integrated model presented here thus has the capability to simulate both natural and anthropogenic flows of water globally at a spatial resolution of 1°x1°, considering dam operation, domestic, industrial and agricultural water withdrawals and environmental flow requirements. The model can also be coupled with climate models to assess the impact of human activities on the climate system. A simple groundwater scheme was also incorporated and the model can be used to assess the change in water table due to groundwater pumping for irrigation. The model was validated by comparing simulated soil moisture, river discharge and Terrestrial Water Storage Anomaly (TWSA) with observations. The model performs well in simulating TWSA as compared to GRACE observation in different river basins ranging from very wet to very dry. Soil moisture cannot be validated globally because of the lack of validation datasets. For Illinois region, where long term soil moisture observations are available, the model captures the seasonal variation quite well. The simulated global potential irrigation demand is about 1100km3/year, which is within the range of previously published estimates based on various water balance models and LSMs. The model has an advanced option to limit water withdrawal from river channels based on water availability and environmental flow requirements. Results showed that about three-fourth of the irrigation demand can be met from surface-water (rivers, small and medium-sized reservoirs). Therefore, one-fourth of the demand must have been supplied by groundwater. Further analysis of modeled groundwater pumping for irrigation is needed to examine the extent of groundwater withdrawal and its impact on water table fluctuations.

Evaluation of integrated assessment model hindcast experiments: a case study of the GCAM 3.0 land use module

DOE Office of Scientific and Technical Information (OSTI.GOV)

Snyder, Abigail C.; Link, Robert P.; Calvin, Katherine V.

Hindcasting experiments (conducting a model forecast for a time period in which observational data are available) are being undertaken increasingly often by the integrated assessment model (IAM) community, across many scales of models. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation-based measures that can be applied on different spatial scales (regional versus global) to make evaluating the large number of variable–region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observationalmore » dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. An ideal evaluation method for hindcast experiments in IAMs would feature both absolute measures for evaluation of a single experiment for a single model and relative measures to compare the results of multiple experiments for a single model or the same experiment repeated across multiple models, such as in community intercomparison studies. The performance benchmarks highlight the use of this scheme for model evaluation in absolute terms, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. To demonstrate the use of and types of results possible with the evaluation method, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. The question of how to more holistically evaluate models as complex as IAMs is an area for future research. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs that require global supply to equal global demand at each time period, such as GCAM. The results of this work indicate it is unlikely that a single evaluation measure for all variables in an IAM exists, and therefore sector-by-sector evaluation may be necessary.« less

Evaluation of integrated assessment model hindcast experiments: a case study of the GCAM 3.0 land use module

DOE PAGES

Snyder, Abigail C.; Link, Robert P.; Calvin, Katherine V.

2017-11-29

Hindcasting experiments (conducting a model forecast for a time period in which observational data are available) are being undertaken increasingly often by the integrated assessment model (IAM) community, across many scales of models. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation-based measures that can be applied on different spatial scales (regional versus global) to make evaluating the large number of variable–region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observationalmore » dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. An ideal evaluation method for hindcast experiments in IAMs would feature both absolute measures for evaluation of a single experiment for a single model and relative measures to compare the results of multiple experiments for a single model or the same experiment repeated across multiple models, such as in community intercomparison studies. The performance benchmarks highlight the use of this scheme for model evaluation in absolute terms, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. To demonstrate the use of and types of results possible with the evaluation method, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. The question of how to more holistically evaluate models as complex as IAMs is an area for future research. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs that require global supply to equal global demand at each time period, such as GCAM. The results of this work indicate it is unlikely that a single evaluation measure for all variables in an IAM exists, and therefore sector-by-sector evaluation may be necessary.« less

Evaluation of integrated assessment model hindcast experiments: a case study of the GCAM 3.0 land use module

NASA Astrophysics Data System (ADS)

Snyder, Abigail C.; Link, Robert P.; Calvin, Katherine V.

2017-11-01

Hindcasting experiments (conducting a model forecast for a time period in which observational data are available) are being undertaken increasingly often by the integrated assessment model (IAM) community, across many scales of models. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation-based measures that can be applied on different spatial scales (regional versus global) to make evaluating the large number of variable-region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observational dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. An ideal evaluation method for hindcast experiments in IAMs would feature both absolute measures for evaluation of a single experiment for a single model and relative measures to compare the results of multiple experiments for a single model or the same experiment repeated across multiple models, such as in community intercomparison studies. The performance benchmarks highlight the use of this scheme for model evaluation in absolute terms, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. To demonstrate the use of and types of results possible with the evaluation method, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. The question of how to more holistically evaluate models as complex as IAMs is an area for future research. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs that require global supply to equal global demand at each time period, such as GCAM. The results of this work indicate it is unlikely that a single evaluation measure for all variables in an IAM exists, and therefore sector-by-sector evaluation may be necessary.

Expansion or extinction: deterministic and stochastic two-patch models with Allee effects.

PubMed

Kang, Yun; Lanchier, Nicolas

2011-06-01

We investigate the impact of Allee effect and dispersal on the long-term evolution of a population in a patchy environment. Our main focus is on whether a population already established in one patch either successfully invades an adjacent empty patch or undergoes a global extinction. Our study is based on the combination of analytical and numerical results for both a deterministic two-patch model and a stochastic counterpart. The deterministic model has either two, three or four attractors. The existence of a regime with exactly three attractors only appears when patches have distinct Allee thresholds. In the presence of weak dispersal, the analysis of the deterministic model shows that a high-density and a low-density populations can coexist at equilibrium in nearby patches, whereas the analysis of the stochastic model indicates that this equilibrium is metastable, thus leading after a large random time to either a global expansion or a global extinction. Up to some critical dispersal, increasing the intensity of the interactions leads to an increase of both the basin of attraction of the global extinction and the basin of attraction of the global expansion. Above this threshold, for both the deterministic and the stochastic models, the patches tend to synchronize as the intensity of the dispersal increases. This results in either a global expansion or a global extinction. For the deterministic model, there are only two attractors, while the stochastic model no longer exhibits a metastable behavior. In the presence of strong dispersal, the limiting behavior is entirely determined by the value of the Allee thresholds as the global population size in the deterministic and the stochastic models evolves as dictated by their single-patch counterparts. For all values of the dispersal parameter, Allee effects promote global extinction in terms of an expansion of the basin of attraction of the extinction equilibrium for the deterministic model and an increase of the probability of extinction for the stochastic model.

A Global System for Transportation Simulation and Visualization in Emergency Evacuation Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Wei; Liu, Cheng; Thomas, Neil

2015-01-01

Simulation-based studies are frequently used for evacuation planning and decision making processes. Given the transportation systems complexity and data availability, most evacuation simulation models focus on certain geographic areas. With routine improvement of OpenStreetMap road networks and LandScanTM global population distribution data, we present WWEE, a uniform system for world-wide emergency evacuation simulations. WWEE uses unified data structure for simulation inputs. It also integrates a super-node trip distribution model as the default simulation parameter to improve the system computational performance. Two levels of visualization tools are implemented for evacuation performance analysis, including link-based macroscopic visualization and vehicle-based microscopic visualization. Formore » left-hand and right-hand traffic patterns in different countries, the authors propose a mirror technique to experiment with both scenarios without significantly changing traffic simulation models. Ten cities in US, Europe, Middle East, and Asia are modeled for demonstration. With default traffic simulation models for fast and easy-to-use evacuation estimation and visualization, WWEE also retains the capability of interactive operation for users to adopt customized traffic simulation models. For the first time, WWEE provides a unified platform for global evacuation researchers to estimate and visualize their strategies performance of transportation systems under evacuation scenarios.« less

Global root zone storage capacity from satellite-based evaporation

NASA Astrophysics Data System (ADS)

Wang-Erlandsson, Lan; Bastiaanssen, Wim G. M.; Gao, Hongkai; Jägermeyr, Jonas; Senay, Gabriel B.; van Dijk, Albert I. J. M.; Guerschman, Juan P.; Keys, Patrick W.; Gordon, Line J.; Savenije, Hubert H. G.

2016-04-01

This study presents an "Earth observation-based" method for estimating root zone storage capacity - a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone storage capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone storage capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale independent. In contrast to a traditional look-up table approach, our method captures the variability in root zone storage capacity within land cover types, including in rainforests where direct measurements of root depths otherwise are scarce. Implementing the estimated root zone storage capacity in the global hydrological model STEAM (Simple Terrestrial Evaporation to Atmosphere Model) improved evaporation simulation overall, and in particular during the least evaporating months in sub-humid to humid regions with moderate to high seasonality. Our results suggest that several forest types are able to create a large storage to buffer for severe droughts (with a very long return period), in contrast to, for example, savannahs and woody savannahs (medium length return period), as well as grasslands, shrublands, and croplands (very short return period). The presented method to estimate root zone storage capacity eliminates the need for poor resolution soil and rooting depth data that form a limitation for achieving progress in the global land surface modelling community.

A Simple Climate Model Program for High School Education

NASA Astrophysics Data System (ADS)

Dommenget, D.

2012-04-01

The future climate change projections of the IPCC AR4 are based on GCM simulations, which give a distinct global warming pattern, with an arctic winter amplification, an equilibrium land sea contrast and an inter-hemispheric warming gradient. While these simulations are the most important tool of the IPCC predictions, the conceptual understanding of these predicted structures of climate change are very difficult to reach if only based on these highly complex GCM simulations and they are not accessible for ordinary people. In this study presented here we will introduce a very simple gridded globally resolved energy balance model based on strongly simplified physical processes, which is capable of simulating the main characteristics of global warming. The model shall give a bridge between the 1-dimensional energy balance models and the fully coupled 4-dimensional complex GCMs. It runs on standard PC computers computing globally resolved climate simulation with 2yrs per second or 100,000yrs per day. The program can compute typical global warming scenarios in a few minutes on a standard PC. The computer code is only 730 line long with very simple formulations that high school students should be able to understand. The simple model's climate sensitivity and the spatial structure of the warming pattern is within the uncertainties of the IPCC AR4 models simulations. It is capable of simulating the arctic winter amplification, the equilibrium land sea contrast and the inter-hemispheric warming gradient with good agreement to the IPCC AR4 models in amplitude and structure. The program can be used to do sensitivity studies in which students can change something (e.g. reduce the solar radiation, take away the clouds or make snow black) and see how it effects the climate or the climate response to changes in greenhouse gases. This program is available for every one and could be the basis for high school education. Partners for a high school project are wanted!

Long-Term Global Morphology of Gravity Wave Activity Using UARS Data

NASA Technical Reports Server (NTRS)

Eckermann, Stephen D.; Bacmeister, Julio T.; Wu, Dong L.

1998-01-01

This is the first quarter's report on research to extract global gravity-wave data from satellite data and to model those observations synoptically. Preliminary analysis of global maps of extracted middle atmospheric temperature variance from the CRISTA instrument is presented, which appear to contain gravity-wave information. Corresponding simulations of global gravity-wave and mountain-wave activity during this mission period are described using global ray-tracing and mountain-wave models, and interesting similarities among simulated data and CRISTA data are noted. Climatological simulations of mesospheric gravity-wave activity using the HWM-03 wind-temperature climatology are also reported, for comparison with UARS MLS data. Preparatory work on modeling of gravity wave observations from space-based platforms and subsequent interpretation of the MLS gravity-wave product are also described. Preliminary interpretation and relation to the research objectives are provided, and further action for the next quarter's research is recommended.

Schwarz-Christoffel Conformal Mapping based Grid Generation for Global Oceanic Circulation Models

NASA Astrophysics Data System (ADS)

Xu, Shiming

2015-04-01

We propose new grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithm are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the conventional grid design problem of pole relocation, it also addresses more advanced issues of computational efficiency and the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily 10 utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling when complex land-ocean distribution is present.

Indices and Dynamics of Global Hydroclimate Over the Past Millennium from Data Assimilation

NASA Astrophysics Data System (ADS)

Steiger, N. J.; Smerdon, J. E.

2017-12-01

Reconstructions based on data assimilation (DA) are at the forefront of model-data syntheses in that such reconstructions optimally fuse proxy data with climate models. DA-based paleoclimate reconstructions have the benefit of being physically-consistent across the reconstructed climate variables and are capable of providing dynamical information about past climate phenomena. Here we use a new implementation of DA, that includes updated proxy system models and climate model bias correction procedures, to reconstruct global hydroclimate on seasonal and annual timescales over the last millennium. This new global hydroclimate product includes reconstructions of the Palmer Drought Severity Index, the Standardized Precipitation Evapotranspiration Index, and global surface temperature along with dynamical variables including the Nino 3.4 index, the latitudinal location of the intertropical convergence zone, and an index of the Atlantic Multidecadal Oscillation. Here we present a validation of the reconstruction product and also elucidate the causes of severe drought in North America and in equatorial Africa. Specifically, we explore the connection between droughts in North America and modes of ocean variability in the Pacific and Atlantic oceans. We also link drought over equatorial Africa to shifts of the intertropical convergence zone and modes of ocean variability.

Southwestern Pine Forests Likely to Disappear

ScienceCinema

McDowell, Nathan

2018-01-16

A new study, led by Los Alamos National Laboratory's Nathan McDowell, suggests that widespread loss of a major forest type, the pine-juniper woodlands of the Southwestern U.S., could be wiped out by the end of this century due to climate change, and that conifers throughout much of the Northern Hemisphere may be on a similar trajectory. New results, reported in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. McDowell and his large international team strove to provide the missing pieces of understanding tree death at three levels: plant, regional and global. The team rigorously developed and evaluated multiple process-based and empirical models against experimental results, and then compared these models to results from global vegetation models to examine independent simulations. They discovered that the global models simulated mortality throughout the Northern Hemisphere that was of similar magnitude, but much broader spatial scale, as the evaluated ecosystem models predicted for in the Southwest.

Southwestern Pine Forests Likely to Disappear

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDowell, Nathan

A new study, led by Los Alamos National Laboratory's Nathan McDowell, suggests that widespread loss of a major forest type, the pine-juniper woodlands of the Southwestern U.S., could be wiped out by the end of this century due to climate change, and that conifers throughout much of the Northern Hemisphere may be on a similar trajectory. New results, reported in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. McDowell and his large international team strove to provide the missing pieces of understanding tree death at three levels: plant, regional and global. The teammore » rigorously developed and evaluated multiple process-based and empirical models against experimental results, and then compared these models to results from global vegetation models to examine independent simulations. They discovered that the global models simulated mortality throughout the Northern Hemisphere that was of similar magnitude, but much broader spatial scale, as the evaluated ecosystem models predicted for in the Southwest.« less

Tuning a physically-based model of the air-sea gas transfer velocity

NASA Astrophysics Data System (ADS)

Jeffery, C. D.; Robinson, I. S.; Woolf, D. K.

Air-sea gas transfer velocities are estimated for one year using a 1-D upper-ocean model (GOTM) and a modified version of the NOAA-COARE transfer velocity parameterization. Tuning parameters are evaluated with the aim of bringing the physically based NOAA-COARE parameterization in line with current estimates, based on simple wind-speed dependent models derived from bomb-radiocarbon inventories and deliberate tracer release experiments. We suggest that A = 1.3 and B = 1.0, for the sub-layer scaling parameter and the bubble mediated exchange, respectively, are consistent with the global average CO 2 transfer velocity k. Using these parameters and a simple 2nd order polynomial approximation, with respect to wind speed, we estimate a global annual average k for CO 2 of 16.4 ± 5.6 cm h -1 when using global mean winds of 6.89 m s -1 from the NCEP/NCAR Reanalysis 1 1954-2000. The tuned model can be used to predict the transfer velocity of any gas, with appropriate treatment of the dependence on molecular properties including the strong solubility dependence of bubble-mediated transfer. For example, an initial estimate of the global average transfer velocity of DMS (a relatively soluble gas) is only 11.9 cm h -1 whilst for less soluble methane the estimate is 18.0 cm h -1.

Global root zone storage capacity from satellite-based evaporation data

NASA Astrophysics Data System (ADS)

Wang-Erlandsson, Lan; Bastiaanssen, Wim; Gao, Hongkai; Jägermeyr, Jonas; Senay, Gabriel; van Dijk, Albert; Guerschman, Juan; Keys, Patrick; Gordon, Line; Savenije, Hubert

2016-04-01

We present an "earth observation-based" method for estimating root zone storage capacity - a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone storage capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone storage capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale-independent. In contrast to traditional look-up table approaches, our method captures the variability in root zone storage capacity within land cover type, including in rainforests where direct measurements of root depth otherwise are scarce. Implementing the estimated root zone storage capacity in the global hydrological model STEAM improved evaporation simulation overall, and in particular during the least evaporating months in sub-humid to humid regions with moderate to high seasonality. We find that evergreen forests are able to create a large storage to buffer for extreme droughts (with a return period of up to 60 years), in contrast to short vegetation and crops (which seem to adapt to a drought return period of about 2 years). The presented method to estimate root zone storage capacity eliminates the need for soils and rooting depth information, which could be a game-changer in global land surface modelling.

Vision-based navigation in a dynamic environment for virtual human

NASA Astrophysics Data System (ADS)

Liu, Yan; Sun, Ji-Zhou; Zhang, Jia-Wan; Li, Ming-Chu

2004-06-01

Intelligent virtual human is widely required in computer games, ergonomics software, virtual environment and so on. We present a vision-based behavior modeling method to realize smart navigation in a dynamic environment. This behavior model can be divided into three modules: vision, global planning and local planning. Vision is the only channel for smart virtual actor to get information from the outside world. Then, the global and local planning module use A* and D* algorithm to find a way for virtual human in a dynamic environment. Finally, the experiments on our test platform (Smart Human System) verify the feasibility of this behavior model.

Mixed geographically weighted regression (MGWR) model with weighted adaptive bi-square for case of dengue hemorrhagic fever (DHF) in Surakarta

NASA Astrophysics Data System (ADS)

Astuti, H. N.; Saputro, D. R. S.; Susanti, Y.

2017-06-01

MGWR model is combination of linear regression model and geographically weighted regression (GWR) model, therefore, MGWR model could produce parameter estimation that had global parameter estimation, and other parameter that had local parameter in accordance with its observation location. The linkage between locations of the observations expressed in specific weighting that is adaptive bi-square. In this research, we applied MGWR model with weighted adaptive bi-square for case of DHF in Surakarta based on 10 factors (variables) that is supposed to influence the number of people with DHF. The observation unit in the research is 51 urban villages and the variables are number of inhabitants, number of houses, house index, many public places, number of healthy homes, number of Posyandu, area width, level population density, welfare of the family, and high-region. Based on this research, we obtained 51 MGWR models. The MGWR model were divided into 4 groups with significant variable is house index as a global variable, an area width as a local variable and the remaining variables vary in each. Global variables are variables that significantly affect all locations, while local variables are variables that significantly affect a specific location.

Gap models and their individual-based relatives in the assessment of the consequences of global change

NASA Astrophysics Data System (ADS)

Shugart, Herman H.; Wang, Bin; Fischer, Rico; Ma, Jianyong; Fang, Jing; Yan, Xiaodong; Huth, Andreas; Armstrong, Amanda H.

2018-03-01

Individual-based models (IBMs) of complex systems emerged in the 1960s and early 1970s, across diverse disciplines from astronomy to zoology. Ecological IBMs arose with seemingly independent origins out of the tradition of understanding the ecosystems dynamics of ecosystems from a ‘bottom-up’ accounting of the interactions of the parts. Individual trees are principal among the parts of forests. Because these models are computationally demanding, they have prospered as the power of digital computers has increased exponentially over the decades following the 1970s. This review will focus on a class of forest IBMs called gap models. Gap models simulate the changes in forests by simulating the birth, growth and death of each individual tree on a small plot of land. The summation of these plots comprise a forest (or set of sample plots on a forested landscape or region). Other, more aggregated forest IBMs have been used in global applications including cohort-based models, ecosystem demography models, etc. Gap models have been used to provide the parameters for these bulk models. Currently, gap models have grown from local-scale to continental-scale and even global-scale applications to assess the potential consequences of climate change on natural forests. Modifications to the models have enabled simulation of disturbances including fire, insect outbreak and harvest. Our objective in this review is to provide the reader with an overview of the history, motivation and applications, including theoretical applications, of these models. In a time of concern over global changes, gap models are essential tools to understand forest responses to climate change, modified disturbance regimes and other change agents. Development of forest surveys to provide the starting points for simulations and better estimates of the behavior of the diversity of tree species in response to the environment are continuing needs for improvement for these and other IBMs.

Interfacing remote sensing and geographic information systems for global environmental change research

NASA Technical Reports Server (NTRS)

Lee, Jae K.; Randolph, J. C.; Lulla, Kamlesh P.; Helfert, Michael R.

1993-01-01

Because changes in the Earth's environment have become major global issues, continuous, longterm scientific information is required to assess global problems such as deforestation, desertification, greenhouse effects and climate variations. Global change studies require understanding of interactions of complex processes regulating the Earth system. Space-based Earth observation is an essential element in global change research for documenting changes in Earth environment. It provides synoptic data for conceptual predictive modeling of future environmental change. This paper provides a brief overview of remote sensing technology from the perspective of global change research.

Digital Elevation Models of the Earth derived from space-based observations: Advances and potential for geomorphological studies

NASA Astrophysics Data System (ADS)

Mouratidis, Antonios

2013-04-01

Digital Elevation Models (DEMs) are an inherently interdisciplinary topic, both due to their production and validation methods, as well as their significance for numerous disciplines. The most utilized contemporary topographic datasets worldwide are those of global DEMs. Several space-based sources have been used for the production of (almost) global DEMs, namely satellite Synthetic Aperture Radar (SAR) Interferometry/InSAR, stereoscopy of multispectral satellite images and altimetry, producing several versions of autonomous or mixed products (i.e. SRTM, ACE, ASTER-GDEM). Complementary space-based observations, such as those of Global Navigation Satellite Systems (GNSS), are also used, mainly for validation purposes. The apparent positive impact of these elevation datasets so far has been consolidated by the plethora of related scientific, civil and military applications. Topography is a prominent element for almost all Earth sciences, but in Geomorphology it is even more fundamental. In geomorphological studies, elevation data and thus DEMs can be extensively used for the extraction of both qualitative and quantitative information, such as relief classification, determination of slope and slope orientation, delineation of drainage basins, extraction of drainage networks and much more. Global DEMs are constantly becoming finer, i.e. of higher spatial resolution and more "sensitive" to elevation changes, i.e. of higher vertical accuracy and these progresses are undoubtedly considered as a major breakthrough, each time a new improved global DEM is released. Nevertheless, for Geomorphology in particular, if not already there, we are close to the point in time, where the need for discrimination between DSM (Digital Surface Model) and DTM (Digital Terrain Model) is becoming critical; if the distinction between vegetation and man-made structures on one side (DSM), and actual terrain elevation on the other side (DTM) cannot be made, then, in many cases, any further increase of elevation accuracy in DEMs will have little impact on geomorphological studies. After shortly reviewing the evolution of satellite-based global DEMs, the purpose of this paper is to address their current limitations and challenges from the perspective of a geomorphologist. Subsequently, the implications for geomorphological studies are discussed, with respect to the expected near-future advances in the field, such as the TanDEM-X Global Digital Elevation Model ("WorldDEM", 2014), as well as spaceborne LIDAR (Light Detection and Ranging) approaches (e.g. Lidar Surface Topography/LIST mission, 2016-2020).

Fractional Gaussian model in global optimization

NASA Astrophysics Data System (ADS)

Dimri, V. P.; Srivastava, R. P.

2009-12-01

Earth system is inherently non-linear and it can be characterized well if we incorporate no-linearity in the formulation and solution of the problem. General tool often used for characterization of the earth system is inversion. Traditionally inverse problems are solved using least-square based inversion by linearizing the formulation. The initial model in such inversion schemes is often assumed to follow posterior Gaussian probability distribution. It is now well established that most of the physical properties of the earth follow power law (fractal distribution). Thus, the selection of initial model based on power law probability distribution will provide more realistic solution. We present a new method which can draw samples of posterior probability density function very efficiently using fractal based statistics. The application of the method has been demonstrated to invert band limited seismic data with well control. We used fractal based probability density function which uses mean, variance and Hurst coefficient of the model space to draw initial model. Further this initial model is used in global optimization inversion scheme. Inversion results using initial models generated by our method gives high resolution estimates of the model parameters than the hitherto used gradient based liner inversion method.

Glossary of AWS Acrinabs. Acronyms, Initialisms, and Abbreviations Commonly Used in Air Weather Service

DTIC Science & Technology

1991-01-01

Foundation FYDP ......... Five Year Defense Plan FSI ............ Fog Stability Index 17 G G ................ gravity, giga- GISM ......... Gridded ...Global Circulation Model GOES-TAP GOES imagery processing & dissemination system GCS .......... grid course GOFS ........ Global Ocean Flux Study GD...Analysis Support System Complex Systems GRID .......... Global Resource Information Data -Base GEMAG ..... geomagnetic GRIST..... grazing-incidence solar

Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland Management

NASA Technical Reports Server (NTRS)

Chang, Jinfeng; Ciais, Philippe; Herrero, Mario; Havlik, Petr; Campioli, Matteo; Zhang, Xianzhou; Bai, Yongfei; Viovy, Nicolas; Joiner, Joanna; Wang, Xuhui;

Global Marine Productivity and Living-Phytoplankton Carbon Biomass Estimated from a Physiological Growth Model

NASA Astrophysics Data System (ADS)

Arteaga, L.; Pahlow, M.; Oschlies, A.

2016-02-01

Primay production by marine phytoplankton essentially drives the oceanic biological carbon pump. Global productivity estimates are commonly founded on chlorophyll-based primary production models. However, a major drawback of most of these models is that variations in chlorophyll concentration do not necessarily account for changes in phytoplankton biomass resulting from the physiological regulation of the chlorophyll-to-carbon ratio (Chl:C). Here we present phytoplankton production rates and surface phytoplankton C concentrations for the global ocean for 2005-2010, obtained by combining satellite Chl observations with a mechanistic model for the acclimation of phytoplankton stoichiometry to variations in nutrients, light and temperature. We compare our inferred phytoplankton C concentrations with an independent estimate of surface particulate organic carbon (POC) to identify for the first time the global contribution of living phytoplankton to total POC in the surface ocean. Our annual primary production (46 Pg C yr-1) is in good agreement with other C-based model estimates obtained from satellite observations. We find that most of the oligotrophic surface ocean is dominated by living phytoplankton biomass (between 30-70% of total particulate carbon). Lower contributions are found in the tropical Pacific (10-30% phytoplankton) and the Southern Ocean (≈ 10%). Our method provides a novel analytical tool for identifying changes in marine plankton communities and carbon cycling.

An improved empirical dynamic control system model of global mean sea level rise and surface temperature change

NASA Astrophysics Data System (ADS)

Wu, Qing; Luu, Quang-Hung; Tkalich, Pavel; Chen, Ge

2018-04-01

Having great impacts on human lives, global warming and associated sea level rise are believed to be strongly linked to anthropogenic causes. Statistical approach offers a simple and yet conceptually verifiable combination of remotely connected climate variables and indices, including sea level and surface temperature. We propose an improved statistical reconstruction model based on the empirical dynamic control system by taking into account the climate variability and deriving parameters from Monte Carlo cross-validation random experiments. For the historic data from 1880 to 2001, we yielded higher correlation results compared to those from other dynamic empirical models. The averaged root mean square errors are reduced in both reconstructed fields, namely, the global mean surface temperature (by 24-37%) and the global mean sea level (by 5-25%). Our model is also more robust as it notably diminished the unstable problem associated with varying initial values. Such results suggest that the model not only enhances significantly the global mean reconstructions of temperature and sea level but also may have a potential to improve future projections.

Large-Scale Variation in Forest Carbon Turnover Rate and its Relation to Climate - Remote Sensing vs. Global Vegetation Models

NASA Astrophysics Data System (ADS)

Carvalhais, N.; Thurner, M.; Beer, C.; Forkel, M.; Rademacher, T. T.; Santoro, M.; Tum, M.; Schmullius, C.

2015-12-01

While vegetation productivity is known to be strongly correlated to climate, there is a need for an improved understanding of the underlying processes of vegetation carbon turnover and their importance at a global scale. This shortcoming has been due to the lack of spatially extensive information on vegetation carbon stocks, which we recently have been able to overcome by a biomass dataset covering northern boreal and temperate forests originating from radar remote sensing. Based on state-of-the-art products on biomass and NPP, we are for the first time able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests. The implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current global vegetation models. In contrast to our observation-based findings, investigated models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well to observation-based NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in global vegetation models and estimating their impact on the land carbon balance.

Aerosol single-scattering albedo over the global oceans: Comparing PARASOL retrievals with AERONET, OMI, and AeroCom models estimates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lacagnina, Carlo; Hasekamp, Otto P.; Bian, Huisheng

2015-09-27

The aerosol Single Scattering Albedo (SSA) over the global oceans is evaluated based on polarimetric measurements by the PARASOL satellite. The retrieved values for SSA and Aerosol Optical Depth (AOD) agree well with the ground-based measurements of the AErosol RObotic NETwork (AERONET). The global coverage provided by the PARASOL observations represents a unique opportunity to evaluate SSA and AOD simulated by atmospheric transport model runs, as performed in the AeroCom framework. The SSA estimate provided by the AeroCom models is generally higher than the SSA retrieved from both PARASOL and AERONET. On the other hand, the mean simulated AOD ismore » about right or slightly underestimated compared with observations. An overestimate of the SSA by the models would suggest that these simulate an overly strong aerosol radiative cooling at top-of-atmosphere (TOA) and underestimate it at surface. This implies that aerosols have a potential stronger impact within the atmosphere than currently simulated.« less

A framework for global river flood risk assessments

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Jongman, B.; Ward, P. J.; Bouwman, A.

2012-08-01

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate. The framework estimates hazard at high resolution (~1 km2) using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood routing model, and importantly, a flood extent downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case-study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard and damage estimates has been performed using the Dartmouth Flood Observatory database and damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

A Robust Response of Precipitation to Global Warming from CMIP5 Models

NASA Technical Reports Server (NTRS)

Lau, K. -M.; Wu, H. -T.; Kim, K. -M.

2012-01-01

How precipitation responds to global warming is a major concern to society and a challenge to climate change research. Based on analyses of rainfall probability distribution functions of 14 state-of-the-art climate models, we find a robust, canonical global rainfall response to a triple CO2 warming scenario, featuring 100 250% more heavy rain, 5-10% less moderate rain, and 10-15% more very light or no-rain events. Regionally, a majority of the models project a consistent response with more heavy rain events over climatologically wet regions of the deep tropics, and more dry events over subtropical and tropical land areas. Results suggest that increased CO2 emissions induce basic structural changes in global rain systems, increasing risks of severe floods and droughts in preferred geographic locations worldwide.

Evaluation of Diagnostic CO2 Flux and Transport Modeling in NU-WRF and GEOS-5

NASA Astrophysics Data System (ADS)

Kawa, S. R.; Collatz, G. J.; Tao, Z.; Wang, J. S.; Ott, L. E.; Liu, Y.; Andrews, A. E.; Sweeney, C.

2015-12-01

We report on recent diagnostic (constrained by observations) model simulations of atmospheric CO2 flux and transport using a newly developed facility in the NASA Unified-Weather Research and Forecast (NU-WRF) model. The results are compared to CO2 data (ground-based, airborne, and GOSAT) and to corresponding simulations from a global model that uses meteorology from the NASA GEOS-5 Modern Era Retrospective analysis for Research and Applications (MERRA). The objective of these intercomparisons is to assess the relative strengths and weaknesses of the respective models in pursuit of an overall carbon process improvement at both regional and global scales. Our guiding hypothesis is that the finer resolution and improved land surface representation in NU-WRF will lead to better comparisons with CO2 data than those using global MERRA, which will, in turn, inform process model development in global prognostic models. Initial intercomparison results, however, have generally been mixed: NU-WRF is better at some sites and times but not uniformly. We are examining the model transport processes in detail to diagnose differences in the CO2 behavior. These comparisons are done in the context of a long history of simulations from the Parameterized Chemistry and Transport Model, based on GEOS-5 meteorology and Carnegie Ames-Stanford Approach-Global Fire Emissions Database (CASA-GFED) fluxes, that capture much of the CO2 variation from synoptic to seasonal to global scales. We have run the NU-WRF model using unconstrained, internally generated meteorology within the North American domain, and with meteorological 'nudging' from Global Forecast System and North American Regional Reanalysis (NARR) in an effort to optimize the CO2 simulations. Output results constrained by NARR show the best comparisons to data. Discrepancies, of course, may arise either from flux or transport errors and compensating errors are possible. Resolving their interplay is also important to using the data in inverse models. Recent analysis is focused on planetary boundary depth, which can be significantly different between MERRA and NU-WRF, along with subgrid transport differences. Characterization of transport differences between the models will allow us to better constrain the CO2 fluxes, which is the major objective of this work.

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling.

PubMed

Ross, C Wade; Prihodko, Lara; Anchang, Julius; Kumar, Sanath; Ji, Wenjie; Hanan, Niall P

2018-05-15

Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product-HYSOGs250m-represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.

Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling

NASA Astrophysics Data System (ADS)

Sasai, T.; Murakami, K.; Kato, S.; Matsunaga, T.; Saigusa, N.; Hiraki, K.

2015-12-01

Global terrestrial carbon cycle largely depends on a spatial pattern in land cover type, which is heterogeneously-distributed over regional and global scales. However, most studies, which aimed at the estimation of carbon exchanges between ecosystem and atmosphere, remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. In this study, we show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. As methodology for computing the exchanges, we 1) developed a global 1km-grid climate and satellite dataset based on the approach in Setoyama and Sasai (2013); 2) used the satellite-driven biosphere model (Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data: BEAMS) (Sasai et al., 2005, 2007, 2011); 3) simulated the carbon exchanges by using the new dataset and BEAMS by the use of a supercomputer that includes 1280 CPU and 320 GPGPU cores (GOSAT RCF of NIES). As a result, we could develop a global uniform system for realistically estimating terrestrial carbon exchange, and evaluate net ecosystem production in each community level; leading to obtain highly detailed understanding of terrestrial carbon exchanges.

[Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model].

PubMed

Zhang, Ling Yu; Liu, Zhao Gang

2017-12-01

Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

Methanol clusters (CH3OH)n: putative global minimum-energy structures from model potentials and dispersion-corrected density functional theory.

PubMed

Kazachenko, Sergey; Bulusu, Satya; Thakkar, Ajit J

2013-06-14

Putative global minima are reported for methanol clusters (CH3OH)n with n ≤ 15. The predictions are based on global optimization of three intermolecular potential energy models followed by local optimization and single-point energy calculations using two variants of dispersion-corrected density functional theory. Recurring structural motifs include folded and/or twisted rings, folded rings with a short branch, and stacked rings. Many of the larger structures are stabilized by weak C-H···O bonds.

The use of Meteonorm weather generator for climate change studies

NASA Astrophysics Data System (ADS)

Remund, J.; Müller, S. C.; Schilter, C.; Rihm, B.

2010-09-01

The global climatological database Meteonorm (www.meteonorm.com) is widely used as meteorological input for simulation of solar applications and buildings. It's a combination of a climate database, a spatial interpolation tool and a stochastic weather generator. Like this typical years with hourly or minute time resolution can be calculated for any site. The input of Meteonorm for global radiation is the Global Energy Balance Archive (GEBA, http://proto-geba.ethz.ch). All other meteorological parameters are taken from databases of WMO and NCDC (periods 1961-90 and 1996-2005). The stochastic generation of global radiation is based on a Markov chain model for daily values and an autoregressive model for hourly and minute values (Aguiar and Collares-Pereira, 1988 and 1992). The generation of temperature is based on global radiation and measured distribution of daily temperature values of approx. 5000 sites. Meteonorm generates also additional parameters like precipitation, wind speed or radiation parameters like diffuse and direct normal irradiance. Meteonorm can also be used for climate change studies. Instead of climate values, the results of IPCC AR4 results are used as input. From all 18 public models an average has been made at a resolution of 1°. The anomalies of the parameters temperature, precipitation and global radiation and the three scenarios B1, A1B and A2 have been included. With the combination of Meteonorm's current database 1961-90, the interpolation algorithms and the stochastic generation typical years can be calculated for any site, for different scenarios and for any period between 2010 and 2200. From the analysis of variations of year to year and month to month variations of temperature, precipitation and global radiation of the past ten years as well of climate model forecasts (from project prudence, http://prudence.dmi.dk) a simple autoregressive model has been formed which is used to generate realistic monthly time series of future periods. Meteonorm can therefore be used as a relatively simple method to enhance the spatial and temporal resolution instead of using complicated and time consuming downscaling methods based on regional climate models. The combination of Meteonorm, gridded historical (based on work of Luterbach et al.) and IPCC results has been used for studies of vegetation simulation between 1660 and 2600 (publication of first version based on IS92a scenario and limited time period 1950 - 2100: http://www.pbl.nl/images/H5_Part2_van%20CCE_opmaak%28def%29_tcm61-46625.pdf). It's also applicable for other adaptation studies for e.g. road surfaces or building simulation. In Meteonorm 6.1 one scenario (IS92a) and one climate model has been included (Hadley CM3). In the new Meteonorm 7 (coming spring 2011) the model averages of the three above mentioned scenarios of the IPCC AR4 will be included.

Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model

NASA Astrophysics Data System (ADS)

Smith, B.; Wårlind, D.; Arneth, A.; Hickler, T.; Leadley, P.; Siltberg, J.; Zaehle, S.

2013-11-01

The LPJ-GUESS dynamic vegetation model uniquely combines an individual- and patch-based representation of vegetation dynamics with ecosystem biogeochemical cycling from regional to global scales. We present an updated version that includes plant and soil N dynamics, analysing the implications of accounting for C-N interactions on predictions and performance of the model. Stand structural dynamics and allometric scaling of tree growth suggested by global databases of forest stand structure and development were well-reproduced by the model in comparison to an earlier multi-model study. Accounting for N cycle dynamics improved the goodness-of-fit for broadleaved forests. N limitation associated with low N mineralisation rates reduces productivity of cold-climate and dry-climate ecosystems relative to mesic temperate and tropical ecosystems. In a model experiment emulating free-air CO2 enrichment (FACE) treatment for forests globally, N-limitation associated with low N mineralisation rates of colder soils reduces CO2-enhancement of NPP for boreal forests, while some temperate and tropical forests exhibit increased NPP enhancement. Under a business-as-usual future climate and emissions scenario, ecosystem C storage globally was projected to increase by c. 10%; additional N requirements to match this increasing ecosystem C were within the high N supply limit estimated on stoichiometric grounds in an earlier study. Our results highlight the importance of accounting for C-N interactions not only in studies of global terrestrial C cycling, but to understand underlying mechanisms on local scales and in different regional contexts.

Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model

NASA Astrophysics Data System (ADS)

Smith, B.; Wårlind, D.; Arneth, A.; Hickler, T.; Leadley, P.; Siltberg, J.; Zaehle, S.

2014-04-01

The LPJ-GUESS dynamic vegetation model uniquely combines an individual- and patch-based representation of vegetation dynamics with ecosystem biogeochemical cycling from regional to global scales. We present an updated version that includes plant and soil N dynamics, analysing the implications of accounting for C-N interactions on predictions and performance of the model. Stand structural dynamics and allometric scaling of tree growth suggested by global databases of forest stand structure and development were well reproduced by the model in comparison to an earlier multi-model study. Accounting for N cycle dynamics improved the goodness of fit for broadleaved forests. N limitation associated with low N-mineralisation rates reduces productivity of cold-climate and dry-climate ecosystems relative to mesic temperate and tropical ecosystems. In a model experiment emulating free-air CO2 enrichment (FACE) treatment for forests globally, N limitation associated with low N-mineralisation rates of colder soils reduces CO2 enhancement of net primary production (NPP) for boreal forests, while some temperate and tropical forests exhibit increased NPP enhancement. Under a business-as-usual future climate and emissions scenario, ecosystem C storage globally was projected to increase by ca. 10%; additional N requirements to match this increasing ecosystem C were within the high N supply limit estimated on stoichiometric grounds in an earlier study. Our results highlight the importance of accounting for C-N interactions in studies of global terrestrial N cycling, and as a basis for understanding mechanisms on local scales and in different regional contexts.

A process-based inventory model for landfill CH4 emissions inclusive of seasonal soil microclimate and CH4 oxidation

USDA-ARS?s Scientific Manuscript database

We have developed and field-validated an annual inventory model for California landfill CH4 emissions that incorporates both site-specific soil properties and soil microclimate modeling coupled to 0.5o scale global climatic models. Based on 1-D diffusion, CALMIM (California Landfill Methane Inventor...

Long-Term Global Morphology of Gravity Wave Activity Using UARS Data

NASA Technical Reports Server (NTRS)

Eckermann, Stephen D.; Bacmeister, Julio T.; Wu, Dong L.

1998-01-01

Progress in research into the global morphology of gravity wave activity using UARS data is described for the period March-June, 1998. Highlights this quarter include further progress in the analysis and interpretation of CRISTA temperature variances; model-generated climatologies of mesospheric gravity wave activity using the HWM-93 wind and temperature model; and modeling of gravity wave detection from space-based platforms. Preliminary interpretations and recommended avenues for further analysis are also described.

Simulation of leaf area index on site scale based on model data fusion

NASA Astrophysics Data System (ADS)

Yang, Y.; Wang, J. B.

2017-12-01

The world's grassland area is about 24 × 108hm2, accounting for about one-fifth of the global land area. It is one of the most widely distributed terrestrial ecosystems on Earth. And currently, it is the most affected area of human activity. A considerable portion of the global CO2 emissions are fixed by grassland, and the grassland carbon cycle plays an important role in the global carbon cycle (Li Bo, Yongshen Peng, Li Yao, China's Prairie, 1990). In recent years, the carbon cycle and its influencing factors of grassland ecosystems have become one of the hotspots in ecology, geology, botany and agronomy under the background of global change ( Mu Shaojie, 2014) . And the model is now as a popular and effective method of research. However, there are still some uncertainties in this approach. CEVSA ( Carbon Exchange between Vegetation, Soil and Atmosphere) is a biogeochemical cycle model based on physiological and ecological processes to simulate plant-soil-atmosphere system energy exchange and water-carbon-nitrogen coupling cycles (Cao at al., 1998a; 1998b; Woodward et al., 1995). In this paper, the remote sensing observation data of leaf area index are integrated into the model, and the CEVSA model of site version is optimized by Markov chain-Monte Carlo method to achieve the purpose of increasing the accuracy of model results.

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

The 5'×5' global geoid model GGM2016

NASA Astrophysics Data System (ADS)

Shen, WenBin; Han, Jiancheng

2016-04-01

We provide an updated 5'×5' global geoid model GGM2016, which is determined based on the shallow layer method (Shen 2006). We choose an inner surface S below the EGM2008 geoid, and the layer bounded by the inner surface S and the Earth's geographical surface E is referred to as the shallow layer. The Earth's geographical surface E is determined by the digital topographic model DTM2006.0 combining with the DNSC2008 mean sea surface. We determine the 3D shallow layer model (SLM) using the refined crust density model CRUST1.0-5min, which is an improved 5'×5' density model of the CRUST1.0 with taking into account the corrections of the areas covered by ice sheets and the land-ocean crossing regions. Based on the SLM and the gravity field EGM2008 defined outside the Earth's geographical surface E, we determine the gravity field EGM2008S defined in the region outside the inner surface S, extending the gravity field's definition domain from the domain outside E to the domain outside S. Based on the geodetic equation W(P)=W0, where W0 is the geopotential constant on the geoid, we determine a 5'×5' global geoid model GGM2016, which provides both the 5'×5' grid values and spherical harmonic coefficient expressions. Comparisons show that the GGM2016 fits the globally available GPS/leveling points better than the EGM2008 geoid. This study is supported by National 973 Project China (grant Nos. 2013CB733301 and 2013CB733305), NSFC (grant Nos. 41174011, 41210006, 41429401, 41128003, 41021061).

Methods for estimating comparable prevalence rates of food insecurity experienced by adults in 147 countries and areas

NASA Astrophysics Data System (ADS)

Nord, Mark; Cafiero, Carlo; Viviani, Sara

2016-11-01

Statistical methods based on item response theory are applied to experiential food insecurity survey data from 147 countries, areas, and territories to assess data quality and develop methods to estimate national prevalence rates of moderate and severe food insecurity at equal levels of severity across countries. Data were collected from nationally representative samples of 1,000 adults in each country. A Rasch-model-based scale was estimated for each country, and data were assessed for consistency with model assumptions. A global reference scale was calculated based on item parameters from all countries. Each country's scale was adjusted to the global standard, allowing for up to 3 of the 8 scale items to be considered unique in that country if their deviance from the global standard exceeded a set tolerance. With very few exceptions, data from all countries were sufficiently consistent with model assumptions to constitute reasonably reliable measures of food insecurity and were adjustable to the global standard with fair confidence. National prevalence rates of moderate-or-severe food insecurity assessed over a 12-month recall period ranged from 3 percent to 92 percent. The correlations of national prevalence rates with national income, health, and well-being indicators provide external validation of the food security measure.

Global long-term ozone trends derived from different observed and modelled data sets

NASA Astrophysics Data System (ADS)

Coldewey-Egbers, M.; Loyola, D.; Zimmer, W.; van Roozendael, M.; Lerot, C.; Dameris, M.; Garny, H.; Braesicke, P.; Koukouli, M.; Balis, D.

2012-04-01

The long-term behaviour of stratospheric ozone amounts during the past three decades is investigated on a global scale using different observed and modelled data sets. Three European satellite sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/METOP are combined and a merged global monthly mean total ozone product has been prepared using an inter-satellite calibration approach. The data set covers the 16-years period from June 1995 to June 2011 and it exhibits an excellent long-term stability, which is required for such trend studies. A multiple linear least-squares regression algorithm using different explanatory variables is applied to the time series and statistically significant positive trends are detected in the northern mid latitudes and subtropics. Global trends are also estimated using a second satellite-based Merged Ozone Data set (MOD) provided by NASA. For few selected geographical regions ozone trends are additionally calculated using well-maintained measurements of individual Dobson/Brewer ground-based instruments. A reasonable agreement in the spatial patterns of the trends is found amongst the European satellite, the NASA satellite, and the ground-based observations. Furthermore, two long-term simulations obtained with the Chemistry-Climate Models E39C-A provided by German Aerospace Center and UMUKCA-UCAM provided by University of Cambridge are analysed.

Increased evapotranspiration demand in a Mediterranean climate might cause a decline in fungal yields under global warming.

PubMed

Ágreda, Teresa; Águeda, Beatriz; Olano, José M; Vicente-Serrano, Sergio M; Fernández-Toirán, Marina

2015-09-01

Wild fungi play a critical role in forest ecosystems, and its recollection is a relevant economic activity. Understanding fungal response to climate is necessary in order to predict future fungal production in Mediterranean forests under climate change scenarios. We used a 15-year data set to model the relationship between climate and epigeous fungal abundance and productivity, for mycorrhizal and saprotrophic guilds in a Mediterranean pine forest. The obtained models were used to predict fungal productivity for the 2021-2080 period by means of regional climate change models. Simple models based on early spring temperature and summer-autumn rainfall could provide accurate estimates for fungal abundance and productivity. Models including rainfall and climatic water balance showed similar results and explanatory power for the analyzed 15-year period. However, their predictions for the 2021-2080 period diverged. Rainfall-based models predicted a maintenance of fungal yield, whereas water balance-based models predicted a steady decrease of fungal productivity under a global warming scenario. Under Mediterranean conditions fungi responded to weather conditions in two distinct periods: early spring and late summer-autumn, suggesting a bimodal pattern of growth. Saprotrophic and mycorrhizal fungi showed differences in the climatic control. Increased atmospheric evaporative demand due to global warming might lead to a drop in fungal yields during the 21st century. © 2015 John Wiley & Sons Ltd.

Contribution of milk production to global greenhouse gas emissions. An estimation based on typical farms.

PubMed

Hagemann, Martin; Ndambi, Asaah; Hemme, Torsten; Latacz-Lohmann, Uwe

2012-02-01

Studies on the contribution of milk production to global greenhouse gas (GHG) emissions are rare (FAO 2010) and often based on crude data which do not appropriately reflect the heterogeneity of farming systems. This article estimates GHG emissions from milk production in different dairy regions of the world based on a harmonised farm data and assesses the contribution of milk production to global GHG emissions. The methodology comprises three elements: (1) the International Farm Comparison Network (IFCN) concept of typical farms and the related globally standardised dairy model farms representing 45 dairy regions in 38 countries; (2) a partial life cycle assessment model for estimating GHG emissions of the typical dairy farms; and (3) standard regression analysis to estimate GHG emissions from milk production in countries for which no typical farms are available in the IFCN database. Across the 117 typical farms in the 38 countries analysed, the average emission rate is 1.50 kg CO(2) equivalents (CO(2)-eq.)/kg milk. The contribution of milk production to the global anthropogenic emissions is estimated at 1.3 Gt CO(2)-eq./year, accounting for 2.65% of total global anthropogenic emissions (49 Gt; IPCC, Synthesis Report for Policy Maker, Valencia, Spain, 2007). We emphasise that our estimates of the contribution of milk production to global GHG emissions are subject to uncertainty. Part of the uncertainty stems from the choice of the appropriate methods for estimating emissions at the level of the individual animal.

Climate change, global warming and coral reefs: modelling the effects of temperature.

PubMed

Crabbe, M James C

2008-10-01

Climate change and global warming have severe consequences for the survival of scleractinian (reef-building) corals and their associated ecosystems. This review summarizes recent literature on the influence of temperature on coral growth, coral bleaching, and modelling the effects of high temperature on corals. Satellite-based sea surface temperature (SST) and coral bleaching information available on the internet is an important tool in monitoring and modelling coral responses to temperature. Within the narrow temperature range for coral growth, corals can respond to rate of temperature change as well as to temperature per se. We need to continue to develop models of how non-steady-state processes such as global warming and climate change will affect coral reefs.

A high-resolution physically-based global flood hazard map

NASA Astrophysics Data System (ADS)

Kaheil, Y.; Begnudelli, L.; McCollum, J.

2016-12-01

We present the results from a physically-based global flood hazard model. The model uses a physically-based hydrologic model to simulate river discharges, and 2D hydrodynamic model to simulate inundation. The model is set up such that it allows the application of large-scale flood hazard through efficient use of parallel computing. For hydrology, we use the Hillslope River Routing (HRR) model. HRR accounts for surface hydrology using Green-Ampt parameterization. The model is calibrated against observed discharge data from the Global Runoff Data Centre (GRDC) network, among other publicly-available datasets. The parallel-computing framework takes advantage of the river network structure to minimize cross-processor messages, and thus significantly increases computational efficiency. For inundation, we implemented a computationally-efficient 2D finite-volume model with wetting/drying. The approach consists of simulating flood along the river network by forcing the hydraulic model with the streamflow hydrographs simulated by HRR, and scaled up to certain return levels, e.g. 100 years. The model is distributed such that each available processor takes the next simulation. Given an approximate criterion, the simulations are ordered from most-demanding to least-demanding to ensure that all processors finalize almost simultaneously. Upon completing all simulations, the maximum envelope of flood depth is taken to generate the final map. The model is applied globally, with selected results shown from different continents and regions. The maps shown depict flood depth and extent at different return periods. These maps, which are currently available at 3 arc-sec resolution ( 90m) can be made available at higher resolutions where high resolution DEMs are available. The maps can be utilized by flood risk managers at the national, regional, and even local levels to further understand their flood risk exposure, exercise certain measures of mitigation, and/or transfer the residual risk financially through flood insurance programs.

Assessing skill of a global bimonthly streamflow ensemble prediction system

NASA Astrophysics Data System (ADS)

van Dijk, A. I.; Peña-Arancibia, J.; Sheffield, J.; Wood, E. F.

2011-12-01

Ideally, a seasonal streamflow forecasting system might be conceived of as a system that ingests skillful climate forecasts from general circulation models and propagates these through thoroughly calibrated hydrological models that are initialised using hydrometric observations. In practice, there are practical problems with each of these aspects. Instead, we analysed whether a comparatively simple hydrological model-based Ensemble Prediction System (EPS) can provide global bimonthly streamflow forecasts with some skill and if so, under what circumstances the greatest skill may be expected. The system tested produces ensemble forecasts for each of six annual bimonthly periods based on the previous 30 years of global daily gridded 1° resolution climate variables and an initialised global hydrological model. To incorporate some of the skill derived from ocean conditions, a post-EPS analog method was used to sample from the ensemble based on El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) index values observed prior to the forecast. Forecasts skill was assessed through a hind-casting experiment for the period 1979-2008. Potential skill was calculated with reference to a model run with the actual forcing for the forecast period (the 'perfect' model) and was compared to actual forecast skill calculated for each of the six forecast times for an average 411 Australian and 51 pan-tropical catchments. Significant potential skill in bimonthly forecasts was largely limited to northern regions during the snow melt period, seasonally wet tropical regions at the transition of wet to dry season, and the Indonesian region where rainfall is well correlated to ENSO. The actual skill was approximately 34-50% of the potential skill. We attribute this primarily to limitations in the model structure, parameterisation and global forcing data. Use of better climate forecasts and remote sensing observations of initial catchment conditions should help to increase actual skill in future. Future work also could address the potential skill gain from using weather and climate forecasts and from a calibrated and/or alternative hydrological model or model ensemble. The approach and data might be useful as a benchmark for joint seasonal forecasting experiments planned under GEWEX.

A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves

NASA Astrophysics Data System (ADS)

Montzka, Carsten; Herbst, Michael; Weihermüller, Lutz; Verhoef, Anne; Vereecken, Harry

2017-07-01

Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller-Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem-van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at https://doi.org/10.1594/PANGAEA.870605.

Addressing global uncertainty and sensitivity in first-principles based microkinetic models by an adaptive sparse grid approach

NASA Astrophysics Data System (ADS)

Döpking, Sandra; Plaisance, Craig P.; Strobusch, Daniel; Reuter, Karsten; Scheurer, Christoph; Matera, Sebastian

2018-01-01

In the last decade, first-principles-based microkinetic modeling has been developed into an important tool for a mechanistic understanding of heterogeneous catalysis. A commonly known, but hitherto barely analyzed issue in this kind of modeling is the presence of sizable errors from the use of approximate Density Functional Theory (DFT). We here address the propagation of these errors to the catalytic turnover frequency (TOF) by global sensitivity and uncertainty analysis. Both analyses require the numerical quadrature of high-dimensional integrals. To achieve this efficiently, we utilize and extend an adaptive sparse grid approach and exploit the confinement of the strongly non-linear behavior of the TOF to local regions of the parameter space. We demonstrate the methodology on a model of the oxygen evolution reaction at the Co3O4 (110)-A surface, using a maximum entropy error model that imposes nothing but reasonable bounds on the errors. For this setting, the DFT errors lead to an absolute uncertainty of several orders of magnitude in the TOF. We nevertheless find that it is still possible to draw conclusions from such uncertain models about the atomistic aspects controlling the reactivity. A comparison with derivative-based local sensitivity analysis instead reveals that this more established approach provides incomplete information. Since the adaptive sparse grids allow for the evaluation of the integrals with only a modest number of function evaluations, this approach opens the way for a global sensitivity analysis of more complex models, for instance, models based on kinetic Monte Carlo simulations.

Scene-aware joint global and local homographic video coding

NASA Astrophysics Data System (ADS)

Peng, Xiulian; Xu, Jizheng; Sullivan, Gary J.

2016-09-01

Perspective motion is commonly represented in video content that is captured and compressed for various applications including cloud gaming, vehicle and aerial monitoring, etc. Existing approaches based on an eight-parameter homography motion model cannot deal with this efficiently, either due to low prediction accuracy or excessive bit rate overhead. In this paper, we consider the camera motion model and scene structure in such video content and propose a joint global and local homography motion coding approach for video with perspective motion. The camera motion is estimated by a computer vision approach, and camera intrinsic and extrinsic parameters are globally coded at the frame level. The scene is modeled as piece-wise planes, and three plane parameters are coded at the block level. Fast gradient-based approaches are employed to search for the plane parameters for each block region. In this way, improved prediction accuracy and low bit costs are achieved. Experimental results based on the HEVC test model show that up to 9.1% bit rate savings can be achieved (with equal PSNR quality) on test video content with perspective motion. Test sequences for the example applications showed a bit rate savings ranging from 3.7 to 9.1%.

Quantifying Risks in the Global Water-Food-Climate Nexus in the Coming Decades: An Integrated Modeling Approach

NASA Astrophysics Data System (ADS)

Schlosser, C. A.; Strzepek, K.; Arndt, C.; Gueneau, A.; Cai, Y.; Gao, X.; Robinson, S.; Sokolov, A. P.; Thurlow, J.

2011-12-01

The growing need for risk-based assessments of impacts and adaptation to regional climate change calls for the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Moreover, our global water resources include energy, agricultural and environmental systems, which are linked together as well as to climate. With the prospect of potential climate change and associated shifts in hydrologic variation and extremes, the MIT Integrated Global Systems Model (IGSM) framework, in collaboration with UNU-WIDER, has enhanced its capabilities to model impacts (or effects) on the managed water-resource systems. We first present a hybrid approach that extends the MIT Integrated Global System Model (IGSM) framework to provide probabilistic projections of regional climate changes. This procedure constructs meta-ensembles of the regional hydro-climate, combining projections from the MIT IGSM that represent global-scale uncertainties with regionally resolved patterns from archived climate-model projections. From these, a river routing and water-resource management module allocates water among irrigation, hydropower, urban/industrial, and in-stream uses and investigate how society might adapt water resources due to shifts in hydro-climate variations and extremes. These results are then incorporated into economic models allowing us to consider the implications of climate for growth, land use, and development prospects. In this model-based investigation, we consider how changes in the regional hydro-climate over major river basins in southern Africa, Vietnam, as well as the United States impact agricultural productivity and water-management systems, and whether adaptive strategies can cope with the more severe climate-related threats to growth and development. All this is cast under a probabilistic description of regional climate changes encompassed by the IGSM framework.

Projection of heat waves over China for eight different global warming targets using 12 CMIP5 models

NASA Astrophysics Data System (ADS)

Guo, Xiaojun; Huang, Jianbin; Luo, Yong; Zhao, Zongci; Xu, Ying

2017-05-01

Simulation and projection of the characteristics of heat waves over China were investigated using 12 CMIP5 global climate models and the CN05.1 observational gridded dataset. Four heat wave indices (heat wave frequency, longest heat wave duration, heat wave days, and high temperature days) were adopted in the analysis. Evaluations of the 12 CMIP5 models and their ensemble indicated that the multi-model ensemble could capture the spatiotemporal characteristics of heat wave variation over China. The inter-decadal variations of heat waves during 1961-2005 can be well simulated by multi-model ensemble. Based on model projections, the features of heat waves over China for eight different global warming targets (1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C) were explored. The results showed that the frequency and intensity of heat waves would increase more dramatically as the global mean temperature rise attained higher warming targets. Under the RCP8.5 scenario, the four China-averaged heat wave indices would increase from about 1.0 times/year, 2.5, 5.4, and 13.8 days/year to about 3.2 times/year, 14.0, 32.0, and 31.9 days/year for 1.5 and 5.0 °C warming targets, respectively. Those regions that suffer severe heat waves in the base climate would experience the heat waves with greater frequency and severity following global temperature rise. It is also noteworthy that the areas in which a greater number of severe heat waves occur displayed considerable expansion. Moreover, the model uncertainties exhibit a gradual enhancement with projected time extending from 2006 to 2099.

Ionospheric error contribution to GNSS single-frequency navigation at the 2014 solar maximum

NASA Astrophysics Data System (ADS)

Orus Perez, Raul

2017-04-01

For single-frequency users of the global satellite navigation system (GNSS), one of the main error contributors is the ionospheric delay, which impacts the received signals. As is well-known, GPS and Galileo transmit global models to correct the ionospheric delay, while the international GNSS service (IGS) computes precise post-process global ionospheric maps (GIM) that are considered reference ionospheres. Moreover, accurate ionospheric maps have been recently introduced, which allow for the fast convergence of the real-time precise point position (PPP) globally. Therefore, testing of the ionospheric models is a key issue for code-based single-frequency users, which constitute the main user segment. Therefore, the testing proposed in this paper is straightforward and uses the PPP modeling applied to single- and dual-frequency code observations worldwide for 2014. The usage of PPP modeling allows us to quantify—for dual-frequency users—the degradation of the navigation solutions caused by noise and multipath with respect to the different ionospheric modeling solutions, and allows us, in turn, to obtain an independent assessment of the ionospheric models. Compared to the dual-frequency solutions, the GPS and Galileo ionospheric models present worse global performance, with horizontal root mean square (RMS) differences of 1.04 and 0.49 m and vertical RMS differences of 0.83 and 0.40 m, respectively. While very precise global ionospheric models can improve the dual-frequency solution globally, resulting in a horizontal RMS difference of 0.60 m and a vertical RMS difference of 0.74 m, they exhibit a strong dependence on the geographical location and ionospheric activity.

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

NASA Technical Reports Server (NTRS)

Muller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven;

A framework for global river flood risk assessment

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Bouwman, A.; Ward, P. J.; Jongman, B.

2012-04-01

There is an increasing need for strategic global assessments of flood risks. Such assessments may be required by: (a) International Financing Institutes and Disaster Management Agencies to evaluate where, when, and which investments in flood risk mitigation are most required; (b) (re-)insurers, who need to determine their required coverage capital; and (c) large companies to account for risks of regional investments. In this contribution, we propose a framework for global river flood risk assessment. The framework combines coarse scale resolution hazard probability distributions, derived from global hydrological model runs (typical scale about 0.5 degree resolution) with high resolution estimates of exposure indicators. The high resolution is required because floods typically occur at a much smaller scale than the typical resolution of global hydrological models, and exposure indicators such as population, land use and economic value generally are strongly variable in space and time. The framework therefore estimates hazard at a high resolution ( 1 km2) by using a) global forcing data sets of the current (or in scenario mode, future) climate; b) a global hydrological model; c) a global flood routing model, and d) importantly, a flood spatial downscaling routine. This results in probability distributions of annual flood extremes as an indicator of flood hazard, at the appropriate resolution. A second component of the framework combines the hazard probability distribution with classical flood impact models (e.g. damage, affected GDP, affected population) to establish indicators for flood risk. The framework can be applied with a large number of datasets and models and sensitivities of such choices can be evaluated by the user. The framework is applied using the global hydrological model PCR-GLOBWB, combined with a global flood routing model. Downscaling of the hazard probability distributions to 1 km2 resolution is performed with a new downscaling algorithm, applied on a number of target regions. We demonstrate the use of impact models in these regions based on global GDP, population, and land use maps. In this application, we show sensitivities of the estimated risks with regard to the use of different climate input datasets, decisions made in the downscaling algorithm, and different approaches to establish distributed estimates of GDP and asset exposure to flooding.

Global forest sector modeling: application to some impacts of climate change

Treesearch

Joseph Buongiorno

2016-01-01

This paper explored the potential long-term effects of a warming climate on the global wood sector, based on Way and Oren's synthesis (Tree Physiology 30,669-688) indicating positive responses of tree growth to higher temperature in boreal and temperative climates, and negative responses in the topics. Changes in forest productivity were introduced in the Global...

OCO-2 Column Carbon Dioxide and Biometric Data Jointly Constrain Parameterization and Projection of a Global Land Model

NASA Astrophysics Data System (ADS)

Shi, Z.; Crowell, S.; Luo, Y.; Rayner, P. J.; Moore, B., III

2015-12-01

Uncertainty in predicted carbon-climate feedback largely stems from poor parameterization of global land models. However, calibration of global land models with observations has been extremely challenging at least for two reasons. First we lack global data products from systematical measurements of land surface processes. Second, computational demand is insurmountable for estimation of model parameter due to complexity of global land models. In this project, we will use OCO-2 retrievals of dry air mole fraction XCO2 and solar induced fluorescence (SIF) to independently constrain estimation of net ecosystem exchange (NEE) and gross primary production (GPP). The constrained NEE and GPP will be combined with data products of global standing biomass, soil organic carbon and soil respiration to improve the community land model version 4.5 (CLM4.5). Specifically, we will first develop a high fidelity emulator of CLM4.5 according to the matrix representation of the terrestrial carbon cycle. It has been shown that the emulator fully represents the original model and can be effectively used for data assimilation to constrain parameter estimation. We will focus on calibrating those key model parameters (e.g., maximum carboxylation rate, turnover time and transfer coefficients of soil carbon pools, and temperature sensitivity of respiration) for carbon cycle. The Bayesian Markov chain Monte Carlo method (MCMC) will be used to assimilate the global databases into the high fidelity emulator to constrain the model parameters, which will be incorporated back to the original CLM4.5. The calibrated CLM4.5 will be used to make scenario-based projections. In addition, we will conduct observing system simulation experiments (OSSEs) to evaluate how the sampling frequency and length could affect the model constraining and prediction.

Spatiotemporal patterns of terrestrial gross primary production: A review

NASA Astrophysics Data System (ADS)

Anav, Alessandro; Friedlingstein, Pierre; Beer, Christian; Ciais, Philippe; Harper, Anna; Jones, Chris; Murray-Tortarolo, Guillermo; Papale, Dario; Parazoo, Nicholas C.; Peylin, Philippe; Piao, Shilong; Sitch, Stephen; Viovy, Nicolas; Wiltshire, Andy; Zhao, Maosheng

2015-09-01

Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.

Simulations of Seismic Wave Propagation on Mars

NASA Astrophysics Data System (ADS)

Bozdağ, Ebru; Ruan, Youyi; Metthez, Nathan; Khan, Amir; Leng, Kuangdai; van Driel, Martin; Wieczorek, Mark; Rivoldini, Attilio; Larmat, Carène S.; Giardini, Domenico; Tromp, Jeroen; Lognonné, Philippe; Banerdt, Bruce W.

2017-10-01

We present global and regional synthetic seismograms computed for 1D and 3D Mars models based on the spectral-element method. For global simulations, we implemented a radially-symmetric Mars model with a 110 km thick crust (Sohl and Spohn in J. Geophys. Res., Planets 102(E1):1613-1635, 1997). For this 1D model, we successfully benchmarked the 3D seismic wave propagation solver SPECFEM3D_GLOBE (Komatitsch and Tromp in Geophys. J. Int. 149(2):390-412, 2002a; 150(1):303-318, 2002b) against the 2D axisymmetric wave propagation solver AxiSEM (Nissen-Meyer et al. in Solid Earth 5(1):425-445, 2014) at periods down to 10 s. We also present higher-resolution body-wave simulations with AxiSEM down to 1 s in a model with a more complex 1D crust, revealing wave propagation effects that would have been difficult to interpret based on ray theory. For 3D global simulations based on SPECFEM3D_GLOBE, we superimposed 3D crustal thickness variations capturing the distinct crustal dichotomy between Mars' northern and southern hemispheres, as well as topography, ellipticity, gravity, and rotation. The global simulations clearly indicate that the 3D crust speeds up body waves compared to the reference 1D model, whereas it significantly changes surface waveforms and their dispersive character depending on its thickness. We also perform regional simulations with the solver SES3D (Fichtner et al. Geophys. J. Int. 179:1703-1725, 2009) based on 3D crustal models derived from surface composition, thereby addressing the effects of various distinct crustal features down to 2 s. The regional simulations confirm the strong effects of crustal variations on waveforms. We conclude that the numerical tools are ready for examining more scenarios, including various other seismic models and sources.

Acetylene (C2H2) and hydrogen cyanide (HCN) from IASI satellite observations: global distributions, validation, and comparison with model

NASA Astrophysics Data System (ADS)

Duflot, V.; Wespes, C.; Clarisse, L.; Hurtmans, D.; Ngadi, Y.; Jones, N.; Paton-Walsh, C.; Hadji-Lazaro, J.; Vigouroux, C.; De Mazière, M.; Metzger, J.-M.; Mahieu, E.; Servais, C.; Hase, F.; Schneider, M.; Clerbaux, C.; Coheur, P.-F.

2015-09-01

We present global distributions of C2H2 and hydrogen cyanide (HCN) total columns derived from the Infrared Atmospheric Sounding Interferometer (IASI) for the years 2008-2010. These distributions are obtained with a fast method allowing to retrieve C2H2 abundance globally with a 5 % precision and HCN abundance in the tropical (subtropical) belt with a 10 % (25 %) precision. IASI data are compared for validation purposes with ground-based Fourier transform infrared (FTIR) spectrometer measurements at four selected stations. We show that there is an overall agreement between the ground-based and space measurements with correlation coefficients for daily mean measurements ranging from 0.28 to 0.81, depending on the site. Global C2H2 and subtropical HCN abundances retrieved from IASI spectra show the expected seasonality linked to variations in the anthropogenic emissions and seasonal biomass burning activity, as well as exceptional events, and are in good agreement with previous spaceborne studies. Total columns simulated by the Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) are compared to the ground-based FTIR measurements at the four selected stations. The model is able to capture the seasonality in the two species in most of the cases, with correlation coefficients for daily mean measurements ranging from 0.50 to 0.86, depending on the site. IASI measurements are also compared to the distributions from MOZART-4. Seasonal cycles observed from satellite data are reasonably well reproduced by the model with correlation coefficients ranging from -0.31 to 0.93 for C2H2 daily means, and from 0.09 to 0.86 for HCN daily means, depending on the considered region. However, the anthropogenic (biomass burning) emissions used in the model seem to be overestimated (underestimated), and a negative global mean bias of 1 % (16 %) of the model relative to the satellite observations was found for C2H2 (HCN).

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value.

PubMed

Zhao, Zhi-Lin; Wang, Chang-Dong; Lai, Jian-Huang

2016-01-01

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density.

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value

PubMed Central

Zhao, Zhi-Lin; Wang, Chang-Dong; Lai, Jian-Huang

2016-01-01

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density. PMID:26828803

Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Luis; Marchante, Ruth; Cony, Marco

2010-10-15

Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time seriesmore » applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models. (author)« less

Morphology of the winter anomaly in NmF2 and Total Electron Content

NASA Astrophysics Data System (ADS)

Yasyukevich, Yury; Ratovsky, Konstantin; Yasyukevich, Anna; Klimenko, Maksim; Klimenko, Vladimir; Chirik, Nikolay

2017-04-01

We analyzed the winter anomaly manifestation in the F2 peak electron density (NmF2) and Total Electron Content (TEC) based on the observation data and model calculation results. For the analysis we used 1998-2015 TEC Global Ionospheric Maps (GIM) and NmF2 ground-based ionosonde observation data from and COSMIC, CHAMP and GRACE radio occultation data. We used Global Self-consistent Model of the Thermosphere, Ionosphere, and Protonosphere (GSM TIP) and International Reference Ionosphere model (IRI-2012). Based on the observation data and model calculation results we constructed the maps of the winter anomaly intensity in TEC and NmF2 for the different solar and geomagnetic activity levels. The winter anomaly intensity was found to be higher in NmF2 than in TEC according to both observation and modeling. In this report we show the similarity and difference in winter anomaly as revealed in experimental data and model results.

EX.MAIN. Expert System Model for Maintenance and Staff Training.

ERIC Educational Resources Information Center

Masturzi, Elio R.

EX.MAIN, a model for maintenance and staff training which combines knowledge based expert systems and computer based training, was developed jointly by the Department of Production Engineering of the University of Naples and CIRCUMVESUVIANA, the largest private railroad in Italy. It is a global model in the maintenance field which contains both…

Landscape-based population viability models demonstrate importance of strategic conservation planning for birds

Treesearch

Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh; D. Todd Jones-Farland

2013-01-01

Efforts to conserve regional biodiversity in the face of global climate change, habitat loss and fragmentation will depend on approaches that consider population processes at multiple scales. By combining habitat and demographic modeling, landscape-based population viability models effectively relate small-scale habitat and landscape patterns to regional population...

CISM: Modeling the Sun-Earth Connection

NASA Astrophysics Data System (ADS)

Hughes, W. J.; Team, T. C.

2003-12-01

The Center for Integrated SpaceWeather Modeling (CISM), an NSF Science and Technology Center that is a consortium of ten institutions headed by Boston University, has as its primary goal the development of a series of ever improving versions of a comprehensive physics-based simulation model that describes the space environment from the Sun to the Earth. CISM will do this by coupling existing models of components of the system. In this paper we review our progress to date and summarize our plans. We discuss results of initial coupling of MHD models of the corona and solar wind, and of a global magnetospheric MHD model with a global ionosphere/thermosphere model, a radiation belt model, and a ring current particle model. Coupling the SAIC coronal MHD model and the U Colorado/SEC solar wind MHD codes allows us to track CMEs from the base of the corona to 1 AU. The results show how shocks form and develop in the heliosphere, and how the CME flattens into a pancake shape by the time it reaches earth. Coupling the Lyon/Fedder/Mobarry global MHD model with the Rice Convection Model and the NCAR TIE-GCM/TING model allows full dynamic coupling between the magnetosphere, the ionosphere/thermosphere, and the hot plasma in the inner magnetosphere. Including the Dartmouth radiation belt model shows how the radiation belts evolve in a realistic magnetosphere.

Global Carbon Cycle Modeling in GISS ModelE2 GCM

NASA Astrophysics Data System (ADS)

Aleinov, I. D.; Kiang, N. Y.; Romanou, A.; Romanski, J.

2014-12-01

Consistent and accurate modeling of the Global Carbon Cycle remains one of the main challenges for the Earth System Models. NASA Goddard Institute for Space Studies (GISS) ModelE2 General Circulation Model (GCM) was recently equipped with a complete Global Carbon Cycle algorithm, consisting of three integrated components: Ent Terrestrial Biosphere Model (Ent TBM), Ocean Biogeochemistry Module and atmospheric CO2 tracer. Ent TBM provides CO2 fluxes from the land surface to the atmosphere. Its biophysics utilizes the well-known photosynthesis functions of Farqhuar, von Caemmerer, and Berry and Farqhuar and von Caemmerer, and stomatal conductance of Ball and Berry. Its phenology is based on temperature, drought, and radiation fluxes, and growth is controlled via allocation of carbon from labile carbohydrate reserve storage to different plant components. Soil biogeochemistry is based on the Carnegie-Ames-Stanford (CASA) model of Potter et al. Ocean biogeochemistry module (the NASA Ocean Biogeochemistry Model, NOBM), computes prognostic distributions for biotic and abiotic fields that influence the air-sea flux of CO2 and the deep ocean carbon transport and storage. Atmospheric CO2 is advected with a quadratic upstream algorithm implemented in atmospheric part of ModelE2. Here we present the results for pre-industrial equilibrium and modern transient simulations and provide comparison to available observations. We also discuss the process of validation and tuning of particular algorithms used in the model.

Tropospheric carbon monoxide over the Pacific during HIPPO: two-way coupled simulation of GEOS-Chem and its multiple nested models

NASA Astrophysics Data System (ADS)

Yan, Y.-Y.; Lin, J.-T.; Kuang, Y.; Yang, D.; Zhang, L.

2014-07-01

Global chemical transport models (CTMs) are used extensively to study air pollution and transport at a global scale. These models are limited by coarse horizontal resolutions, not allowing for detailed representation of small-scale nonlinear processes over the pollutant source regions. Here we couple the global GEOS-Chem CTM and its three high-resolution nested models to simulate the tropospheric carbon monoxide (CO) over the Pacific Ocean during five HIAPER Pole-to-Pole Observations (HIPPO) campaigns between 2009 and 2011. We develop a two-way coupler, PKUCPL, to integrate simulation results for chemical constituents from the global model (at 2.5° long. × 2° lat.) and the three nested models (at 0.667° long. × 0.5° lat.) covering Asia, North America and Europe, respectively. The coupler obtains nested model results to modify the global model simulation within the respective nested domains, and simultaneously acquires global model results to provide lateral boundary conditions for the nested models. Compared to the global model alone, the two-way coupled simulation results in enhanced CO concentrations in the nested domains. Sensitivity tests suggest the enhancement to be a result of improved representation of the spatial distributions of CO, nitrogen oxides and non-methane volatile organic compounds, the meteorological dependence of natural emissions, and other resolution-dependent processes. The relatively long lifetime of CO allows for the enhancement to be accumulated and carried across the globe. We find that the two-way coupled simulation increases the global tropospheric mean CO concentrations in 2009 by 10.4%, with a greater enhancement at 13.3% in the Northern Hemisphere. Coincidently, the global tropospheric mean hydroxyl radical (OH) is reduced by 4.2% (as compared to the interannual variability of OH at 2.3%), resulting in a 4.2% enhancement in the methyl chloroform lifetime (MCF, via reaction with tropospheric OH). The resulting CO and OH contents and MCF lifetime are closer to observation-based estimates. Both the global and the two-way coupled models capture the general spatiotemporal patterns of HIPPO CO over the Pacific. The two-way coupled simulation is much closer to HIPPO CO, with a mean bias of 1.1 ppb (1.4%) below 9 km compared to the bias at -7.2 ppb (-9.2%) for the global model. The improvement is most apparent over the North Pacific. Our test simulations show that the global model could resemble the two-way coupled simulation (especially below 4 km) by increasing its global CO emissions by 15% for HIPPO-1 and HIPPO-3, by 25% for HIPPO-2 and HIPPO-4, and by 35% for HIPPO-5. This has important implications for using the global model to constrain CO emissions. Thus, the two-way coupled simulation is a significantly improved model tool to studying the global impacts of air pollutants from major anthropogenic source regions.

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

DOE PAGES

Bennett, Katrina Eleanor; Urrego Blanco, Jorge Rolando; Jonko, Alexandra; ...

2017-11-20

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model.more » Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.« less

Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bennett, Katrina Eleanor; Urrego Blanco, Jorge Rolando; Jonko, Alexandra

The Colorado River basin is a fundamentally important river for society, ecology and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model.more » Here, we combine global sensitivity analysis with a space-filling Latin Hypercube sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach.« less

A new, accurate, global hydrography data for remote sensing and modelling of river hydrodynamics

NASA Astrophysics Data System (ADS)

Yamazaki, D.

2017-12-01

A high-resolution hydrography data is an important baseline data for remote sensing and modelling of river hydrodynamics, given the spatial scale of river network is much smaller than that of land hydrology or atmosphere/ocean circulations. For about 10 years, HydroSHEDS, developed based on the SRTM3 DEM, has been the only available global-scale hydrography data. However, the data availability at the time of HydroSHEDS development limited the quality of the represented river networks. Here, we developed a new global hydrography data using latest geodata such as the multi-error-removed elevation data (MERIT DEM), Landsat-based global water body data (GSWO & G3WBM), cloud-sourced open geography database (OpenStreetMap). The new hydrography data covers the entire globe (including boreal regions above 60N), and it represents more detailed structure of the world river network and contains consistent supplementary data layers such as hydrologically adjusted elevations and river channel width. In the AGU meeting, the developing methodology, assessed quality, and potential applications of the new global hydrography data will be introduced.

What do we mean by sensitivity analysis? The need for comprehensive characterization of "global" sensitivity in Earth and Environmental systems models

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2015-05-01

Sensitivity analysis is an essential paradigm in Earth and Environmental Systems modeling. However, the term "sensitivity" has a clear definition, based in partial derivatives, only when specified locally around a particular point (e.g., optimal solution) in the problem space. Accordingly, no unique definition exists for "global sensitivity" across the problem space, when considering one or more model responses to different factors such as model parameters or forcings. A variety of approaches have been proposed for global sensitivity analysis, based on different philosophies and theories, and each of these formally characterizes a different "intuitive" understanding of sensitivity. These approaches focus on different properties of the model response at a fundamental level and may therefore lead to different (even conflicting) conclusions about the underlying sensitivities. Here we revisit the theoretical basis for sensitivity analysis, summarize and critically evaluate existing approaches in the literature, and demonstrate their flaws and shortcomings through conceptual examples. We also demonstrate the difficulty involved in interpreting "global" interaction effects, which may undermine the value of existing interpretive approaches. With this background, we identify several important properties of response surfaces that are associated with the understanding and interpretation of sensitivities in the context of Earth and Environmental System models. Finally, we highlight the need for a new, comprehensive framework for sensitivity analysis that effectively characterizes all of the important sensitivity-related properties of model response surfaces.

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE PAGES

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.; ...

2016-05-02

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Global spatiotemporal distribution of soil respiration modeled using a global database

NASA Astrophysics Data System (ADS)

Hashimoto, S.; Carvalhais, N.; Ito, A.; Migliavacca, M.; Nishina, K.; Reichstein, M.

2015-07-01

The flux of carbon dioxide from the soil to the atmosphere (soil respiration) is one of the major fluxes in the global carbon cycle. At present, the accumulated field observation data cover a wide range of geographical locations and climate conditions. However, there are still large uncertainties in the magnitude and spatiotemporal variation of global soil respiration. Using a global soil respiration data set, we developed a climate-driven model of soil respiration by modifying and updating Raich's model, and the global spatiotemporal distribution of soil respiration was examined using this model. The model was applied at a spatial resolution of 0.5°and a monthly time step. Soil respiration was divided into the heterotrophic and autotrophic components of respiration using an empirical model. The estimated mean annual global soil respiration was 91 Pg C yr-1 (between 1965 and 2012; Monte Carlo 95 % confidence interval: 87-95 Pg C yr-1) and increased at the rate of 0.09 Pg C yr-2. The contribution of soil respiration from boreal regions to the total increase in global soil respiration was on the same order of magnitude as that of tropical and temperate regions, despite a lower absolute magnitude of soil respiration in boreal regions. The estimated annual global heterotrophic respiration and global autotrophic respiration were 51 and 40 Pg C yr-1, respectively. The global soil respiration responded to the increase in air temperature at the rate of 3.3 Pg C yr-1 °C-1, and Q10 = 1.4. Our study scaled up observed soil respiration values from field measurements to estimate global soil respiration and provide a data-oriented estimate of global soil respiration. The estimates are based on a semi-empirical model parameterized with over one thousand data points. Our analysis indicates that the climate controls on soil respiration may translate into an increasing trend in global soil respiration and our analysis emphasizes the relevance of the soil carbon flux from soil to the atmosphere in response to climate change. Further approaches should additionally focus on climate controls in soil respiration in combination with changes in vegetation dynamics and soil carbon stocks, along with their effects on the long temporal dynamics of soil respiration. We expect that these spatiotemporal estimates will provide a benchmark for future studies and also help to constrain process-oriented models.

A new physics-based modeling approach for tsunami-ionosphere coupling

NASA Astrophysics Data System (ADS)

Meng, X.; Komjathy, A.; Verkhoglyadova, O. P.; Yang, Y.-M.; Deng, Y.; Mannucci, A. J.

2015-06-01

Tsunamis can generate gravity waves propagating upward through the atmosphere, inducing total electron content (TEC) disturbances in the ionosphere. To capture this process, we have implemented tsunami-generated gravity waves into the Global Ionosphere-Thermosphere Model (GITM) to construct a three-dimensional physics-based model WP (Wave Perturbation)-GITM. WP-GITM takes tsunami wave properties, including the wave height, wave period, wavelength, and propagation direction, as inputs and time-dependently characterizes the responses of the upper atmosphere between 100 km and 600 km altitudes. We apply WP-GITM to simulate the ionosphere above the West Coast of the United States around the time when the tsunami associated with the March 2011 Tohuku-Oki earthquke arrived. The simulated TEC perturbations agree with Global Positioning System observations reasonably well. For the first time, a fully self-consistent and physics-based model has reproduced the GPS-observed traveling ionospheric signatures of an actual tsunami event.

Global scale predictability of floods

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Gijsbers, Peter; Sperna Weiland, Frederiek

2016-04-01

Flood (and storm surge) forecasting at the continental and global scale has only become possible in recent years (Emmerton et al., 2016; Verlaan et al., 2015) due to the availability of meteorological forecast, global scale precipitation products and global scale hydrologic and hydrodynamic models. Deltares has setup GLOFFIS a research-oriented multi model operational flood forecasting system based on Delft-FEWS in an open experimental ICT facility called Id-Lab. In GLOFFIS both the W3RA and PCRGLOB-WB model are run in ensemble mode using GEFS and ECMWF-EPS (latency 2 days). GLOFFIS will be used for experiments into predictability of floods (and droughts) and their dependency on initial state estimation, meteorological forcing and the hydrologic model used. Here we present initial results of verification of the ensemble flood forecasts derived with the GLOFFIS system. Emmerton, R., Stephens, L., Pappenberger, F., Pagano, T., Weerts, A., Wood, A. Salamon, P., Brown, J., Hjerdt, N., Donnelly, C., Cloke, H. Continental and Global Scale Flood Forecasting Systems, WIREs Water (accepted), 2016 Verlaan M, De Kleermaeker S, Buckman L. GLOSSIS: Global storm surge forecasting and information system 2015, Australasian Coasts & Ports Conference, 15-18 September 2015,Auckland, New Zealand.

SURFEX v8.0 interface with OASIS3-MCT to couple atmosphere with hydrology, ocean, waves and sea-ice models, from coastal to global scales

NASA Astrophysics Data System (ADS)

Voldoire, Aurore; Decharme, Bertrand; Pianezze, Joris; Lebeaupin Brossier, Cindy; Sevault, Florence; Seyfried, Léo; Garnier, Valérie; Bielli, Soline; Valcke, Sophie; Alias, Antoinette; Accensi, Mickael; Ardhuin, Fabrice; Bouin, Marie-Noëlle; Ducrocq, Véronique; Faroux, Stéphanie; Giordani, Hervé; Léger, Fabien; Marsaleix, Patrick; Rainaud, Romain; Redelsperger, Jean-Luc; Richard, Evelyne; Riette, Sébastien

2017-11-01

This study presents the principles of the new coupling interface based on the SURFEX multi-surface model and the OASIS3-MCT coupler. As SURFEX can be plugged into several atmospheric models, it can be used in a wide range of applications, from global and regional coupled climate systems to high-resolution numerical weather prediction systems or very fine-scale models dedicated to process studies. The objective of this development is to build and share a common structure for the atmosphere-surface coupling of all these applications, involving on the one hand atmospheric models and on the other hand ocean, ice, hydrology, and wave models. The numerical and physical principles of SURFEX interface between the different component models are described, and the different coupled systems in which the SURFEX OASIS3-MCT-based coupling interface is already implemented are presented.

A Future-Oriented, Globally Based Curriculum Model for Industrial Technology.

ERIC Educational Resources Information Center

Hacker, Michael

1982-01-01

Presents a future-oriented curriculum approach for industrial technology programs. Major global issues provide the basic structure for curriculum development. These issues include energy management, resource management, technological advancement, and international relations. Rationales for industrial technology are discussed and a curriculum…

Simulating the effects of climate and agricultural management practices on global crop yield

NASA Astrophysics Data System (ADS)

Deryng, D.; Sacks, W. J.; Barford, C. C.; Ramankutty, N.

2011-06-01

Climate change is expected to significantly impact global food production, and it is important to understand the potential geographic distribution of yield losses and the means to alleviate them. This study presents a new global crop model, PEGASUS 1.0 (Predicting Ecosystem Goods And Services Using Scenarios) that integrates, in addition to climate, the effect of planting dates and cultivar choices, irrigation, and fertilizer application on crop yield for maize, soybean, and spring wheat. PEGASUS combines carbon dynamics for crops with a surface energy and soil water balance model. It also benefits from the recent development of a suite of global data sets and analyses that serve as model inputs or as calibration data. These include data on crop planting and harvesting dates, crop-specific irrigated areas, a global analysis of yield gaps, and harvested area and yield of major crops. Model results for present-day climate and farm management compare reasonably well with global data. Simulated planting and harvesting dates are within the range of crop calendar observations in more than 75% of the total crop-harvested areas. Correlation of simulated and observed crop yields indicates a weighted coefficient of determination, with the weighting based on crop-harvested area, of 0.81 for maize, 0.66 for soybean, and 0.45 for spring wheat. We found that changes in temperature and precipitation as predicted by global climate models for the 2050s lead to a global yield reduction if planting and harvesting dates remain unchanged. However, adapting planting dates and cultivar choices increases yield in temperate regions and avoids 7-18% of global losses.

The data quality analyzer: a quality control program for seismic data

USGS Publications Warehouse

Ringler, Adam; Hagerty, M.T.; Holland, James F.; Gonzales, A.; Gee, Lind S.; Edwards, J.D.; Wilson, David; Baker, Adam

2015-01-01

The quantification of data quality is based on the evaluation of various metrics (e.g., timing quality, daily noise levels relative to long-term noise models, and comparisons between broadband data and event synthetics). Users may select which metrics contribute to the assessment and those metrics are aggregated into a “grade” for each station. The DQA is being actively used for station diagnostics and evaluation based on the completed metrics (availability, gap count, timing quality, deviation from a global noise model, deviation from a station noise model, coherence between co-located sensors, and comparison between broadband data and synthetics for earthquakes) on stations in the Global Seismographic Network and Advanced National Seismic System.

Chemical OSSEs in Global Modeling and Assimilation Office (GMAO)

NASA Technical Reports Server (NTRS)

Pawson, Steven

2008-01-01

This presentation will summarize ongoing 'chemical observing system simulation experiment (OSSE)' work in the Global Modeling and Assimilation Office (GMAO). Weather OSSEs are being studied in detail, with a 'nature run' based on the European Centre for Medium-Range Weather Forecasts (ECMWF) model that can be sampled by a synthesized suite of satellites that reproduces present-day observations. Chemical OSSEs are based largely on the carbon-cycle project and aim to study (1) how well we can reproduce the observed carbon distribution with the Atmospheric Infrared Sounder (AIRS) and Orbiting Carbon Observatory (OCO) sensors and (2) with what accuracy can we deduce surface sources and sinks of carbon species in an assimilation system.

An integrated model for the assessment of global water resources - Part 2: Anthropogenic activities modules and assessments

NASA Astrophysics Data System (ADS)

Hanasaki, N.; Kanae, S.; Oki, T.; Shirakawa, N.

2007-10-01

To assess global water resources from the perspective of subannual variation in water resources and water use, an integrated water resources model was developed. In a companion report, we presented the global meteorological forcing input used to drive the model and two natural hydrological cycle modules, namely, the land surface hydrology module and the river routing module. Here, we present the remaining four modules, which represent anthropogenic activities: a crop growth module, a reservoir operation module, an environmental flow requirement module, and an anthropogenic withdrawal module. In addition, we discuss the results of a global water resources assessment using the integrated model. The crop growth module is a relatively simple model based on heat unit theory and potential biomass and harvest index concepts. The performance of the crop growth module was examined extensively because agricultural water comprises approximately 70% of total water withdrawal in the world. The estimated crop calendar showed good agreement with earlier reports for wheat, maize, and rice in major countries of production. The estimated irrigation water withdrawal also showed fair agreement with country statistics, but tended to underestimate countries in the Asian monsoon region. In the reservoir operation module, 452 major reservoirs with more than 1 km³ each of storage capacity store and release water according to their own rules of operation. Operating rules were determined for each reservoir using an algorithm that used currently available global data such as reservoir storage capacity, intended purposes, simulated inflow, and water demand in the lower reaches. The environmental flow requirement module was newly developed based on case studies from around the world. The integrated model closes both energy and water balances on land surfaces. Global water resources were assessed on a subannual basis using a newly devised index that locates water-stressed regions that were undetected in earlier studies. These regions, which are indicated by a gap in the subannual distribution of water resources and water use, include the Sahel, the Asian monsoon region, and southern Africa. The integrated model is applicable to assess various global environmental projections such as climate change.

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Zhen; Zimmermann, Niklaus E.; Kaplan, Jed O.; Poulter, Benjamin

2016-03-01

Simulations of the spatiotemporal dynamics of wetlands are key to understanding the role of wetland biogeochemistry under past and future climate. Hydrologic inundation models, such as the TOPography-based hydrological model (TOPMODEL), are based on a fundamental parameter known as the compound topographic index (CTI) and offer a computationally cost-efficient approach to simulate wetland dynamics at global scales. However, there remains a large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl (Lund-Potsdam-Jena Wald Schnee und Landschaft version) Dynamic Global Vegetation Model (DGVM) and quantifies uncertainties by comparing three digital elevation model (DEM) products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. In addition, we found that calibrating TOPMODEL with a benchmark wetland data set can help to successfully delineate the seasonal and interannual variation of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows the best accuracy for capturing the spatiotemporal dynamics of wetlands among the three DEM products. The estimate of global wetland potential/maximum is ˜ 10.3 Mkm2 (106 km2), with a mean annual maximum of ˜ 5.17 Mkm2 for 1980-2010. When integrated with wetland methane emission submodule, the uncertainty of global annual CH4 emissions from topography inputs is estimated to be 29.0 Tg yr-1. This study demonstrates the feasibility of TOPMODEL to capture spatial heterogeneity of inundation at a large scale and highlights the significance of correcting maximum wetland extent to improve modeling of interannual variations in wetland area. It additionally highlights the importance of an adequate investigation of topographic indices for simulating global wetlands and shows the opportunity to converge wetland estimates across LSMs by identifying the uncertainty associated with existing wetland products.

Global dynamics of a network-based SIQRS epidemic model with demographics and vaccination

NASA Astrophysics Data System (ADS)

Huang, Shouying; Chen, Fengde; Chen, Lijuan

2017-02-01

This paper investigates a new SIQRS epidemic model with demographics and vaccination on complex heterogeneous networks. We analytically derive the basic reproduction number R0, which determines not only the existence of endemic equilibrium but also the global dynamics of the model. The permanence of the disease and the globally asymptotical stability of disease-free equilibrium are proved in detail. By using a monotone iterative technique, we show that the unique endemic equilibrium is globally attractive under certain conditions. Our results really improve and enrich the results in Li et al (2014) [14]. Interestingly, the basic reproduction number R0 bears no relation to the degree-dependent birth, but our simulations indicate that the degree-dependent birth does affect the epidemic dynamics. Furthermore, we find that quarantine plays a more active role than vaccination in controlling the disease.

Ionospheric propagation correction modeling for satellite altimeters

NASA Technical Reports Server (NTRS)

Nesterczuk, G.

1981-01-01

The theoretical basis and avaliable accuracy verifications were reviewed and compared for ionospheric correction procedures based on a global ionsopheric model driven by solar flux, and a technique in which measured electron content (using Faraday rotation measurements) for one path is mapped into corrections for a hemisphere. For these two techniques, RMS errors for correcting satellite altimeters data (at 14 GHz) are estimated to be 12 cm and 3 cm, respectively. On the basis of global accuracy and reliability after implementation, the solar flux model is recommended.

Recent Achievements of the Collaboratory for the Study of Earthquake Predictability

NASA Astrophysics Data System (ADS)

Jordan, T. H.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Jackson, D. D.; Rhoades, D. A.; Zechar, J. D.; Marzocchi, W.

2016-12-01

The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 442 models under evaluation. The California testing center, started by SCEC, Sept 1, 2007, currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. Our tests are now based on the hypocentral locations and magnitudes of cataloged earthquakes, but we plan to test focal mechanisms, seismic hazard models, ground motion forecasts, and finite rupture forecasts as well. We have increased computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model, introduced Bayesian ensemble models, and implemented support for non-Poissonian simulation-based forecasts models. We are currently developing formats and procedures to evaluate externally hosted forecasts and predictions. CSEP supports the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. We found that earthquakes as small as magnitude 2.5 provide important information on subsequent earthquakes larger than magnitude 5. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence showed that some physics-based and hybrid models outperform catalog-based (e.g., ETAS) models. This experiment also demonstrates the ability of the CSEP infrastructure to support retrospective forecast testing. Current CSEP development activities include adoption of the Comprehensive Earthquake Catalog (ComCat) as an authorized data source, retrospective testing of simulation-based forecasts, and support for additive ensemble methods. We describe the open-source CSEP software that is available to researchers as they develop their forecast models. We also discuss how CSEP procedures are being adapted to intensity and ground motion prediction experiments as well as hazard model testing.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghimire, Bardan; Riley, William J.; Koven, Charles D.

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis ratesmore » are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO 2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.« less

Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

DOE PAGES

Ghimire, Bardan; Riley, William J.; Koven, Charles D.; ...

2016-05-01

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis ratesmore » are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO 2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.« less

Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

NASA Astrophysics Data System (ADS)

Ghimire, Bardan; Riley, William J.; Koven, Charles D.; Mu, Mingquan; Randerson, James T.

2016-06-01

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.

Medium range flood forecasts at global scale

NASA Astrophysics Data System (ADS)

Voisin, N.; Wood, A. W.; Lettenmaier, D. P.; Wood, E. F.

2006-12-01

While weather and climate forecast methods have advanced greatly over the last two decades, this capability has yet to be evidenced in mitigation of water-related natural hazards (primarily floods and droughts), especially in the developing world. Examples abound of extreme property damage and loss of life due to floods in the underdeveloped world. For instance, more than 4.5 million people were affected by the July 2000 flooding of the Mekong River and its tributaries in Cambodia, Vietnam, Laos and Thailand. The February- March 2000 floods in the Limpopo River of Mozambique caused extreme disruption to that country's fledgling economy. Mitigation of these events through advance warning has typically been modest at best. Despite the above noted improvement in weather and climate forecasts, there is at present no system for forecasting of floods globally, notwithstanding that the potential clearly exists. We describe a methodology that is eventually intended to generate global flood predictions routinely. It draws heavily from the experimental North American Land Data Assimilation System (NLDAS) and the companion Global Land Data Assimilation System (GLDAS) for development of nowcasts, and the University of Washington Experimental Hydrologic Prediction System to develop ensemble hydrologic forecasts based on Numerical Weather Prediction (NWP) models which serve both as nowcasts (and hence reduce the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient) and provide forecasts for lead times as long as fifteen days. The heart of the hydrologic modeling system is the University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype (tested using retrospective data), VIC is driven globally up to the time of forecast with daily ERA40 precipitation (rescaled on a monthly basis to a station-based global climatology), ERA40 wind, and ERA40 average surface air temperature (with temperature ranges adjusted to a station-based climatology). In the retrospective forecasting mode, VIC is driven by global NCEP ensemble 15-day reforecasts provided by Tom Hamill (NOAA/ERL), bias corrected with respect to the adjusted ERA40 data and further downscaled spatially using higher spatial resolution Global Precipitation Climatology Project (GPCP) 1dd daily precipitation. Downward solar and longwave radiation, surface relative humidity, and other model forcings are derived from relationships with the daily temperature range during both the retrospective (spinup) and forecast period. The initial system is implemented globally at one-half degree spatial resolution. We evaluate model performance retrospectively for predictions of major floods for the Oder River in 1997, the Mekong River in 2000 and the Limpopo River in 2000.

Hierarchical information fusion for global displacement estimation in microsensor motion capture.

PubMed

Meng, Xiaoli; Zhang, Zhi-Qiang; Wu, Jian-Kang; Wong, Wai-Choong

2013-07-01

This paper presents a novel hierarchical information fusion algorithm to obtain human global displacement for different gait patterns, including walking, running, and hopping based on seven body-worn inertial and magnetic measurement units. In the first-level sensor fusion, the orientation for each segment is achieved by a complementary Kalman filter (CKF) which compensates for the orientation error of the inertial navigation system solution through its error state vector. For each foot segment, the displacement is also estimated by the CKF, and zero velocity update is included for the drift reduction in foot displacement estimation. Based on the segment orientations and left/right foot locations, two global displacement estimates can be acquired from left/right lower limb separately using a linked biomechanical model. In the second-level geometric fusion, another Kalman filter is deployed to compensate for the difference between the two estimates from the sensor fusion and get more accurate overall global displacement estimation. The updated global displacement will be transmitted to left/right foot based on the human lower biomechanical model to restrict the drifts in both feet displacements. The experimental results have shown that our proposed method can accurately estimate human locomotion for the three different gait patterns with regard to the optical motion tracker.

Carbon stock and carbon turnover in boreal and temperate forests - Integration of remote sensing data and global vegetation models

NASA Astrophysics Data System (ADS)

Thurner, Martin; Beer, Christian; Carvalhais, Nuno; Forkel, Matthias; Tito Rademacher, Tim; Santoro, Maurizio; Tum, Markus; Schmullius, Christiane

2016-04-01

Long-term vegetation dynamics are one of the key uncertainties of the carbon cycle. There are large differences in simulated vegetation carbon stocks and fluxes including productivity, respiration and carbon turnover between global vegetation models. Especially the implementation of climate-related mortality processes, for instance drought, fire, frost or insect effects, is often lacking or insufficient in current models and their importance at global scale is highly uncertain. These shortcomings have been due to the lack of spatially extensive information on vegetation carbon stocks, which cannot be provided by inventory data alone. Instead, we recently have been able to estimate northern boreal and temperate forest carbon stocks based on radar remote sensing data. Our spatially explicit product (0.01° resolution) shows strong agreement to inventory-based estimates at a regional scale and allows for a spatial evaluation of carbon stocks and dynamics simulated by global vegetation models. By combining this state-of-the-art biomass product and NPP datasets originating from remote sensing, we are able to study the relation between carbon turnover rate and a set of climate indices in northern boreal and temperate forests along spatial gradients. We observe an increasing turnover rate with colder winter temperatures and longer winters in boreal forests, suggesting frost damage and the trade-off between frost adaptation and growth being important mortality processes in this ecosystem. In contrast, turnover rate increases with climatic conditions favouring drought and insect outbreaks in temperate forests. Investigated global vegetation models from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT, are able to reproduce observation-based spatial climate - turnover rate relationships only to a limited extent. While most of the models compare relatively well in terms of NPP, simulated vegetation carbon stocks are severely biased compared to our biomass dataset. Current limitations lead to considerable uncertainties in the estimated vegetation carbon turnover, contributing substantially to the forest feedback to climate change. Our results are the basis for improving mortality concepts in models and estimating their impact on the land carbon balance.

DATA-CONSTRAINED CORONAL MASS EJECTIONS IN A GLOBAL MAGNETOHYDRODYNAMICS MODEL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, M.; Manchester, W. B.; Van der Holst, B.

We present a first-principles-based coronal mass ejection (CME) model suitable for both scientific and operational purposes by combining a global magnetohydrodynamics (MHD) solar wind model with a flux-rope-driven CME model. Realistic CME events are simulated self-consistently with high fidelity and forecasting capability by constraining initial flux rope parameters with observational data from GONG, SOHO /LASCO, and STEREO /COR. We automate this process so that minimum manual intervention is required in specifying the CME initial state. With the newly developed data-driven Eruptive Event Generator using Gibson–Low configuration, we present a method to derive Gibson–Low flux rope parameters through a handful ofmore » observational quantities so that the modeled CMEs can propagate with the desired CME speeds near the Sun. A test result with CMEs launched with different Carrington rotation magnetograms is shown. Our study shows a promising result for using the first-principles-based MHD global model as a forecasting tool, which is capable of predicting the CME direction of propagation, arrival time, and ICME magnetic field at 1 au (see the companion paper by Jin et al. 2016a).« less

A new voxel-based model for the determination of atmospheric weighted mean temperature in GPS atmospheric sounding

NASA Astrophysics Data System (ADS)

He, Changyong; Wu, Suqin; Wang, Xiaoming; Hu, Andong; Wang, Qianxin; Zhang, Kefei

2017-06-01

The Global Positioning System (GPS) is a powerful atmospheric observing system for determining precipitable water vapour (PWV). In the detection of PWV using GPS, the atmospheric weighted mean temperature (Tm) is a crucial parameter for the conversion of zenith tropospheric delay (ZTD) to PWV since the quality of PWV is affected by the accuracy of Tm. In this study, an improved voxel-based Tm model, named GWMT-D, was developed using global reanalysis data over a 4-year period from 2010 to 2013 provided by the United States National Centers for Environmental Prediction (NCEP). The performance of GWMT-D was assessed against three existing empirical Tm models - GTm-III, GWMT-IV, and GTmN - using different data sources in 2014 - the NCEP reanalysis data, surface Tm data provided by Global Geodetic Observing System and radiosonde measurements. The results show that the new GWMT-D model outperforms all the other three models with a root-mean-square error of less than 5.0 K at different altitudes over the globe. The new GWMT-D model can provide a practical alternative Tm determination method in real-time GPS-PWV remote sensing systems.

A study of the sources and sinks of methane and methyl chloroform using a global three-dimensional Lagrangian tropospheric tracer transport model

NASA Technical Reports Server (NTRS)

Taylor, John A.; Brasseur, G. P.; Zimmerman, P. R.; Cicerone, R. J.

1991-01-01

Sources and sinks of methane and methyl chloroform are investigated using a global three-dimensional Lagrangian tropospheric tracer transport model with parameterized hydroxyl and temperature fields. Using the hydroxyl radical field calibrated to the methyl chloroform observations, the globally averaged release of methane and its spatial and temporal distribution were investigated. Two source function models of the spatial and temporal distribution of the flux of methane to the atmosphere were developed. The first model was based on the assumption that methane is emitted as a proportion of net primary productivity (NPP). The second model identified source regions for methane from rice paddies, wetlands, enteric fermentation, termites, and biomass burning based on high-resolution land use data. The most significant difference between the two models were predictions of methane fluxes over China and South East Asia, the location of most of the world's rice paddies, indicating that either the assumption that a uniform fraction of NPP is converted to methane is not valid for rice paddies, or that NPP is underestimated for rice paddies, or that present methane emission estimates from rice paddies are too high.

The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework

PubMed Central

Warszawski, Lila; Frieler, Katja; Huber, Veronika; Piontek, Franziska; Serdeczny, Olivia; Schewe, Jacob

2014-01-01

The Inter-Sectoral Impact Model Intercomparison Project offers a framework to compare climate impact projections in different sectors and at different scales. Consistent climate and socio-economic input data provide the basis for a cross-sectoral integration of impact projections. The project is designed to enable quantitative synthesis of climate change impacts at different levels of global warming. This report briefly outlines the objectives and framework of the first, fast-tracked phase of Inter-Sectoral Impact Model Intercomparison Project, based on global impact models, and provides an overview of the participating models, input data, and scenario set-up. PMID:24344316

Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

2013-04-01

Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a world map, with detailed reports for individual gauging sites. A comparison of discharge estimates from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS) with observations for representative climatic zones is presented. Both systems have demonstrated strong potential in forecasting and detecting recent catastrophic floods. The usefulness of their combined information on global scale for decision makers at different levels is discussed. Combining space-based monitoring and global forecasting models is an innovative approach and has significant benefits for international river commissions as well as international aid organisations. This is in line with the objectives of the Hyogo and the Post-2015 Framework that aim at the development of systems which involve trans-boundary collaboration, space-based earth observation, flood forecasting and early warning.

How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stavrakou, T.; Muller, J. F.; Bauwens, M.

2015-10-26

The vertical columns of formaldehyde (HCHO) retrieved from two satellite instruments, the Global Ozone Monitoring Instrument-2 (GOME-2) on Metop-A and the Ozone Monitoring Instrument (OMI) on Aura, are used to constrain global emissions of HCHO precursors from open fires, vegetation and human activities in the year 2010. To this end, the emissions are varied and optimized using the ad-joint model technique in the IMAGESv2 global CTM (chem-ical transport model) on a monthly basis and at the model res-olution. Given the different local overpass times of GOME- 2 (09:30 LT) and OMI (13:30 LT), the simulated diurnal cy-cle of HCHO columnsmore » is investigated and evaluated against ground-based optical measurements at seven sites in Europe, China and Africa. The modeled diurnal cycle exhibits large variability, reflecting competition between photochemistry and emission variations, with noon or early afternoon max-ima at remote locations (oceans) and in regions dominated by anthropogenic emissions, late afternoon or evening max-ima over fire scenes, and midday minima in isoprene-rich re-gions. The agreement between simulated and ground-based columns is generally better in summer (with a clear after-noon maximum at mid-latitude sites) than in winter, and the annually averaged ratio of afternoon to morning columns is slightly higher in the model (1.126) than in the ground-based measurements (1.043).The anthropogenic VOC (volatile organic compound) sources are found to be weakly constrained by the inversions on the global scale, mainly owing to their generally minor contribution to the HCHO columns, except over strongly pol-luted regions, like China. The OMI-based inversion yields total flux estimates over China close to the bottom-up inven-tory (24.6 vs. 25.5 TgVOC yr -1 in the a priori) with, how-ever, pronounced increases in the northeast of China and re-ductions in the south. Lower fluxes are estimated based on GOME-2 HCHO columns (20.6 TgVOC yr -1), in particular over the northeast, likely reflecting mismatches between the observed and the modeled diurnal cycle in this region.« less

A CPT for Improving Turbulence and Cloud Processes in the NCEP Global Models

NASA Astrophysics Data System (ADS)

Krueger, S. K.; Moorthi, S.; Randall, D. A.; Pincus, R.; Bogenschutz, P.; Belochitski, A.; Chikira, M.; Dazlich, D. A.; Swales, D. J.; Thakur, P. K.; Yang, F.; Cheng, A.

2016-12-01

Our Climate Process Team (CPT) is based on the premise that the NCEP (National Centers for Environmental Prediction) global models can be improved by installing an integrated, self-consistent description of turbulence, clouds, deep convection, and the interactions between clouds and radiative and microphysical processes. The goal of our CPT is to unify the representation of turbulence and subgrid-scale (SGS) cloud processes and to unify the representation of SGS deep convective precipitation and grid-scale precipitation as the horizontal resolution decreases. We aim to improve the representation of small-scale phenomena by implementing a PDF-based SGS turbulence and cloudiness scheme that replaces the boundary layer turbulence scheme, the shallow convection scheme, and the cloud fraction schemes in the GFS (Global Forecast System) and CFS (Climate Forecast System) global models. We intend to improve the treatment of deep convection by introducing a unified parameterization that scales continuously between the simulation of individual clouds when and where the grid spacing is sufficiently fine and the behavior of a conventional parameterization of deep convection when and where the grid spacing is coarse. We will endeavor to improve the representation of the interactions of clouds, radiation, and microphysics in the GFS/CFS by using the additional information provided by the PDF-based SGS cloud scheme. The team is evaluating the impacts of the model upgrades with metrics used by the NCEP short-range and seasonal forecast operations.

Nested Global Inversion for the Carbon Flux Distribution in Canada and USA from 1994 to 2003

NASA Astrophysics Data System (ADS)

Chen, J. M.; Deng, F.; Ishizawa, M.; Ju, W.; Mo, G.; Chan, D.; Higuchi, K.; Maksyutov, S.

2007-12-01

Based on TransCom inverse modeling for 22 global regions, we developed a nested global inversion system for estimating carbon fluxes of 30 regions in North America (2 of the 22 regions are divided into 30). Irregular boundaries of these 30 regions are delineated based on ecosystem types and provincial/state borders. Synthesis Bayesian inversion is conducted in monthly steps using CO2 concentration measurements at 88 coastal and continental stations of the globe for the 1994-2003 period (NOAA GlobalView database). Responses of these stations to carbon fluxes from the 50 regions are simulated using the transport model of National Institute for Environmental Studies of Japan and reanalysis wind fields of the National Centers for Environmental Prediction (NCEP). Terrestrial carbon flux fields modeled using BEPS and Biome-BGC driven by NCEP reanalysis meteorological data are used as two different a priori to constrain the inversion. The inversion (top- down) results are compared with remote sensing-based ecosystem modeling (bottom-up) results in Canada's forests and wetlands. There is a broad consistency in the spatial pattern of the carbon source and sink distributions obtained using these two independent methods. Both sets of results also indicate that Canada's forests and wetlands are carbon sinks in 1994-2003, but the top-down method produces consistently larger sinks than the bottom-up results. Reasons for this discrepancy may lie in both methods, and several issues are identified for further investigation.

Sensitivity analysis of Repast computational ecology models with R/Repast.

PubMed

Prestes García, Antonio; Rodríguez-Patón, Alfonso

2016-12-01

Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom-up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in-silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.

Multi-Decadal Variation of Aerosols: Sources, Transport, and Climate Effects

NASA Technical Reports Server (NTRS)

Chin, Mian; Diehl, Thomas; Bian, Huisheng; Streets, David

2008-01-01

We present a global model study of multi-decadal changes of atmospheric aerosols and their climate effects using a global chemistry transport model along with the near-term to longterm data records. We focus on a 27-year time period of satellite era from 1980 to 2006, during which a suite of aerosol data from satellite observations, ground-based measurements, and intensive field experiments have become available. We will use the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, which involves a time-varying, comprehensive global emission dataset that we put together in our previous investigations and will be improved/extended in this project. This global emission dataset includes emissions of aerosols and their precursors from fuel combustion, biomass burning, volcanic eruptions, and other sources from 1980 to the present. Using the model and satellite data, we will analyze (1) the long-term global and regional aerosol trends and their relationship to the changes of aerosol and precursor emissions from anthropogenic and natural sources, (2) the intercontinental source-receptor relationships controlled by emission, transport pathway, and climate variability.

An Event-Based Approach to Distributed Diagnosis of Continuous Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

2010-01-01

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

NASA Astrophysics Data System (ADS)

Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten

2017-12-01

Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

A framework for process-based assessment of regional climate model experiments: applied to projections of southern African precipitation

NASA Astrophysics Data System (ADS)

James, Rachel; Washington, Richard; Jones, Richard

2015-04-01

There is a demand from adaptation planners for regional climate change projections, particularly the finer resolution data delivered by regional models. However, climate models are subject to important uncertainties, and their projections diverge substantially, particularly for precipitation. So how should decision makers know which futures to consider and which to disregard? Model evaluation is clearly a priority. The majority of studies seeking to assess the validity of projections are based on comparison of the models' twentieth century climatologies with observations or reanalysis. Whilst this work is very important, examination of the modelled mean state it is not sufficient to assess the credibility of modelled changes. Direct investigation of the mechanisms for change is also vital. In this study, a framework for process-based analysis of projections is presented, whereby circulation changes accompanying future responses are examined, and then compared to atmospheric dynamics during historical years in models and reanalyses. This framework has previously been applied to investigate a drying signal in West Africa, and will here be used to examine projected precipitation change in southern Africa. An ensemble of five global and regional model experiments will be employed, consisting of five perturbed versions of HadCM3 and five corresponding runs of HadRM3P (PRECIS), run over the CORDEX Africa domain. The global and regional model runs show contrasting future responses: there is a strong drying in the global models over southern Africa during the rainy season, but the regional models show drying over Madagascar and the south west Indian Ocean. Circulation changes associated with these projections will be presented as a first step towards understanding the mechanisms for change and the reasons for difference between the global and regional models. The interannual variability will also be examined and compared to reanalysis to explore how well the models represent the dipole between southern Africa and Madagascar in the twentieth century simulations. This analysis could shed light on the credibility of the projected changes, and the relative trustworthiness of the global and regional models. This research makes a valuable contribution to the understanding of mechanisms for change in southern Africa. It also has wider relevance for regional climate model studies, in highlighting the need to evaluate models on a case by case basis, and providing a framework for assessment which could be applied to other models and other regions.

Deep Water Ocean Acoustics

DTIC Science & Technology

2015-07-17

under- ice scattering, bathymetric diffraction and the application of the ocean acoustic Parabolic Equation to infrasound. 2. Tasks a. Task 1...and Climate of the Ocean, Phase II (ECCO2): High-Resolution Global-Ocean and Sea- Ice Data Synthesis) model re- analysis for the years 1992 and 1993...The ECCO2 model is a state estimation based upon data syntheses obtained by least squares fitting of the global ocean and sea- ice configuration of

Decision Support Tool for Deep Energy Efficiency Retrofits in DoD Installations

DTIC Science & Technology

2014-01-01

representations (HDMR). Chemical Engineering Science, 57, 4445–4460. 2. Sobol ’, I., 2001. Global sensitivity indices for nonlinear mathematical...models and their Monte Carlo estimates. Mathematics and computers in simulation, 55, 271–280. 3. Sobol , I. and Kucherenko, S., 2009. Derivative based...representations (HDMR). Chemical Engineering Science, 57, 4445–4460. 16. Sobol ’, I., 2001. Global sensitivity indices for nonlinear mathematical models and

Operational Space Weather Models: Trials, Tribulations and Rewards

NASA Astrophysics Data System (ADS)

Schunk, R. W.; Scherliess, L.; Sojka, J. J.; Thompson, D. C.; Zhu, L.

2009-12-01

There are many empirical, physics-based, and data assimilation models that can probably be used for space weather applications and the models cover the entire domain from the surface of the Sun to the Earth’s surface. At Utah State University we developed two physics-based data assimilation models of the terrestrial ionosphere as part of a program called Global Assimilation of Ionospheric Measurements (GAIM). One of the data assimilation models is now in operational use at the Air Force Weather Agency (AFWA) in Omaha, Nebraska. This model is a Gauss-Markov Kalman Filter (GAIM-GM) model, and it uses a physics-based model of the ionosphere and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. The physics-based model is the Ionosphere Forecast Model (IFM), which is global and covers the E-region, F-region, and topside ionosphere from 90 to 1400 km. It takes account of five ion species (NO+, O2+, N2+, O+, H+), but the main output of the model is a 3-dimensional electron density distribution at user specified times. The second data assimilation model uses a physics-based Ionosphere-Plasmasphere Model (IPM) and an ensemble Kalman filter technique as a basis for assimilating a diverse set of real-time (or near real-time) measurements. This Full Physics model (GAIM-FP) is global, covers the altitude range from 90 to 30,000 km, includes six ions (NO+, O2+, N2+, O+, H+, He+), and calculates the self-consistent ionospheric drivers (electric fields and neutral winds). The GAIM-FP model is scheduled for delivery in 2012. Both of these GAIM models assimilate bottom-side Ne profiles from a variable number of ionosondes, slant TEC from a variable number of ground GPS/TEC stations, in situ Ne from four DMSP satellites, line-of-sight UV emissions measured by satellites, and occultation data. Quality control algorithms for all of the data types are provided as an integral part of the GAIM models and these models take account of latent data (up to 3 hours). The trials, tribulations and rewards of constructing and maintaining operational data assimilation models will be discussed.

Generation of the 30 M-Mesh Global Digital Surface Model by Alos Prism

NASA Astrophysics Data System (ADS)

Tadono, T.; Nagai, H.; Ishida, H.; Oda, F.; Naito, S.; Minakawa, K.; Iwamoto, H.

2016-06-01

Topographical information is fundamental to many geo-spatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. Several satellite-based digital elevation datasets were provided to examine global terrains with medium resolutions e.g. the Shuttle Radar Topography Mission (SRTM), the global digital elevation model by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM). A new global digital surface model (DSM) dataset using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed "Daichi") has been completed on March 2016 by Japan Aerospace Exploration Agency (JAXA) collaborating with NTT DATA Corp. and Remote Sensing Technology Center, Japan. This project is called "ALOS World 3D" (AW3D), and its dataset consists of the global DSM dataset with 0.15 arcsec. pixel spacing (approx. 5 m mesh) and ortho-rectified PRISM image with 2.5 m resolution. JAXA is also processing the global DSM with 1 arcsec. spacing (approx. 30 m mesh) based on the AW3D DSM dataset, and partially releasing it free of charge, which calls "ALOS World 3D 30 m mesh" (AW3D30). The global AW3D30 dataset will be released on May 2016. This paper describes the processing status, a preliminary validation result of the AW3D30 DSM dataset, and its public release status. As a summary of the preliminary validation of AW3D30 DSM, 4.40 m (RMSE) of the height accuracy of the dataset was confirmed using 5,121 independent check points distributed in the world.

Influence of global heterogeneities on regional imaging based upon full waveform inversion of teleseismic wavefield

NASA Astrophysics Data System (ADS)

Monteiller, Vadim; Beller, Stephen; Operto, Stephane; Virieux, Jean

2015-04-01

The current development of dense seismic arrays and high performance computing make feasible today application of full-waveform inversion (FWI) on teleseismic data for high-resolution lithospheric imaging. In teleseismic configuration, the source is often considered to first order as a planar wave that impinges the base of the lithospheric target located below the receiver array. Recently, injection methods coupling global propagation in 1D or axisymmetric earth model with regional 3D methods (Discontinuous Galerkin finite element methods, Spectral elements methods or finite differences) allow us to consider more realistic teleseismic phases. Those teleseismic phases can be propagated inside 3D regional model in order to exploit not only the forward-scattered waves propagating up to the receiver but also second-order arrivals that are back-scattered from the free-surface and the reflectors before their recordings on the surface. However, those computation are performed assuming simple global model. In this presentation, we review some key specifications that might be considered for mitigating the effect on FWI of heterogeneities situated outside the regional domain. We consider synthetic models and data computed using our recently developed hybrid method AxiSEM/SEM. The global simulation is done by AxiSEM code which allows us to consider axisymmetric anomalies. The 3D regional computation is performed by Spectral Element Method. We investigate the effect of external anomalies on the regional model obtained by FWI when one neglects them by considering only 1D global propagation. We also investigate the effect of the source time function and the focal mechanism on results of the FWI approach.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE PAGES

Xi, Maolong; Lu, Dan; Gui, Dongwei; ...

2016-11-27

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

NASA Astrophysics Data System (ADS)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xi, Maolong; Lu, Dan; Gui, Dongwei

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Modeling the global emission, transport and deposition of trace elements associated with mineral dust

DOE PAGES

Zhang, Y.; Mahowald, N.; Scanza, R. A.; ...

2015-10-12

Trace element deposition from desert dust has important impacts on ocean primary productivity, the quantification of which could be useful in determining the magnitude and sign of the biogeochemical feedback on radiative forcing. However, the impact of elemental deposition to remote ocean regions is not well understood and is not currently included in global climate models. In this study, emission inventories for eight elements primarily of soil origin, Mg, P, Ca, Mn, Fe, K, Al, and Si are determined based on a global mineral data set and a soil data set. The resulting elemental fractions are used to drive themore » desert dust model in the Community Earth System Model (CESM) in order to simulate the elemental concentrations of atmospheric dust. Spatial variability of mineral dust elemental fractions is evident on a global scale, particularly for Ca. Simulations of global variations in the Ca / Al ratio, which typically range from around 0.1 to 5.0 in soils, are consistent with observations, suggesting that this ratio is a good signature for dust source regions. The simulated variable fractions of chemical elements are sufficiently different; estimates of deposition should include elemental variations, especially for Ca, Al and Fe. The model results have been evaluated with observations of elemental aerosol concentrations from desert regions and dust events in non-dust regions, providing insights into uncertainties in the modeling approach. The ratios between modeled and observed elemental fractions range from 0.7 to 1.6, except for Mg and Mn (3.4 and 3.5, respectively). Using the soil database improves the correspondence of the spatial heterogeneity in the modeling of several elements (Ca, Al and Fe) compared to observations. Total and soluble dust element fluxes to different ocean basins and ice sheet regions have been estimated, based on the model results. The annual inputs of soluble Mg, P, Ca, Mn, Fe and K associated with dust using the mineral data set are 0.30 Tg, 16.89 Gg, 1.32 Tg, 22.84 Gg, 0.068 Tg, and 0.15 Tg to global oceans and ice sheets.« less

GLADIS: GLobal AIS & Data-X International Satellite Constellation

DTIC Science & Technology

2008-01-01

1Approved for public release; distribution is unlimited GLADIS : GLobal AIS & Data-X International Satellite Constellation Space-Based System for...TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE GLADIS : GLobal AIS & Data-X International Satellite Constellation 5a. CONTRACT NUMBER 5b...Maritime & Technology Challenges • GLADIS Mission Objective • AIS & Data-X capabilities • GLADIS Architecture • International Strategy – MSSIS as Model

Challenges in the global-scale quantification of permafrost changes

NASA Astrophysics Data System (ADS)

Gruber, S.

2012-12-01

Permafrost underlies much of Earth's surface and interacts with climate, land-surface phenomena and human systems. This presentation highlights heterogeneity and near-isothermal ground, two simple and well-known phenomena, as important challenges for investigating current and future states of permafrost. Heterogeneity, which can be introduced by e.g., topography, vegetation or subsurface material, is shown to be important for large parts of the global permafrost areas based on two proxies calculated from a global model of permafrost distribution. The model is based on a 1km DEM and NCEP-NCAR as well as CRU TS 2.0 air temperature data. Near-isothermal ground occurs when heat flow into a volume of ground material is accompanied by only a minute temperature change due to the dominance of latent heat transfer near 0°C. This causes our monitoring systems, which are to a large part based on temperature measurements, to lose much of their sensitivity as an instrument to measure permafrost changes. The importance of this is argued for based on (a) the long duration that soil columns are usually exposed to this effect, (b) the abundance of boreholes with temperatures close to 0°C based on the IPY-TSP data set, and (c) the global abundance and relative importance of ground near 0°C. The results presented indicated that systems and methods of gathering permafrost evidence and monitoring data need to better account for heterogeneity and isothermal ground in order to maintain long-term relevance, and that in large-area models sub-grid heterogeneity needs explicit attention.

School Gardens: Situating Students within a Global Context

ERIC Educational Resources Information Center

Wolsey, Thomas DeVere; Lapp, Diane

2014-01-01

School-based gardens are increasingly common. The benefits to students reflect principles of global education by modeling sustainability through responsible ecological planning and service to the community, the environment, and humanity. The authors propose a pedagogical framework for planning school gardens and related experiences that…

Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century

PubMed Central

Emanuel, Kerry A.

2013-01-01

A recently developed technique for simulating large [O(104)] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones downscaled from the climate of the period 1950–2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of downscaled tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones. PMID:23836646

Fast Magnetotail Reconnection: Challenge to Global MHD Modeling

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.; Hesse, M.; Rastaetter, L.; Toth, G.; de Zeeuw, D.; Gombosi, T.

2005-05-01

Representation of fast magnetotail reconnection rates during substorm onset is one of the major challenges to global MHD modeling. Our previous comparative study of collisionless magnetic reconnection in GEM Challenge geometry demonstrated that the reconnection rate is controlled by ion nongyrotropic behavior near the reconnection site and that it can be described in terms of nongyrotropic corrections to the magnetic induction equation. To further test the approach we performed MHD simulations with nongyrotropic corrections of forced reconnection for the Newton Challenge setup. As a next step we employ the global MHD code BATSRUS and test different methods to model fast magnetotail reconnection rates by introducing non-ideal corrections to the induction equation in terms of nongyrotropic corrections, spatially localized resistivity, or current dependent resistivity. The BATSRUS adaptive grid structure allows to perform global simulations with spatial resolution near the reconnection site comparable with spatial resolution of local MHD simulations for the Newton Challenge. We select solar wind conditions which drive the accumulation of magnetic field in the tail lobes and subsequent magnetic reconnection and energy release. Testing the ability of global MHD models to describe magnetotail evolution during substroms is one of the elements of science based validation efforts at the Community Coordinated Modeling Center.

A Global Ocean Tide Model From TOPEX/POSEIDON Altimetry: GOT99.2

NASA Technical Reports Server (NTRS)

Ray, Richard D.

1999-01-01

Goddard Ocean Tide model GOT99.2 is a new solution for the amplitudes and phases of the global oceanic tides, based on over six years of sea-surface height measurements by the TOPEX/POSEIDON satellite altimeter. Comparison with deep-ocean tide-gauge measurements show that this new tidal solution is an improvement over previous global models, with accuracies for the main semidiurnal lunar constituent M2 now below 1.5 cm (deep water only). The new solution benefits from use of prior hydrodynamic models, several in shallow and inland seas as well as the global finite-element model FES94.1. This report describes some of the data processing details involved in handling the altimetry, and it provides a comprehensive set of global cotidal charts of the resulting solutions. Various derived tidal charts are also provided, including tidal loading deformation charts, tidal gravimetric charts, and tidal current velocity (or transport) charts. Finally, low-degree spherical harmonic coefficients are computed by numerical quadrature and are tabulated for the major short-period tides; these are useful for a variety of geodetic and geophysical purposes, especially in combination with similar estimates from satellite laser ranging.

Artifact-Based Transformation of IBM Global Financing

NASA Astrophysics Data System (ADS)

Chao, Tian; Cohn, David; Flatgard, Adrian; Hahn, Sandy; Linehan, Mark; Nandi, Prabir; Nigam, Anil; Pinel, Florian; Vergo, John; Wu, Frederick Y.

IBM Global Financing (IGF) is transforming its business using the Business Artifact Method, an innovative business process modeling technique that identifies key business artifacts and traces their life cycles as they are processed by the business. IGF is a complex, global business operation with many business design challenges. The Business Artifact Method is a fundamental shift in how to conceptualize, design and implement business operations. The Business Artifact Method was extended to solve the problem of designing a global standard for a complex, end-to-end process while supporting local geographic variations. Prior to employing the Business Artifact method, process decomposition, Lean and Six Sigma methods were each employed on different parts of the financing operation. Although they provided critical input to the final operational model, they proved insufficient for designing a complete, integrated, standard operation. The artifact method resulted in a business operations model that was at the right level of granularity for the problem at hand. A fully functional rapid prototype was created early in the engagement, which facilitated an improved understanding of the redesigned operations model. The resulting business operations model is being used as the basis for all aspects of business transformation in IBM Global Financing.

Combined use of local and global hydrometeorological data with regional and global hydrological models in the Magdalena - Cauca river basin, Colombia

NASA Astrophysics Data System (ADS)

Rodriguez, Erasmo; Sanchez, Ines; Duque, Nicolas; Lopez, Patricia; Kaune, Alexander; Werner, Micha; Arboleda, Pedro

2017-04-01

The Magdalena Cauca Macrobasin (MCMB) in Colombia, with an area of about 257,000 km2, is the largest and most important water resources system in the country. With almost 80% of the Colombian population (46 million people) settled in the basin, it is the main source of water for demands including human consumption, agriculture, hydropower generation, industrial activities and ecosystems. Despite its importance, the basin has witnessed enormous changes in land-cover and extensive deforestation during the last three decades. To make things more complicated, the MCMB currently lacks a set of tools to support planning and decision making processes at scale of the whole watershed. Considering this, the MCMB has been selected as one of the six different regional case studies in the eartH2Observe research project, in which hydrological and meteorological reanalysis products are being validated for the period 1980-2012. The combined use of the hydrological and meteorological reanalysis data, with local hydrometeorological data (precipitation, temperature and streamflow) provided by the National Hydrometeorological Agency (IDEAM), has given us the opportunity to implement and test three hydrological models (VIC, WFLOW and a Water Balance Model based on the Budyko framework) at the basin scale. Additionally, results from the global models in the eartH2Observe hydrological reanalysis have been used to evaluate their performance against the observed streamflow data. This paper discusses the comparison between streamflow observations and simulations from the global hydrological models forced with the WFDEI data, and regional models forced with a combination of observed and meteorological reanalysis data, in the whole domain of the MCMB. For the three regional models analysed results show good performances for some sub-basins and poor performances for others. This can be due to the smoothing of the precipitation fields, interpolated from point daily rainfall data, the effect of horizontal precipitation (not included in the models) and weaknesses in the models structures; for example the poor performance of the VIC model in base flow dominated basins. In order to improve these simulations a strategy based on a hydrological model ensemble is currently being developed in the case study. Results from the global models, show that these consistently tend to overestimate runoff. This may be due to the coarse resolution used (50 km), biases in the ERA-Interim precipitation forcing, and the different partitioning within the global models of the precipitation into evapotranspiration and runoff. It is expected that within the Tier II hydrological reanalysis, where the models will produce outputs at 25 km resolution, some improvements may be identified.

A 4D global respiratory motion model of the thorax based on CT images: A proof of concept.

PubMed

Fayad, Hadi; Gilles, Marlene; Pan, Tinsu; Visvikis, Dimitris

2018-05-17

Respiratory motion reduces the sensitivity and specificity of medical images especially in the thoracic and abdominal areas. It may affect applications such as cancer diagnostic imaging and/or radiation therapy (RT). Solutions to this issue include modeling of the respiratory motion in order to optimize both diagnostic and therapeutic protocols. Personalized motion modeling required patient-specific four-dimensional (4D) imaging which in the case of 4D computed tomography (4D CT) acquisition is associated with an increased dose. The goal of this work was to develop a global respiratory motion model capable of relating external patient surface motion to internal structure motion without the need for a patient-specific 4D CT acquisition. The proposed global model is based on principal component analysis and can be adjusted to a given patient anatomy using only one or two static CT images in conjunction with a respiratory synchronized patient external surface motion. It is based on the relation between the internal motion described using deformation fields obtained by registering 4D CT images and patient surface maps obtained either from optical imaging devices or extracted from CT image-based patient skin segmentation. 4D CT images of six patients were used to generate the global motion model which was validated by adapting it on four different patients having skin segmented surfaces and two other patients having time of flight camera acquired surfaces. The reproducibility of the proposed model was also assessed on two patients with two 4D CT series acquired within 2 weeks of each other. Profile comparison shows the efficacy of the global respiratory motion model and an improvement while using two CT images in order to adapt the model. This was confirmed by the correlation coefficient with a mean correlation of 0.9 and 0.95 while using one or two CT images respectively and when comparing acquired to model generated 4D CT images. For the four patients with segmented surfaces, expert validation indicates an error of 2.35 ± 0.26 mm compared to 6.07 ± 0.76 mm when using a simple interpolation between full inspiration (FI) and full expiration (FE) CT only; i.e., without specific modeling of the respiratory motion. For the two patients with acquired surfaces, this error was of 2.48 ± 0.18 mm. In terms of reproducibility, model error changes of 0.12 and 0.17 mm were measured for the two patients concerned. The framework for the derivation of a global respiratory motion model was developed. A single or two static CT images and associated patient surface motion, as a surrogate measure, are only needed to personalize the model. This model accuracy and reproducibility were assessed by comparing acquired vs model generated 4D CT images. Future work will consist of assessing extensively the proposed model for radiotherapy applications. © 2018 American Association of Physicists in Medicine.

Numerical Parameter Optimization of the Ignition and Growth Model for HMX Based Plastic Bonded Explosives

NASA Astrophysics Data System (ADS)

Gambino, James; Tarver, Craig; Springer, H. Keo; White, Bradley; Fried, Laurence

2017-06-01

We present a novel method for optimizing parameters of the Ignition and Growth reactive flow (I&G) model for high explosives. The I&G model can yield accurate predictions of experimental observations. However, calibrating the model is a time-consuming task especially with multiple experiments. In this study, we couple the differential evolution global optimization algorithm to simulations of shock initiation experiments in the multi-physics code ALE3D. We develop parameter sets for HMX based explosives LX-07 and LX-10. The optimization finds the I&G model parameters that globally minimize the difference between calculated and experimental shock time of arrival at embedded pressure gauges. This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. LLNS, LLC LLNL-ABS- 724898.

An effective automatic procedure for testing parameter identifiability of HIV/AIDS models.

PubMed

Saccomani, Maria Pia

2011-08-01

Realistic HIV models tend to be rather complex and many recent models proposed in the literature could not yet be analyzed by traditional identifiability testing techniques. In this paper, we check a priori global identifiability of some of these nonlinear HIV models taken from the recent literature, by using a differential algebra algorithm based on previous work of the author. The algorithm is implemented in a software tool, called DAISY (Differential Algebra for Identifiability of SYstems), which has been recently released (DAISY is freely available on the web site http://www.dei.unipd.it/~pia/ ). The software can be used to automatically check global identifiability of (linear and) nonlinear models described by polynomial or rational differential equations, thus providing a general and reliable tool to test global identifiability of several HIV models proposed in the literature. It can be used by researchers with a minimum of mathematical background.

Assimilative modeling of low latitude ionosphere

NASA Technical Reports Server (NTRS)

Pi, Xiaoqing; Wang, Chunining; Hajj, George A.; Rosen, I. Gary; Wilson, Brian D.; Mannucci, Anthony J.

2004-01-01

In this paper we present an observation system simulation experiment for modeling low-latitude ionosphere using a 3-dimensional (3-D) global assimilative ionospheric model (GAIM). The experiment is conducted to test the effectiveness of GAIM with a 4-D variational approach (4DVAR) in estimation of the ExB drift and thermospheric wind in the magnetic meridional planes simultaneously for all longitude or local time sectors. The operational Global Positioning System (GPS) satellites and the ground-based global GPS receiver network of the International GPS Service are used in the experiment as the data assimilation source. 'The optimization of the ionospheric state (electron density) modeling is performed through a nonlinear least-squares minimization process that adjusts the dynamical forces to reduce the difference between the modeled and observed slant total electron content in the entire modeled region. The present experiment for multiple force estimations reinforces our previous assessment made through single driver estimations conducted for the ExB drift only.

Comparison of Water Vapor Measurements from Ground-based and Space-based GPS Atmospheric Remote Sensing Techniques

NASA Astrophysics Data System (ADS)

Colon-Pagan, Ian; Kuo, Ying-Hwa

2008-10-01

In this study, we compare precipitable water vapor (PWV) values from ground-based GPS water vapor sensing and COSMIC radio occultation (RO) measurements over the Caribbean Sea, Gulf of Mexico, and United States regions as well as global analyses from NCEP and ECMWF models. The results show good overall agreement; however, the PWV values estimated by ground-based GPS receivers tend to have a slight dry bias for low PWV values and a slight wet bias for higher PWV values, when compared with GPS RO measurements and global analyses. An application of a student T-test indicates that there is a significant difference between both ground- and space-based GPS measured datasets. The dry bias associated with space-based GPS is attributed to the missing low altitude data, where the concentration of water vapor is large. The close agreements between space-based and global analyses are due to the fact that these global analyses assimilate space-based GPS RO data from COSMIC, and the retrieval of water vapor profiles from space-based technique requires the use of global analyses as the first guess. This work is supported by UCAR SOARS and a grant from the National Oceanic and Atmospheric Administration, Educational Partnership Program under the cooperative agreement NA06OAR4810187.

Octree-based Global Earthquake Simulations

NASA Astrophysics Data System (ADS)

Ramirez-Guzman, L.; Juarez, A.; Bielak, J.; Salazar Monroy, E. F.

2017-12-01

Seismological research has motivated recent efforts to construct more accurate three-dimensional (3D) velocity models of the Earth, perform global simulations of wave propagation to validate models, and also to study the interaction of seismic fields with 3D structures. However, traditional methods for seismogram computation at global scales are limited by computational resources, relying primarily on traditional methods such as normal mode summation or two-dimensional numerical methods. We present an octree-based mesh finite element implementation to perform global earthquake simulations with 3D models using topography and bathymetry with a staircase approximation, as modeled by the Carnegie Mellon Finite Element Toolchain Hercules (Tu et al., 2006). To verify the implementation, we compared the synthetic seismograms computed in a spherical earth against waveforms calculated using normal mode summation for the Preliminary Earth Model (PREM) for a point source representation of the 2014 Mw 7.3 Papanoa, Mexico earthquake. We considered a 3 km-thick ocean layer for stations with predominantly oceanic paths. Eigen frequencies and eigen functions were computed for toroidal, radial, and spherical oscillations in the first 20 branches. Simulations are valid at frequencies up to 0.05 Hz. Matching among the waveforms computed by both approaches, especially for long period surface waves, is excellent. Additionally, we modeled the Mw 9.0 Tohoku-Oki earthquake using the USGS finite fault inversion. Topography and bathymetry from ETOPO1 are included in a mesh with more than 3 billion elements; constrained by the computational resources available. We compared estimated velocity and GPS synthetics against observations at regional and teleseismic stations of the Global Seismological Network and discuss the differences among observations and synthetics, revealing that heterogeneity, particularly in the crust, needs to be considered.

A global validation of ERA-Interim integrated water vapor estimates using ground-based GNSS observations

NASA Astrophysics Data System (ADS)

Ahmed, F.; Dousa, J.; Hunegnaw, A.; Teferle, F. N.; Bingley, R.

2017-12-01

Integrated water vapor (IWV) derived from climate reanalysis models, such as the European Centre for Medium-range Weather Forecasts (ECMWF) ReAnalysis-Interim (ERA-Interim), is widely used in many atmospheric applications. Therefore, it is of interest to assess the quality of this reanalysis product using available observations. Observations from Global Navigation Satellite Systems (GNSS) are, as of now, available for a period of over 2 decades and their global availability makes it possible to validate the IWV obtained from climate reanalysis models in different geographical and climatic regions. In this study, primarily, three 5-year long homogeneously reprocessed GNSS-derived IWV datasets containing over 400 globally distributed ground-based GNSS stations have been used to validate the IWV estimates obtained from the ERA-Interim climate reanalysis model in 25 different climate zones. The IWV from ERA-Interim has been obtained by vertically integrating the specific humidity at all model levels above the locations of GNSS stations. It has been studied how the difference between the ERA-Interim IWV and the GNSS-derived IWV varies with respect to the different climate zones as well as with respect to the difference in the model orography and latitude. The results show a dependence of the ability of ERA-Interim to model the IWV on difference in climate types and latitude. This dependence, however, is dictated by the concentration of water vapor in different climate zones and at different latitudes. Furthermore, as a secondary focus of this study, the weighted mean atmospheric temperature (Tm) obtained from ERA-Interim has been compared to its equivalent obtained using two widely used approximations globally.

Ecosystem shifts under climate change - a multi-model analysis from ISI-MIP

NASA Astrophysics Data System (ADS)

Warszawski, Lila; Beerling, David; Clark, Douglas; Friend, Andrew; Ito, Akihito; Kahana, Ron; Keribin, Rozenn; Kleidon, Axel; Lomas, Mark; Lucht, Wolfgang; Nishina, Kazuya; Ostberg, Sebastian; Pavlick, Ryan; Tito Rademacher, Tim; Schaphoff, Sibyll

2013-04-01

Dramatic ecosystem shifts, relating to vegetation composition and water and carbon stocks and fluxes, are potential consequences of climate change in the twenty-first century. Shifting climatic conditions, resulting in changes in biogeochemical properties of the ecosystem, will render it difficult for endemic plant and animal species to continue to survive in their current habitat. The potential for major shifts in biomes globally will also have severe consequences for the humans who rely on vital ecosystem services. Here we employ a novel metric of ecosystem shift to quantify the magnitude and uncertainty in these shifts at different levels of global warming, based on the response of seven biogeochemical Earth models to different future climate scenarios, in the context of the Intersectoral Impact Model Intercomparison Project (ISI-MIP). Based on this ensemble, 15% of the Earth's land surface will experience severe ecosystem shifts at 2°C degrees of global warming above 1980-2010 levels. This figure rises monotonically with global mean temperature for all models included in this study, reaching a median value of 60% of the land surface in a 4°C warmer world. At both 2°C and 4°C of warming, the most pronounced shifts occur in south-eastern India and south-western China, large swathes of the northern lattitudes above 60°N, the Amazon region and sub-Saharan Africa. Where dynamic vegetation composition is modelled, these shifts correspond to significant reductions in the land surface of vunerable vegetation types. We show that global mean temperature is a robust predictor of ecosystem shifts, whilst the spread across impact models is the greatest contributor to uncertainty.

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, Jorge R.; Urban, Nathan M.; Hunke, Elizabeth C.; Turner, Adrian K.; Jeffery, Nicole

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mao, Jiafu; Shi, Xiaoying; Ricciuto, Daniel M.

Here, we examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982-2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increased trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO 2 ranked second in these models after the predominant climatic influences, and yielded a decreasing trend in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increased nitrogen deposition slightly amplifiedmore » global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.« less

A Global Rapid Integrated Monitoring System for Water Cycle and Water Resource Assessment (Global-RIMS)

NASA Technical Reports Server (NTRS)

Roads, John; Voeroesmarty, Charles

2005-01-01

The main focus of our work was to solidify underlying data sets, the data processing tools and the modeling environment needed to perform a series of long-term global and regional hydrological simulations leading eventually to routine hydrometeorological predictions. A water and energy budget synthesis was developed for the Mississippi River Basin (Roads et al. 2003), in order to understand better what kinds of errors exist in current hydrometeorological data sets. This study is now being extended globally with a larger number of observations and model based data sets under the new NASA NEWS program. A global comparison of a number of precipitation data sets was subsequently carried out (Fekete et al. 2004) in which it was further shown that reanalysis precipitation has substantial problems, which subsequently led us to the development of a precipitation assimilation effort (Nunes and Roads 2005). We believe that with current levels of model skill in predicting precipitation that precipitation assimilation is necessary to get the appropriate land surface forcing.

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends

DOE PAGES

Mao, Jiafu; Shi, Xiaoying; Ricciuto, Daniel M.; ...

2015-09-08

Here, we examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982-2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increased trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO 2 ranked second in these models after the predominant climatic influences, and yielded a decreasing trend in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increased nitrogen deposition slightly amplifiedmore » global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.« less

Climate change air toxic co-reduction in the context of macroeconomic modelling.

PubMed

Crawford-Brown, Douglas; Chen, Pi-Cheng; Shi, Hsiu-Ching; Chao, Chia-Wei

2013-08-15

This paper examines the health implications of global PM reduction accompanying greenhouse gas emissions reductions in the 180 national economies of the global macroeconomy. A human health effects module based on empirical data on GHG emissions, PM emissions, background PM concentrations, source apportionment and human health risk coefficients is used to estimate reductions in morbidity and mortality from PM exposures globally as co-reduction of GHG reductions. These results are compared against the "fuzzy bright line" that often underlies regulatory decisions for environmental toxics, and demonstrate that the risk reduction through PM reduction would usually be considered justified in traditional risk-based decisions for environmental toxics. It is shown that this risk reduction can be on the order of more than 4 × 10(-3) excess lifetime mortality risk, with global annual cost savings of slightly more than $10B, when uniform GHG reduction measures across all sectors of the economy form the basis for climate policy ($2.2B if only Annex I nations reduce). Consideration of co-reduction of PM-10 within a climate policy framework harmonized with other environmental policies can therefore be an effective driver of climate policy. An error analysis comparing results of the current model against those of significantly more spatially resolved models at city and national scales indicates errors caused by the low spatial resolution of the global model used here may be on the order of a factor of 2. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Earth's Population Can Reach 14 Billion in the 23rd Century without Significant Adverse Effects on Survivability.

PubMed

Krapivin, Vladimir F; Varotsos, Costas A; Soldatov, Vladimir Yu

2017-08-07

This paper presents the results obtained from the study of the sustainable state between nature and human society on a global scale, focusing on the most critical interactions between the natural and anthropogenic processes. Apart from the conventional global models, the basic tool employed herein is the newly proposed complex model entitled "nature-society system (NSS) model", through which a reliable modeling of the processes taking place in the global climate-nature-society system (CNSS) is achieved. This universal tool is mainly based on the information technology that allows the adaptive conformance of the parametric and functional space of this model. The structure of this model includes the global biogeochemical cycles, the hydrological cycle, the demographic processes and a simple climate model. In this model, the survivability indicator is used as a criterion for the survival of humanity, which defines a trend in the dynamics of the total biomass of the biosphere, taking into account the trends of the biocomplexity dynamics of the land and hydrosphere ecosystems. It should be stressed that there are no other complex global models comparable to those of the CNSS model developed here. The potential of this global model is demonstrated through specific examples in which the classification of the terrestrial ecosystem is accomplished by separating 30 soil-plant formations for geographic pixels 4° × 5°. In addition, humanity is considered to be represented by three groups of economic development status (high, transition, developing) and the World Ocean is parameterized by three latitude zones (low, middle, high). The modelling results obtained show the dynamics of the CNSS at the beginning of the 23rd century, according to which the world population can reach the level of 14 billion without the occurrence of major negative impacts.

Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes.

PubMed

Naujokaitis-Lewis, Ilona; Curtis, Janelle M R

2016-01-01

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options.

Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes

PubMed Central

Curtis, Janelle M.R.

2016-01-01

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options. PMID:27547529

Simulation of Drought-induced Tree Mortality Using a New Individual and Hydraulic Trait-based Model (S-TEDy)

NASA Astrophysics Data System (ADS)

Sinha, T.; Gangodagamage, C.; Ale, S.; Frazier, A. G.; Giambelluca, T. W.; Kumagai, T.; Nakai, T.; Sato, H.

2017-12-01

Drought-related tree mortality at a regional scale causes drastic shifts in carbon and water cycling in Southeast Asian tropical rainforests, where severe droughts are projected to occur more frequently, especially under El Niño conditions. To provide a useful tool for projecting the tropical rainforest dynamics under climate change conditions, we developed the Spatially Explicit Individual-Based (SEIB) Dynamic Global Vegetation Model (DGVM) applicable to simulating mechanistic tree mortality induced by the climatic impacts via individual-tree-scale ecophysiology such as hydraulic failure and carbon starvation. In this study, we present the new model, SEIB-originated Terrestrial Ecosystem Dynamics (S-TEDy) model, and the computation results were compared with observations collected at a field site in a Bornean tropical rainforest. Furthermore, after validating the model's performance, numerical experiments addressing a future of the tropical rainforest were conducted using some global climate model (GCM) simulation outputs.

The sense and non-sense of plot-scale, catchment-scale, continental-scale and global-scale hydrological modelling

NASA Astrophysics Data System (ADS)

Bronstert, Axel; Heistermann, Maik; Francke, Till

2017-04-01

Hydrological models aim at quantifying the hydrological cycle and its constituent processes for particular conditions, sites or periods in time. Such models have been developed for a large range of spatial and temporal scales. One must be aware that the question which is the appropriate scale to be applied depends on the overall question under study. Therefore, it is not advisable to give a general applicable guideline on what is "the best" scale for a model. This statement is even more relevant for coupled hydrological, ecological and atmospheric models. Although a general statement about the most appropriate modelling scale is not recommendable, it is worth to have a look on what are the advantages and the shortcomings of micro-, meso- and macro-scale approaches. Such an appraisal is of increasing importance, since increasingly (very) large / global scale approaches and models are under operation and therefore the question arises how far and for what purposes such methods may yield scientifically sound results. It is important to understand that in most hydrological (and ecological, atmospheric and other) studies process scale, measurement scale, and modelling scale differ from each other. In some cases, the differences between theses scales can be of different orders of magnitude (example: runoff formation, measurement and modelling). These differences are a major source of uncertainty in description and modelling of hydrological, ecological and atmospheric processes. Let us now summarize our viewpoint of the strengths (+) and weaknesses (-) of hydrological models of different scales: Micro scale (e.g. extent of a plot, field or hillslope): (+) enables process research, based on controlled experiments (e.g. infiltration; root water uptake; chemical matter transport); (+) data of state conditions (e.g. soil parameter, vegetation properties) and boundary fluxes (e.g. rainfall or evapotranspiration) are directly measurable and reproducible; (+) equations based on first principals, partly pde-type, are available for several processes (but not for all), because measurement and modelling scale are compatible (-) the spatial model domain are hardly representative for larger spatial entities, including regions for which water resources management decisions are to be taken; straightforward upsizing is also limited by data availability and computational requirements. Meso scale (e.g. extent of a small to large catchment or region): (+) the spatial extent of the model domain has approximately the same extent as the regions for which water resources management decisions are to be taken. I.e., such models enable water resources quantification at the scale of most water management decisions; (+) data of some state conditions (e.g. vegetation cover, topography, river network and cross sections) are available; (+) data of some boundary fluxes (in particular surface runoff / channel flow) are directly measurable with mostly sufficient certainty; (+) equations, partly based on simple water budgeting, partly variants of pde-type equations, are available for most hydrological processes. This enables the construction of meso-scale distributed models reflecting the spatial heterogeneity of regions/landscapes; (-) process scale, measurement scale, and modelling scale differ from each other for a number of processes, e.g., such as runoff generation; (-) the process formulation (usually derived from micro-scale studies) cannot directly be transferred to the modelling domain. Upscaling procedures for this purpose are not readily and generally available. Macro scale (e.g. extent of a continent up to global): (+) the spatial extent of the model may cover the whole Earth. This enables an attractive global display of model results; (+) model results might be technically interchangeable or at least comparable with results from other global models, such as global climate models; (-) process scale, measurement scale, and modelling scale differ heavily from each other for all hydrological and associated processes; (-) the model domain and its results are not representative regions for which water resources management decisions are to be taken. (-) both state condition and boundary flux data are hardly available for the whole model domain. Water management data and discharge data from remote regions are particular incomplete / unavailable for this scale. This undermines the model's verifiability; (-) since process formulation and resulting modelling reliability at this scale is very limited, such models can hardly show any explanatory skills or prognostic power; (-) since both the entire model domain and the spatial sub-units cover large areas, model results represent values averaged over at least the spatial sub-unit's extent. In many cases, the applied time scale implies a long-term averaging in time, too. We emphasize the importance to be aware of the above mentioned strengths and weaknesses of those scale-specific models. (Many of the) results of the current global model studies do not reflect such limitations. In particular, we consider the averaging over large model entities in space and/or time inadequate. Many hydrological processes are of a non-linear nature, including threshold-type behaviour. Such features cannot be reflected by such large scale entities. The model results therefore can be of little or no use for water resources decisions and/or even misleading for public debates or decision making. Some rather newly developed sustainability concepts, e.g. "Planetary Boundaries" in which humanity may "continue to develop and thrive for generations to come" are based on such global-scale approaches and models. However, many of the major problems regarding sustainability on Earth, e.g. water scarcity, do not exhibit on a global but on a regional scale. While on a global scale water might look like being available in sufficient quantity and quality, there are many regions where water problems already have very harmful or even devastating effects. Therefore, it is the challenge to derive models and observation programmes for regional scales. In case a global display is desired future efforts should be directed towards the development of a global picture based on a mosaic of regional sound assessments, rather than "zooming into" the results of large-scale simulations. Still, a key question remains to be discussed, i.e. for which purpose models at this (global) scale can be used.

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Feng, Cong; Cui, Mingjian

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern ismore » determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.« less

On the use of Schwarz-Christoffel conformal mappings to the grid generation for global ocean models

NASA Astrophysics Data System (ADS)

Xu, S.; Wang, B.; Liu, J.

2015-02-01

In this article we propose two conformal mapping based grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithms are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the basic grid design problem of pole relocation, these new algorithms also address more advanced issues such as smoothed scaling factor, or the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling where complex land-ocean distribution is present.

Comparison of Global and Mode of Action-Based Models for Aquatic Toxicity

EPA Science Inventory

The ability to estimate aquatic toxicity for a wide variety of chemicals is a critical need for ecological risk assessment and chemical regulation. The consensus in the literature is that mode of action (MOA) based QSAR (Quantitative Structure Activity Relationship) models yield ...

Adding ecosystem function to agent-based land use models

USDA-ARS?s Scientific Manuscript database

The objective of this paper is to examine issues in the inclusion of simulations of ecosystem functions in agent-based models of land use decision-making. The reasons for incorporating these simulations include local interests in land fertility and global interests in carbon sequestration. Biogeoche...

Combined constraints on global ocean primary production using observations and models

NASA Astrophysics Data System (ADS)

Buitenhuis, Erik T.; Hashioka, Taketo; Quéré, Corinne Le

2013-09-01

production is at the base of the marine food web and plays a central role for global biogeochemical cycles. Yet global ocean primary production is known to only a factor of 2, with previous estimates ranging from 38 to 65 Pg C yr-1 and no formal uncertainty analysis. Here, we present an improved global ocean biogeochemistry model that includes a mechanistic representation of photosynthesis and a new observational database of net primary production (NPP) in the ocean. We combine the model and observations to constrain particulate NPP in the ocean with statistical metrics. The PlankTOM5.3 model includes a new photosynthesis formulation with a dynamic representation of iron-light colimitation, which leads to a considerable improvement of the interannual variability of surface chlorophyll. The database includes a consistent set of 50,050 measurements of 14C primary production. The model best reproduces observations when global NPP is 58 ± 7 Pg C yr-1, with a most probable value of 56 Pg C yr-1. The most probable value is robust to the model used. The uncertainty represents 95% confidence intervals. It considers all random errors in the model and observations, but not potential biases in the observations. We show that tropical regions (23°S-23°N) contribute half of the global NPP, while NPPs in the Northern and Southern Hemispheres are approximately equal in spite of the larger ocean area in the South.

Structure and conformational dynamics of scaffolded DNA origami nanoparticles

DTIC Science & Technology

2017-05-08

all-atom molecular dynamics and coarse-grained finite element modeling to DX-based nanoparticles to elucidate their fine-scale and global conforma... finite element (FE) modeling approach CanDo is also routinely used to predict the 3D equilibrium conformation of programmed DNA assemblies based on a...model with both experimental cryo-electron microscopy (cryo-EM) data and all-atom modeling. MATERIALS AND METHODS Lattice-free finite element model

Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin

USGS Publications Warehouse

Shrestha, M.S.; Artan, Guleid A.; Bajracharya, S.R.; Sharma, R. R.

2008-01-01

In this study, we have described a hydrologic modelling system that uses satellite-based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

ShakeMap Atlas 2.0: an improved suite of recent historical earthquake ShakeMaps for global hazard analyses and loss model calibration

USGS Publications Warehouse

Garcia, D.; Mah, R.T.; Johnson, K.L.; Hearne, M.G.; Marano, K.D.; Lin, K.-W.; Wald, D.J.

2012-01-01

We introduce the second version of the U.S. Geological Survey ShakeMap Atlas, which is an openly-available compilation of nearly 8,000 ShakeMaps of the most significant global earthquakes between 1973 and 2011. This revision of the Atlas includes: (1) a new version of the ShakeMap software that improves data usage and uncertainty estimations; (2) an updated earthquake source catalogue that includes regional locations and finite fault models; (3) a refined strategy to select prediction and conversion equations based on a new seismotectonic regionalization scheme; and (4) vastly more macroseismic intensity and ground-motion data from regional agencies All these changes make the new Atlas a self-consistent, calibrated ShakeMap catalogue that constitutes an invaluable resource for investigating near-source strong ground-motion, as well as for seismic hazard, scenario, risk, and loss-model development. To this end, the Atlas will provide a hazard base layer for PAGER loss calibration and for the Earthquake Consequences Database within the Global Earthquake Model initiative.

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

NASA Technical Reports Server (NTRS)

Epperson, David L.; Davis, Jerry M.; Bloomfield, Peter; Karl, Thomas R.; Mcnab, Alan L.; Gallo, Kevin P.

1995-01-01

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

An online mineral dust model within the global/regional NMMB: current progress and plans

NASA Astrophysics Data System (ADS)

Perez, C.; Haustein, K.; Janjic, Z.; Jorba, O.; Baldasano, J. M.; Black, T.; Nickovic, S.

2008-12-01

While mineral dust distribution and effects are important on global scales, they strongly depend on dust emissions that are occurring on small spatial and temporal scales. Indeed, the accuracy of surface wind speed used in dust models is crucial. Due to the high-order power dependency on wind friction velocity and the threshold behaviour of dust emissions, small errors in surface wind speed lead to large dust emission errors. Most global dust models use prescribed wind fields provided by major meteorological centres (e.g., NCEP and ECMWF) and their spatial resolution is currently about 1 degree x 1 degree . Such wind speeds tend to be strongly underestimated over arid and semi-arid areas and do not account for mesoscale systems responsible for a significant fraction of dust emissions regionally and globally. Other significant uncertainties in dust emissions resulting from such approaches are related to the misrepresentation of high subgrid-scale spatial heterogeneity in soil and vegetation boundary conditions, mainly in semi-arid areas. In order to significantly reduce these uncertainties, the Barcelona Supercomputing Center is currently implementing a mineral dust model coupled on-line with the new global/regional NMMB atmospheric model using the ESMF framework under development in NOAA/NCEP/EMC. The NMMB is an evolution of the operational WRF-NMME extending from meso to global scales, and including non-hydrostatic option and improved tracer advection. This model is planned to become the next-generation NCEP mesoscale model for operational weather forecasting in North America. Current implementation is based on the well established regional dust model and forecast system Eta/DREAM (http://www.bsc.es/projects/earthscience/DREAM/). First successful global simulations show the potentials of such an approach and compare well with DREAM regionally. Ongoing developments include improvements in dust size distribution representation, sedimentation, dry deposition, wet scavenging and dust-radiation feedback, as well as the efficient implementation of the model on High Performance Supercomputers for global simulations and forecasts at high resolution.

An Accurate Absorption-Based Net Primary Production Model for the Global Ocean

NASA Astrophysics Data System (ADS)

Silsbe, G.; Westberry, T. K.; Behrenfeld, M. J.; Halsey, K.; Milligan, A.

2016-02-01

As a vital living link in the global carbon cycle, understanding how net primary production (NPP) varies through space, time, and across climatic oscillations (e.g. ENSO) is a key objective in oceanographic research. The continual improvement of ocean observing satellites and data analytics now present greater opportunities for advanced understanding and characterization of the factors regulating NPP. In particular, the emergence of spectral inversion algorithms now permits accurate retrievals of the phytoplankton absorption coefficient (aΦ) from space. As NPP is the efficiency in which absorbed energy is converted into carbon biomass, aΦ measurements circumvents chlorophyll-based empirical approaches by permitting direct and accurate measurements of phytoplankton energy absorption. It has long been recognized, and perhaps underappreciated, that NPP and phytoplankton growth rates display muted variability when normalized to aΦ rather than chlorophyll. Here we present a novel absorption-based NPP model that parameterizes the underlying physiological mechanisms behind this muted variability, and apply this physiological model to the global ocean. Through a comparison against field data from the Hawaii and Bermuda Ocean Time Series, we demonstrate how this approach yields more accurate NPP measurements than other published NPP models. By normalizing NPP to satellite estimates of phytoplankton carbon biomass, this presentation also explores the seasonality of phytoplankton growth rates across several oceanic regions. Finally, we discuss how future advances in remote-sensing (e.g. hyperspectral satellites, LIDAR, autonomous profilers) can be exploited to further improve absorption-based NPP models.

Evaluation of globally available precipitation data products as input for water balance models

NASA Astrophysics Data System (ADS)

Lebrenz, H.; Bárdossy, A.

2009-04-01

Subject of this study is the evaluation of globally available precipitation data products, which are intended to be used as input variables for water balance models in ungauged basins. The selected data sources are a) the Global Precipitation Climatology Centre (GPCC), b) the Global Precipitation Climatology Project (GPCP) and c) the Climate Research Unit (CRU), resulting into twelve globally available data products. The data products imply different data bases, different derivation routines and varying resolutions in time and space. For validation purposes, the ground data from South Africa were screened on homogeneity and consistency by various tests and an outlier detection using multi-linear regression was performed. External Drift Kriging was subsequently applied on the ground data and the resulting precipitation arrays were compared to the different products with respect to quantity and variance.

Can the concept of fundamental and realized niches be applied to the distribution of dominant phytoplankton in the global ocean?

NASA Astrophysics Data System (ADS)

Dowell, M.; Moore, T.; Follows, M.; Dutkiewicz, S.

2006-12-01

In recent years there has been significant progress both in the use of satellite ocean colour remote sensing and coupled hydrodynamic biological models for producing maps of different dominant phytoplankton groups in the global ocean. In parallel to these initiatives, there is ongoing research largely following on from Alan Longhurst's seminal work on defining a template of distinct ecological and biogeochemical provinces for the oceans based on their physical and biochemical characteristics. For these products and models to be of maximum use in their subsequent inclusion in re-analysis and climate scale models, there is a need to understand how the "observed" distributions of dominant phytoplankton (realized niche) coincide with of the environmental constraints in which they occur (fundamental niche). In the current paper, we base our analysis on the recently published results on the distribution of dominant phytoplankton species at global scale, resulting both from satellite and model analysis. Furthermore, we will present research in defining biogeochemical provinces using satellite and model data inputs and a fuzzy logic based approach. This will be compared with ongoing modelling efforts, which include competitive exclusion and therefore compatible with the definition of the realized ecological niche, to define the emergent distribution of dominant phytoplankton species. Ultimately we investigate the coherence of these two distinct approaches in studying phytoplankton distributions and propose the significance of this in the context of modelling and analysis at various scales.

Global terrain classification using Multiple-Error-Removed Improved-Terrain (MERIT) to address susceptibility of landslides and other geohazards

NASA Astrophysics Data System (ADS)

Iwahashi, J.; Yamazaki, D.; Matsuoka, M.; Thamarux, P.; Herrick, J.; Yong, A.; Mital, U.

2017-12-01

A seamless model of landform classifications with regional accuracy will be a powerful platform for geophysical studies that forecast geologic hazards. Spatial variability as a function of landform on a global scale was captured in the automated classifications of Iwahashi and Pike (2007) and additional developments are presented here that incorporate more accurate depictions using higher-resolution elevation data than the original 1-km scale Shuttle Radar Topography Mission digital elevation model (DEM). We create polygon-based terrain classifications globally by using the 280-m DEM interpolated from the Multi-Error-Removed Improved-Terrain DEM (MERIT; Yamazaki et al., 2017). The multi-scale pixel-image analysis method, known as Multi-resolution Segmentation (Baatz and Schäpe, 2000), is first used to classify the terrains based on geometric signatures (slope and local convexity) calculated from the 280-m DEM. Next, we apply the machine learning method of "k-means clustering" to prepare the polygon-based classification at the globe-scale using slope, local convexity and surface texture. We then group the divisions with similar properties by hierarchical clustering and other statistical analyses using geological and geomorphological data of the area where landslides and earthquakes are frequent (e.g. Japan and California). We find the 280-m DEM resolution is only partially sufficient for classifying plains. We nevertheless observe that the categories correspond to reported landslide and liquefaction features at the global scale, suggesting that our model is an appropriate platform to forecast ground failure. To predict seismic amplification, we estimate site conditions using the time-averaged shear-wave velocity in the upper 30-m (VS30) measurements compiled by Yong et al. (2016) and the terrain model developed by Yong (2016; Y16). We plan to test our method on finer resolution DEMs and report our findings to obtain a more globally consistent terrain model as there are known errors in DEM derivatives at higher-resolutions. We expect the improvement in DEM resolution (4 times greater detail) and the combination of regional and global coverage will yield a consistent dataset of polygons that have the potential to improve relations to the Y16 estimates significantly.

Global soil consumption of atmospheric carbon monoxide: an analysis using a process-based biogeochemistry model

NASA Astrophysics Data System (ADS)

Liu, Licheng; Zhuang, Qianlai; Zhu, Qing; Liu, Shaoqing; van Asperen, Hella; Pihlatie, Mari

2018-06-01

Carbon monoxide (CO) plays an important role in controlling the oxidizing capacity of the atmosphere by reacting with OH radicals that affect atmospheric methane (CH4) dynamics. We develop a process-based biogeochemistry model to quantify the CO exchange between soils and the atmosphere with a 5 min internal time step at the global scale. The model is parameterized using the CO flux data from the field and laboratory experiments for 11 representative ecosystem types. The model is then extrapolated to global terrestrial ecosystems using monthly climate forcing data. Global soil gross consumption, gross production, and net flux of the atmospheric CO are estimated to be from -197 to -180, 34 to 36, and -163 to -145 Tg CO yr-1 (1 Tg = 1012 g), respectively, when the model is driven with satellite-based atmospheric CO concentration data during 2000-2013. Tropical evergreen forest, savanna and deciduous forest areas are the largest sinks at 123 Tg CO yr-1. The soil CO gross consumption is sensitive to air temperature and atmospheric CO concentration, while the gross production is sensitive to soil organic carbon (SOC) stock and air temperature. By assuming that the spatially distributed atmospheric CO concentrations ( ˜ 128 ppbv) are not changing over time, the global mean CO net deposition velocity is estimated to be 0.16-0.19 mm s-1 during the 20th century. Under the future climate scenarios, the CO deposition velocity will increase at a rate of 0.0002-0.0013 mm s-1 yr-1 during 2014-2100, reaching 0.20-0.30 mm s-1 by the end of the 21st century, primarily due to the increasing temperature. Areas near the Equator, the eastern US, Europe and eastern Asia will be the largest sinks due to optimum soil moisture and high temperature. The annual global soil net flux of atmospheric CO is primarily controlled by air temperature, soil temperature, SOC and atmospheric CO concentrations, while its monthly variation is mainly determined by air temperature, precipitation, soil temperature and soil moisture.

GNSS-Based Space Weather Systems Including COSMIC Ionospheric Measurements

NASA Technical Reports Server (NTRS)

Komjathy, Attila; Mandrake, Lukas; Wilson, Brian; Iijima, Byron; Pi, Xiaoqing; Hajj, George; Mannucci, Anthony J.

2006-01-01

The presentation outline includes University Corporation for Atmospheric Research (UCAR) and Jet Propulsion Laboratory (JPL) product comparisons, assimilating ground-based global positioning satellites (GPS) and COSMIC into JPL/University of Southern California (USC) Global Assimilative Ionospheric Model (GAIM), and JPL/USC GAIM validation. The discussion of comparisons examines Abel profiles and calibrated TEC. The JPL/USC GAIM validation uses Arecibo ISR, Jason-2 VTEC, and Abel profiles.

The Role of Scale and Model Bias in ADAPT's Photospheric Eatimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Godinez Vazquez, Humberto C.; Hickmann, Kyle Scott; Arge, Charles Nicholas

2015-05-20

The Air Force Assimilative Photospheric flux Transport model (ADAPT), is a magnetic flux propagation based on Worden-Harvey (WH) model. ADAPT would be used to provide a global photospheric map of the Earth. A data assimilation method based on the Ensemble Kalman Filter (EnKF), a method of Monte Carlo approximation tied with Kalman filtering, is used in calculating the ADAPT models.

Consistency between satellite-derived and modeled estimates of the direct aerosol effect.

PubMed

Myhre, Gunnar

2009-07-10

In the Intergovernmental Panel on Climate Change Fourth Assessment Report, the direct aerosol effect is reported to have a radiative forcing estimate of -0.5 Watt per square meter (W m(-2)), offsetting the warming from CO2 by almost one-third. The uncertainty, however, ranges from -0.9 to -0.1 W m(-2), which is largely due to differences between estimates from global aerosol models and observation-based estimates, with the latter tending to have stronger (more negative) radiative forcing. This study demonstrates consistency between a global aerosol model and adjustment to an observation-based method, producing a global and annual mean radiative forcing that is weaker than -0.5 W m(-2), with a best estimate of -0.3 W m(-2). The physical explanation for the earlier discrepancy is that the relative increase in anthropogenic black carbon (absorbing aerosols) is much larger than the overall increase in the anthropogenic abundance of aerosols.

Development of an integrated chemical weather prediction system for environmental applications at meso to global scales: NMMB/BSC-CHEM

NASA Astrophysics Data System (ADS)

Jorba, O.; Pérez, C.; Karsten, K.; Janjic, Z.; Dabdub, D.; Baldasano, J. M.

2009-09-01

This contribution presents the ongoing developments of a new fully on-line chemical weather prediction system for meso to global scale applications. The modeling system consists of a mineral dust module and a gas-phase chemistry module coupled on-line to a unified global-regional atmospheric driver. This approach allows solving small scale processes and their interactions at local to global scales. Its unified environment maintains the consistency of all the physico-chemical processes involved. The atmospheric driver is the NCEP/NMMB numerical weather prediction model (Janjic and Black, 2007) developed at National Centers for Environmental Prediction (NCEP). It represents an evolution of the operational WRF-NMME model extending from meso to global scales. Its unified non-hydrostatic dynamical core supports regional and global simulations. The Barcelona Supercomputing Center is currently designing and implementing a chemistry transport model coupled online with the new global/regional NMMB. The new modeling system is intended to be a powerful tool for research and to provide efficient global and regional chemical weather forecasts at sub-synoptic and mesoscale resolutions. The online coupling of the chemistry follows the approach similar to that of the mineral dust module already coupled to the atmospheric driver, NMMB/BSC-DUST (Pérez et al., 2008). Chemical species are advected and mixed at the corresponding time steps of the meteorological tracers using the same numerical scheme. Advection is eulerian, positive definite and monotone. The chemical mechanism and chemistry solver is based on the Kinetic PreProcessor KPP (Damian et al., 2002) package with the main purpose of maintaining a wide flexibility when configuring the model. Such approach will allow using a simplified chemical mechanism for global applications or a more complete mechanism for high-resolution local or regional studies. Moreover, it will permit the implementation of a specific configuration for forecasting applications in regional or global domains. An emission process allows the coupling of different emission inventories sources such as RETRO, EDGAR and GEIA for the global domain, EMEP for Europe and HERMES for Spain. The photolysis scheme is based on the Fast-J scheme, coupled with physics of each model layer (e.g., aerosols, clouds, absorbers as ozone) and it considers grid-scale clouds from the atmospheric driver. The dry deposition scheme follows the deposition velocity analogy for gases, enabling the calculation of deposition fluxes from airborne concentrations. No cloud-chemistry processes are included in the system yet (no wet deposition, scavenging and aqueous chemistry). The modeling system developments will be presented and first results of the gas-phase chemistry at global scale will be discussed. REFERENCES Janjic, Z.I., and Black, T.L., 2007. An ESMF unified model for a broad range of spatial and temporal scales, Geophysical Research Abstracts, 9, 05025. Pérez, C., Haustein, K., Janjic, Z.I., Jorba, O., Baldasano, J.M., Black, T.L., and Nickovic, S., 2008. An online dust model within the meso to global NMMB: current progress and plans. AGU Fall Meeting, San Francisco, A41K-03, 2008. Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G.R., 2002. The kinetic preprocessor KPP - A software environment for solving chemical kinetics. Comp. Chem. Eng., 26, 1567-1579. Sandu, A., and Sander, R., 2006. Technical note:Simulating chemical systems in Fortran90 and Matlab with the Kinetic PreProcessor KPP-2.1. Atmos. Chem. and Phys., 6, 187-195.

Seismic waves and earthquakes in a global monolithic model

NASA Astrophysics Data System (ADS)

Roubíček, Tomáš

2018-03-01

The philosophy that a single "monolithic" model can "asymptotically" replace and couple in a simple elegant way several specialized models relevant on various Earth layers is presented and, in special situations, also rigorously justified. In particular, global seismicity and tectonics is coupled to capture, e.g., (here by a simplified model) ruptures of lithospheric faults generating seismic waves which then propagate through the solid-like mantle and inner core both as shear (S) or pressure (P) waves, while S-waves are suppressed in the fluidic outer core and also in the oceans. The "monolithic-type" models have the capacity to describe all the mentioned features globally in a unified way together with corresponding interfacial conditions implicitly involved, only when scaling its parameters appropriately in different Earth's layers. Coupling of seismic waves with seismic sources due to tectonic events is thus an automatic side effect. The global ansatz is here based, rather for an illustration, only on a relatively simple Jeffreys' viscoelastic damageable material at small strains whose various scaling (limits) can lead to Boger's viscoelastic fluid or even to purely elastic (inviscid) fluid. Self-induced gravity field, Coriolis, centrifugal, and tidal forces are counted in our global model, as well. The rigorous mathematical analysis as far as the existence of solutions, convergence of the mentioned scalings, and energy conservation is briefly presented.

Global Modeling Study of the Bioavailable Atmospheric Iron Supply to the Global Ocean

NASA Astrophysics Data System (ADS)

Myriokefalitakis, S.; Krol, M. C.; van Noije, T.; Le Sager, P.

2017-12-01

Atmospheric deposition of trace constituents acts as a nutrient source to the open ocean and affect marine ecosystem. Dust is known as a major source of nutrients to the global ocean, but only a fraction of these nutrients is released in a bioavailable form that can be assimilated by the marine biota. Iron (Fe) is a key micronutrient that significantly modulates gross primary production in the High-Nutrient-Low-Chlorophyll (HNLC) oceans, where macronutrients like nitrate are abundant, but primary production is limited by Fe scarcity. The global atmospheric Fe cycle is here parameterized in the state-of-the-art global Earth System Model EC-Earth. The model takes into account the primary emissions of both insoluble and soluble Fe forms, associated with mineral dust and combustion aerosols. The impact of atmospheric acidity and organic ligands on mineral dissolution processes, is parameterized based on updated experimental and theoretical findings. Model results are also evaluated against available observations. Overall, the link between the labile Fe atmospheric deposition and atmospheric composition changes is here demonstrated and quantified. This work has been financed by the Marie-Curie H2020-MSCA-IF-2015 grant (ID 705652) ODEON (Online DEposition over OceaNs; modeling the effect of air pollution on ocean bio-geochemistry in an Earth System Model).

Assessing the Impact of Land Use and Land Cover Change on Global Water Resources

NASA Astrophysics Data System (ADS)

Batra, N.; Yang, Y. E.; Choi, H. I.; Islam, A.; Charlotte, D. F.; Cai, X.; Kumar, P.

2007-12-01

Land use and land cover changes (LULCC) significantly modify the hydrological regime of the watersheds, affecting water resources and environment from regional to global scale. This study seeks to advance and integrate water and energy cycle observation, scientific understanding, and human impacts to assess future water availability. To achieve the research objective, we integrate and interpret past and current space based and in situ observations into a global hydrologic model (GHM). GHM is developed with enhanced spatial and temporal resolution, physical complexity, hydrologic theory and processes to quantify the impact of LULCC on physical variables: surface runoff, subsurface flow, groundwater, infiltration, ET, soil moisture, etc. Coupled with the common land model (CLM), a 3-dimensional volume averaged soil-moisture transport (VAST) model is expanded to incorporate the lateral flow and subgrid heterogeneity. The model consists of 11 soil-hydrology layers to predict lateral as well as vertical moisture flux transport based on Richard's equations. The primary surface boundary conditions (SBCs) include surface elevation and its derivatives, land cover category, sand and clay fraction profiles, bedrock depth and fractional vegetation cover. A consistent global GIS-based dataset is constructed for the SBCs of the model from existing observational datasets comprising of various resolutions, map projections and data formats. Global ECMWF data at 6-hour time steps for the period 1971 through 2000 is processed to get the forcing data which includes incoming longwave and shortwave radiation, precipitation, air temperature, pressure, wind components, boundary layer height and specific humidity. Land use land cover data, generated using IPCC scenarios for every 10 years from 2000 to 2100 is used for future assessment on water resources. Alterations due to LULCC on surface water balance components: ET, groundwater recharge and runoff are then addressed in the study. Land use change disrupts the hydrological cycle through increasing the water yield at some places leading to floods while diminishing, or even eliminating the low flow at other places.

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

PubMed

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

2014-04-15

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

Cosmological backreaction within the Szekeres model and emergence of spatial curvature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolejko, Krzysztof, E-mail: krzysztof.bolejko@sydney.edu.au

This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature Ω{sub R} (in the FLRW limit Ω{sub R} → Ω {sub k} ). If averaged over global scales the result depends on the assumed global model of the Universe. Withinmore » the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from Ω{sub R} =0 at the CMB to Ω{sub R} ∼ 0.1 at 0 z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ω {sub k} ≠ 0, even if in the early Universe Ω {sub k} = 0) and therefore when analysing low- z cosmological data one should keep Ω {sub k} as a free parameter and independent from the CMB constraints.« less

Cosmological backreaction within the Szekeres model and emergence of spatial curvature

NASA Astrophysics Data System (ADS)

Bolejko, Krzysztof

2017-06-01

This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature ΩScript R (in the FLRW limit ΩScript R → Ωk). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from ΩScript R =0 at the CMB to ΩScript R ~ 0.1 at 0z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ωk ≠ 0, even if in the early Universe Ωk = 0) and therefore when analysing low-z cosmological data one should keep Ωk as a free parameter and independent from the CMB constraints.

Tropospheric carbon monoxide over the Pacific during HIPPO: two-way coupled simulation of GEOS-Chem and its multiple nested models

NASA Astrophysics Data System (ADS)

Yan, Y.-Y.; Lin, J.-T.; Kuang, Y.; Yang, D.; Zhang, L.

2014-12-01

Global chemical transport models (CTMs) are used extensively to study air pollution and transport at a global scale. These models are limited by coarse horizontal resolutions that do not allow for a detailed representation of small-scale nonlinear processes over the pollutant source regions. Here we couple the global GEOS-Chem CTM and its three high-resolution nested models to simulate the tropospheric carbon monoxide (CO) over the Pacific Ocean during five High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) campaigns between 2009 and 2011. We develop a two-way coupler, the PeKing University CouPLer (PKUCPL), allowing for the exchange and interaction of chemical constituents between the global model (at 2.5° long. × 2° lat.) and the three nested models (at 0.667° long. × 0.5° lat.) covering Asia, North America, and Europe. The coupler obtains nested model results to modify the global model simulation within the respective nested domains, and simultaneously acquires global model results to provide lateral boundary conditions (LBCs) for the nested models. Compared to the global model alone, the two-way coupled simulation results in enhanced CO concentrations in the nested domains. Sensitivity tests suggest the enhancement to be a result of improved representation of the spatial distributions of CO, nitrogen oxides, and non-methane volatile organic compounds, the meteorological dependence of natural emissions, and other resolution-dependent processes. The relatively long lifetime of CO allows for the enhancement to be accumulated and carried across the globe. We found that the two-way coupled simulation increased the global tropospheric mean CO concentrations in 2009 by 10.4%, with a greater enhancement at 13.3% in the Northern Hemisphere. Coincidently, the global tropospheric mean hydroxyl radical (OH) was reduced by 4.2%, resulting in a 4.2% enhancement in the methyl chloroform lifetime (MCF; via reaction with tropospheric OH). The resulting CO and OH contents and MCF lifetime are closer to observation-based estimates. Both the global and the two-way coupled models capture the general spatiotemporal patterns of HIPPO CO over the Pacific. The two-way coupled simulation is much closer to HIPPO CO, with a mean bias of 1.1 ppb (1.4%) below 9 km compared to the bias at -7.2 ppb (-9.2%) for the global model alone. The improvement is most apparent over the North Pacific. Our test simulations show that the global model alone could resemble the two-way coupled simulation (especially below 4 km) by increasing its global CO emissions by 15% for HIPPO-1 and HIPPO-3, by 25% for HIPPO-2 and HIPPO-4, and by 35% for HIPPO-5. This has important implications for using the global model alone to constrain CO emissions. Thus, the two-way coupled simulation is a significantly improved model tool for studying the global impacts of air pollutants from major anthropogenic source regions.

Evaluation of NCAR CAM5 Simulated Marine Boundary Layer Cloud Properties Using a Combination of Satellite and Surface Observations

NASA Astrophysics Data System (ADS)

Zhang, Z.; Song, H.; Wang, M.; Ghan, S. J.; Dong, X.

2016-12-01

he main objective of this study is to systematically evaluate the MBL cloud properties simulated in CAM5 family models using a combination of satellite-based CloudSat/MODIS observations and ground-based observations from the ARM Azores site, with a special focus on MBL cloud microphysics and warm rain process. First, we will present a global evaluation based on satellite observations and retrievals. We will compare global cloud properties (e.g., cloud fraction, cloud vertical structure, cloud CER, COT, and LWP, as well as drizzle frequency and intensity diagnosed using the CAM5-COSP instrumental simulators) simulated in the CAM5 models with the collocated CloudSat and MODIS observations. We will also present some preliminary results from a regional evaluation based mainly on ground observations from ARM Azores site. We will compare MBL cloud properties simulated in CAM5 models over the ARM Azores site with collocated satellite (MODIS and CloudSat) and ground-based observations from the ARM site.

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

DOE PAGES

Bai, Yun; Zhang, Jiahua; Zhang, Sha; ...

2017-01-04

Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, fourmore » older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 ( RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 ( RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.« less

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, Yun; Zhang, Jiahua; Zhang, Sha

Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, fourmore » older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 ( RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 ( RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.« less

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

NASA Astrophysics Data System (ADS)

Bai, Yun; Zhang, Jiahua; Zhang, Sha; Koju, Upama Ashish; Yao, Fengmei; Igbawua, Tertsea

2017-03-01

Recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 (RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 (RMSE = 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 (RMSE = 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

DOE PAGES

Urrego-Blanco, Jorge Rolando; Urban, Nathan Mark; Hunke, Elizabeth Clare; ...

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less

Observationally constrained estimates of carbonaceous aerosol radiative forcing.

PubMed

Chung, Chul E; Ramanathan, V; Decremer, Damien

2012-07-17

Carbonaceous aerosols (CA) emitted by fossil and biomass fuels consist of black carbon (BC), a strong absorber of solar radiation, and organic matter (OM). OM scatters as well as absorbs solar radiation. The absorbing component of OM, which is ignored in most climate models, is referred to as brown carbon (BrC). Model estimates of the global CA radiative forcing range from 0 to 0.7 Wm(-2), to be compared with the Intergovernmental Panel on Climate Change's estimate for the pre-Industrial to the present net radiative forcing of about 1.6 Wm(-2). This study provides a model-independent, observationally based estimate of the CA direct radiative forcing. Ground-based aerosol network data is integrated with field data and satellite-based aerosol observations to provide a decadal (2001 through 2009) global view of the CA optical properties and direct radiative forcing. The estimated global CA direct radiative effect is about 0.75 Wm(-2) (0.5 to 1.0). This study identifies the global importance of BrC, which is shown to contribute about 20% to 550-nm CA solar absorption globally. Because of the inclusion of BrC, the net effect of OM is close to zero and the CA forcing is nearly equal to that of BC. The CA direct radiative forcing is estimated to be about 0.65 (0.5 to about 0.8) Wm(-2), thus comparable to or exceeding that by methane. Caused in part by BrC absorption, CAs have a net warming effect even over open biomass-burning regions in Africa and the Amazon.

Observationally constrained estimates of carbonaceous aerosol radiative forcing

PubMed Central

Chung, Chul E.; Ramanathan, V.; Decremer, Damien

2012-01-01

Carbonaceous aerosols (CA) emitted by fossil and biomass fuels consist of black carbon (BC), a strong absorber of solar radiation, and organic matter (OM). OM scatters as well as absorbs solar radiation. The absorbing component of OM, which is ignored in most climate models, is referred to as brown carbon (BrC). Model estimates of the global CA radiative forcing range from 0 to 0.7 Wm-2, to be compared with the Intergovernmental Panel on Climate Change’s estimate for the pre-Industrial to the present net radiative forcing of about 1.6 Wm-2. This study provides a model-independent, observationally based estimate of the CA direct radiative forcing. Ground-based aerosol network data is integrated with field data and satellite-based aerosol observations to provide a decadal (2001 through 2009) global view of the CA optical properties and direct radiative forcing. The estimated global CA direct radiative effect is about 0.75 Wm-2 (0.5 to 1.0). This study identifies the global importance of BrC, which is shown to contribute about 20% to 550-nm CA solar absorption globally. Because of the inclusion of BrC, the net effect of OM is close to zero and the CA forcing is nearly equal to that of BC. The CA direct radiative forcing is estimated to be about 0.65 (0.5 to about 0.8) Wm-2, thus comparable to or exceeding that by methane. Caused in part by BrC absorption, CAs have a net warming effect even over open biomass-burning regions in Africa and the Amazon. PMID:22753522

Declining vulnerability to river floods and the global benefits of adaptation

NASA Astrophysics Data System (ADS)

Jongman, Brenden; Winsemius, Hessel; Aerts, Jeroen; Coughlan de Perez, Erin; Van Aalst, Maarten; Kron, Wolfgang; Ward, Philip

2016-04-01

The global impacts of river floods are substantial and rising. Effective adaptation to the increasing risks requires an in-depth understanding of the physical and socioeconomic drivers of risk. Whilst the modeling of flood hazard and exposure has improved greatly, compelling evidence on spatiotemporal patterns in vulnerability of societies around the world is lacking. Hence, the effects of vulnerability on global flood risk are not fully understood, and future projections of fatalities and losses available today are based on simplistic assumptions or do not include vulnerability. In this study, we show that trends and fluctuations in vulnerability to river floods around the world can be estimated by dynamic high-resolution modeling of flood hazard and exposure. We show that fatalities and losses as a share of exposed population and gross domestic product are decreasing with rising income. We also show that there is a tendency of convergence in vulnerability levels between low- and high-income countries. Based on these findings, we simulate future flood impacts per country using traditional assumptions of static vulnerability through time, but also using future assumptions on reduced vulnerability in the future. We show that future risk increases can be largely contained using effective disaster risk reduction strategies, including a reduction of vulnerability. The study was carried out using the global flood risk model, GLOFRIS, combined with high-resolution time-series maps of hazard and exposure at the global scale. Based on: Jongman et al., 2015. Proceedings of the National Academy of Sciences of the United States of America, doi:10.1073/pnas.1414439112.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

Interpretation of Trace Gas Data Using Inverse Methods and Global Chemical Transport Models

NASA Technical Reports Server (NTRS)

Prinn, Ronald G.

1997-01-01

This is a theoretical research project aimed at: (1) development, testing, and refining of inverse methods for determining regional and global transient source and sink strengths for long lived gases important in ozone depletion and climate forcing, (2) utilization of inverse methods to determine these source/sink strengths which use the NCAR/Boulder CCM2-T42 3-D model and a global 3-D Model for Atmospheric Transport and Chemistry (MATCH) which is based on analyzed observed wind fields (developed in collaboration by MIT and NCAR/Boulder), (3) determination of global (and perhaps regional) average hydroxyl radical concentrations using inverse methods with multiple titrating gases, and, (4) computation of the lifetimes and spatially resolved destruction rates of trace gases using 3-D models. Important goals include determination of regional source strengths of methane, nitrous oxide, and other climatically and chemically important biogenic trace gases and also of halocarbons restricted by the Montreal Protocol and its follow-on agreements and hydrohalocarbons used as alternatives to the restricted halocarbons.

Global distributions of ionospheric electric potentials for variable IMF conditions: climatology and near-real time specification

NASA Astrophysics Data System (ADS)

Kartalev, M. D.; Papitashvili, V. O.; Keremidarska, V. I.; Grigorov, K. G.; Romanov, D. K.

2002-03-01

We report a study of global climatology in the ionospheric electric potentials obtained from combining two algorithms used for mapping of high- and middle/low latitude ionospheric electrodynamics: the LiMIE (http://www.sprl.umich.edu/mist/limie.html) and IMEH (http://geospace.nat.bg) models, respectively. In this combination, the latter model utilizes high-latitude field-aligned current distributions provided by LiMIE for various IMF conditions and different seasons (summer, winter, equinox). For the testing purposes, we developed a Web-based interface which provides global distributions of the ionospheric electric potential in near-real time utilizing solar wind observations made onboard the NASA's ACE spacecraft upstream at L1. We discuss the electric potential global modeling over both the northern and southern hemispheres and consider some implications for the solar cycle studies and space weather forecasting.

Improved simulation of tropospheric ozone by a global-multi-regional two-way coupling model system

NASA Astrophysics Data System (ADS)

Yan, Y.-Y.; Lin, J.-T.; Chen, J.; Hu, L.

2015-09-01

Small-scale nonlinear chemical and physical processes over pollution source regions affect the global ozone (O3) chemistry, but these processes are not captured by current global chemical transport models (CTMs) and chemistry-climate models that are limited by coarse horizontal resolutions (100-500 km, typically 200 km). These models tend to contain large (and mostly positive) tropospheric O3 biases in the Northern Hemisphere. Here we use a recently built two-way coupling system of the GEOS-Chem CTM to simulate the global tropospheric O3 in 2009. The system couples the global model (at 2.5° long. × 2° lat.) and its three nested models (at 0.667° long. × 0.5° lat.) covering Asia, North America and Europe, respectively. Benefiting from the high resolution, the nested models better capture small-scale processes than the global model alone. In the coupling system, the nested models provide results to modify the global model simulation within respective nested domains while taking the lateral boundary conditions from the global model. Due to the "coupling" effects, the two-way system significantly improves the tropospheric O3 simulation upon the global model alone, as found by comparisons with a suite of ground (1420 sites from WDCGG, GMD, EMEP, and AQS), aircraft (HIPPO and MOZAIC), and satellite measurements (two OMI products). Compared to the global model alone, the two-way coupled simulation enhances the correlation in day-to-day variation of afternoon mean O3 with the ground measurements from 0.53 to 0.68, and it reduces the mean model bias from 10.8 to 6.7 ppb in annual average afternoon O3. Regionally, the coupled model reduces the bias by 4.6 ppb over Europe, 3.9 ppb over North America, and 3.1 ppb over other regions. The two-way coupling brings O3 vertical profiles much closer to the HIPPO (for remote areas) and MOZAIC (for polluted regions) data, reducing the tropospheric (0-9 km) mean bias by 3-10 ppb at most MOZAIC sites and by 5.3 ppb for HIPPO profiles. The two-way coupled simulation also reduces the global tropospheric column ozone by 3.0 DU (9.5 %, annual mean), bringing them closer to the OMI data in all seasons. Simulation improvements are more significant in the northern hemisphere, and are primarily a result of improved representation of urban-rural contrast and other small-scale processes. The two-way coupled simulation also reduces the global tropospheric mean hydroxyl radical by 5 % with enhancements by 5 % in the lifetimes of methyl chloroform (from 5.58 to 5.87 yr) and methane (from 9.63 to 10.12 yr), bringing them closer to observation-based estimates. Improving model representations of small-scale processes are a critical step forward to understanding the global tropospheric chemistry.

A climatology of gravity wave parameters based on satellite limb soundings

NASA Astrophysics Data System (ADS)

Ern, Manfred; Trinh, Quang Thai; Preusse, Peter; Riese, Martin

2017-04-01

Gravity waves are one of the main drivers of atmospheric dynamics. The resolution of most global circulation models (GCMs) and chemistry climate models (CCMs), however, is too coarse to properly resolve the small scales of gravity waves. Horizontal scales of gravity waves are in the range of tens to a few thousand kilometers. Gravity wave source processes involve even smaller scales. Therefore GCMs/CCMs usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified, and comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. In our study, we present a global data set of gravity wave distributions observed in the stratosphere and the mesosphere by the infrared limb sounding satellite instruments High Resolution Dynamics Limb Sounder (HIRDLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). We provide various gravity wave parameters (for example, gravity variances, potential energies and absolute momentum fluxes). This comprehensive climatological data set can serve for comparison with other instruments (ground based, airborne, or other satellite instruments), as well as for comparison with gravity wave distributions, both resolved and parametrized, in GCMs and CCMs. The purpose of providing various different parameters is to make our data set useful for a large number of potential users and to overcome limitations of other observation techniques, or of models, that may be able to provide only one of those parameters. We present a climatology of typical average global distributions and of zonal averages, as well as their natural range of variations. In addition, we discuss seasonal variations of the global distribution of gravity waves, as well as limitations of our method of deriving gravity wave parameters from satellite data.

Terrestrial Feedbacks Incorporated in Global Vegetation Models through Observed Trait-Environment Responses

NASA Astrophysics Data System (ADS)

Bodegom, P. V.

2015-12-01

Most global vegetation models used to evaluate climate change impacts rely on plant functional types to describe vegetation responses to environmental stresses. In a traditional set-up in which vegetation characteristics are considered constant within a vegetation type, the possibility to implement and infer feedback mechanisms are limited as feedback mechanisms will likely involve a changing expression of community trait values. Based on community assembly concepts, we implemented functional trait-environment relationships into a global dynamic vegetation model to quantitatively assess this feature. For the current climate, a different global vegetation distribution was calculated with and without the inclusion of trait variation, emphasizing the importance of feedbacks -in interaction with competitive processes- for the prevailing global patterns. These trait-environmental responses do, however, not necessarily imply adaptive responses of vegetation to changing conditions and may locally lead to a faster turnover in vegetation upon climate change. Indeed, when running climate projections, simulations with trait variation did not yield a more stable or resilient vegetation than those without. Through the different feedback expressions, global and regional carbon and water fluxes were -however- strongly altered. At a global scale, model projections suggest an increased productivity and hence an increased carbon sink in the next decades to come, when including trait variation. However, by the end of the century, a reduced carbon sink is projected. This effect is due to a downregulation of photosynthesis rates, particularly in the tropical regions, even when accounting for CO2-fertilization effects. Altogether, the various global model simulations suggest the critical importance of including vegetation functional responses to changing environmental conditions to grasp terrestrial feedback mechanisms at global scales in the light of climate change.

Global water dynamics: issues for the 21st century.

PubMed

Simonovic, Slobodan P

2002-01-01

The WorldWater system dynamics model has been developed for modeling the global world water balance and capturing the dynamic character of the main variables affecting water availability and use in the future. Despite not being a novel approach, system dynamics offers a new way of addressing complex systems. WorldWater simulations are clearly demonstrating the strong feedback relation between water availability and different aspects of world development. Results of numerous simulations are contradictory to the assumption made by many global modelers that water is not an issue on the global scale. Two major observations can be made from early simulations: (a) the use of clean water for dilution and transport of wastewater, if not dealt with in other ways, imposes a major stress on the global world water balance; and (b) water use by different sectors is demonstrating quite different dynamics than predicted by classical forecasting tools and other water-models. Inherent linkages between water quantity and quality sectors with food, industry, persistent pollution, technology, and non-renewable resources sectors of the model create shoot and collapse behavior in water use dynamics. This paper discusses a number of different water-related scenarios and their implications on the global water balance. In particular, two extreme scenarios (business as usual - named "Chaos", and unlimited desalination - named "Ocean") are presented in the paper. Based on the conclusions derived from these two extreme cases a set of more moderate and realistic scenarios (named "Conservation") is proposed and their consequences on the global water balance are evaluated.

Mapping local and global variability in plant trait distributions.

PubMed

Butler, Ethan E; Datta, Abhirup; Flores-Moreno, Habacuc; Chen, Ming; Wythers, Kirk R; Fazayeli, Farideh; Banerjee, Arindam; Atkin, Owen K; Kattge, Jens; Amiaud, Bernard; Blonder, Benjamin; Boenisch, Gerhard; Bond-Lamberty, Ben; Brown, Kerry A; Byun, Chaeho; Campetella, Giandiego; Cerabolini, Bruno E L; Cornelissen, Johannes H C; Craine, Joseph M; Craven, Dylan; de Vries, Franciska T; Díaz, Sandra; Domingues, Tomas F; Forey, Estelle; González-Melo, Andrés; Gross, Nicolas; Han, Wenxuan; Hattingh, Wesley N; Hickler, Thomas; Jansen, Steven; Kramer, Koen; Kraft, Nathan J B; Kurokawa, Hiroko; Laughlin, Daniel C; Meir, Patrick; Minden, Vanessa; Niinemets, Ülo; Onoda, Yusuke; Peñuelas, Josep; Read, Quentin; Sack, Lawren; Schamp, Brandon; Soudzilovskaia, Nadejda A; Spasojevic, Marko J; Sosinski, Enio; Thornton, Peter E; Valladares, Fernando; van Bodegom, Peter M; Williams, Mathew; Wirth, Christian; Reich, Peter B

2017-12-19

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

A framework for global river flood risk assessments

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Jongman, B.; Ward, P. J.; Bouwman, A.

2013-05-01

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ~ 1 km2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

Regional TEC model under quiet geomagnetic conditions and low-to-moderate solar activity based on CODE GIMs

NASA Astrophysics Data System (ADS)

Feng, Jiandi; Jiang, Weiping; Wang, Zhengtao; Zhao, Zhenzhen; Nie, Linjuan

2017-08-01

Global empirical total electron content (TEC) models based on TEC maps effectively describe the average behavior of the ionosphere. However, the accuracy of these global models for a certain region may not be ideal. Due to the number and distribution of the International GNSS Service (IGS) stations, the accuracy of TEC maps is geographically different. The modeling database derived from the global TEC maps with different accuracy is likely one of the main reasons that limits the accuracy of the new models. Moreover, many anomalies in the ionosphere are geographic or geomagnetic dependent, and as such the accuracy of global models can deteriorate if these anomalies are not fully incorporated into the modeling approach. For regional models built in small areas, these influences on modeling are immensely weakened. Thus, the regional TEC models may better reflect the temporal and spatial variations of TEC. In our previous work (Feng et al., 2016), a regional TEC model TECM-NEC is proposed for northeast China. However, this model is only directed against the typical region of Mid-latitude Summer Nighttime Anomaly (MSNA) occurrence, which is meaningless in other regions without MSNA. Following the technique of TECM-NEC model, this study proposes another regional empirical TEC model for other regions in mid-latitudes. Taking a small area BeiJing-TianJin-Tangshan (JJT) region (37.5°-42.5° N, 115°-120° E) in China as an example, a regional empirical TEC model (TECM-JJT) is proposed using the TEC grid data from January 1, 1999 to June 30, 2015 provided by the Center for Orbit Determination in Europe (CODE) under quiet geomagnetic conditions. The TECM-JJT model fits the input CODE TEC data with a bias of 0.11TECU and a root mean square error of 3.26TECU. Result shows that the regional model TECM-JJT is consistent with CODE TEC data and GPS-TEC data.

A thermal-based remote sensing modeling system for estimating evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/s...

Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training

ERIC Educational Resources Information Center

Baschera, Gian-Marco; Gross, Markus

2010-01-01

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006

USGS Publications Warehouse

Xia, Jiangzhou; Liu, Shuguang; Liang, Shunlin; Chen, Yang; Xu, Wenfang; Yuan, Wenping

2014-01-01

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.

A Robust, Scalable Framework for Conducting Climate Change Susceptibility Analyses

DTIC Science & Technology

2014-05-01

for identifying areas of heightened risk from varying forms of climate forcings is needed. Based on global climate model projections, deviations from...framework provides an opportunity to easily combine multiple data sources — that are often freely available from many federal, state, and global ...Climate change and extreme weather events: implications for food production, plant diseases, and pests. Global Change and Human Health 2:90–104. ERDC/EL

Development of global sea ice 6.0 CICE configuration for the Met Office global coupled model

DOE PAGES

Rae, J. . G. L; Hewitt, H. T.; Keen, A. B.; ...

2015-03-05

The new sea ice configuration GSI6.0, used in the Met Office global coupled configuration GC2.0, is described and the sea ice extent, thickness and volume are compared with the previous configuration and with observationally-based datasets. In the Arctic, the sea ice is thicker in all seasons than in the previous configuration, and there is now better agreement of the modelled concentration and extent with the HadISST dataset. In the Antarctic, a warm bias in the ocean model has been exacerbated at the higher resolution of GC2.0, leading to a large reduction in ice extent and volume; further work is requiredmore » to rectify this in future configurations.« less

Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach

PubMed Central

Appia, Vikram; Ganapathy, Balaji; Yezzi, Anthony; Faber, Tracy

2014-01-01

We propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape. Thus, in a sense, we are clustering the variations exhibited in the training data set. We then use a parametric model to implicitly represent each localized segmentation curve as a combination of the local shape priors obtained by representing the training shapes and the masks as a collection of signed distance functions. We also propose a parametric model to combine the locally evolved segmentation curves into a single hybrid (global) segmentation. Finally, we combine the evolution of these semilocal and global parameters to minimize an objective energy function. The resulting algorithm thus provides a globally accurate solution, which retains the local variations in shape. We present some results to illustrate how our approach performs better than the traditional approach with fully global PCA. PMID:25520901

A Canonical Response in Rainfall Characteristics to Global Warming: Projections by IPCC CMIP5 Models

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Wu, H. T.; Kim, K. M.

2012-01-01

Changes in rainfall characteristics induced by global warming are examined based on probability distribution function (PDF) analysis, from outputs of 14 IPCC (Intergovernmental Panel on Climate Change), CMIP (5th Coupled Model Intercomparison Project) models under various scenarios of increased CO2 emissions. Results show that collectively CMIP5 models project a robust and consistent global and regional rainfall response to CO2 warming. Globally, the models show a 1-3% increase in rainfall per degree rise in temperature, with a canonical response featuring large increase (100-250 %) in frequency of occurrence of very heavy rain, a reduction (5-10%) of moderate rain, and an increase (10-15%) of light rain events. Regionally, even though details vary among models, a majority of the models (>10 out of 14) project a consistent large scale response with more heavy rain events in climatologically wet regions, most pronounced in the Pacific ITCZ and the Asian monsoon. Moderate rain events are found to decrease over extensive regions of the subtropical and extratropical oceans, but increases over the extratropical land regions, and the Southern Oceans. The spatial distribution of light rain resembles that of moderate rain, but mostly with opposite polarity. The majority of the models also show increase in the number of dry events (absence or only trace amount of rain) over subtropical and tropical land regions in both hemispheres. These results suggest that rainfall characteristics are changing and that increased extreme rainfall events and droughts occurrences are connected, as a consequent of a global adjustment of the large scale circulation to global warming.

Spatial modeling of agricultural land use change at global scale

NASA Astrophysics Data System (ADS)

Meiyappan, P.; Dalton, M.; O'Neill, B. C.; Jain, A. K.

2014-11-01

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by hypothesized landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns. We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

PubMed

Lin, Xin; Moussawi, Alaa; Korniss, Gyorgy; Bakdash, Jonathan Z; Szymanski, Boleslaw K

2017-07-27

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.

Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa

2018-08-01

The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC 50 ) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors (EA) and/or charge transfer (ΔN) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑Q Sul ) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC 50 ) values. Copyright © 2018 Elsevier Ltd. All rights reserved.

High resolution global flood hazard map from physically-based hydrologic and hydraulic models.

NASA Astrophysics Data System (ADS)

Begnudelli, L.; Kaheil, Y.; McCollum, J.

2017-12-01

The global flood map published online at http://www.fmglobal.com/research-and-resources/global-flood-map at 90m resolution is being used worldwide to understand flood risk exposure, exercise certain measures of mitigation, and/or transfer the residual risk financially through flood insurance programs. The modeling system is based on a physically-based hydrologic model to simulate river discharges, and 2D shallow-water hydrodynamic model to simulate inundation. The model can be applied to large-scale flood hazard mapping thanks to several solutions that maximize its efficiency and the use of parallel computing. The hydrologic component of the modeling system is the Hillslope River Routing (HRR) hydrologic model. HRR simulates hydrological processes using a Green-Ampt parameterization, and is calibrated against observed discharge data from several publicly-available datasets. For inundation mapping, we use a 2D Finite-Volume Shallow-Water model with wetting/drying. We introduce here a grid Up-Scaling Technique (UST) for hydraulic modeling to perform simulations at higher resolution at global scale with relatively short computational times. A 30m SRTM is now available worldwide along with higher accuracy and/or resolution local Digital Elevation Models (DEMs) in many countries and regions. UST consists of aggregating computational cells, thus forming a coarser grid, while retaining the topographic information from the original full-resolution mesh. The full-resolution topography is used for building relationships between volume and free surface elevation inside cells and computing inter-cell fluxes. This approach almost achieves computational speed typical of the coarse grids while preserving, to a significant extent, the accuracy offered by the much higher resolution available DEM. The simulations are carried out along each river of the network by forcing the hydraulic model with the streamflow hydrographs generated by HRR. Hydrographs are scaled so that the peak corresponds to the return period corresponding to the hazard map being produced (e.g. 100 years, 500 years). Each numerical simulation models one river reach, except for the longest reaches which are split in smaller parts. Here we show results for selected river basins worldwide.

Towards a climate-dependent paradigm of ammonia emission and deposition

PubMed Central

Sutton, Mark A.; Reis, Stefan; Riddick, Stuart N.; Dragosits, Ulrike; Nemitz, Eiko; Theobald, Mark R.; Tang, Y. Sim; Braban, Christine F.; Vieno, Massimo; Dore, Anthony J.; Mitchell, Robert F.; Wanless, Sarah; Daunt, Francis; Fowler, David; Blackall, Trevor D.; Milford, Celia; Flechard, Chris R.; Loubet, Benjamin; Massad, Raia; Cellier, Pierre; Personne, Erwan; Coheur, Pierre F.; Clarisse, Lieven; Van Damme, Martin; Ngadi, Yasmine; Clerbaux, Cathy; Skjøth, Carsten Ambelas; Geels, Camilla; Hertel, Ole; Wichink Kruit, Roy J.; Pinder, Robert W.; Bash, Jesse O.; Walker, John T.; Simpson, David; Horváth, László; Misselbrook, Tom H.; Bleeker, Albert; Dentener, Frank; de Vries, Wim

2013-01-01

Existing descriptions of bi-directional ammonia (NH3) land–atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission–deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28–67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45–85) Tg N in 2008 to reach 132 (89–179) Tg by 2100. PMID:23713128

Towards a climate-dependent paradigm of ammonia emission and deposition.

PubMed

Sutton, Mark A; Reis, Stefan; Riddick, Stuart N; Dragosits, Ulrike; Nemitz, Eiko; Theobald, Mark R; Tang, Y Sim; Braban, Christine F; Vieno, Massimo; Dore, Anthony J; Mitchell, Robert F; Wanless, Sarah; Daunt, Francis; Fowler, David; Blackall, Trevor D; Milford, Celia; Flechard, Chris R; Loubet, Benjamin; Massad, Raia; Cellier, Pierre; Personne, Erwan; Coheur, Pierre F; Clarisse, Lieven; Van Damme, Martin; Ngadi, Yasmine; Clerbaux, Cathy; Skjøth, Carsten Ambelas; Geels, Camilla; Hertel, Ole; Wichink Kruit, Roy J; Pinder, Robert W; Bash, Jesse O; Walker, John T; Simpson, David; Horváth, László; Misselbrook, Tom H; Bleeker, Albert; Dentener, Frank; de Vries, Wim

2013-07-05

Existing descriptions of bi-directional ammonia (NH3) land-atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission-deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28-67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45-85) Tg N in 2008 to reach 132 (89-179) Tg by 2100.

US medical specialty global health training and the global burden of disease

PubMed Central

Kerry, Vanessa B.; Walensky, Rochelle P.; Tsai, Alexander C.; Bergmark, Regan W.; Bergmark, Brian A.; Rouse, Chaturia; Bangsberg, David R.

2013-01-01

Background Rapid growth in global health activity among US medical specialty education programs has lead to heterogeneity in types of activities and global health training models. The breadth and scope of this activity is not well chronicled. Methods Using a standardized search protocol, we examined the characteristics of US medical residency global health programs by number of programs, clinical specialty, nature of activity (elective, research, extended curriculum based field training), and geographic location across seven different clinical medical residency education specialties. We tabulated programmatic activity by clinical discipline, region and country. We calculated the Spearman's rank correlation coefficient to estimate the association between programmatic activity and country–level disease burden. Results Of the 1856 programs assessed between January and June 2011, there were 380 global health residency training programs (20%) working in 141 countries. 529 individual programmatic activities (elective–based rotations, research programs, extended curriculum–based field training, or other) occurred at 1337 specific sites. The majority of the activities consisted of elective–based rotations. At the country level, disease burden had a statistically significant association with programmatic activity (Spearman's ρ = 0.17) but only explained 3% of the total variation between countries. Conclusions There were a substantial number of US medical specialty global health programs, but a relative paucity of surgical and mental health programs. Elective–based programs were more common than programs that offer longitudinal experiences. Despite heterogeneity, there was a small but statistically significant association between program location and the global burden of disease. Areas for further study include the degree to which US–based programs develop partnerships with their program sites, the significance of this activity for training, and number and breadth of programs in medical specialty global health education in other countries around the world. PMID:24363924

How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI?

NASA Astrophysics Data System (ADS)

Stavrakou, T.; Müller, J.-F.; Bauwens, M.; De Smedt, I.; Van Roozendael, M.; De Mazière, M.; Vigouroux, C.; Hendrick, F.; George, M.; Clerbaux, C.; Coheur, P.-F.; Guenther, A.

2015-10-01

The vertical columns of formaldehyde (HCHO) retrieved from two satellite instruments, the Global Ozone Monitoring Instrument-2 (GOME-2) on Metop-A and the Ozone Monitoring Instrument (OMI) on Aura, are used to constrain global emissions of HCHO precursors from open fires, vegetation and human activities in the year 2010. To this end, the emissions are varied and optimized using the adjoint model technique in the IMAGESv2 global CTM (chemical transport model) on a monthly basis and at the model resolution. Given the different local overpass times of GOME-2 (09:30 LT) and OMI (13:30 LT), the simulated diurnal cycle of HCHO columns is investigated and evaluated against ground-based optical measurements at seven sites in Europe, China and Africa. The modeled diurnal cycle exhibits large variability, reflecting competition between photochemistry and emission variations, with noon or early afternoon maxima at remote locations (oceans) and in regions dominated by anthropogenic emissions, late afternoon or evening maxima over fire scenes, and midday minima in isoprene-rich regions. The agreement between simulated and ground-based columns is generally better in summer (with a clear afternoon maximum at mid-latitude sites) than in winter, and the annually averaged ratio of afternoon to morning columns is slightly higher in the model (1.126) than in the ground-based measurements (1.043). The anthropogenic VOC (volatile organic compound) sources are found to be weakly constrained by the inversions on the global scale, mainly owing to their generally minor contribution to the HCHO columns, except over strongly polluted regions, like China. The OMI-based inversion yields total flux estimates over China close to the bottom-up inventory (24.6 vs. 25.5 TgVOC yr-1 in the a priori) with, however, pronounced increases in the northeast of China and reductions in the south. Lower fluxes are estimated based on GOME-2 HCHO columns (20.6 TgVOC yr-1), in particular over the northeast, likely reflecting mismatches between the observed and the modeled diurnal cycle in this region. The resulting biogenic and pyrogenic flux estimates from both optimizations generally show a good degree of consistency. A reduction of the global annual biogenic emissions of isoprene is derived, of 9 and 13 % according to GOME-2 and OMI, respectively, compared to the a priori estimate of 363 Tg in 2010. The reduction is largest (up to 25-40 %) in the Southeastern US, in accordance with earlier studies. The GOME-2 and OMI satellite columns suggest a global pyrogenic flux decrease by 36 and 33 %, respectively, compared to the GFEDv3 (Global Fire Emissions Database) inventory. This decrease is especially pronounced over tropical forests, such as in Amazonia, Thailand and Myanmar, and is supported by comparisons with CO observations from IASI (Infrared Atmospheric Sounding Interferometer). In contrast to these flux reductions, the emissions due to harvest waste burning are strongly enhanced over the northeastern China plain in June (by ca. 70 % in June according to OMI) as well as over Indochina in March. Sensitivity inversions showed robustness of the inferred estimates, which were found to lie within 7 % of the standard inversion results at the global scale.

Protein structure modeling for CASP10 by multiple layers of global optimization.

PubMed

Joo, Keehyoung; Lee, Juyong; Sim, Sangjin; Lee, Sun Young; Lee, Kiho; Heo, Seungryong; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

2014-02-01

In the template-based modeling (TBM) category of CASP10 experiment, we introduced a new protocol called protein modeling system (PMS) to generate accurate protein structures in terms of side-chains as well as backbone trace. In the new protocol, a global optimization algorithm, called conformational space annealing (CSA), is applied to the three layers of TBM procedure: multiple sequence-structure alignment, 3D chain building, and side-chain re-modeling. For 3D chain building, we developed a new energy function which includes new distance restraint terms of Lorentzian type (derived from multiple templates), and new energy terms that combine (physical) energy terms such as dynamic fragment assembly (DFA) energy, DFIRE statistical potential energy, hydrogen bonding term, etc. These physical energy terms are expected to guide the structure modeling especially for loop regions where no template structures are available. In addition, we developed a new quality assessment method based on random forest machine learning algorithm to screen templates, multiple alignments, and final models. For TBM targets of CASP10, we find that, due to the combination of three stages of CSA global optimizations and quality assessment, the modeling accuracy of PMS improves at each additional stage of the protocol. It is especially noteworthy that the side-chains of the final PMS models are far more accurate than the models in the intermediate steps. Copyright © 2013 Wiley Periodicals, Inc.

V and V Efforts of Auroral Precipitation Models: Preliminary Results

NASA Technical Reports Server (NTRS)

Zheng, Yihua; Kuznetsova, Masha; Rastaetter, Lutz; Hesse, Michael

2011-01-01

Auroral precipitation models have been valuable both in terms of space weather applications and space science research. Yet very limited testing has been performed regarding model performance. A variety of auroral models are available, including empirical models that are parameterized by geomagnetic indices or upstream solar wind conditions, now casting models that are based on satellite observations, or those derived from physics-based, coupled global models. In this presentation, we will show our preliminary results regarding V&V efforts of some of the models.

Orographic precipitation at global and regional scales: Observational uncertainty and evaluation of 25-km global model simulations

NASA Astrophysics Data System (ADS)

Schiemann, Reinhard; Roberts, Charles J.; Bush, Stephanie; Demory, Marie-Estelle; Strachan, Jane; Vidale, Pier Luigi; Mizielinski, Matthew S.; Roberts, Malcolm J.

2015-04-01

Precipitation over land exhibits a high degree of variability due to the complex interaction of the precipitation generating atmospheric processes with coastlines, the heterogeneous land surface, and orography. Global general circulation models (GCMs) have traditionally had very limited ability to capture this variability on the mesoscale (here ~50-500 km) due to their low resolution. This has changed with recent investments in resolution and ensembles of multidecadal climate simulations of atmospheric GCMs (AGCMs) with ~25 km grid spacing are becoming increasingly available. Here, we evaluate the mesoscale precipitation distribution in one such set of simulations obtained in the UPSCALE (UK on PrACE - weather-resolving Simulations of Climate for globAL Environmental risk) modelling campaign with the HadGEM-GA3 AGCM. Increased model resolution also poses new challenges to the observational datasets used to evaluate models. Global gridded data products such as those provided by the Global Precipitation Climatology Project (GPCP) are invaluable for assessing large-scale features of the precipitation distribution but may not sufficiently resolve mesoscale structures. In the absence of independent estimates, the intercomparison of different observational datasets may be the only way to get some insight into the uncertainties associated with these observations. Here, we focus on mid-latitude continental regions where observations based on higher-density gauge networks are available in addition to the global data sets: Europe/the Alps, South and East Asia, and the continental US. The ability of GCMs to represent mesoscale variability is of interest in its own right, as climate information on this scale is required by impact studies. An additional motivation for the research proposed here arises from continuing efforts to quantify the components of the global radiation budget and water cycle. Recent estimates based on radiation measurements suggest that the global mean precipitation/evaporation may be up to 10 Wm-2 (about 0.35 mm day-1) larger than the estimate obtained from GPCP. While the main part of this discrepancy is thought to be due to the underestimation of remotely-sensed ocean precipitation, there is also considerable uncertainty about 'unobserved' precipitation over land, in particular in the form of snow in regions of high latitude/altitude. We aim to contribute to this discussion, at least at a qualitative level, by considering case studies of how area-averaged mountain precipitation is represented in different observational datasets and by HadGEM3-GA3 at different resolutions. Our results show that the AGCM simulates considerably more orographic precipitation at higher resolution. We find this at the global scale both for the winter and summer hemispheres, as well as in several case studies in mid-latitude regions. Gridded observations based on gauge measurements generally capture the mesoscale spatial variability of precipitation, but differ strongly from one another in the magnitude of area-averaged precipitation, so that they are of very limited use for evaluating this aspect of the modelled climate. We are currently conducting a sensitivity experiment (coarse-grained orography in high-resolution HadGEM3) to further investigate the resolution sensitivity seen in the model.

Downscaling Global Land Cover Projections from an Integrated Assessment Model for Use in Regional Analyses: Results and Evaluation for the US from 2005 to 2095

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Tristram O.; Le Page, Yannick LB; Huang, Maoyi

2014-06-05

Projections of land cover change generated from Integrated Assessment Models (IAM) and other economic-based models can be applied for analyses of environmental impacts at subregional and landscape scales. For those IAM and economic models that project land use at the sub-continental or regional scale, these projections must be downscaled and spatially distributed prior to use in climate or ecosystem models. Downscaling efforts to date have been conducted at the national extent with relatively high spatial resolution (30m) and at the global extent with relatively coarse spatial resolution (0.5 degree).

AERONET Version 3 Release: Providing Significant Improvements for Multi-Decadal Global Aerosol Database and Near Real-Time Validation

NASA Technical Reports Server (NTRS)

Holben, Brent; Slutsker, Ilya; Giles, David; Eck, Thomas; Smirnov, Alexander; Sinyuk, Aliaksandr; Schafer, Joel; Sorokin, Mikhail; Rodriguez, Jon; Kraft, Jason;

Comparisons of Radiative Flux Distributions from Satellite Observations and Global Models

NASA Astrophysics Data System (ADS)

Raschke, Ehrhard; Kinne, Stefan; Wild, Martin; Stackhouse, Paul; Rossow, Bill

2014-05-01

Radiative flux distributions at the top of the atmosphere (TOA) and at the surface are compared between typical data from satellite observations and from global modeling. Averages of CERES, ISCCP and SRB data-products (for the same 4-year period) represent satellite observations. Central values of IPCC-4AR output (over a 12-year period) represent global modeling. At TOA, differences are dominated by differences for cloud-effects, which are extracted from the differences between all-sky and clear-sky radiative flux products. As satellite data are considered as TOA reference, these differences document the poor representation of clouds in global modeling, especially for low altitude clouds over oceans. At the surface the differences, caused by the different cloud treatment are overlaid by a general offset. Satellite products suggest a ca 15Wm-2 stronger surface net-imbalance (and with it stronger precipitation). Since surface products of satellite and modeling are based on simulations and many assumptions, this difference has remained an open issue. BSRN surface monitoring is too short and too sparsely distributed for clear answers to provide a reliable basis for validation.

Statistical models of global Langmuir mixing

NASA Astrophysics Data System (ADS)

Li, Qing; Fox-Kemper, Baylor; Breivik, Øyvind; Webb, Adrean

2017-05-01

The effects of Langmuir mixing on the surface ocean mixing may be parameterized by applying an enhancement factor which depends on wave, wind, and ocean state to the turbulent velocity scale in the K-Profile Parameterization. Diagnosing the appropriate enhancement factor online in global climate simulations is readily achieved by coupling with a prognostic wave model, but with significant computational and code development expenses. In this paper, two alternatives that do not require a prognostic wave model, (i) a monthly mean enhancement factor climatology, and (ii) an approximation to the enhancement factor based on the empirical wave spectra, are explored and tested in a global climate model. Both appear to reproduce the Langmuir mixing effects as estimated using a prognostic wave model, with nearly identical and substantial improvements in the simulated mixed layer depth and intermediate water ventilation over control simulations, but significantly less computational cost. Simpler approaches, such as ignoring Langmuir mixing altogether or setting a globally constant Langmuir number, are found to be deficient. Thus, the consequences of Stokes depth and misaligned wind and waves are important.

Trade in water and commodities as adaptations to global change

NASA Astrophysics Data System (ADS)

Lammers, R. B.; Hertel, T. W.; Prousevitch, A.; Baldos, U. L. C.; Frolking, S. E.; Liu, J.; Grogan, D. S.

2015-12-01

The human capacity for altering the water cycle has been well documented and given the expected change due to population, income growth, biofuels, climate, and associated land use change, there remains great uncertainty in both the degree of increased pressure on land and water resources and in our ability to adapt to these changes. Alleviating regional shortages in water supply can be carried out in a spatial hierarchy through i) direct trade of water between all regions, ii) development of infrastructure to improve water availability within regions (e.g. impounding rivers), iii) via inter-basin hydrological transfer between neighboring regions and, iv) via virtual water trade. These adaptation strategies can be managed via market trade in water and commodities to identify those strategies most likely to be adopted. This work combines the physically-based University of New Hampshire Water Balance Model (WBM) with the macro-scale Purdue University Simplified International Model of agricultural Prices Land use and the Environment (SIMPLE) to explore the interaction of supply and demand for fresh water globally. In this work we use a newly developed grid cell-based version of SIMPLE to achieve a more direct connection between the two modeling paradigms of physically-based models with optimization-driven approaches characteristic of economic models. We explore questions related to the global and regional impact of water scarcity and water surplus on the ability of regions to adapt to future change. Allowing for a variety of adaptation strategies such as direct trade of water and expanding the built water infrastructure, as well as indirect trade in commodities, will reduce overall global water stress and, in some regions, significantly reduce their vulnerability to these future changes.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography.

PubMed

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-09-01

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details. A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy. A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

PubMed Central

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E.; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-01-01

Purpose Partial volume effects (PVE) are consequences of the limited spatial resolution in emission tomography leading to under-estimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multi-resolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model which may introduce artefacts in regions where no significant correlation exists between anatomical and functional details. Methods A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Results Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present the new model outperformed the 2D global approach, avoiding artefacts and significantly improving quality of the corrected images and their quantitative accuracy. Conclusions A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multi-resolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information. PMID:21978037

Partitioning CloudSat Ice Water Content for Comparison with Upper-Tropospheric Ice in Global Atmospheric Models

NASA Astrophysics Data System (ADS)

Chen, W. A.; Woods, C. P.; Li, J. F.; Waliser, D. E.; Chern, J.; Tao, W.; Jiang, J. H.; Tompkins, A. M.

2010-12-01

CloudSat provides important estimates of vertically resolved ice water content (IWC) on a global scale based on radar reflectivity. These estimates of IWC have proven beneficial in evaluating the representations of ice clouds in global models. An issue when performing model-data comparisons of IWC particularly germane to this investigation, is the question of which component(s) of the frozen water mass are represented by retrieval estimates and how they relate to what is represented in models. The present study developed and applied a new technique to partition CloudSat total IWC into small and large ice hydrometeors, based on the CloudSat-retrieved ice particle size distribution (PSD) parameters. The new method allows one to make relevant model-data comparisons and provides new insights into the model’s representation of atmospheric IWC. The partitioned CloudSat IWC suggests that the small ice particles contribute to 20-30% of the total IWC in the upper troposphere when a threshold size of 100 μm is used. Sensitivity measures with respect to the threshold size, the PSD parameters, and the retrieval algorithms are presented. The new dataset is compared to model estimates, pointing to areas for model improvement. Cloud ice analyses from the European Centre for Medium-Range Weather Forecasts model agree well with the small IWC from CloudSat. The finite-volume multi-scale modeling framework model underestimates total IWC at 147 and 215 hPa, while overestimating the fractional contribution from the small ice species. These results are discussed in terms of their applications to, and implications for, the evaluation of global atmospheric models, providing constraints on the representations of cloud feedback and precipitation in global models, which in turn can help reduce uncertainties associated with climate change projections. Figure 1. A sample lognormal ice number distribution (red curve), and the corresponding mass distribution (black curve). The dotted line represents the cutoff size for IWC partitioning (Dc = 100 µm as an example). The partial integrals of the mass distribution for particles smaller and larger than Dc correspond to IWC<100 (green area) and IWC>100 (blue area), respectively.

A simple inertial model for Neptune's zonal circulation

NASA Technical Reports Server (NTRS)

Allison, Michael; Lumetta, James T.

1990-01-01

Voyager imaging observations of zonal cloud-tracked winds on Neptune revealed a strongly subrotational equatorial jet with a speed approaching 500 m/s and generally decreasing retrograde motion toward the poles. The wind data are interpreted with a speculative but revealingly simple model based on steady gradient flow balance and an assumed global homogenization of potential vorticity for shallow layer motion. The prescribed model flow profile relates the equatorial velocity to the mid-latitude shear, in reasonable agreement with the available data, and implies a global horizontal deformation scale L(D) of about 3000 km.

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.

We present a general Global Minimization Algorithm (GMA) to identify basic or thermally coupled distillation configurations that require the least vapor duty under minimum reflux conditions for separating any ideal or near-ideal multicomponent mixture into a desired number of product streams. In this algorithm, global optimality is guaranteed by modeling the system using Underwood equations and reformulating the resulting constraints to bilinear inequalities. The speed of convergence to the globally optimal solution is increased by using appropriate feasibility and optimality based variable-range reduction techniques and by developing valid inequalities. As a result, the GMA can be coupled with already developedmore » techniques that enumerate basic and thermally coupled distillation configurations, to provide for the first time, a global optimization based rank-list of distillation configurations.« less

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

PubMed

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation

NASA Astrophysics Data System (ADS)

Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten

2015-04-01

Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.

Image-optimized Coronal Magnetic Field Models

NASA Astrophysics Data System (ADS)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-08-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.

An Exospheric Temperature Model Based On CHAMP Observations and TIEGCM Simulations

NASA Astrophysics Data System (ADS)

Ruan, Haibing; Lei, Jiuhou; Dou, Xiankang; Liu, Siqing; Aa, Ercha

2018-02-01

In this work, thermospheric densities from the accelerometer measurement on board the CHAMP satellite during 2002-2009 and the simulations from the National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model (NCAR-TIEGCM) are employed to develop an empirical exospheric temperature model (ETM). The two-dimensional basis functions of the ETM are first provided from the principal component analysis of the TIEGCM simulations. Based on the exospheric temperatures derived from CHAMP thermospheric densities, a global distribution of the exospheric temperatures is reconstructed. A parameterization is conducted for each basis function amplitude as a function of solar-geophysical and seasonal conditions. Thus, the ETM can be utilized to model the thermospheric temperature and mass density under a specified condition. Our results showed that the averaged standard deviation of the ETM is generally less than 10% than approximately 30% in the MSIS model. Besides, the ETM reproduces the global thermospheric evolutions including the equatorial thermosphere anomaly.

Image-Optimized Coronal Magnetic Field Models

NASA Technical Reports Server (NTRS)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-01-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work we presented early tests of the method which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane, and the effect on the outcome of the optimization of errors in localization of constraints. We find that substantial improvement in the model field can be achieved with this type of constraints, even when magnetic features in the images are located outside of the image plane.

A 7.5-Year Dataset of SSM/I-Derived Surface Turbulent Fluxes Over Global Oceans

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Adizzone, Joe; Nelkin, Eric; Starr, David OC. (Technical Monitor)

2001-01-01

The global air-sea turbulent fluxes are needed for driving ocean models and validating coupled ocean-atmosphere global models. A method was developed to retrieve surface air humidity from the radiances measured by the Special Sensor Microwave/Imager (SSM/I) Using both SSM/I-retrieved surface wind and air humidity, they computed daily turbulent fluxes over global oceans with a stability-dependent bulk scheme. Based on this method, we have produced Version 1 of Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) dataset from the SSM/I data and other data. It provides daily- and monthly-mean surface turbulent fluxes and some relevant parameters over global oceans for individual F8, F10, and F11 satellites covering the period July 1987-December 1994. It also provides 1988-94 annual- and monthly-mean climatologies of the same variables, using only F8 and F1 1 satellite data. It has a spatial resolution of 2.0 degrees x 2.5 degrees lat-long and is archived at the NASA/GSFC DAAC. The purpose of this paper is to present an updated assessment of the GSSTF 1.0 dataset.

Detailed assessment of global transport-energy models’ structures and projections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yeh, Sonia; Mishra, Gouri Shankar; Fulton, Lew

This paper focuses on comparing the frameworks and projections from four major global transportation models with considerable transportation technology and behavioral detail. We analyze and compare the modeling frameworks, underlying data, assumptions, intermediate parameters, and projections to identify the sources of divergence or consistency, as well as key knowledge gaps. We find that there are significant differences in the base-year data and key parameters for future projections, especially for developing countries. These include passenger and freight activity, mode shares, vehicle ownership rates, and even energy consumption by mode, particularly for shipping, aviation and trucking. This may be due in partmore » to a lack of previous efforts to do such consistency-checking and “bench-marking.” We find that the four models differ in terms of the relative roles of various mitigation strategies to achieve a 2°C / 450 ppm CO2e target: the economics-based integrated assessment models favor the use of low carbon fuels as the primary mitigation option followed by efficiency improvements, whereas transport-only and expert-based models favor efficiency improvements of vehicles followed by mode shifts. We offer recommendations for future modeling improvements focusing on (1) reducing data gaps; (2) translating the findings from this study into relevant policy implications such as feasibility of current policy goals, additional policy targets needed, regional vs. global reductions, etc.; (3) modeling strata of demographic groups to improve understanding of vehicle ownership levels, travel behavior, and urban vs. rural considerations; and (4) conducting coordinated efforts in aligning input assumptions and historical data, policy analysis, and modeling insights.« less

Optimizing Land and Water Use at the Local Level to Enhance Global Food Security through Virtual Resources Trade in the World

NASA Astrophysics Data System (ADS)

Cai, X.; Zhang, X.; Zhu, T.

2014-12-01

Global food security is constrained by local and regional land and water availability, as well as other agricultural input limitations and inappropriate national and global regulations. In a theoretical context, this study assumes that optimal water and land uses in local food production to maximize food security and social welfare at the global level can be driven by global trade. It follows the context of "virtual resources trade", i.e., utilizing international trade of agricultural commodities to reduce dependency on local resources, and achieves land and water savings in the world. An optimization model based on the partial equilibrium of agriculture is developed for the analysis, including local commodity production and land and water resources constraints, demand by country, and global food market. Through the model, the marginal values (MVs) of social welfare for water and land at the level of so-called food production units (i.e., sub-basins with similar agricultural production conditions) are derived and mapped in the world. In this personation, we will introduce the model structure, explain the meaning of MVs at the local level and their distribution around the world, and discuss the policy implications for global communities to enhance global food security. In particular, we will examine the economic values of water and land under different world targets of food security (e.g., number of malnourished population or children in a future year). In addition, we will also discuss the opportunities on data to improve such global modeling exercises.

How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI?

NASA Astrophysics Data System (ADS)

Stavrakou, T.; Müller, J.-F.; Bauwens, M.; De Smedt, I.; Van Roozendael, M.; De Mazière, M.; Vigouroux, C.; Hendrick, F.; George, M.; Clerbaux, C.; Coheur, P.-F.; Guenther, A.

2015-04-01

The vertical columns of formaldehyde (HCHO) retrieved from two satellite instruments, the Global Ozone Monitoring Instrument-2 (GOME-2) on Metop-A and the Ozone Monitoring Instrument (OMI) on Aura, are used to constrain global emissions of HCHO precursors from open fires, vegetation and human activities in the year 2010. To this end, the emissions are varied and optimized using the adjoint model technique in the IMAGESv2 global CTM (chemistry-transport model) on a monthly basis and at the model resolution. Given the different local overpass times of GOME-2 (09:30 LT) and OMI (13:30 LT), the simulated diurnal cycle of HCHO columns is investigated and evaluated against ground-based optical measurements at 7 sites in Europe, China and Africa. The modelled diurnal cycle exhibits large variability, reflecting competition between photochemistry and emission variations, with noon or early afternoon maxima at remote locations (oceans) and in regions dominated by anthropogenic emissions, late afternoon or evening maxima over fire scenes, and midday minima in isoprene-rich regions. The agreement between simulated and ground-based columns is found to be generally better in summer (with a clear afternoon maximum at mid-latitude sites) than in winter, and the annually averaged ratio of afternoon to morning columns is slightly higher in the model (1.126) than in the ground-based measurements (1.043). The anthropogenic VOC (volatile organic compound) sources are found to be weakly constrained by the inversions on the global scale, mainly owing to their generally minor contribution to the HCHO columns, except over strongly polluted regions, like China. The OMI-based inversion yields total flux estimates over China close to the bottom-up inventory (24.6 vs. 25.5 in the a priori) with, however, pronounced increases in the Northeast China and reductions in the south. Lower fluxes are estimated based on GOME-2 HCHO columns (20.6 TgVOC), in particular over the Northeast, likely reflecting mismatches between the observed and the modelled diurnal cycle in this region. The resulting biogenic and pyrogenic flux estimates from both optimizations generally show a good degree of consistency. A reduction of the global annual biogenic emissions of isoprene is derived, by 9 and by 13% according to GOME-2 and OMI, respectively, compared to the a priori estimate of 363 Tg in 2010. The reduction is largest (up to 25-40%) in the Southeastern US, in accordance with earlier studies. The GOME-2 and OMI satellite columns suggest a global pyrogenic flux decrease by 36 and 33%, respectively, compared to the GFEDv3 inventory. This decrease is especially pronounced over tropical forests such as Amazonia and Thailand/Myanmar, and is supported by comparisons with IASI CO observations. In contrast to these flux reductions, the emissions due to harvest waste burning are strongly enhanced in the Northeastern China plain in June (by ca. 70% in June according to OMI) as well as over Indochina in March. Sensitivity inversions showed robustness of the inferred estimates, which were found to lie within 7% of the standard inversion results at the global scale.

Internally Consistent MODIS Estimate of Aerosol Clear-Sky Radiative Effect Over the Global Oceans

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Kaufman, Yoram J.

2004-01-01

Modern satellite remote sensing, and in particular the MODerate resolution Imaging Spectroradiometer (MODIS), offers a measurement-based pathway to estimate global aerosol radiative effects and aerosol radiative forcing. Over the Oceans, MODIS retrieves the total aerosol optical thickness, but also reports which combination of the 9 different aerosol models was used to obtain the retrieval. Each of the 9 models is characterized by a size distribution and complex refractive index, which through Mie calculations correspond to a unique set of single scattering albedo, assymetry parameter and spectral extinction for each model. The combination of these sets of optical parameters weighted by the optical thickness attributed to each model in the retrieval produces the best fit to the observed radiances at the top of the atmosphere. Thus the MODIS Ocean aerosol retrieval provides us with (1) An observed distribution of global aerosol loading, and (2) An internally-consistent, observed, distribution of aerosol optical models that when used in combination will best represent the radiances at the top of the atmosphere. We use these two observed global distributions to initialize the column climate model by Chou and Suarez to calculate the aerosol radiative effect at top of the atmosphere and the radiative efficiency of the aerosols over the global oceans. We apply the analysis to 3 years of MODIS retrievals from the Terra satellite and produce global and regional, seasonally varying, estimates of aerosol radiative effect over the clear-sky oceans.

Top-down constraints on disturbance dynamics in the terrestrial carbon cycle: effects at global and regional scales

NASA Astrophysics Data System (ADS)

Bloom, A. A.; Exbrayat, J. F.; van der Velde, I.; Peters, W.; Williams, M.

2014-12-01

Large uncertainties preside over terrestrial carbon flux estimates on a global scale. In particular, the strongly coupled dynamics between net ecosystem productivity and disturbance C losses are poorly constrained. To gain an improved understanding of ecosystem C dynamics from regional to global scale, we apply a Markov Chain Monte Carlo based model-data-fusion approach into the CArbon DAta-MOdel fraMework (CARDAMOM). We assimilate MODIS LAI and burned area, plant-trait data, and use the Harmonized World Soil Database (HWSD) and maps of above ground biomass as prior knowledge for initial conditions. We optimize model parameters based on (a) globally spanning observations and (b) ecological and dynamic constraints that force single parameter values and parameter inter-dependencies to be representative of real world processes. We determine the spatial and temporal dynamics of major terrestrial C fluxes and model parameter values on a global scale (GPP = 123 +/- 8 Pg C yr-1 & NEE = -1.8 +/- 2.7 Pg C yr-1). We further show that the incorporation of disturbance fluxes, and accounting for their instantaneous or delayed effect, is of critical importance in constraining global C cycle dynamics, particularly in the tropics. In a higher resolution case study centred on the Amazon Basin we show how fires not only trigger large instantaneous emissions of burned matter, but also how they are responsible for a sustained reduction of up to 50% in plant uptake following the depletion of biomass stocks. The combination of these two fire-induced effects leads to a 1 g C m-2 d-1reduction in the strength of the net terrestrial carbon sink. Through our simulations at regional and global scale, we advocate the need to assimilate disturbance metrics in global terrestrial carbon cycle models to bridge the gap between globally spanning terrestrial carbon cycle data and the full dynamics of the ecosystem C cycle. Disturbances are especially important because their quick occurrence may have long-term effects on ecosystems. Our synthetic simulations show that while tropical ecosystems uptake may reach pre-disturbance level after a decade, biomass stocks would most likely need more than a century to recover from a single extreme disturbance event.

Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

NASA Technical Reports Server (NTRS)

Van Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael; Hsu, N. Christina; Kahn, Ralph A.; Levy, Robert C.; Lyapustin, Alexei; Sayer, Andrew M.; Winker, David M.

2016-01-01

We estimated global fine particulate matter (PM(sub 2.5)) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically-based satellite-derived PM(sub 2.5) estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM(sub 2.5) estimates were highly consistent (R(sup 2) equals 0.81) with out-of-sample cross-validated PM(sub 2.5) concentrations from monitors. The global population-weighted annual average PM(sub 2.5) concentrations were 3-fold higher than the 10 micrograms per cubic meter WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM(sub 2.5) data sources can yield valuable improvements to PM(sub 2.5) characterization on a global scale.

Improved parameterization of managed grassland in a global process-based vegetation model using Bayesian statistics

NASA Astrophysics Data System (ADS)

Rolinski, S.; Müller, C.; Lotze-Campen, H.; Bondeau, A.

2010-12-01

More than a quarter of the Earth’s land surface is covered by grassland, which is also the major part (~ 70 %) of the agricultural area. Most of this area is used for livestock production in different degrees of intensity. The dynamic global vegetation model LPJmL (Sitch et al., Global Change Biology, 2003; Bondeau et al., Global Change Biology, 2007) is one of few process-based model that simulates biomass production on managed grasslands at the global scale. The implementation of managed grasslands and its evaluation has received little attention so far, as reference data on grassland productivity are scarce and the definition of grassland extent and usage are highly uncertain. However, grassland productivity is related to large areas, and strongly influences global estimates of carbon and water budgets and should thus be improved. Plants are implemented in LPJmL in an aggregated form as plant functional types assuming that processes concerning carbon and water fluxes are quite similar between species of the same type. Therefore, the parameterization of a functional type is possible with parameters in a physiologically meaningful range of values. The actual choice of the parameter values from the possible and reasonable phase space should satisfy the condition of the best fit of model results and measured data. In order to improve the parameterization of managed grass we follow a combined procedure using model output and measured data of carbon and water fluxes. By comparing carbon and water fluxes simultaneously, we expect well-balanced refinements and avoid over-tuning of the model in only one direction. The comparison of annual biomass from grassland to data from the Food and Agriculture Organization of the United Nations (FAO) per country provide an overview about the order of magnitude and the identification of deviations. The comparison of daily net primary productivity, soil respiration and water fluxes at specific sites (FluxNet Data) provides information on boundary conditions such as water and light availability or temperature sensibility. Based on the given limitation factors, a number of sensitive parameters are chosen, e.g. for the phenological development, biomass allocation, and different management regimes. These are introduced to a sensitivity analysis and Bayesian parameter evaluation using the R package FME (Soetart & Petzoldt, Journal of Statistical Software, 2010). Given the extremely different climatic conditions at the FluxNet grass sites, the premises for the global sensitivity analysis are very promising.

A new model for graduate education and innovation in medical technology.

PubMed

Yazdi, Youseph; Acharya, Soumyadipta

2013-09-01

We describe a new model of graduate education in bioengineering innovation and design- a year long Master's degree program that educates engineers in the process of healthcare technology innovation for both advanced and low-resource global markets. Students are trained in an iterative "Spiral Innovation" approach that ensures early, staged, and repeated examination of all key elements of a successful medical device. This includes clinical immersion based problem identification and assessment (at Johns Hopkins Medicine and abroad), team based concept and business model development, and project planning based on iterative technical and business plan de-risking. The experiential, project based learning process is closely supported by several core courses in business, design, and engineering. Students in the program work on two team based projects, one focused on addressing healthcare needs in advanced markets and a second focused on low-resource settings. The program recently completed its fourth year of existence, and has graduated 61 students, who have continued on to industry or startups (one half), additional graduate education, or medical school (one third), or our own Global Health Innovation Fellowships. Over the 4 years, the program has sponsored 10 global health teams and 14 domestic/advanced market medtech teams, and launched 5 startups, of which 4 are still active. Projects have attracted over US$2.5M in follow-on awards and grants, that are supporting the continued development of over a dozen projects.

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

DOE PAGES

Blanc, Élodie

2017-01-26

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanc, Élodie

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals.

PubMed

Önlü, Serli; Saçan, Melek Türker

2017-04-01

The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC. © 2016 SETAC.

The Space Weather Modeling Framework (SWMF): Models and Validation

NASA Astrophysics Data System (ADS)

Gombosi, Tamas; Toth, Gabor; Sokolov, Igor; de Zeeuw, Darren; van der Holst, Bart; Ridley, Aaron; Manchester, Ward, IV

In the last decade our group at the Center for Space Environment Modeling (CSEM) has developed the Space Weather Modeling Framework (SWMF) that efficiently couples together different models describing the interacting regions of the space environment. Many of these domain models (such as the global solar corona, the inner heliosphere or the global magneto-sphere) are based on MHD and are represented by our multiphysics code, BATS-R-US. SWMF is a powerful tool for coupling regional models describing the space environment from the solar photosphere to the bottom of the ionosphere. Presently, SWMF contains over a dozen components: the solar corona (SC), eruptive event generator (EE), inner heliosphere (IE), outer heliosphere (OH), solar energetic particles (SE), global magnetosphere (GM), inner magnetosphere (IM), radiation belts (RB), plasmasphere (PS), ionospheric electrodynamics (IE), polar wind (PW), upper atmosphere (UA) and lower atmosphere (LA). This talk will present an overview of SWMF, new results obtained with improved physics as well as some validation studies.

Estimating daily global solar radiation by day of the year in Algeria

NASA Astrophysics Data System (ADS)

Aoun, Nouar; Bouchouicha, Kada

2017-05-01

This study presents six empirical models based on the day-of-the-year number for estimating global solar radiation on a horizontal surface. For this case study, 21 years of experimental data sets for 21 cities over the whole Algerian territory are utilized to develop these models for each city and for all of Algeria. In this study, the territory of Algeria was divided into four different climatic zones, i.e., Arid, Semi-arid, Highlands and Mediterranean. The accuracy of the all-Algeria model was tested for each city and for each climate zone. To evaluate the accuracy of the models, the RMSE, rRMSE, MABE, MAPE, and R, which are the most commonly applied statistical parameters, were utilized. The results show that the six developed models provide excellent predictions for global solar radiation for each city and for all-Algeria. Furthermore, the model showing the greatest accuracy is the sine and cosine wave trigonometric model.

The North American Forest Sector Outlook Study 2006-2030

Treesearch

Jeffrey P. Prestemon; Joseph Buongiorno

2012-01-01

Projections for the United States and Canada to 2030 have been made with a global model to account for concurrent changes in other countries. Three future scenarios were investigated: two IPCC-based scenarios assuming the rapid growth of wood-based energy, and one IPCC-based scenario without this assumption. The model, under the IPCC scenarios, accounted for trends in...

AgMIP 1.5°C Assessment: Mitigation and Adaptation at Coordinated Global and Regional Scales

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2016-12-01

The AgMIP 1.5°C Coordinated Global and Regional Integrated Assessments of Climate Change and Food Security (AgMIP 1.5 CGRA) is linking site-based crop and livestock models with similar models run on global grids, and then links these biophysical components with economics models and nutrition metrics at regional and global scales. The AgMIP 1.5 CGRA assessment brings together experts in climate, crop, livestock, economics, nutrition, and food security to define the 1.5°C Protocols and guide the process throughout the assessment. Scenarios are designed to consistently combine elements of intertwined storylines of future society including socioeconomic development (Shared Socioeconomic Pathways), greenhouse gas concentrations (Representative Concentration Pathways), and specific pathways of agricultural sector development (Representative Agricultural Pathways). Shared Climate Policy Assumptions will be extended to provide additional agricultural detail on mitigation and adaptation strategies. The multi-model, multi-disciplinary, multi-scale integrated assessment framework is using scenarios of economic development, adaptation, mitigation, food policy, and food security. These coordinated assessments are grounded in the expertise of AgMIP partners around the world, leading to more consistent results and messages for stakeholders, policymakers, and the scientific community. The early inclusion of nutrition and food security experts has helped to ensure that assessment outputs include important metrics upon which investment and policy decisions may be based. The CGRA builds upon existing AgMIP research groups (e.g., the AgMIP Wheat Team and the AgMIP Global Gridded Crop Modeling Initiative; GGCMI) and regional programs (e.g., AgMIP Regional Teams in Sub-Saharan Africa and South Asia), with new protocols for cross-scale and cross-disciplinary linkages to ensure the propagation of expert judgment and consistent assumptions.